07.00-tratamento-dados-desbalanceados.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Importação das bibliotecas para analise e manipluação de dados\n",
    "\n",
    "import pandas as pd                                                # Analise e tratamento de dados\n",
    "import numpy as np                                                 # Tratamento de dados numéricos\n",
    "import matplotlib.pyplot as plt                                    # Plotagem de gráficos\n",
    "import seaborn as sns                                              # Plotagem de gráficos\n",
    "from sklearn.model_selection import train_test_split               # Divisão de conjuntos de dados de treino e teste\n",
    "from sklearn.preprocessing import LabelEncoder                     # tratamento variaveis categoricas         \n",
    "from sklearn.preprocessing import OneHotEncoder                    # tratamento de variaveis \n",
    "from sklearn.compose import ColumnTransformer                      # tratamento de variaveis\n",
    "from sklearn.ensemble import RandomForestClassifier                # Algoritmo Random Forest(Árvore de decisão)\n",
    "from sklearn.metrics import confusion_matrix, accuracy_score       # Matriz de confusão e métricas de precisão e acurácia\n",
    "from sklearn.metrics import classification_report                  # metricas de avaliação do modelo \n",
    "from imblearn.under_sampling import TomekLinks                     # Subamostragem aleatória (undersampling)\n",
    "from imblearn.over_sampling import SMOTE                           # Sobreamostragem aleatória (OverSampling)\n",
    "\n",
    "# ignorando warnings\n",
    "import warnings\n",
    "warnings.filterwarnings('ignore')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "# carregando a base de dados\n",
    "\n",
    "base_census =pd.read_csv('./data/census.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>age</th>\n",
       "      <th>workclass</th>\n",
       "      <th>final-weight</th>\n",
       "      <th>education</th>\n",
       "      <th>education-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-loos</th>\n",
       "      <th>hour-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>39</td>\n",
       "      <td>State-gov</td>\n",
       "      <td>77516</td>\n",
       "      <td>Bachelors</td>\n",
       "      <td>13</td>\n",
       "      <td>Never-married</td>\n",
       "      <td>Adm-clerical</td>\n",
       "      <td>Not-in-family</td>\n",
       "      <td>White</td>\n",
       "      <td>Male</td>\n",
       "      <td>2174</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>50</td>\n",
       "      <td>Self-emp-not-inc</td>\n",
       "      <td>83311</td>\n",
       "      <td>Bachelors</td>\n",
       "      <td>13</td>\n",
       "      <td>Married-civ-spouse</td>\n",
       "      <td>Exec-managerial</td>\n",
       "      <td>Husband</td>\n",
       "      <td>White</td>\n",
       "      <td>Male</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>13</td>\n",
       "      <td>United-States</td>\n",
       "      <td>&lt;=50K</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>38</td>\n",
       "      <td>Private</td>\n",
       "      <td>215646</td>\n",
       "      <td>HS-grad</td>\n",
       "      <td>9</td>\n",
