Pre-Processing-Template-Example-2.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "#medium article: https://towardsdatascience.com/the-complete-beginners-guide-to-data-cleaning-and-preprocessing-2070b7d4c6d?gi=c5101f25cd16"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "import pandas as pd\n",
    "from sklearn.preprocessing import Imputer\n",
    "from sklearn.preprocessing import LabelEncoder\n",
    "from sklearn.preprocessing import OneHotEncoder\n",
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn.preprocessing import StandardScaler\n",
    "from sklearn.preprocessing import Normalizer\n",
    "from sklearn.preprocessing import MinMaxScaler\n",
    "from sklearn.preprocessing import StandardScaler\n",
    "from sklearn.preprocessing import RobustScaler\n",
    "from sklearn.preprocessing import QuantileTransformer\n",
    "from sklearn.preprocessing import PowerTransformer\n",
    "from sklearn.feature_selection import SelectKBest\n",
    "from sklearn.feature_selection import chi2\n",
    "from sklearn.ensemble import ExtraTreesClassifier\n",
    "from sklearn.feature_selection import SelectFromModel\n",
    "from sklearn.decomposition import PCA\n",
    "from sklearn.model_selection import KFold"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "metadata": {},
   "outputs": [],
   "source": [
    "dataset = pd.read_csv('fake_data_2.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "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>cargo</th>\n",
       "      <th>idade</th>\n",
       "      <th>salario</th>\n",
       "      <th>bonus</th>\n",
       "      <th>sócio</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Diretor</td>\n",
       "      <td>45</td>\n",
       "      <td>24000.0</td>\n",
       "      <td>10000.0</td>\n",
       "      <td>sim</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Analista</td>\n",
       "      <td>22</td>\n",
       "      <td>8000.0</td>\n",
       "      <td>2000.0</td>\n",
       "      <td>não</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Programador</td>\n",
       "      <td>30</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1000.0</td>\n",
       "      <td>não</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Gerente</td>\n",
       "      <td>24</td>\n",
       "      <td>15100.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>não</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Gerente</td>\n",
       "      <td>30</td>\n",
       "      <td>35000.0</td>\n",
       "      <td>6000.0</td>\n",
       "      <td>sim</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "         cargo  idade  salario    bonus sócio\n",
       "0      Diretor     45  24000.0  10000.0   sim\n",
       "1     Analista     22   8000.0   2000.0   não\n",
       "2  Programador     30      NaN   1000.0   não\n",
       "3      Gerente     24  15100.0      NaN   não\n",
       "4      Gerente     30  35000.0   6000.0   sim"
      ]
     },
     "execution_count": 62,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dataset.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "metadata": {},
   "outputs": [],
   "source": [
    "X = dataset.iloc[:, :-1].values\n",
    "Y = dataset.iloc[:, 4].values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/usr/local/lib/python3.6/dist-packages/sklearn/utils/deprecation.py:58: DeprecationWarning: Class Imputer is deprecated; Imputer was deprecated in version 0.20 and will be removed in 0.22. Import impute.SimpleImputer from sklearn instead.\n",
      "  warnings.warn(msg, category=DeprecationWarning)\n"
     ]
    }
   ],
   "source": [
    "imputer = Imputer(missing_values = np.nan, strategy = 'mean', axis = 0) #This will handle missing data with the mean of the column"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[24000.0, 10000.0],\n",
       "       [8000.0, 2000.0],\n",
       "       [nan, 1000.0],\n",
       "       [15100.0, nan],\n",
       "       [35000.0, 6000.0],\n",
       "       [5300.0, 2000.0],\n",
       "       [nan, 1200.0],\n",
       "       [18000.0, 8000.0],\n",
       "       [38000.0, 28000.0],\n",
       "       [7300.0, 4000.0],\n",
       "       [2344.0, nan],\n",
       "       [4500.0, 2200.0],\n",
       "       [30000.0, 12000.0],\n",
       "       [14000.0, 10000.0]], dtype=object)"
      ]
     },
     "execution_count": 65,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X[:, 2:4]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "metadata": {},
   "outputs": [],
   "source": [
    "imputer = imputer.fit(X[:, 2:4]) #put only the columns with possible missing data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "metadata": {},
   "outputs": [],
   "source": [
    "X[:, 2:4] = imputer.transform(X[:, 2:4]) #put only the columns with possible missing data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([['Diretor', 45, 24000.0, 10000.0],\n",
