Projeto minerando dados/Dados_do_Censo.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Dataset: \n",
    "O conjunto de dados para este projeto é originário do Repositório de A prendizado de Máquina da UCI(https://archive.ics.uci.edu/ml/datasets/Adult)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Importando os módulos"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.preprocessing import LabelEncoder\n",
    "from sklearn.model_selection import StratifiedKFold\n",
    "from sklearn.pipeline import Pipeline\n",
    "from sklearn.model_selection import RandomizedSearchCV\n",
    "from sklearn import model_selection\n",
    "from sklearn.linear_model import LogisticRegression\n",
    "from sklearn.tree import DecisionTreeClassifier\n",
    "from sklearn.neighbors import KNeighborsClassifier\n",
    "from sklearn.ensemble import RandomForestClassifier\n",
    "from sklearn.naive_bayes import GaussianNB\n",
    "from sklearn.preprocessing import StandardScaler\n",
    "from sklearn.metrics import accuracy_score\n",
    "from sklearn.model_selection import cross_val_predict\n",
    "from sklearn.metrics import confusion_matrix\n",
    "from sklearn.metrics import classification_report\n",
    "from warnings import simplefilter\n",
    "import pickle\n",
    "import pandas as pd\n",
    "import seaborn as sns\n",
    "import matplotlib.pyplot as plt\n",
    "from matplotlib import style\n",
    "style.use('ggplot')\n",
    "simplefilter(action='ignore', category=FutureWarning)\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Carregando e visualizando os Dados"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "df = pd.DataFrame()\n",
    "for chunk in pd.read_csv('adult.data',na_values= '?', chunksize = 100000):\n",
    "    df = pd.concat([df,chunk])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>age</th>\n",
       "      <th>workclass</th>\n",
       "      <th>education</th>\n",
       "      <th>educational-num</th>\n",
       "      <th>marital-status</th>\n",
       "      <th>occupation</th>\n",
       "      <th>relationship</th>\n",
       "      <th>race</th>\n",
       "      <th>sex</th>\n",
       "      <th>capital-gain</th>\n",
       "      <th>capital-loss</th>\n",
       "      <th>hours-per-week</th>\n",
       "      <th>native-country</th>\n",
       "      <th>income</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>25</td>\n",
       "      <td>Private</td>\n",
       "      <td>11th</td>\n",
       "      <td>7</td>\n",
       "      <td>Never-married</td>\n",
       "      <td>Machine-op-inspct</td>\n",
       "      <td>Own-child</td>\n",
       "      <td>Black</td>\n",
       "      <td>Male</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>40</td>\n",
       "      <td>United-States</td>\n",
       "      <td>&lt;=50K</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>38</td>\n",
       "      <td>Private</td>\n",
       "      <td>HS-grad</td>\n",
       "      <td>9</td>\n",
       "      <td>Married-civ-spouse</td>\n",
       "      <td>Farming-fishing</td>\n",
       "      <td>Husband</td>\n",
       "      <td>White</td>\n",
       "      <td>Male</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>50</td>\n",
       "      <td>United-States</td>\n",
       "      <td>&lt;=50K</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>28</td>\n",
       "      <td>Local-gov</td>\n",
       "      <td>Assoc-acdm</td>\n",
       "      <td>12</td>\n",
       "      <td>Married-civ-spouse</td>\n",
       "      <td>Protective-serv</td>\n",
       "      <td>Husband</td>\n",
       "      <td>White</td>\n",
       "      <td>Male</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>40</td>\n",
       "      <td>United-States</td>\n",
       "      <td>&gt;50K</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>44</td>\n",
       "      <td>Private</td>\n",
       "      <td>Some-college</td>\n",
       "      <td>10</td>\n",
       "      <td>Married-civ-spouse</td>\n",
       "      <td>Machine-op-inspct</td>\n",
       "      <td>Husband</td>\n",
       "      <td>Black</td>\n",
       "      <td>Male</td>\n",
       "      <td>7688</td>\n",
       "      <td>0</td>\n",
       "      <td>40</td>\n",
       "      <td>United-States</td>\n",
       "      <td>&gt;50K</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>18</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Some-college</td>\n",
       "      <td>10</td>\n",
       "      <td>Never-married</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Own-child</td>\n",
       "      <td>White</td>\n",
       "      <td>Female</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>30</td>\n",
       "      <td>United-States</td>\n",
       "      <td>&lt;=50K</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   age  workclass     education  educational-num      marital-status  \\\n",
       "0   25    Private          11th                7       Never-married   \n",
       "1   38    Private       HS-grad                9  Married-civ-spouse   \n",
       "2   28  Local-gov    Assoc-acdm               12  Married-civ-spouse   \n",
       "3   44    Private  Some-college               10  Married-civ-spouse   \n",