       "      <td>Divorced</td>\n",
       "      <td>Handlers-cleaners</td>\n",
       "      <td>Not-in-family</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>&lt;=50K</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>53</td>\n",
       "      <td>Private</td>\n",
       "      <td>234721</td>\n",
       "      <td>11th</td>\n",
       "      <td>7</td>\n",
       "      <td>Married-civ-spouse</td>\n",
       "      <td>Handlers-cleaners</td>\n",
       "      <td>Husband</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>4</th>\n",
       "      <td>28</td>\n",
       "      <td>Private</td>\n",
       "      <td>338409</td>\n",
       "      <td>Bachelors</td>\n",
       "      <td>13</td>\n",
       "      <td>Married-civ-spouse</td>\n",
       "      <td>Prof-specialty</td>\n",
       "      <td>Wife</td>\n",
       "      <td>Black</td>\n",
       "      <td>Female</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>40</td>\n",
       "      <td>Cuba</td>\n",
       "      <td>&lt;=50K</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   age          workclass  final-weight   education  education-num  \\\n",
       "0   39          State-gov         77516   Bachelors             13   \n",
       "1   50   Self-emp-not-inc         83311   Bachelors             13   \n",
       "2   38            Private        215646     HS-grad              9   \n",
       "3   53            Private        234721        11th              7   \n",
       "4   28            Private        338409   Bachelors             13   \n",
       "\n",
       "        marital-status          occupation    relationship    race      sex  \\\n",
       "0        Never-married        Adm-clerical   Not-in-family   White     Male   \n",
       "1   Married-civ-spouse     Exec-managerial         Husband   White     Male   \n",
       "2             Divorced   Handlers-cleaners   Not-in-family   White     Male   \n",
       "3   Married-civ-spouse   Handlers-cleaners         Husband   Black     Male   \n",
       "4   Married-civ-spouse      Prof-specialty            Wife   Black   Female   \n",
       "\n",
       "   capital-gain  capital-loos  hour-per-week  native-country  income  \n",
       "0          2174             0             40   United-States   <=50K  \n",
       "1             0             0             13   United-States   <=50K  \n",
       "2             0             0             40   United-States   <=50K  \n",
       "3             0             0             40   United-States   <=50K  \n",
       "4             0             0             40            Cuba   <=50K  "
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# primeiros registros\n",
    "\n",
    "base_census.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      " <=50K    24720\n",
      " >50K      7841\n",
      "Name: income, dtype: int64\n",
      " <=50K    0.75919\n",
      " >50K     0.24081\n",
      "Name: income, dtype: float64\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# quantidade registros por classe\n",
    "# quantidade, percentual e visual \n",
    "\n",
    "print(base_census['income'].value_counts())\n",
    "print(base_census['income'].value_counts(1))\n",
    "sns.countplot(base_census['income']);"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 32561 entries, 0 to 32560\n",