       "       ['Analista', 22, 8000.0, 2000.0],\n",
       "       ['Programador', 30, 16795.333333333332, 1000.0],\n",
       "       ['Gerente', 24, 15100.0, 7200.0],\n",
       "       ['Gerente', 30, 35000.0, 6000.0],\n",
       "       ['Programador', 22, 5300.0, 2000.0],\n",
       "       ['Analista', 20, 16795.333333333332, 1200.0],\n",
       "       ['Diretor', 50, 18000.0, 8000.0],\n",
       "       ['Fundador', 65, 38000.0, 28000.0],\n",
       "       ['Analista', 32, 7300.0, 4000.0],\n",
       "       ['Programador', 35, 2344.0, 7200.0],\n",
       "       ['Programador', 28, 4500.0, 2200.0],\n",
       "       ['Fundador', 28, 30000.0, 12000.0],\n",
       "       ['Programador', 30, 14000.0, 10000.0]], dtype=object)"
      ]
     },
     "execution_count": 68,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "metadata": {},
   "outputs": [],
   "source": [
    "X_cargo = pd.DataFrame({'cargo':X[:,0]})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 70,
   "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>cargo</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Diretor</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Analista</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Programador</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Gerente</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Gerente</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>Programador</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>Analista</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>Diretor</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>Fundador</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>Analista</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>Programador</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>Programador</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>Fundador</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>Programador</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "          cargo\n",
       "0       Diretor\n",
       "1      Analista\n",
       "2   Programador\n",
       "3       Gerente\n",
       "4       Gerente\n",
       "5   Programador\n",
       "6      Analista\n",
       "7       Diretor\n",
       "8      Fundador\n",
       "9      Analista\n",
       "10  Programador\n",
       "11  Programador\n",
       "12     Fundador\n",
       "13  Programador"
      ]
     },
     "execution_count": 70,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X_cargo"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 71,
   "metadata": {},
   "outputs": [],
   "source": [
    "X_cargo = pd.get_dummies(X_cargo)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 72,
   "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>cargo_Analista</th>\n",
       "      <th>cargo_Diretor</th>\n",
       "      <th>cargo_Fundador</th>\n",
       "      <th>cargo_Gerente</th>\n",
       "      <th>cargo_Programador</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\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>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\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>4</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   cargo_Analista  cargo_Diretor  cargo_Fundador  cargo_Gerente  \\\n",
       "0               0              1               0              0   \n",
       "1               1              0               0              0   \n",
       "2               0              0               0              0   \n",
       "3               0              0               0              1   \n",
       "4               0              0               0              1   \n",
       "\n",
       "   cargo_Programador  \n",
       "0                  0  \n",
       "1                  0  \n",
       "2                  1  \n",
       "3                  0  \n",
       "4                  0  "
      ]
     },
     "execution_count": 72,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X_cargo.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 73,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([['Diretor', 45, 24000.0, 10000.0],\n",
       "       ['Analista', 22, 8000.0, 2000.0],\n",
       "       ['Programador', 30, 16795.333333333332, 1000.0],\n",
       "       ['Gerente', 24, 15100.0, 7200.0],\n",
       "       ['Gerente', 30, 35000.0, 6000.0],\n",
       "       ['Programador', 22, 5300.0, 2000.0],\n",
       "       ['Analista', 20, 16795.333333333332, 1200.0],\n",
       "       ['Diretor', 50, 18000.0, 8000.0],\n",
       "       ['Fundador', 65, 38000.0, 28000.0],\n",
       "       ['Analista', 32, 7300.0, 4000.0],\n",
       "       ['Programador', 35, 2344.0, 7200.0],\n",