       "4   18        NaN  Some-college               10       Never-married   \n",
       "\n",
       "          occupation relationship   race     sex  capital-gain  capital-loss  \\\n",
       "0  Machine-op-inspct    Own-child  Black    Male             0             0   \n",
       "1    Farming-fishing      Husband  White    Male             0             0   \n",
       "2    Protective-serv      Husband  White    Male             0             0   \n",
       "3  Machine-op-inspct      Husband  Black    Male          7688             0   \n",
       "4                NaN    Own-child  White  Female             0             0   \n",
       "\n",
       "   hours-per-week native-country income  \n",
       "0              40  United-States  <=50K  \n",
       "1              50  United-States  <=50K  \n",
       "2              40  United-States   >50K  \n",
       "3              40  United-States   >50K  \n",
       "4              30  United-States  <=50K  "
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Descrição das principais Features"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "age: Idade.\n",
    "\n",
    "workclass: Classe trabalhadora. Se a pessoa é funcionário público, autônomo, empresário etc.\n",
    "\n",
    "education: Níveis de Escolaridade.\n",
    "\n",
    "education-num: Código do Nível de Escolaridade.\n",
    "\n",
    "marital-status: Estado Civil.\n",
    "\n",
    "occupation: Ocupação (Suporte técnico, Conserto de arte, Outros serviços, Vendas, Exec-managerial, Prof-specialty, Manipuladores de limpeza, Machine-op-inspct, Adm-clerical, Pesca agropecuária, Transport-moving, Priv-house-serv, Serviço de Proteção, Forças Armadas)\n",
    "\n",
    "relationship: Relacionamento (Esposa, Filha Própria, Marido, Não-familiar, Outro-parente, Solteira.)\n",
    "\n",
    "race: Raça (Preto, branco, asiático-Pac-Islander, Amer-Indian-Eskimo, Outro.)\n",
    "\n",
    "sex: Sexo(Feminino, Masculino).\n",
    "\n",
    "capital-gain: Ganho de Capital.\n",
    "\n",
    "capital-loss: Perda de Capital.\n",
    "\n",
    "hours-per-week: Horas de trabalho por Semana.\n",
    "\n",
    "native-country: País Nativo (Estados Unidos, Camboja, Inglaterra, Porto Rico, Canadá, Alemanha, EUA (Guam-USVI-etc), Índia, México, Portugal etc)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Informações sobre as colunas"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 48842 entries, 0 to 48841\n",
      "Data columns (total 14 columns):\n",
      "age                48842 non-null int64\n",
      "workclass          46043 non-null object\n",
      "education          48842 non-null object\n",
      "educational-num    48842 non-null int64\n",
      "marital-status     48842 non-null object\n",
      "occupation         46033 non-null object\n",
      "relationship       48842 non-null object\n",
      "race               48842 non-null object\n",
      "sex                48842 non-null object\n",
      "capital-gain       48842 non-null int64\n",
      "capital-loss       48842 non-null int64\n",
      "hours-per-week     48842 non-null int64\n",
      "native-country     47985 non-null object\n",
      "income             48842 non-null object\n",
      "dtypes: int64(5), object(9)\n",
      "memory usage: 5.2+ MB\n"
     ]
    }
   ],
   "source": [
    "df.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "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>educational-num</th>\n",
       "      <th>capital-gain</th>\n",
       "      <th>capital-loss</th>\n",
       "      <th>hours-per-week</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>48842.000000</td>\n",
       "      <td>48842.000000</td>\n",
       "      <td>48842.000000</td>\n",
       "      <td>48842.000000</td>\n",
       "      <td>48842.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>38.643585</td>\n",
       "      <td>10.078089</td>\n",
       "      <td>1079.067626</td>\n",
       "      <td>87.502314</td>\n",
       "      <td>40.422382</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>13.710510</td>\n",
       "      <td>2.570973</td>\n",
       "      <td>7452.019058</td>\n",
       "      <td>403.004552</td>\n",
       "      <td>12.391444</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>17.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>28.000000</td>\n",
       "      <td>9.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>40.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>37.000000</td>\n",
       "      <td>10.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>40.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>48.000000</td>\n",
       "      <td>12.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>45.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>90.000000</td>\n",
       "      <td>16.000000</td>\n",
       "      <td>99999.000000</td>\n",
       "      <td>4356.000000</td>\n",
       "      <td>99.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                age  educational-num  capital-gain  capital-loss  \\\n",
       "count  48842.000000     48842.000000  48842.000000  48842.000000   \n",
       "mean      38.643585        10.078089   1079.067626     87.502314   \n",
