scripts/analise_e_modelos.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import seaborn as sns\n",
    "import matplotlib.pyplot as plt\n",
    "from matplotlib.pyplot import figure\n",
    "import numpy as np\n",
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn.ensemble import RandomForestRegressor\n",
    "from sklearn.ensemble import RandomForestClassifier\n",
    "from sklearn.metrics import accuracy_score\n",
    "from sklearn.metrics import classification_report\n",
    "from sklearn.model_selection import cross_val_score\n",
    "\n",
    "from sklearn.datasets import make_classification\n",
    "from sklearn.linear_model import LogisticRegression\n",
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn.pipeline import make_pipeline\n",
    "from sklearn.preprocessing import StandardScaler\n",
    "\n",
    "from sklearn.model_selection import RandomizedSearchCV"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 1. Análise de dados"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "# carregamos o dataset\n",
    "df = pd.read_csv(\"../data/adult.csv\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
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       "        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>fnlwgt</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>gender</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>226802</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>89814</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>336951</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>160323</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>?</td>\n",
       "      <td>103497</td>\n",
       "      <td>Some-college</td>\n",
       "      <td>10</td>\n",
       "      <td>Never-married</td>\n",
       "      <td>?</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  fnlwgt     education  educational-num      marital-status  \\\n",
       "0   25    Private  226802          11th                7       Never-married   \n",
       "1   38    Private   89814       HS-grad                9  Married-civ-spouse   \n",
       "2   28  Local-gov  336951    Assoc-acdm               12  Married-civ-spouse   \n",
       "3   44    Private  160323  Some-college               10  Married-civ-spouse   \n",
       "4   18          ?  103497  Some-college               10       Never-married   \n",
       "\n",
       "          occupation relationship   race  gender  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                  ?    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": [
    "# overview do dataset\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(48842, 15)"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Private             33906\n",
       "Self-emp-not-inc     3862\n",
       "Local-gov            3136\n",
       "?                    2799\n",
       "State-gov            1981\n",
       "Self-emp-inc         1695\n",
       "Federal-gov          1432\n",
       "Without-pay            21\n",
       "Never-worked           10\n",
       "Name: workclass, dtype: int64"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# tem NA values, precisaremos decidir o que faremos com estes NA\n",
    "# provavelmente podemos converter estes NA para one-hot-encoding\n",
    "df['workclass'].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "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": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# nao tem NA values\n",
    "df['education'].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Married-civ-spouse       22379\n",
       "Never-married            16117\n",
       "Divorced                  6633\n",
       "Separated                 1530\n",
       "Widowed                   1518\n",
       "Married-spouse-absent      628\n",
       "Married-AF-spouse           37\n",
       "Name: marital-status, dtype: int64"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# nao ha NA values\n",
    "df['marital-status'].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "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",
       "?                    2809\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": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# tem NA values (\"?\")\n",
    "df['occupation'].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Husband           19716\n",
       "Not-in-family     12583\n",
       "Own-child          7581\n",
       "Unmarried          5125\n",
       "Wife               2331\n",
       "Other-relative     1506\n",
       "Name: relationship, dtype: int64"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# nao ha NA values\n",
    "df['relationship'].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "White                 41762\n",
       "Black                  4685\n",
       "Asian-Pac-Islander     1519\n",
       "Amer-Indian-Eskimo      470\n",
       "Other                   406\n",
       "Name: race, dtype: int64"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# nao ha NA values\n",
    "df['race'].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<=50K    37155\n",
       ">50K     11687\n",
       "Name: income, dtype: int64"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# income\n",
    "df['income'].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "9     15784\n",
       "10    10878\n",
       "13     8025\n",
       "14     2657\n",
       "11     2061\n",
       "7      1812\n",
       "12     1601\n",
       "6      1389\n",
       "4       955\n",
       "15      834\n",
       "5       756\n",
       "8       657\n",
       "16      594\n",
       "3       509\n",
       "2       247\n",
       "1        83\n",
       "Name: educational-num, dtype: int64"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# anos de estudo\n",
    "df['educational-num'].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "age                   0\n",