      "Data columns (total 15 columns):\n",
      " #   Column          Non-Null Count  Dtype \n",
      "---  ------          --------------  ----- \n",
      " 0   age             32561 non-null  int64 \n",
      " 1   workclass       32561 non-null  object\n",
      " 2   final-weight    32561 non-null  int64 \n",
      " 3   education       32561 non-null  object\n",
      " 4   education-num   32561 non-null  int64 \n",
      " 5   marital-status  32561 non-null  object\n",
      " 6   occupation      32561 non-null  object\n",
      " 7   relationship    32561 non-null  object\n",
      " 8   race            32561 non-null  object\n",
      " 9   sex             32561 non-null  object\n",
      " 10  capital-gain    32561 non-null  int64 \n",
      " 11  capital-loos    32561 non-null  int64 \n",
      " 12  hour-per-week   32561 non-null  int64 \n",
      " 13  native-country  32561 non-null  object\n",
      " 14  income          32561 non-null  object\n",
      "dtypes: int64(6), object(9)\n",
      "memory usage: 3.7+ MB\n"
     ]
    }
   ],
   "source": [
    "# informações da base de dados\n",
    "\n",
    "base_census.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[39, ' State-gov', 77516, ..., 0, 40, ' United-States'],\n",
       "       [50, ' Self-emp-not-inc', 83311, ..., 0, 13, ' United-States'],\n",
       "       [38, ' Private', 215646, ..., 0, 40, ' United-States'],\n",
       "       ...,\n",
       "       [58, ' Private', 151910, ..., 0, 40, ' United-States'],\n",
       "       [22, ' Private', 201490, ..., 0, 20, ' United-States'],\n",
       "       [52, ' Self-emp-inc', 287927, ..., 0, 40, ' United-States']],\n",
       "      dtype=object)"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# definindo variaveis\n",
    "\n",
    "# previsores\n",
    "X_census = base_census.iloc[ :, 0:14].values\n",
    "X_census"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([' <=50K', ' <=50K', ' <=50K', ..., ' <=50K', ' <=50K', ' >50K'],\n",
       "      dtype=object)"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# classes\n",
    "\n",
    "y_census = base_census.iloc[ :, 14].values\n",
    "y_census"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "# label encoder categoricos"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "# tratamento nas variaveis categoricas com o LabelEncoder\n",
    "\n",
    "label_encoder_workclass = LabelEncoder()\n",
    "label_encoder_education = LabelEncoder()\n",
    "label_encoder_marital = LabelEncoder()\n",
    "label_encoder_occupation = LabelEncoder()\n",
    "label_encoder_relationship = LabelEncoder()\n",
    "label_encoder_race = LabelEncoder()\n",
    "label_encoder_sex = LabelEncoder()\n",
    "label_encoder_country = LabelEncoder()\n",
    "\n",
    "X_census[ :, 1] = label_encoder_workclass.fit_transform(X_census[ :, 1])\n",
    "X_census[ :, 3] = label_encoder_education.fit_transform(X_census[ :, 3])\n",
    "X_census[ :, 5] = label_encoder_marital.fit_transform(X_census[ :, 5])\n",
    "X_census[ :, 6] = label_encoder_occupation.fit_transform(X_census[ :, 6])\n",
    "X_census[ :, 7] = label_encoder_relationship.fit_transform(X_census[ :, 7])\n",
    "X_census[ :, 8] = label_encoder_race.fit_transform(X_census[ :, 8])\n",
    "X_census[ :, 9] = label_encoder_sex.fit_transform(X_census[ :, 9])\n",