       "std       13.710510         2.570973   7452.019058    403.004552   \n",
       "min       17.000000         1.000000      0.000000      0.000000   \n",
       "25%       28.000000         9.000000      0.000000      0.000000   \n",
       "50%       37.000000        10.000000      0.000000      0.000000   \n",
       "75%       48.000000        12.000000      0.000000      0.000000   \n",
       "max       90.000000        16.000000  99999.000000   4356.000000   \n",
       "\n",
       "       hours-per-week  \n",
       "count    48842.000000  \n",
       "mean        40.422382  \n",
       "std         12.391444  \n",
       "min          1.000000  \n",
       "25%         40.000000  \n",
       "50%         40.000000  \n",
       "75%         45.000000  \n",
       "max         99.000000  "
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Variáveis numéricas.\n",
    "df.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>workclass</th>\n",
       "      <th>education</th>\n",
       "      <th>marital-status</th>\n",
       "      <th>occupation</th>\n",
       "      <th>relationship</th>\n",
       "      <th>race</th>\n",
       "      <th>sex</th>\n",
       "      <th>native-country</th>\n",
       "      <th>income</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>46043</td>\n",
       "      <td>48842</td>\n",
       "      <td>48842</td>\n",
       "      <td>46033</td>\n",
       "      <td>48842</td>\n",
       "      <td>48842</td>\n",
       "      <td>48842</td>\n",
       "      <td>47985</td>\n",
       "      <td>48842</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>unique</th>\n",
       "      <td>8</td>\n",
       "      <td>16</td>\n",
       "      <td>7</td>\n",
       "      <td>14</td>\n",
       "      <td>6</td>\n",
       "      <td>5</td>\n",
       "      <td>2</td>\n",
       "      <td>41</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>top</th>\n",
       "      <td>Private</td>\n",
       "      <td>HS-grad</td>\n",
       "      <td>Married-civ-spouse</td>\n",
       "      <td>Prof-specialty</td>\n",
       "      <td>Husband</td>\n",
       "      <td>White</td>\n",
       "      <td>Male</td>\n",
       "      <td>United-States</td>\n",
       "      <td>&lt;=50K</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>freq</th>\n",
       "      <td>33906</td>\n",
       "      <td>15784</td>\n",
       "      <td>22379</td>\n",
       "      <td>6172</td>\n",
       "      <td>19716</td>\n",
       "      <td>41762</td>\n",
       "      <td>32650</td>\n",
       "      <td>43832</td>\n",
       "      <td>37155</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       workclass education      marital-status      occupation relationship  \\\n",
       "count      46043     48842               48842           46033        48842   \n",
       "unique         8        16                   7              14            6   \n",
       "top      Private   HS-grad  Married-civ-spouse  Prof-specialty      Husband   \n",
       "freq       33906     15784               22379            6172        19716   \n",
       "\n",
       "         race    sex native-country income  \n",
       "count   48842  48842          47985  48842  \n",
       "unique      5      2             41      2  \n",
       "top     White   Male  United-States  <=50K  \n",
       "freq    41762  32650          43832  37155  "
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Variáveis categóricas.\n",
    "df.describe(include=['O'])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Análise Exporatória"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<seaborn.axisgrid.FacetGrid at 0x7ff69b0fdb00>"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 360x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Distribuição das classes (variável income)\n",
    "sns.catplot(x='income',kind='count',data=df,orient=\"h\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<=50K    37155\n",
       ">50K     11687\n",
       "Name: income, dtype: int64"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['income'].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(array([ 0,  1,  2,  3,  4,  5,  6,  7,  8,  9, 10, 11, 12, 13, 14, 15]),\n",
       " <a list of 16 Text xticklabel objects>)"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1440x720 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Distribuição das classes por Nível de Escolaridade\n",
    "plt.figure(figsize=(20,10))\n",
    "sns.countplot(data=df, x = 'education',  hue = 'income')\n",
    "plt.xticks(rotation=65)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "HS-grad         15784\n",
       "Some-college    10878\n",
       "Bachelors        8025\n",
       "Masters          2657\n",
       "Assoc-voc        2061\n",
       "11th             1812\n",
       "Assoc-acdm       1601\n",
       "10th             1389\n",
       "7th-8th           955\n",
       "Prof-school       834\n",
       "9th               756\n",
       "12th              657\n",
       "Doctorate         594\n",
       "5th-6th           509\n",
       "1st-4th           247\n",
       "Preschool          83\n",
       "Name: education, dtype: int64"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['education'].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1\n",