       "       ['Programador', 28, 4500.0, 2200.0],\n",
       "       ['Fundador', 28, 30000.0, 12000.0],\n",
       "       ['Programador', 30, 14000.0, 10000.0]], dtype=object)"
      ]
     },
     "execution_count": 73,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 74,
   "metadata": {},
   "outputs": [],
   "source": [
    "X = X[:, 1:]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 75,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[45, 24000.0, 10000.0],\n",
       "       [22, 8000.0, 2000.0],\n",
       "       [30, 16795.333333333332, 1000.0],\n",
       "       [24, 15100.0, 7200.0],\n",
       "       [30, 35000.0, 6000.0],\n",
       "       [22, 5300.0, 2000.0],\n",
       "       [20, 16795.333333333332, 1200.0],\n",
       "       [50, 18000.0, 8000.0],\n",
       "       [65, 38000.0, 28000.0],\n",
       "       [32, 7300.0, 4000.0],\n",
       "       [35, 2344.0, 7200.0],\n",
       "       [28, 4500.0, 2200.0],\n",
       "       [28, 30000.0, 12000.0],\n",
       "       [30, 14000.0, 10000.0]], dtype=object)"
      ]
     },
     "execution_count": 75,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 76,
   "metadata": {},
   "outputs": [],
   "source": [
    "X = pd.DataFrame({'idade':X[:,0], 'salario':X[:,1], 'bonus':X[:,2]})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 77,
   "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>idade</th>\n",
       "      <th>salario</th>\n",
       "      <th>bonus</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>45</td>\n",
       "      <td>24000</td>\n",
       "      <td>10000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>22</td>\n",
       "      <td>8000</td>\n",
       "      <td>2000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>30</td>\n",
       "      <td>16795.3</td>\n",
       "      <td>1000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>24</td>\n",
       "      <td>15100</td>\n",
       "      <td>7200</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>30</td>\n",
       "      <td>35000</td>\n",
       "      <td>6000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  idade  salario  bonus\n",
       "0    45    24000  10000\n",
       "1    22     8000   2000\n",
       "2    30  16795.3   1000\n",
       "3    24    15100   7200\n",
       "4    30    35000   6000"
      ]
     },
     "execution_count": 77,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 78,
   "metadata": {},
   "outputs": [],
   "source": [
    "X = X.join(X_cargo)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 79,
   "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>idade</th>\n",
       "      <th>salario</th>\n",
       "      <th>bonus</th>\n",
       "      <th>cargo_Analista</th>\n",
       "      <th>cargo_Diretor</th>\n",
       "      <th>cargo_Fundador</th>\n",
       "      <th>cargo_Gerente</th>\n",
       "      <th>cargo_Programador</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>45</td>\n",
       "      <td>24000</td>\n",
       "      <td>10000</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>22</td>\n",
       "      <td>8000</td>\n",
       "      <td>2000</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>30</td>\n",
       "      <td>16795.3</td>\n",
       "      <td>1000</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>24</td>\n",
       "      <td>15100</td>\n",
       "      <td>7200</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>4</th>\n",
       "      <td>30</td>\n",
       "      <td>35000</td>\n",
       "      <td>6000</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>5</th>\n",
       "      <td>22</td>\n",
       "      <td>5300</td>\n",
       "      <td>2000</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>20</td>\n",
       "      <td>16795.3</td>\n",
       "      <td>1200</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>50</td>\n",
       "      <td>18000</td>\n",
       "      <td>8000</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>8</th>\n",
       "      <td>65</td>\n",
       "      <td>38000</td>\n",
       "      <td>28000</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>32</td>\n",
       "      <td>7300</td>\n",
       "      <td>4000</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>35</td>\n",
       "      <td>2344</td>\n",
       "      <td>7200</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>28</td>\n",
       "      <td>4500</td>\n",
       "      <td>2200</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>28</td>\n",
       "      <td>30000</td>\n",
       "      <td>12000</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>30</td>\n",
       "      <td>14000</td>\n",
       "      <td>10000</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   idade  salario  bonus  cargo_Analista  cargo_Diretor  cargo_Fundador  \\\n",