    "X_census[ :, 13] = label_encoder_country.fit_transform(X_census[ :, 13])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[39, 7, 77516, ..., 0, 40, 39],\n",
       "       [50, 6, 83311, ..., 0, 13, 39],\n",
       "       [38, 4, 215646, ..., 0, 40, 39],\n",
       "       ...,\n",
       "       [58, 4, 151910, ..., 0, 40, 39],\n",
       "       [22, 4, 201490, ..., 0, 20, 39],\n",
       "       [52, 5, 287927, ..., 0, 40, 39]], dtype=object)"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# visualização após tratamento\n",
    "\n",
    "X_census"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "# implementação Tomek Links"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "# uso do tomek links(majority, all) que retira os registros para equilibrar as classes\n",
    "\n",
    "# tl = TomekLinks(sampling_strategy='majority') # 85.52% apenas da maior classe\n",
    "tl = TomekLinks(sampling_strategy='all')        # 88.74% das duas classes\n",
    "X_under, y_under = tl.fit_resample(X_census, y_census)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "((27763, 14), (27763,))"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# visualização após tratamento\n",
    "\n",
    "X_under.shape, y_under.shape\n",
    "# algoritmo reduziu 2_000('majority') registros da base original para equilibrar a base"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(array([' <=50K', ' >50K'], dtype=object), array([24720,  7841], dtype=int64))\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# quantidade registros por classe\n",
    "# quantidade, visual \n",
    "\n",
    "print(np.unique(y_census, return_counts=True))\n",
    "sns.countplot(y_census);"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(array([' <=50K', ' >50K'], dtype=object), array([22321,  5442], dtype=int64))\n"
     ]
    },
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# quantidade registros por classe\n",
    "# quantidade, visual \n",
    "\n",
    "print(np.unique(y_under, return_counts=True))\n",
    "sns.countplot(y_under);"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[    0.,     0.,     0., ...,  2174.,     0.,    40.],\n",
       "       [    0.,     0.,     0., ...,     0.,     0.,    40.],\n",
       "       [    0.,     0.,     0., ...,     0.,     0.,    40.],\n",
       "       ...,\n",
       "       [    0.,     0.,     0., ...,     0.,     0.,    40.],\n",
       "       [    0.,     0.,     0., ...,     0.,     0.,    20.],\n",
       "       [    0.,     0.,     0., ..., 15024.,     0.,    40.]])"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# tratamento Onehotencoder\n",
    "\n",
    "one_hot_encoder = ColumnTransformer(transformers=[('OneHot', OneHotEncoder(), [1, 3, 5, 6, 7, 8, 9, 13])], remainder='passthrough')\n",
    "X_under = one_hot_encoder.fit_transform(X_under).toarray()\n",
    "X_under"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "((27763, 108),)"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# após os tratamentos\n",
    "\n",
    "X_under.shape,"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "((22210, 108), (5553, 108))"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# separação treino e teste Hold-out validation\n",
    "\n",