      "99\n"
     ]
    }
   ],
   "source": [
    "# Verificando a menor e a maior jornada de trabalho.\n",
    "print(df['hours-per-week'].min())\n",
    "print(df['hours-per-week'].max())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(array([0, 1, 2]), <a list of 3 Text xticklabel objects>)"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 576x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Para uma melhor visualização vamos agrupar as horas trabalhadas em 3 categorias:\n",
    "# 0-30 (jornada curta)\n",
    "# 30-40 (jornada normal)\n",
    "# 40-100 (jornada extra)\n",
    "\n",
    "df['hours-per-week'] = pd.cut(df['hours-per-week'],[0,30,40,100], labels =['short','normal','extra'])\n",
    "\n",
    "# Distribuição das classes por Horas trabalhadas\n",
    "plt.figure(figsize=(8,6))\n",
    "sns.countplot(data=df, x = 'hours-per-week',  hue = 'income')\n",
    "plt.xticks(rotation=65)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "normal    26639\n",
       "extra     14352\n",
       "short      7851\n",
       "Name: hours-per-week, dtype: int64"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['hours-per-week'].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(array([ 0,  1,  2,  3,  4,  5,  6,  7,  8,  9, 10, 11, 12, 13]),\n",
       " <a list of 14 Text xticklabel objects>)"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1440x720 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Distribuição das classes de acordo com a ocupação\n",
    "plt.figure(figsize=(20,10))\n",
    "sns.countplot(data=df, x = 'occupation',  hue = 'income')\n",
    "plt.xticks(rotation=65)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Prof-specialty       6172\n",
       "Craft-repair         6112\n",
       "Exec-managerial      6086\n",
       "Adm-clerical         5611\n",
       "Sales                5504\n",
       "Other-service        4923\n",
       "Machine-op-inspct    3022\n",
       "Transport-moving     2355\n",
       "Handlers-cleaners    2072\n",
       "Farming-fishing      1490\n",
       "Tech-support         1446\n",
       "Protective-serv       983\n",
       "Priv-house-serv       242\n",
       "Armed-Forces           15\n",
       "Name: occupation, dtype: int64"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['occupation'].value_counts()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Relação das colunas age,sex e race com a coluna income."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x7ff69b1eab38>"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 576x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(8,6))\n",
    "sns.countplot(y=\"sex\", hue='income', data=df)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x7ff69af6f128>"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 576x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(8,6))\n",
    "sns.countplot(y=\"race\", hue='income', data=df)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "A idade mínima é: 17 anos\n",
      "A idade máxima é: 90 anos\n"
     ]
    }
   ],
   "source": [
    "# Verificando a idade mínima e máxima do dataset.\n",
    "print(f'A idade mínima é: {df.age.min()} anos')\n",
    "print(f'A idade máxima é: {df.age.max()} anos')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(array([0, 1, 2]), <a list of 3 Text xticklabel objects>)"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 576x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Para uma melhor visualização vamos agrupar as idades em 3 categorias:\n",
    "# 0-25 anos (jovens)\n",
    "# 25-50 anos (adultos)\n",
    "# 50-100 anos (idosos)\n",
    "df['age'] = pd.cut(df['age'],[0,25,50,100], labels =['young','adult','old'])\n",
    "\n",
    "# Verificando como ficou a distribuição.\n",
    "plt.figure(figsize=(8,6))\n",
    "sns.countplot(data=df, x = 'age',  hue = 'income')\n",
    "plt.xticks(rotation=70)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Análise de Dados "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "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>total</th>\n",
       "      <th>percent</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>occupation</th>\n",
       "      <td>2809</td>\n",
       "      <td>0.057512</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>workclass</th>\n",
       "      <td>2799</td>\n",
       "      <td>0.057307</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>native-country</th>\n",
       "      <td>857</td>\n",
       "      <td>0.017546</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                total   percent\n",
       "occupation       2809  0.057512\n",
       "workclass        2799  0.057307\n",
       "native-country    857  0.017546"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Verificando a quantidade de valores nulos no dataset.\n",
    "total = df.isnull().sum().sort_values(ascending=False)\n",
    "percent = df.isnull().sum()/df.isnull().count().sort_values(ascending=False)\n",
    "missing_data = pd.concat([total, percent], axis=1, sort=False, keys=['total', 'percent'])\n",
    "missing_data[missing_data['percent']!=0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Preenchendo os valores nulos da coluna 'workclass'.\n",