       "0     45    24000  10000               0              1               0   \n",
       "1     22     8000   2000               1              0               0   \n",
       "2     30  16795.3   1000               0              0               0   \n",
       "3     24    15100   7200               0              0               0   \n",
       "4     30    35000   6000               0              0               0   \n",
       "5     22     5300   2000               0              0               0   \n",
       "6     20  16795.3   1200               1              0               0   \n",
       "7     50    18000   8000               0              1               0   \n",
       "8     65    38000  28000               0              0               1   \n",
       "9     32     7300   4000               1              0               0   \n",
       "10    35     2344   7200               0              0               0   \n",
       "11    28     4500   2200               0              0               0   \n",
       "12    28    30000  12000               0              0               1   \n",
       "13    30    14000  10000               0              0               0   \n",
       "\n",
       "    cargo_Gerente  cargo_Programador  \n",
       "0               0                  0  \n",
       "1               0                  0  \n",
       "2               0                  1  \n",
       "3               1                  0  \n",
       "4               1                  0  \n",
       "5               0                  1  \n",
       "6               0                  0  \n",
       "7               0                  0  \n",
       "8               0                  0  \n",
       "9               0                  0  \n",
       "10              0                  1  \n",
       "11              0                  1  \n",
       "12              0                  0  \n",
       "13              0                  1  "
      ]
     },
     "execution_count": 79,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(['sim', 'não', 'não', 'não', 'sim', 'não', 'não', 'sim', 'sim',\n",
       "       'não', 'não', 'não', 'sim', 'sim'], dtype=object)"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Y"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [],
   "source": [
    "Y = pd.get_dummies(Y)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "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>não</th>\n",
       "      <th>sim</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    não  sim\n",
       "0     0    1\n",
       "1     1    0\n",
       "2     1    0\n",
       "3     1    0\n",
       "4     0    1\n",
       "5     1    0\n",
       "6     1    0\n",
       "7     0    1\n",
       "8     0    1\n",
       "9     1    0\n",
       "10    1    0\n",
       "11    1    0\n",
       "12    0    1\n",
       "13    0    1"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Y"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [],
   "source": [
    "Y = Y['sim'].values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([1, 0, 0, 0, 1, 0, 0, 1, 1, 0, 0, 0, 1, 1], dtype=uint8)"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Y"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 80,
   "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>idade</th>\n",
       "      <th>salario</th>\n",
       "      <th>bonus</th>\n",
       "      <th>cargo_Analista</th>\n",
       "      <th>cargo_Diretor</th>\n",
       "      <th>cargo_Fundador</th>\n",
       "      <th>cargo_Gerente</th>\n",
       "      <th>cargo_Programador</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0.001731</td>\n",
       "      <td>0.923076</td>\n",
       "      <td>0.384615</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>0.002668</td>\n",
       "      <td>0.970139</td>\n",
       "      <td>0.242535</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0.001783</td>\n",
       "      <td>0.998231</td>\n",
       "      <td>0.059435</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0.001435</td>\n",
       "      <td>0.902638</td>\n",
       "      <td>0.430397</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>4</th>\n",
       "      <td>0.000845</td>\n",
       "      <td>0.985622</td>\n",
       "      <td>0.168964</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      idade   salario     bonus  cargo_Analista  cargo_Diretor  \\\n",
       "0  0.001731  0.923076  0.384615               0              1   \n",
       "1  0.002668  0.970139  0.242535               1              0   \n",
       "2  0.001783  0.998231  0.059435               0              0   \n",
       "3  0.001435  0.902638  0.430397               0              0   \n",
       "4  0.000845  0.985622  0.168964               0              0   \n",