       "workclass          2799\n",
       "fnlwgt                0\n",
       "education             0\n",
       "educational-num       0\n",
       "marital-status        0\n",
       "occupation         2809\n",
       "relationship          0\n",
       "race                  0\n",
       "gender                0\n",
       "capital-gain          0\n",
       "capital-loss          0\n",
       "hours-per-week        0\n",
       "native-country      857\n",
       "income                0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# os valores NAN estao representados como \"?\" neste df\n",
    "# desta forma podemos observar quantos NAN tem em cada coluna\n",
    "df[df == '?'].count()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(48842, 15)"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# número de rows e colunas do dataset\n",
    "df.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# com os histogramas podemos fazer um EDA dos dados numericos\n",
    "df.hist(bins=50, figsize=(10, 10));\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 2. Limpeza dos dados"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 1.1 transformacao get_dummies das variaveis categoricas"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['age', 'workclass', 'fnlwgt', 'education', 'educational-num',\n",
       "       'marital-status', 'occupation', 'relationship', 'race', 'gender',\n",
       "       'capital-gain', 'capital-loss', 'hours-per-week', 'native-country',\n",
       "       'income'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [],
   "source": [
    "# vamos definir as variaveis que serao tratadas como categoricas para aplicar a transformacao get_dummies\n",
    "cat_cols = ['workclass', 'education', 'educational-num', 'marital-status', 'occupation', 'relationship', \n",
    "            'race', 'gender', 'native-country', 'income']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [],
   "source": [
    "def transformacao_get_dummies(df=df, cat_cols=cat_cols):\n",
    "    '''\n",
    "    esta funcao aplica a transformacao get_dummies nas variaveis categoricas\n",
    "    do dataset df\n",
    "    '''\n",
    "    # aplicamos a transformação get_dummies nas variaveis categoricas\n",
    "    for cat_col in cat_cols:\n",
    "        df = pd.get_dummies(df, columns=[cat_col], dummy_na=False, prefix=\"{}_\".format(cat_col))\n",
    "    # vamos remover esta coluna, pois ja esta explicita na coluna \"income__>50K\"\n",
    "    df.pop('income__<=50K')\n",
    "    return df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [],
   "source": [
    "df = transformacao_get_dummies(df=df, cat_cols=cat_cols)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(48842, 124)"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# após a transformação get_dummies, aumentou o número de colunas para 125\n",
    "df.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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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>fnlwgt</th>\n",
       "      <th>capital-gain</th>\n",
       "      <th>capital-loss</th>\n",
       "      <th>hours-per-week</th>\n",
       "      <th>workclass__?</th>\n",
       "      <th>workclass__Federal-gov</th>\n",
       "      <th>workclass__Local-gov</th>\n",
       "      <th>workclass__Never-worked</th>\n",
       "      <th>workclass__Private</th>\n",
       "      <th>...</th>\n",
       "      <th>native-country__Puerto-Rico</th>\n",
       "      <th>native-country__Scotland</th>\n",
       "      <th>native-country__South</th>\n",
       "      <th>native-country__Taiwan</th>\n",
       "      <th>native-country__Thailand</th>\n",
       "      <th>native-country__Trinadad&amp;Tobago</th>\n",
       "      <th>native-country__United-States</th>\n",
       "      <th>native-country__Vietnam</th>\n",
       "      <th>native-country__Yugoslavia</th>\n",
       "      <th>income__&gt;50K</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>25</td>\n",
       "      <td>226802</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>40</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</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",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>38</td>\n",
       "      <td>89814</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>50</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</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",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>28</td>\n",
       "      <td>336951</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>40</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</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>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>44</td>\n",
       "      <td>160323</td>\n",
       "      <td>7688</td>\n",
       "      <td>0</td>\n",
       "      <td>40</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</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>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>18</td>\n",
       "      <td>103497</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>30</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</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",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 124 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "   age  fnlwgt  capital-gain  capital-loss  hours-per-week  workclass__?  \\\n",
       "0   25  226802             0             0              40             0   \n",
       "1   38   89814             0             0              50             0   \n",
       "2   28  336951             0             0              40             0   \n",
       "3   44  160323          7688             0              40             0   \n",
       "4   18  103497             0             0              30             1   \n",
       "\n",