    "top = 'Private'\n",
    "df['workclass'] = df['workclass'].fillna(top)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Preenchendo os valores nulos da coluna 'occupation'.\n",
    "top = 'United-States'\n",
    "df['occupation'] = df['occupation'].fillna(top)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Preenchendo os valores nulos da coluna 'native-country'.\n",
    "top = 'Prof-specialty'\n",
    "df['native-country'] = df['native-country'].fillna(top)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Existe valores nulos no dataset? False\n"
     ]
    }
   ],
   "source": [
    "# Verificando se ainda existem valores nulos no Dataset.\n",
    "if df.isnull().sum().sort_values(ascending=False).any() != 0:\n",
    "    print(f'Existe valores nulos no dataset? {True}')\n",
    "else:\n",
    "    print(f'Existe valores nulos no dataset? {False}')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Criando uma nova coluna com a renda final.\n",
    "df['capital_last'] = df['capital-gain'] - df['capital-loss']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Removendo as features irrelevantes.\n",
    "# A coluna 'educational-num' está preenchida com o código referente a cada nível de escolaridade, dessa maneira podemos excluir a coluna 'education'.\n",
    "# As colunas 'capital-gain' e 'capital-loss' estão representadas pela nova coluna que criamos anteriormente.\n",
    "def remove_features(lista_features):\n",
    "    for i in lista_features:\n",
    "        df.drop(i, axis=1, inplace=True)\n",
    "remove_features(['capital-gain','capital-loss','education'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Trasnformando as variáveis categóricas em numéricas.\n",
    "cols = df[df.select_dtypes(['object']).columns]\n",
    "\n",
    "for c in cols:\n",
    "    encoding = LabelEncoder()\n",
    "    encoding.fit(list(df[c].values))\n",
    "    df[c] = encoding.transform(list(df[c].values))\n",
    "df = pd.get_dummies(df)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "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>workclass</th>\n",
       "      <th>educational-num</th>\n",
       "      <th>marital-status</th>\n",
       "      <th>occupation</th>\n",
       "      <th>relationship</th>\n",
       "      <th>race</th>\n",
       "      <th>sex</th>\n",
       "      <th>native-country</th>\n",
       "      <th>income</th>\n",
       "      <th>capital_last</th>\n",
       "      <th>age_young</th>\n",
       "      <th>age_adult</th>\n",
       "      <th>age_old</th>\n",
       "      <th>hours-per-week_short</th>\n",
       "      <th>hours-per-week_normal</th>\n",
       "      <th>hours-per-week_extra</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>3</td>\n",
       "      <td>7</td>\n",
       "      <td>4</td>\n",
       "      <td>6</td>\n",
       "      <td>3</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>39</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>3</td>\n",
       "      <td>9</td>\n",
       "      <td>2</td>\n",
       "      <td>4</td>\n",
       "      <td>0</td>\n",
       "      <td>4</td>\n",
       "      <td>1</td>\n",
       "      <td>39</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1</td>\n",
       "      <td>12</td>\n",
       "      <td>2</td>\n",
       "      <td>10</td>\n",
       "      <td>0</td>\n",
       "      <td>4</td>\n",
       "      <td>1</td>\n",
       "      <td>39</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>3</td>\n",
       "      <td>10</td>\n",
       "      <td>2</td>\n",
       "      <td>6</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>39</td>\n",
       "      <td>1</td>\n",
       "      <td>7688</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>3</td>\n",
       "      <td>10</td>\n",
       "      <td>4</td>\n",
       "      <td>14</td>\n",
       "      <td>3</td>\n",
       "      <td>4</td>\n",
       "      <td>0</td>\n",
       "      <td>39</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   workclass  educational-num  marital-status  occupation  relationship  race  \\\n",
       "0          3                7               4           6             3     2   \n",
       "1          3                9               2           4             0     4   \n",
       "2          1               12               2          10             0     4   \n",
       "3          3               10               2           6             0     2   \n",
       "4          3               10               4          14             3     4   \n",
       "\n",
       "   sex  native-country  income  capital_last  age_young  age_adult  age_old  \\\n",
       "0    1              39       0             0          1          0        0   \n",
       "1    1              39       0             0          0          1        0   \n",
       "2    1              39       1             0          0          1        0   \n",
       "3    1              39       1          7688          0          1        0   \n",
       "4    0              39       0             0          1          0        0   \n",
       "\n",
       "   hours-per-week_short  hours-per-week_normal  hours-per-week_extra  \n",