    "X_census_treinamento_under, X_census_teste_under, y_census_treinamento_under, y_census_teste_under = train_test_split(X_under, y_under, test_size=0.2, stratify=y_under)\n",
    "X_census_treinamento_under.shape, X_census_teste_under.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "RandomForestClassifier(criterion='entropy', min_samples_split=5)"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# uso de algoritmo de classificação\n",
    "# randomForest\n",
    "\n",
    "# definindo paramtros\n",
    "random_forest_classifier = RandomForestClassifier(n_estimators=100,\n",
    "                                                  criterion='entropy',\n",
    "                                                  min_samples_split=5,\n",
    "                                                  min_samples_leaf=1)\n",
    "\n",
    "# treino\n",
    "random_forest_classifier.fit(X_census_treinamento_under, y_census_treinamento_under)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([' <=50K', ' >50K', ' <=50K', ..., ' <=50K', ' <=50K', ' >50K'],\n",
       "      dtype=object)"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# previsão do modelo\n",
    "\n",
    "previsoes = random_forest_classifier.predict(X_census_teste_under)\n",
    "previsoes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.8908698001080497"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# avaliação\n",
    "\n",
    "accuracy_score(y_census_teste_under, previsoes)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[4267  198]\n",
      " [ 408  680]]\n"
     ]
    },
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# confusion matrix\n",
    "\n",
    "cnn = confusion_matrix(y_census_teste_under, previsoes)\n",
    "print(cnn)\n",
    "sns.heatmap(cnn, annot=True);"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "              precision    recall  f1-score   support\n",
      "\n",
      "       <=50K       0.91      0.96      0.93      4465\n",
      "        >50K       0.77      0.62      0.69      1088\n",
      "\n",
      "    accuracy                           0.89      5553\n",
      "   macro avg       0.84      0.79      0.81      5553\n",
      "weighted avg       0.89      0.89      0.89      5553\n",
      "\n"
     ]
    }
   ],
   "source": [
    "# classification report \n",
    "\n",
    "print(classification_report(y_census_teste_under, previsoes))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [],
   "source": [
    "# implemetação do SMOTE "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[39, 7, 77516, ..., 0, 40, 39],\n",
       "       [50, 6, 83311, ..., 0, 13, 39],\n",
       "       [38, 4, 215646, ..., 0, 40, 39],\n",
       "       ...,\n",
       "       [58, 4, 151910, ..., 0, 40, 39],\n",
       "       [22, 4, 201490, ..., 0, 20, 39],\n",
       "       [52, 5, 287927, ..., 0, 40, 39]], dtype=object)"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# visualização da base de dados\n",
    "\n",
    "X_census"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([' <=50K', ' <=50K', ' <=50K', ..., ' <=50K', ' <=50K', ' >50K'],\n",
       "      dtype=object)"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y_census"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [],
   "source": [
    "# over sampling\n",
    "\n",
    "smote = SMOTE(sampling_strategy='minority')\n",