       "   workclass__Federal-gov  workclass__Local-gov  workclass__Never-worked  \\\n",
       "0                       0                     0                        0   \n",
       "1                       0                     0                        0   \n",
       "2                       0                     1                        0   \n",
       "3                       0                     0                        0   \n",
       "4                       0                     0                        0   \n",
       "\n",
       "   workclass__Private  ...  native-country__Puerto-Rico  \\\n",
       "0                   1  ...                            0   \n",
       "1                   1  ...                            0   \n",
       "2                   0  ...                            0   \n",
       "3                   1  ...                            0   \n",
       "4                   0  ...                            0   \n",
       "\n",
       "   native-country__Scotland  native-country__South  native-country__Taiwan  \\\n",
       "0                         0                      0                       0   \n",
       "1                         0                      0                       0   \n",
       "2                         0                      0                       0   \n",
       "3                         0                      0                       0   \n",
       "4                         0                      0                       0   \n",
       "\n",
       "   native-country__Thailand  native-country__Trinadad&Tobago  \\\n",
       "0                         0                                0   \n",
       "1                         0                                0   \n",
       "2                         0                                0   \n",
       "3                         0                                0   \n",
       "4                         0                                0   \n",
       "\n",
       "   native-country__United-States  native-country__Vietnam  \\\n",
       "0                              1                        0   \n",
       "1                              1                        0   \n",
       "2                              1                        0   \n",
       "3                              1                        0   \n",
       "4                              1                        0   \n",
       "\n",
       "   native-country__Yugoslavia  income__>50K  \n",
       "0                           0             0  \n",
       "1                           0             0  \n",
       "2                           0             1  \n",
       "3                           0             1  \n",
       "4                           0             0  \n",
       "\n",
       "[5 rows x 124 columns]"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 3. Modelos de predição"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Random forest classifier"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [],
   "source": [
    "# dividimos o df em X e y\n",
    "y = df['income__>50K'].astype('category')\n",
    "X = df.iloc[:, :-1]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [],
   "source": [
    "# separamos o dataframe em train e test\n",
    "train_X, val_X, train_y, val_y = train_test_split(X, y, random_state = 0, shuffle=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "RandomForestClassifier()"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# modelo random forest de classificação\n",
    "rf = RandomForestClassifier()\n",
    "rf.fit(train_X, train_y)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([0, 1, 0, ..., 1, 1, 0], dtype=uint64)"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pred_rf = rf.predict(val_X)\n",
    "pred_rf"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Accuracy do modelo random forest:  0.8537384325608058\n"
     ]
    }
   ],
   "source": [
    "# acurácia do rf\n",
    "accuracy_rf = accuracy_score(val_y, pred_rf)\n",
    "print('Accuracy do modelo random forest: ', accuracy_rf)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       0.89      0.93      0.91      9273\n",
      "           1       0.73      0.63      0.67      2938\n",
      "\n",
      "    accuracy                           0.85     12211\n",
      "   macro avg       0.81      0.78      0.79     12211\n",
      "weighted avg       0.85      0.85      0.85     12211\n",
      "\n"
     ]
    }
   ],
   "source": [
    "# métricas do tuned model rf\n",
    "print(classification_report(val_y,pred_rf))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([0.85075238, 0.8515713 , 0.8515561 , 0.85513923, 0.85708436])"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# scores do modelo random forest em cross validation\n",
    "scores_rf = cross_val_score(rf, X, y, cv=5)\n",
    "scores_rf"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.85 accuracy with a standard deviation of 0.00\n"
     ]
    }
   ],
   "source": [
    "# acurácia do rf em cross validation\n",
    "print(\"%0.2f accuracy with a standard deviation of %0.2f\" % (scores_rf.mean(), scores_rf.std()))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Random forest tunned "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "{'n_estimators': [50, 200, 350, 500], 'max_features': ['auto', 'sqrt'], 'max_depth': [10, 43, 76, 110, None], 'bootstrap': [True, False]}\n"
     ]
    }
   ],
   "source": [
    "# Number of trees in random forest\n",
    "n_estimators = [int(x) for x in np.linspace(start = 50, stop = 500, num = 4)]\n",
    "\n",
    "# Number of features to consider at every split\n",
    "max_features = ['auto', 'sqrt']\n",
    "\n",
    "# Maximum number of levels in tree\n",
    "max_depth = [int(x) for x in np.linspace(10, 110, num = 4)]\n",
    "max_depth.append(None)\n",
    "\n",
    "# Method of selecting samples for training each tree\n",