       "\n",
       "   cargo_Fundador  cargo_Gerente  cargo_Programador  \n",
       "0               0              0                  0  \n",
       "1               0              0                  0  \n",
       "2               0              0                  1  \n",
       "3               0              1                  0  \n",
       "4               0              1                  0  "
      ]
     },
     "execution_count": 80,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X_normalize = X.copy()\n",
    "X_normalize[['idade', 'salario', 'bonus']] = Normalizer().fit_transform(X[['idade', 'salario', 'bonus']])\n",
    "X_normalize.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 81,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/usr/local/lib/python3.6/dist-packages/sklearn/preprocessing/data.py:323: DataConversionWarning: Data with input dtype object were all converted to float64 by MinMaxScaler.\n",
      "  return self.partial_fit(X, y)\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>idade</th>\n",
       "      <th>salario</th>\n",
       "      <th>bonus</th>\n",
       "      <th>cargo_Analista</th>\n",
       "      <th>cargo_Diretor</th>\n",
       "      <th>cargo_Fundador</th>\n",
       "      <th>cargo_Gerente</th>\n",
       "      <th>cargo_Programador</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0.555556</td>\n",
       "      <td>0.607359</td>\n",
       "      <td>0.333333</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>0.044444</td>\n",
       "      <td>0.158627</td>\n",
       "      <td>0.037037</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0.222222</td>\n",
       "      <td>0.405299</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0.088889</td>\n",
       "      <td>0.357752</td>\n",
       "      <td>0.229630</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>4</th>\n",
       "      <td>0.222222</td>\n",
       "      <td>0.915863</td>\n",
       "      <td>0.185185</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      idade   salario     bonus  cargo_Analista  cargo_Diretor  \\\n",
       "0  0.555556  0.607359  0.333333               0              1   \n",
       "1  0.044444  0.158627  0.037037               1              0   \n",
       "2  0.222222  0.405299  0.000000               0              0   \n",
       "3  0.088889  0.357752  0.229630               0              0   \n",
       "4  0.222222  0.915863  0.185185               0              0   \n",
       "\n",
       "   cargo_Fundador  cargo_Gerente  cargo_Programador  \n",
       "0               0              0                  0  \n",
       "1               0              0                  0  \n",
       "2               0              0                  1  \n",
       "3               0              1                  0  \n",
       "4               0              1                  0  "
      ]
     },
     "execution_count": 81,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X_minMax = X.copy()\n",
    "X_minMax[['idade', 'salario', 'bonus']] = MinMaxScaler().fit_transform(X[['idade', 'salario', 'bonus']])\n",
    "X_minMax.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 82,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/usr/local/lib/python3.6/dist-packages/sklearn/preprocessing/data.py:617: DataConversionWarning: Data with input dtype object were all converted to float64 by StandardScaler.\n",
      "  return self.partial_fit(X, y)\n",
      "/usr/local/lib/python3.6/dist-packages/sklearn/base.py:462: DataConversionWarning: Data with input dtype object were all converted to float64 by StandardScaler.\n",
      "  return self.fit(X, **fit_params).transform(X)\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>idade</th>\n",
       "      <th>salario</th>\n",
       "      <th>bonus</th>\n",
       "      <th>cargo_Analista</th>\n",
       "      <th>cargo_Diretor</th>\n",
       "      <th>cargo_Fundador</th>\n",
       "      <th>cargo_Gerente</th>\n",
       "      <th>cargo_Programador</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1.002248</td>\n",
       "      <td>0.657862</td>\n",
       "      <td>0.415034</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>-0.907361</td>\n",
       "      <td>-0.803107</td>\n",
       "      <td>-0.770778</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>-0.243149</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>-0.919005</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>-0.741308</td>\n",
       "      <td>-0.154802</td>\n",
       "      <td>0.000000</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>4</th>\n",
       "      <td>-0.243149</td>\n",
       "      <td>1.662279</td>\n",