    "X_over, y_over = smote.fit_resample(X_census, y_census)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(array([' <=50K', ' >50K'], dtype=object), array([24720, 24720], dtype=int64))\n"
     ]
    },
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# quantidade registros por classe\n",
    "# quantidade, visual \n",
    "\n",
    "print(np.unique(y_over, return_counts=True))\n",
    "sns.countplot(y_over);"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "((39552, 14), (9888, 14))"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#  separação treino e teste\n",
    "\n",
    "X_census_treinamento_over, X_census_teste_over, y_census_treinamento_over, y_census_teste_over = train_test_split(X_over, y_over, test_size=0.2, stratify=y_over)\n",
    "X_census_treinamento_over.shape, X_census_teste_over.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "RandomForestClassifier(criterion='entropy', min_samples_split=5)"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# uso de algoritmo de classificação\n",
    "# randomForest\n",
    "\n",
    "# definindo paramtros\n",
    "random_forest_classifier = RandomForestClassifier(n_estimators=100,\n",
    "                                                  criterion='entropy',\n",
    "                                                  min_samples_split=5,\n",
    "                                                  min_samples_leaf=1)\n",
    "\n",
    "# treino\n",
    "random_forest_classifier.fit(X_census_treinamento_over, y_census_treinamento_over)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([' <=50K', ' <=50K', ' <=50K', ..., ' <=50K', ' <=50K', ' >50K'],\n",
       "      dtype=object)"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# previsão do modelo\n",
    "\n",
    "previsoes = random_forest_classifier.predict(X_census_teste_over)\n",
    "previsoes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.904126213592233"
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# avaliação\n",
    "\n",
    "accuracy_score(y_census_teste_over, previsoes)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[4573  371]\n",
      " [ 577 4367]]\n"
     ]
    },
    {
     "data": {
      "image/png": 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OdHH3nwBtge5m1pHEJq/3uPtRwCagf2jfH9gU4veEdphZa6AX0AboDjwY9iUslZKziMRLYTlKKTzhq/C1aigOdAGmhvh44PzwuWf4Tqg/I+zQ3ROY5O7b3f1jEhvAti/rMpScRSRWvDD1UhYzq2xmy4D1QBbwH2Czu+eHJjlA0/C5KbAaINRvAQ5NjhdzTImUnEUkXsoxcjazDDNbklQykk/l7gXu3hZoRmK0e+y+ugyt1hCRWCnPLlXunglkptBus5nNBk4E6ppZlTA6bgbkhma5QHMgx8yqAHWAz5PiOyUfUyKNnEUkVjw/9VIaM2toZnXD54OBs4CVwGzgotCsLzAtfJ4evhPqZ3niFXnTgV5hNUdLoBWwqKzr0MhZRGIljfu7NgbGh5UVlYAp7v6Cmb0PTDKz24B3gDGh/RjgMTPLBvJIrNDA3VeY2RTgfSAfGODuZW7XYnv73ac7Nn4Uj8d1JK0ObnLK/v4JEkH53+ZaRc+x7vTTUs45jWa/XuH+9haNnEUkXjyy+bZclJxFJFbSOK2xXyk5i0iseKFGziIikVNYoOQsIhI5mtYQEYkgTWuIiETQXl4dvM8oOYtIrGjkLCISQbohKCISQRo5i4hEkOsJQRGR6NFSOhGRCCrUyFlEJHo0rSEiEkFarSEiEkFarSEiEkFxmXPWHoIiEivulnIpjZk1N7PZZva+ma0ws0EhPszMcs1sWSg9ko650cyyzewDM+uWFO8eYtlmNiSV69DIWURiJY3v1sgHrnP3t83sEGCpmWWFunvc/a7kxmbWmsS+gW2AJsBrZnZ0qH6AxAaxOcBiM5vu7u+X1rmSs4jESrqmNdx9DbAmfP7SzFYCTUs5pCcwyd23Ax+HjV7bh7psd/8IwMwmhbalJmdNa4hIrBQWWsolVWbWAvgpsDCEBprZcjMba2b1QqwpsDrpsJwQKyleKiVnEYmVQreUi5llmNmSpJKx5/nMrBbwNHCNu38BjAaOBNqSGFmP3BvXsdenNRof0X1vdyEHoK3vTd7fP0FiqjwPobh7JpBZUr2ZVSWRmCe6+zPhmHVJ9