       "0                     0                      1                     0  \n",
       "1                     0                      0                     1  \n",
       "2                     0                      1                     0  \n",
       "3                     0                      1                     0  \n",
       "4                     1                      0                     0  "
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Matriz de correlação"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x7ff69ae725c0>"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x648 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Possibilita a análise simultânea da associação entre variáveis.\n",
    "corr = df.corr()\n",
    "sns.set(rc={'axes.facecolor':'white', 'figure.facecolor':'white'})\n",
    "plt.subplots(figsize=(12, 9))\n",
    "sns.heatmap(corr, vmax=.8,annot_kws={'size': 10}, annot=True, fmt='.2f')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "educational-num          0.332613\n",
       "hours-per-week_extra     0.243127\n",
       "sex                      0.214628\n",
       "capital_last             0.214356\n",
       "age_adult                0.133381\n",
       "age_old                  0.092733\n",
       "race                     0.070934\n",
       "native-country           0.020073\n",
       "occupation               0.007275\n",
       "workclass               -0.000511\n",
       "hours-per-week_normal   -0.092061\n",
       "hours-per-week_short    -0.176724\n",
       "marital-status          -0.199072\n",
       "relationship            -0.253214\n",
       "age_young               -0.257496\n",
       "Name: income, dtype: float64"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "corr_list = corr['income'].sort_values(axis=0,ascending=False).iloc[1:]\n",
    "corr_list"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Verificando as features mais importantes para o modelo."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Separando a classe dos Dados.\n",
    "previsores = df.drop('income',axis=1)\n",
    "classe = df['income']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "RandomForestClassifier(bootstrap=True, class_weight=None, criterion='gini',\n",
       "                       max_depth=None, max_features='auto', max_leaf_nodes=None,\n",
       "                       min_impurity_decrease=0.0, min_impurity_split=None,\n",
       "                       min_samples_leaf=1, min_samples_split=2,\n",
       "                       min_weight_fraction_leaf=0.0, n_estimators=100,\n",
       "                       n_jobs=None, oob_score=False, random_state=7, verbose=0,\n",
       "                       warm_start=False)"
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "clf=RandomForestClassifier(n_estimators=100, random_state=7)\n",
    "clf.fit(previsores, classe)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "capital_last             0.227708\n",
       "educational-num          0.185721\n",
       "relationship             0.144930\n",
       "occupation               0.109679\n",
       "marital-status           0.108843\n",
       "workclass                0.058868\n",
       "native-country           0.036384\n",
       "hours-per-week_extra     0.024598\n",
       "age_young                0.024525\n",
       "race                     0.023116\n",
       "sex                      0.018543\n",
       "hours-per-week_short     0.012996\n",
       "hours-per-week_normal    0.008717\n",
       "age_adult                0.007869\n",
       "age_old                  0.007505\n",
       "dtype: float64"
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "feature_imp = pd.Series(clf.feature_importances_,index=previsores.columns).sort_values(ascending=False)\n",
    "feature_imp"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 576x432 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(8,6))\n",
    "sns.barplot(x=feature_imp, y=feature_imp.index)\n",
    "plt.xlabel('Feature Importance Score')\n",
    "plt.ylabel('Features')\n",
    "plt.title(\"Visualizing Important Features\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "capital_last: 23%\n",
      "educational-num: 19%\n",
      "relationship: 14%\n",
      "occupation: 11%\n",
      "marital-status: 11%\n",
      "workclass: 6%\n",
      "native-country: 4%\n"
     ]
    }
   ],
   "source": [
    "# Selecionando as features de maior importância.\n",
    "features_selected = []\n",
    "for feature,importance in feature_imp.iteritems():\n",
    "    if importance > 0.03:\n",
    "        print(f'{feature}: {round(importance * 100)}%')\n",
    "        features_selected.append(feature)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Treinando os modelos utilizando apenas as features selecionadas.\n",
    "previsores = df[features_selected]\n",
    "classe = df['income']"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Avaliação dos modelos de Machine Learning"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Scaled-LR: 0.817309 (0.004340)\n",
      "Scaled-KNN: 0.834917 (0.006556)\n",
      "Scaled-CART: 0.855739 (0.003079)\n",
      "Scaled-NB: 0.802813 (0.001707)\n",