       "      <td>-0.177872</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      idade   salario     bonus  cargo_Analista  cargo_Diretor  \\\n",
       "0  1.002248  0.657862  0.415034               0              1   \n",
       "1 -0.907361 -0.803107 -0.770778               1              0   \n",
       "2 -0.243149  0.000000 -0.919005               0              0   \n",
       "3 -0.741308 -0.154802  0.000000               0              0   \n",
       "4 -0.243149  1.662279 -0.177872               0              0   \n",
       "\n",
       "   cargo_Fundador  cargo_Gerente  cargo_Programador  \n",
       "0               0              0                  0  \n",
       "1               0              0                  0  \n",
       "2               0              0                  1  \n",
       "3               0              1                  0  \n",
       "4               0              1                  0  "
      ]
     },
     "execution_count": 82,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X_standard = X.copy()\n",
    "X_standard[['idade', 'salario', 'bonus']] = StandardScaler().fit_transform(X[['idade', 'salario', 'bonus']])\n",
    "X_standard.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 83,
   "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>idade</th>\n",
       "      <th>salario</th>\n",
       "      <th>bonus</th>\n",
       "      <th>cargo_Analista</th>\n",
       "      <th>cargo_Diretor</th>\n",
       "      <th>cargo_Fundador</th>\n",
       "      <th>cargo_Gerente</th>\n",
       "      <th>cargo_Programador</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1.621622</td>\n",
       "      <td>0.535929</td>\n",
       "      <td>0.456376</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>-0.864865</td>\n",
       "      <td>-0.528963</td>\n",
       "      <td>-0.617450</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.056417</td>\n",
       "      <td>-0.751678</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>-0.648649</td>\n",
       "      <td>-0.056417</td>\n",
       "      <td>0.080537</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>4</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.268042</td>\n",
       "      <td>-0.080537</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      idade   salario     bonus  cargo_Analista  cargo_Diretor  \\\n",
       "0  1.621622  0.535929  0.456376               0              1   \n",
       "1 -0.864865 -0.528963 -0.617450               1              0   \n",
       "2  0.000000  0.056417 -0.751678               0              0   \n",
       "3 -0.648649 -0.056417  0.080537               0              0   \n",
       "4  0.000000  1.268042 -0.080537               0              0   \n",
       "\n",
       "   cargo_Fundador  cargo_Gerente  cargo_Programador  \n",
       "0               0              0                  0  \n",
       "1               0              0                  0  \n",
       "2               0              0                  1  \n",
       "3               0              1                  0  \n",
       "4               0              1                  0  "
      ]
     },
     "execution_count": 83,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X_robust = X.copy()\n",
    "X_robust[['idade', 'salario', 'bonus']] = RobustScaler().fit_transform(X[['idade', 'salario', 'bonus']])\n",
    "X_robust.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 84,
   "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>idade</th>\n",
       "      <th>salario</th>\n",
       "      <th>bonus</th>\n",
       "      <th>cargo_Analista</th>\n",
       "      <th>cargo_Diretor</th>\n",
       "      <th>cargo_Fundador</th>\n",
       "      <th>cargo_Gerente</th>\n",
       "      <th>cargo_Programador</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0.846317</td>\n",
       "      <td>0.769231</td>\n",
       "      <td>8.078078e-01</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>0.115115</td>\n",
       "      <td>0.307375</td>\n",
       "      <td>1.921922e-01</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0.538539</td>\n",
       "      <td>0.576577</td>\n",
       "      <td>1.000000e-07</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0.230599</td>\n",
       "      <td>0.461513</td>\n",
       "      <td>5.765766e-01</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>4</th>\n",
       "      <td>0.538539</td>\n",
       "      <td>0.923156</td>\n",
       "      <td>4.615835e-01</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      idade   salario         bonus  cargo_Analista  cargo_Diretor  \\\n",
       "0  0.846317  0.769231  8.078078e-01               0              1   \n",
       "1  0.115115  0.307375  1.921922e-01               1              0   \n",
       "2  0.538539  0.576577  1.000000e-07               0              0   \n",