Q8DL4SvuUDzpMObhRilxEukkbOIxEp5Rs6lMTMDxgAr3f3upHjjpGYXAO+Fz9OBXmZW3cxaAq2ARcBioJWZtTSzaiRuGk4v6zp0Q1BEYiWNG6F0Aq4A3jWzZSF2E9DbzNqGrj4Bfg3g7ivMbAqJG335wAB3LwAws4HAK0BlYKy7ryircyVnEYmVgsL0TAi4+zyguOH1jFKOGQ4MLyY+o7TjiqPkLCKxEpM3hio5i0i8eLGD3QOPkrOIxEqhdt8WEYmeQo2cRUSiR9MaIiIRVKDkLCISPVqtISISQUrOIiIRpDlnEZEIiskWgkrOIhIvWkonIhJBBfv7B6SJkrOIxEqhaeQsIhI5MXl6W8lZROJFS+lERCJIqzVERCIoLo9vaw9BEYmVQku9lMbMmpvZbDN738xWmNmgEK9vZllm9mH4Z70QNzO7z8yyzWy5mR2XdK6+of2HZtY3letQchaRWCksRylDPnCdu7cGOgIDzKw1MASY6e6tgJnhO8DZJDZ1bQVkAKMhkcyBoUAHoD0wdGdCL42Ss4jEipejlHoe9zXu/nb4/CWwEmgK9ATGh2bjgfPD557ABE94C6gbduruBmS5e567bwKygO5lXYfmnEUkVvbGDUEzawH8FFgINHL3NaFqLdAofG4KrE46LCfESoqXSiNnEYmV8kxrmFmGmS1JKhl7ns/MagFPA9e4+xfJde6eyiD8O9HIWURipaAcI2d3zwQyS6o3s6okEvNEd38mhNeZWWN3XxOmLdaHeC7QPOnwZiGWC3TeIz6nrN+mkbOIxEq6bgiamQFjgJXufndS1XRg54qLvsC0pHifsGqjI7AlTH+8AnQ1s3rhRmDXECuVRs4iEitpfEKwE3AF8K6ZLQuxm4C/A1PMrD+wCrgk1M0AegDZwDagH4C755nZrcDi0O4Wd88rq3MlZxGJlXRNALv7PCjxiZYzimnvwIASzjUWGFue/pWcRSRW9Pi2iEgE6cVHIiIRpJfti4hEkKY1REQiSNMaIiIRpJ1QREQiqDAm6VnJWURiRTcERUQiSHPOIiIRpNUaIiIRpDlnEZEIikdqVnIWkZjRnLOISAQVxGTsrOQsIrGikbOISATF5YagtqkSkVjxcpSymNlYM1tvZu8lxYaZWa6ZLQulR1LdjWaWbWYfmFm3pHj3EMs2syGpXIeSs4jESrr2EAzGAd2Lid/j7m1DmQFgZq2BXkCbcMyDZlbZzCoDDwBnA62B3qFtqTStISKxks4bgu4+18xapNi8JzDJ3bcDH5tZNtA+1GW7+0cAZjYptH2/tJNp5CwisVKIp1wqYKCZLQ/THvVCrCmwOqlNToiVFC+VknMp3n53FnMXPM/sedN4bc7TReoPqV2LiZMfYs6b05m38EV6/+LnFe6zbr06TH3uURa98ypTn3uUOnVrA3DRJefy+vzpzF3wPDOyJtHmh8dWuC/57goKCrlk0DAG3jyqxDZZby7hx+f2Z8WHn1S4v5y1G7jsuts4J+NGrr/jIXbsyAdgyktz+PnAv3Lx1cPoe8Pt/OfTzyrc14GuPHPOZpZhZkuSSkYKXYwGjgTaAmuAkWm/CJScy3T+OX04/eSenNn5wiJ1/a+6nA/+nU3nTufRs8fl3PK3IVStWjWl83Y6uT3/GP33IvFB12Yw9/UFtP9pV+a+voBB1yb+rKz6JIfzelzOqSeey8gRD3L3fbdW7MKkQiY+n0XLZk1KrN+67WsmPv8aPzrmiHKdd9pr83jwiWlF4veOm8oVPc/ixczbqV2rBs9kvQFAj9M68Mz9t/DUfcO48sKzuXPM5HL1F0flGTm7e6a7t0sqmWWd393XuXuBuxcCD/PfqYtcoHlS02YhVlK8VErOFeDu1DqkJgA1a9Vk06Yt5OcnRjQDr+5P1pyneX3+dAbfdHXK5zz7nDOY/MSzAEx+4ll6/OxMABYveoctm78AYMniZTRpcng6L0XKYe3GPOYuXs7Pu55SYpv7Jz7HLy88m+pJ/7EuKChk5Ngp9L72Vi78/VCeemlOSv25O4uW/5uzOrUD4LwzTmL2W+8AUKvGwbvaff3NdmLyzp8KSfMNwSLMrHHS1wuAnSs5pgO9zKy6mbUEWgGLgMVAKzNraWbVSNw0nF5WP9/5hqCZ9XP3R7/r8QcCd2fqc2Nxd8Y/OpkJ43YflYzJfJzHJ41mxf/No2atmlzV71rcnc5dOnHEkS04q/OFmBkTJz/EiSe1Y8H8JWX22bBhA9at2wDAunUbaNiwQZE2l19xETOz5qbnIqXcRjw8iT/0u5itX39TbP372atYuyGPU0/4CeOeeWVX/NmsNzik5sE8ec9f+HbHDvrccDsn/rQNzQ5vWGp/m7/4ikNq1aBK5coANDq0Pus+37SrftKLs5jw3KvsyM/nkeHXp+EKD2yexhuCZvYk0BloYGY5wFCgs5m1JTEz8gnwawB3X2FmU0jc6MsHBrh7QTjPQOAVoDIw1t1XlNV3RVZr3AwUm5zDvE0GQM3qh3FQtToV6Gb/OafbZaxds44GDeozddo4Pvy//+yWYE8/42Tee3cl5/+sDy2P+B5Tn3uUBfMXc3qXk+ncpROz5yX+elqzVg2OOLIFC+Yv4ZVZT1GtWjVq1qpBvXp1drW5ZeidzJ45r8hvcN/9D9rJp3TgF30u5pxuvffilUtJXl/0L+rXOYTWR7Vg8bv/LlJfWFjIXWMmc+s1vyxSN/+dFXz4SQ5Zby4F4MutX/PpZ+uoVeNgrvrzXQBs+XIrO/Lzd42Mh//hVzSsV/q/P73O6UKvc7rw4py3yJz8AsOv7V/RyzygpXm1RnH/oo0ppf1wYHgx8RnAjPL0XWpyNrPlJVUBjUr5gZlAJkCD2kcfsI/rrF2zDoCNG/OY8UIWxx3/492S82WXX8iouxNTVB9/9Cmfrsqh1dFHYmaMuvufjH+06Pxfty4XA4k5516/+Dm//+3u69E3bNhIo0YNWbduA40aNWTjxs931bVucwz33D+cXhf+ik15m9N9uZKCZSuzmbPoX8xb+i7bv93B1m3fcOPIh7n9uqsA2Pr1N2SvyqX/TSMA2LhpC1ffdh/3/flq3J0hv76MTsf9sMh5n7pvGJCYc85d/zm/u6znrjp358uvtpFfUECVypVZ93kejQ6tV+QcZ5/anuGjH0//RR9g4vL4dllzzo2APsC5xZTPSznugFejxsHUqlVz1+fOXTqxcuWHu7XJWf0Zp3Y+EYCGDQ/lqFZHsOrj1cya+QaXXXERNWvWAODwxo1o0KB+Sv2+PGMWl152AQCXXnYBL704E4CmzRozbuL9/O6q6/lP9ifpuET5Dgb1vZDXxt3Fy2NGMOKGX9P+x8fuSswAh9SswdwnRvHymBG8PGYEPz7mSO7789W0adWCTsf9kCkzZrMj3Jf4JHct277ZXmafZsYJPz6GrDcTA4PpM+fTuUNbAFZ9tm5Xu7lLlvO9Joel8WoPTIXuKZcoK2ta4wWglrsv27PCzObsjR8UFQ0Pa8D4iQ8AUKVKZZ5+6nlmvfYGV/6yFwDjxk5i5IgH+cdDf2fugucxM24Zeid5eZuYM+tNjj7mSF56LTFy3rp1G7+96o9s3JhXZr+j7slkzLhRXN7nIlZ/+hn9rxwEwPWDB1K/Xl1G3D0MgIL8/GJXkMj+8cDjz9G6VQtOD0mzOD/vegq56zdy6TW34O7Ur3MI9/5pYErnv/bKi7lhxD+5//HnOPaI5rtuRj75wkwWLltJlSqVqV2rBrdd8789pQHxeZ+z7TmnmW4H8rSG7D25S8bu758gEVT96JMrvODksu9fkHLOeWLVs5Fd4KLHt0UkVtK5WmN/UnIWkVjJV3IWEYkejZxFRCIoLkvplJxFJFb29iKHfUXJWURiJS7bVCk5i0isaPdtEZEI0shZRCSCNOcsIhJBWq0hIhJBWucsIhJBmnMWEYmgAo/HxIb2EBSRWPFy/K8sZjbWzNab2XtJsfpmlmVmH4Z/1gtxM7P7zCzbzJab2XFJx/QN7T80s76pXIeSs4jESppftj8O6L5HbAgw091bATPDd4CzSWzq2orENn2jIZHMSew92IHETt1Ddyb00ig5i0iseDlKmedynwvsuUtGT2B8+DweOD8pPsET3gLqhp26uwFZ7p7n7puALIom/CI05ywisbIPbgg2cvc14fNa/rufalNgdVK7nBArKV4qjZxFJFYK8ZSLmWWY2ZKkklGevjzxxMte+a+BRs4iEivlWa3h7plAZjm7WGdmjd19TZi2WB/iuUDzpHbNQiwX6LxHfE5ZnWjkLCKxks7VGiWYDuxccdEXmJYU7xNWbXQEtoTpj1eArmZWL9wI7BpipdLIWURiJZ3v1jCzJ0mMehuYWQ6JVRd/B6aYWX9gFXBJaD4D6AFkA9uAfuH35JnZrcDi0O4Wd9/zJmMRSs4iEivpvCHo7r1LqDqjmLYODCjhPGOBcm05r+QsIrGit9KJiERQQUzeS6fkLCKxkuKTf5Gn5CwisaJXhoqIRJBGziIiEaSRs4hIBGnkLCISQXF52b6Ss4jEiqY1REQiyDVyFhGJHm3wKiISQXp8W0QkgjRyFhGJoIJCzTmLiESOVmuIiESQ5pxFRCIoLnPO2kNQRGLF3VMuZTGzT8zsXTNbZmZLQqy+mWWZ2Yfhn/VC3MzsPjPLNrPlZnZcRa5DyVlEYqWgsDDlkqLT3b2tu7cL34cAM929FTAzfAc4G2gVSgYwuiLXoeQsIrFSiKdcvqOewPjweTxwflJ8gie8BdQ1s8bftRMlZxGJlXROawAOvGpmS80sI8Qaufua8Hkt0Ch8bgqsTjo2J8S+E90QFJFYKc8rQ0PCzUgKZbp7ZtL3k90918wOA7LM7N/Jx7u7m9leuQOp5CwisVKedc4hEWeWUp8b/rnezJ4F2gPrzKyxu68J0xbrQ/NcoHnS4c1C7DvRtIaIxEqhe8qlNGZW08wO2fkZ6Aq8B0wH+oZmfYFp4fN0oE9YtdER2JI0/VFuGjmLSKwUpu+VoY2AZ80MErnyCXd/2cwWA1PMrD+wCrgktJ8B9ACygW1Av4p0ruQsIrGSricE3f0j4CfFxD8Hzigm7sCAtHSOkrOIxIwe3xYRiaB4pGawuPxX5kBgZhl7LNMR0Z8LKZZWa+xbGWU3kf9B+nMhRSg5i4hEkJKziEgEKTnvW5pXlOLoz4UUoRuCIiIRpJGziEgEKTnvI2bW3cw+CLskDCn7CIk7MxtrZuvN7L39/VskepSc9wEzqww8QGKnhNZAbzNrvX9/lUTAOKD7/v4REk1KzvtGeyDb3T9y92+BSSR2TZD/Ye4+F8jb379DoknJed9I6w4JIhJ/Ss4iIhGk5LxvpHWHBBGJPyXnfWMx0MrMWppZNaAXiV0TRESKpeS8D7h7PjAQeAVYCUxx9xX791fJ/mZmTwILgGPMLCfsrCEC6AlBEZFI0shZRCSClJxFRCJIyVlEJIKUnEVEIkjJWUQkgpScRUQiSMlZRCSClJxFRCLo/wH4mcIWKn5AtgAAAABJRU5ErkJggg==",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# confusion matrix\n",
    "cnn = confusion_matrix(y_census_teste_over, previsoes)\n",
    "print(cnn)\n",
    "sns.heatmap(cnn, annot=True);"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "              precision    recall  f1-score   support\n",
      "\n",
      "       <=50K       0.89      0.92      0.91      4944\n",
      "        >50K       0.92      0.88      0.90      4944\n",
      "\n",
      "    accuracy                           0.90      9888\n",
      "   macro avg       0.90      0.90      0.90      9888\n",
      "weighted avg       0.90      0.90      0.90      9888\n",
      "\n"
     ]
    }
   ],
   "source": [
    "# classification report \n",
    "\n",
    "print(classification_report(y_census_teste_over, previsoes))"
   ]
  }
 ],
 "metadata": {
  "interpreter": {
   "hash": "ad2bdc8ecc057115af97d19610ffacc2b4e99fae6737bb82f5d7fb13d2f2c186"
  },
  "kernelspec": {
   "display_name": "Python 3.8.8 ('base')",
   "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": "3.8.8"
  },
  "orig_nbformat": 4
 },
 "nbformat": 4,
 "nbformat_minor": 2
}