      "Scaled-RF: 0.856886 (0.003339)\n"
     ]
    }
   ],
   "source": [
    "# Criando modelo de Machine Learning a partir de cada algoritmo\n",
    "# Os dados estão em diferentes escalas e isso pode prejudicar a performance de alguns algoritmos.\n",
    "# Vamos aplicar a  Padronização ao conjunto de dados (colocando-os na mesma escala).\n",
    "# Nesta técnica, os dados serão transformados de modo que estejam com uma distribuição normal, com média igual a zero e\n",
    "# desvio padrão igual a 1.\n",
    "\n",
    "# Não utilizarei o modelo SVC, pois ele não funciona bem quando temos um grande conjunto de dados\n",
    "# porque o tempo de treinamento necessário é maior. \n",
    "\n",
    "pipelines = []\n",
    "pipelines.append(('Scaled-LR', Pipeline([('Scaler', StandardScaler()),('LR', LogisticRegression())]))),\n",
    "pipelines.append(('Scaled-KNN', Pipeline([('Scaler', StandardScaler()),('KNN', KNeighborsClassifier())]))),\n",
    "pipelines.append(('Scaled-CART', Pipeline([('Scaler', StandardScaler()),('CART', DecisionTreeClassifier())]))),\n",
    "pipelines.append(('Scaled-NB', Pipeline([('Scaler', StandardScaler()),('NB', GaussianNB())]))),\n",
    "#pipelines.append(('Scaled-SVM', Pipeline([('Scaler', StandardScaler()),('SVM', SVC())])))\n",
    "pipelines.append(('Scaled-RF', Pipeline([('Scaler', StandardScaler()),('RF', RandomForestClassifier())])))\n",
    "resultados = []\n",
    "nomes = []\n",
    "\n",
    "# Percorrendo cada um dos modelos\n",
    "for nome, modelo in pipelines:\n",
    "    kfold = StratifiedKFold(n_splits=10, shuffle = True, random_state=7)\n",
    "    cross_val_result = model_selection.cross_val_score(modelo, \n",
    "                                                     previsores, \n",
    "                                                     classe, \n",
    "                                                     cv = kfold, \n",
    "                                                     scoring = 'accuracy')\n",
    "    resultados.append(cross_val_result)\n",
    "    nomes.append(nome)\n",
    "    texto = \"%s: %f (%f)\" % (nome, cross_val_result.mean(), cross_val_result.std())\n",
    "    print(texto)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# De acordo com os resultados anteriores, os modelos criados com DecisionTree e Random Forest apresentaram \n",
    "# os melhores valores de acurácia e portanto os melhores resultados.\n",
    "# Isso pode ser confirmado, comparando os algoritmos através de boxplots.\n",
    "fig = plt.figure()\n",
    "fig.suptitle('Comparando os Algoritmos Padronizados')\n",
    "ax = fig.add_subplot(111)\n",
    "plt.boxplot(resultados)\n",
    "ax.set_xticklabels(nomes)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Tunning dos modelos criados com Random Forest e DecisionTree."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Grid scores on development set:\n",
      "mean:0.85697,std:0.00319,params:{'n_estimators': 20, 'criterion': 'gini'}\n",
      "mean:0.85754,std:0.00386,params:{'n_estimators': 50, 'criterion': 'gini'}\n",
      "mean:0.85815,std:0.00333,params:{'n_estimators': 100, 'criterion': 'gini'}\n",
      "mean:0.85793,std:0.00316,params:{'n_estimators': 150, 'criterion': 'gini'}\n",
      "mean:0.85815,std:0.0033,params:{'n_estimators': 200, 'criterion': 'gini'}\n",
      "mean:0.85734,std:0.00306,params:{'n_estimators': 20, 'criterion': 'entropy'}\n",
      "mean:0.85764,std:0.00357,params:{'n_estimators': 50, 'criterion': 'entropy'}\n",
      "mean:0.85807,std:0.00271,params:{'n_estimators': 100, 'criterion': 'entropy'}\n",
      "mean:0.85793,std:0.00322,params:{'n_estimators': 150, 'criterion': 'entropy'}\n",
      "mean:0.85811,std:0.00304,params:{'n_estimators': 200, 'criterion': 'entropy'}\n",
      "\n",
      "Melhor parâmetro:{'n_estimators': 100, 'criterion': 'gini'}, Score:0.8581548667130748\n"
     ]
    }
   ],
   "source": [
    "# Embora o Random Forest tenha apresentado a menor taxa de erro após a padronização dos dados, podemos ainda otimizá-lo\n",
    "# com o ajuste dos parâmetros.\n",
    "\n",
    "# # Definindo a escala\n",
    "scaler = StandardScaler()\n",
    "previsores = scaler.fit_transform(previsores)\n",
    "\n",
    "# Possíveis valores de estimators\n",
    "val_estimators = [20, 50, 100, 150, 200]\n",
    "\n",
    "# Possíveis valores para o critério de divisão\n",
    "val_criterion = ['gini', 'entropy']\n",
    "\n",
    "# Definindo um dicionário que recebe as listas de parâmetros e valores\n",
    "parametros_grid = dict(n_estimators = val_estimators,\n",
    "                       criterion = val_criterion)\n",
    "                      \n",
    "                       \n",
    "# Criando o modelo\n",
    "modelo_random = RandomForestClassifier()\n",
    "\n",
    "# Definindo K\n",
    "kfold = StratifiedKFold(n_splits=10, shuffle = True, random_state=7)\n",
    "\n",
    "# Testando diferenets combinações com os parâmetros\n",
    "grid = RandomizedSearchCV(estimator = modelo_random, param_distributions = parametros_grid, cv = kfold, scoring = 'accuracy')\n",
    "grid.fit(previsores, classe)\n",
    "\n",
    "# Print do resultado\n",
    "print(\"Grid scores on development set:\")\n",