       "3  0.230599  0.461513  5.765766e-01               0              0   \n",
       "4  0.538539  0.923156  4.615835e-01               0              0   \n",
       "\n",
       "   cargo_Fundador  cargo_Gerente  cargo_Programador  \n",
       "0               0              0                  0  \n",
       "1               0              0                  0  \n",
       "2               0              0                  1  \n",
       "3               0              1                  0  \n",
       "4               0              1                  0  "
      ]
     },
     "execution_count": 84,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X_quantile = X.copy()\n",
    "X_quantile[['idade', 'salario', 'bonus']] = QuantileTransformer().fit_transform(X[['idade', 'salario', 'bonus']])\n",
    "X_quantile.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 85,
   "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>idade</th>\n",
       "      <th>salario</th>\n",
       "      <th>bonus</th>\n",
       "      <th>cargo_Analista</th>\n",
       "      <th>cargo_Diretor</th>\n",
       "      <th>cargo_Fundador</th>\n",
       "      <th>cargo_Gerente</th>\n",
       "      <th>cargo_Programador</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1.143230</td>\n",
       "      <td>0.768685</td>\n",
       "      <td>0.775384</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>-1.075613</td>\n",
       "      <td>-0.711676</td>\n",
       "      <td>-0.943097</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>-0.120103</td>\n",
       "      <td>0.222110</td>\n",
       "      <td>-1.639884</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>-0.808699</td>\n",
       "      <td>0.072326</td>\n",
       "      <td>0.412848</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>4</th>\n",
       "      <td>-0.120103</td>\n",
       "      <td>1.428193</td>\n",
       "      <td>0.214291</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      idade   salario     bonus  cargo_Analista  cargo_Diretor  \\\n",
       "0  1.143230  0.768685  0.775384               0              1   \n",
       "1 -1.075613 -0.711676 -0.943097               1              0   \n",
       "2 -0.120103  0.222110 -1.639884               0              0   \n",
       "3 -0.808699  0.072326  0.412848               0              0   \n",
       "4 -0.120103  1.428193  0.214291               0              0   \n",
       "\n",
       "   cargo_Fundador  cargo_Gerente  cargo_Programador  \n",
       "0               0              0                  0  \n",
       "1               0              0                  0  \n",
       "2               0              0                  1  \n",
       "3               0              1                  0  \n",
       "4               0              1                  0  "
      ]
     },
     "execution_count": 85,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X_power = X.copy()\n",
    "X_power[['idade', 'salario', 'bonus']] = PowerTransformer().fit_transform(X[['idade', 'salario', 'bonus']])\n",
    "X_power.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [],
   "source": [
    "X_new = SelectKBest(chi2, k=6).fit_transform(X_minMax, Y)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[0.55555556, 0.60735921, 0.33333333, 0.        , 1.        ,\n",
       "        0.        ],\n",
       "       [0.04444444, 0.15862688, 0.03703704, 1.        , 0.        ,\n",
       "        0.        ],\n",
       "       [0.22222222, 0.40529878, 0.        , 0.        , 0.        ,\n",
       "        0.        ],\n",
       "       [0.08888889, 0.35775185, 0.22962963, 0.        , 0.        ,\n",
       "        0.        ],\n",
       "       [0.22222222, 0.91586269, 0.18518519, 0.        , 0.        ,\n",
       "        0.        ],\n",
       "       [0.04444444, 0.0829033 , 0.03703704, 0.        , 0.        ,\n",
       "        0.        ],\n",
       "       [0.        , 0.40529878, 0.00740741, 1.        , 0.        ,\n",
       "        0.        ],\n",
       "       [0.66666667, 0.43908459, 0.25925926, 0.        , 1.        ,\n",
       "        0.        ],\n",
       "       [1.        , 1.        , 1.        , 0.        , 0.        ,\n",
       "        1.        ],\n",
       "       [0.26666667, 0.13899484, 0.11111111, 1.        , 0.        ,\n",
       "        0.        ],\n",
       "       [0.33333333, 0.        , 0.22962963, 0.        , 0.        ,\n",
       "        0.        ],\n",
       "       [0.17777778, 0.06046668, 0.04444444, 0.        , 0.        ,\n",
       "        0.        ],\n",
       "       [0.17777778, 0.77563383, 0.40740741, 0.        , 0.        ,\n",
       "        1.        ],\n",
       "       [0.22222222, 0.3269015 , 0.33333333, 0.        , 0.        ,\n",