    "bootstrap = [True, False]# Create the random grid\n",
    "random_grid = {'n_estimators': n_estimators,\n",
    "               'max_features': max_features,\n",
    "               'max_depth': max_depth,\n",
    "               'bootstrap': bootstrap}\n",
    "\n",
    "print(random_grid)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Fitting 3 folds for each of 10 candidates, totalling 30 fits\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[Parallel(n_jobs=-1)]: Using backend LokyBackend with 8 concurrent workers.\n",
      "[Parallel(n_jobs=-1)]: Done   2 tasks      | elapsed:    6.9s\n",
      "[Parallel(n_jobs=-1)]: Done   9 tasks      | elapsed:   47.2s\n",
      "[Parallel(n_jobs=-1)]: Done  19 out of  30 | elapsed:  1.5min remaining:   53.2s\n",
      "[Parallel(n_jobs=-1)]: Done  23 out of  30 | elapsed:  1.6min remaining:   30.0s\n",
      "[Parallel(n_jobs=-1)]: Done  27 out of  30 | elapsed:  1.7min remaining:   11.4s\n",
      "[Parallel(n_jobs=-1)]: Done  30 out of  30 | elapsed:  2.1min finished\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "RandomizedSearchCV(cv=3, estimator=RandomForestClassifier(), n_jobs=-1,\n",
       "                   param_distributions={'bootstrap': [True, False],\n",
       "                                        'max_depth': [10, 43, 76, 110, None],\n",
       "                                        'max_features': ['auto', 'sqrt'],\n",
       "                                        'n_estimators': [50, 200, 350, 500]},\n",
       "                   random_state=42, verbose=10)"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Use the random grid to search for best hyperparameters\n",
    "# First create the base model to tune\n",
    "rf_t = RandomForestClassifier()\n",
    "# Random search of parameters, using 3 fold cross validation, \n",
    "# search across 100 different combinations, and use all available cores\n",
    "rf_tuned = RandomizedSearchCV(estimator = rf_t, param_distributions = random_grid, n_iter = 10, cv = 3, verbose=10, random_state=42, n_jobs = -1)\n",
    "# Fit the random search model\n",
    "rf_tuned.fit(train_X, train_y)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [],
   "source": [
    "# predições do tuned model rf\n",
    "tuned_preds = rf_tuned.predict(val_X)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Accuracy do modelo random forest com grid search:  0.856440913930063\n"
     ]
    }
   ],
   "source": [
    "# acurácia do tuned model rf\n",
    "accuracy_tuned = accuracy_score(val_y, tuned_preds)\n",
    "print('Accuracy do modelo random forest com grid search: ', accuracy_tuned)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       0.87      0.96      0.91      9273\n",
      "           1       0.80      0.54      0.64      2938\n",
      "\n",
      "    accuracy                           0.86     12211\n",
      "   macro avg       0.83      0.75      0.78     12211\n",
      "weighted avg       0.85      0.86      0.85     12211\n",
      "\n"
     ]
    }
   ],
   "source": [
    "# métricas do tuned model rf\n",
    "print(classification_report(val_y,tuned_preds))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'n_estimators': 50,\n",
       " 'max_features': 'auto',\n",
       " 'max_depth': 10,\n",
       " 'bootstrap': True}"
      ]
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# parámetros escolhidos no grid search\n",
    "rf_tuned.best_params_"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Logistic regression classifier "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Pipeline(steps=[('standardscaler', StandardScaler()),\n",
       "                ('logisticregression', LogisticRegression())])"
      ]
     },
     "execution_count": 36,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pipe = make_pipeline(StandardScaler(), LogisticRegression())\n",
    "pipe.fit(train_X, train_y)  # apply scaling on training data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.8528376054377201"
      ]
     },
     "execution_count": 37,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pipe.score(val_X, val_y)  # apply scaling on testing data, without leaking training data."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [],
   "source": [
    "# predições do modelo lr\n",
    "lr_preds = pipe.predict(val_X)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Accuracy do modelo logistic regression:  0.8528376054377201\n"
     ]
    }
   ],
   "source": [
    "# acurácia do modelo lr\n",
    "accuracy_lr = accuracy_score(val_y, lr_preds)\n",
    "print('Accuracy do modelo logistic regression: ', accuracy_lr)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       0.88      0.93      0.91      9273\n",
      "           1       0.74      0.60      0.66      2938\n",
      "\n",
      "    accuracy                           0.85     12211\n",
      "   macro avg       0.81      0.77      0.78     12211\n",
      "weighted avg       0.85      0.85      0.85     12211\n",
      "\n"
     ]
    }
   ],
   "source": [
    "# métricas do modelo logistic regression\n",
    "print(classification_report(val_y,lr_preds))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Comparação dos modelos"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Accuracy do modelo random forest:  0.8537384325608058\n",
      "Accuracy do modelo random forest com grid search:  0.856440913930063\n",
      "Accuracy do modelo logistic regression:  0.8528376054377201\n"
     ]
    }
   ],
   "source": [
    "print('Accuracy do modelo random forest: ', accuracy_rf)\n",
    "print('Accuracy do modelo random forest com grid search: ', accuracy_tuned)\n",
    "print('Accuracy do modelo logistic regression: ', accuracy_lr)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "A acurácia dos três modelos foi bem parecida. Entretanto,  modelo com otimização de hiperparámetros mostrou a maior acurácia"
   ]
  }
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