    "means = grid.cv_results_['mean_test_score'].round(5)\n",
    "stds = grid.cv_results_['std_test_score'].round(5)\n",
    "\n",
    "for mean, std, params in zip(means, stds, grid.cv_results_['params']):\n",
    "    print(f'mean:{mean},std:{std},params:{params}')\n",
    "print()\n",
    "print(f'Melhor parâmetro:{grid.best_params_}, Score:{grid.best_score_}')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Grid scores on development set:\n",
      "mean:0.85564,std:0.00319,params:{'max_depth': 30, 'criterion': 'gini'}\n",
      "mean:0.85594,std:0.003,params:{'max_depth': 29, 'criterion': 'entropy'}\n",
      "mean:0.85871,std:0.00421,params:{'max_depth': 10, 'criterion': 'gini'}\n",
      "mean:0.85906,std:0.00417,params:{'max_depth': 11, 'criterion': 'gini'}\n",
      "mean:0.85543,std:0.00364,params:{'max_depth': 29, 'criterion': 'gini'}\n",
      "mean:0.84438,std:0.00466,params:{'max_depth': 4, 'criterion': 'entropy'}\n",
      "mean:0.85959,std:0.00375,params:{'max_depth': 13, 'criterion': 'entropy'}\n",
      "mean:0.85906,std:0.00339,params:{'max_depth': 15, 'criterion': 'entropy'}\n",
      "mean:0.76072,std:6e-05,params:{'max_depth': 1, 'criterion': 'gini'}\n",
      "mean:0.85539,std:0.00329,params:{'max_depth': 28, 'criterion': 'gini'}\n",
      "\n",
      "Melhor parâmetro:{'max_depth': 13, 'criterion': 'entropy'}, Score:0.8595880594570247\n"
     ]
    }
   ],
   "source": [
    "# Vamos agora ajustar os parâmeros do DecisionTree.\n",
    "\n",
    "# Definindo a escala\n",
    "scaler = StandardScaler()\n",
    "previsores = scaler.fit_transform(previsores)\n",
    "\n",
    "# Definindo a profundidade máxima da árvore.\n",
    "max_depth = list(range(1, 31))\n",
    "\n",
    "# Possíveis valores para o critério de divisão\n",
    "val_criterion = ['gini', 'entropy']\n",
    "\n",
    "\n",
    "# Definindo um dicionário que recebe as listas de parâmetros e valores\n",
    "valores_grid = dict(criterion = val_criterion,\n",
    "                    max_depth = max_depth)\n",
    "\n",
    "# Criando o modelo\n",
    "tree = DecisionTreeClassifier()\n",
    "\n",
    "# Definindo K\n",
    "kfold = StratifiedKFold(n_splits=10, shuffle = True, random_state=7)\n",
    "\n",
    "# Testando diferenets combinações com os valores de K\n",
    "grid = RandomizedSearchCV(estimator = tree, param_distributions = valores_grid, cv = kfold, scoring = 'accuracy')\n",
    "grid.fit(previsores, classe)\n",
    "\n",
    "# Print do resultado  \n",
    "print(\"Grid scores on development set:\")\n",
    "means = grid.cv_results_['mean_test_score'].round(5)\n",
    "stds = grid.cv_results_['std_test_score'].round(5)\n",
    "\n",
    "for mean, std, params in zip(means, stds, grid.cv_results_['params']):\n",
    "    print(f'mean:{mean},std:{std},params:{params}')\n",
    "print()\n",
    "print(f'Melhor parâmetro:{grid.best_params_}, Score:{grid.best_score_}')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Finalizando o Modelo\n",
    "\n",
    "O modelo criado com DecisionTree apresentou a melhor acurária entre todos os modelos criados e portanto será usado na construção da versão final do modelo preditivo.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Acurácia: 0.858789566356824\n",
      "\n",
      " [[35055  2100]\n",
      " [ 4797  6890]]\n",
      "\n",
      "               precision    recall  f1-score   support\n",
      "\n",
      "           0       0.88      0.94      0.91     37155\n",
      "           1       0.77      0.59      0.67     11687\n",
      "\n",
      "    accuracy                           0.86     48842\n",
      "   macro avg       0.82      0.77      0.79     48842\n",
      "weighted avg       0.85      0.86      0.85     48842\n",
      "\n"
     ]
    }
   ],
   "source": [
    "# Preparando a versão final do modelo.\n",
    "scaler = StandardScaler()\n",
    "previsores = scaler.fit_transform(previsores)\n",
    "modelo_tree = DecisionTreeClassifier(criterion='entropy', max_depth=14, random_state=7)\n",
    "modelo_tree.fit(previsores,classe)\n",
    "result_tree = cross_val_predict(modelo_tree,previsores, classe, cv=10)\n",
    "\n",
    "print(f'Acurácia: {accuracy_score(classe,result_tree)}')\n",
    "print('\\n',confusion_matrix(classe,result_tree))\n",
    "print('\\n',classification_report(classe,result_tree))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Fazendo a persistência do modelo treinado para o disco."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "DecisionTreeClassifier(class_weight=None, criterion='entropy', max_depth=14,\n",
       "                       max_features=None, max_leaf_nodes=None,\n",
       "                       min_impurity_decrease=0.0, min_impurity_split=None,\n",
       "                       min_samples_leaf=1, min_samples_split=2,\n",
       "                       min_weight_fraction_leaf=0.0, presort=False,\n",
       "                       random_state=7, splitter='best')"
      ]
     },
     "execution_count": 42,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#filename = 'model_final.sav'\n",
    "pickle.dump(modelo_tree, open('modelo.sav', 'wb'))\n",
    "pickle.load(open('modelo.sav', 'rb'))"
   ]
  }
 ],
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