       "        0.        ]])"
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X_new"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [],
   "source": [
    "clf = ExtraTreesClassifier()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/usr/local/lib/python3.6/dist-packages/sklearn/ensemble/forest.py:248: FutureWarning: The default value of n_estimators will change from 10 in version 0.20 to 100 in 0.22.\n",
      "  \"10 in version 0.20 to 100 in 0.22.\", FutureWarning)\n"
     ]
    }
   ],
   "source": [
    "clf = clf.fit(X_minMax, Y)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([0.12823864, 0.24412879, 0.27216225, 0.07169613, 0.11600694,\n",
       "       0.11073338, 0.02269676, 0.03433712])"
      ]
     },
     "execution_count": 37,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "clf.feature_importances_ "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [],
   "source": [
    "model = SelectFromModel(clf, prefit=True, threshold=\"median\")\n",
    "X_new = model.transform(X_minMax)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[0.55555556, 0.60735921, 0.33333333, 1.        ],\n",
       "       [0.04444444, 0.15862688, 0.03703704, 0.        ],\n",
       "       [0.22222222, 0.40529878, 0.        , 0.        ],\n",
       "       [0.08888889, 0.35775185, 0.22962963, 0.        ],\n",
       "       [0.22222222, 0.91586269, 0.18518519, 0.        ],\n",
       "       [0.04444444, 0.0829033 , 0.03703704, 0.        ],\n",
       "       [0.        , 0.40529878, 0.00740741, 0.        ],\n",
       "       [0.66666667, 0.43908459, 0.25925926, 1.        ],\n",
       "       [1.        , 1.        , 1.        , 0.        ],\n",
       "       [0.26666667, 0.13899484, 0.11111111, 0.        ],\n",
       "       [0.33333333, 0.        , 0.22962963, 0.        ],\n",
       "       [0.17777778, 0.06046668, 0.04444444, 0.        ],\n",
       "       [0.17777778, 0.77563383, 0.40740741, 0.        ],\n",
       "       [0.22222222, 0.3269015 , 0.33333333, 0.        ]])"
      ]
     },
     "execution_count": 39,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X_new"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 86,
   "metadata": {},
   "outputs": [],
   "source": [
    "pca = PCA(n_components='mle')\n",
    "X_new = pca.fit_transform(X_minMax) "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 88,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[ 0.53866608, -0.13267348, -0.3357473 ,  0.79791559,  0.08466453,\n",
       "        -0.05714549,  0.07021413],\n",
       "       [-0.1368266 ,  0.93283828, -0.21270972, -0.16271694, -0.0028759 ,\n",
       "         0.04533437,  0.01861439],\n",
       "       [-0.60363558, -0.37644469,  0.01636196, -0.04850587,  0.07254725,\n",
       "        -0.23334703, -0.09461641],\n",
       "       [ 0.20186796,  0.18208778,  0.90457672,  0.15443852, -0.06257746,\n",
       "         0.26362392,  0.03431419],\n",
       "       [ 0.4695611 ,  0.08126605,  0.95831876,  0.12838664, -0.03621429,\n",
       "        -0.22627592, -0.0374659 ],\n",
       "       [-0.78274982, -0.29609104,  0.01031809, -0.04706371,  0.11946656,\n",
       "         0.05719386,  0.01365582],\n",
       "       [-0.0501463 ,  0.91612012, -0.16114226, -0.18467334,  0.08231014,\n",
       "        -0.16703018,  0.03887834],\n",
       "       [ 0.47351528, -0.11871599, -0.38245359,  0.83764906,  0.01517078,\n",
       "         0.06756885, -0.06232372],\n",
       "       [ 1.21379905, -0.62653624, -0.30107854, -0.5921759 , -0.31002738,\n",
       "        -0.02640987, -0.0082365 ],\n",
       "       [-0.0615711 ,  0.86322067, -0.28071454, -0.1471028 , -0.18805305,\n",
       "         0.06181369, -0.0542862 ],\n",
       "       [-0.6810227 , -0.40144957, -0.09634357, -0.03739642, -0.17453742,\n",
       "         0.14340386, -0.01148692],\n",
       "       [-0.7534116 , -0.32754108, -0.02838242, -0.03074252,  0.02326011,\n",
       "         0.07001957, -0.05915848],\n",
       "       [ 0.70951022, -0.25128851, -0.06224468, -0.57218556,  0.46819143,\n",
       "         0.11267141, -0.00293375],\n",
       "       [-0.53755599, -0.4447923 , -0.02875891, -0.09582675, -0.09132531,\n",
       "        -0.11142105,  0.15483102]])"
      ]
     },
     "execution_count": 88,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X_new"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 89,
   "metadata": {},
   "outputs": [],
   "source": [
    "X_train, X_test, Y_train, Y_test = train_test_split(X_minMax, Y, test_size = 0.2, random_state = 0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 90,
   "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>idade</th>\n",
       "      <th>salario</th>\n",
       "      <th>bonus</th>\n",
       "      <th>cargo_Analista</th>\n",
       "      <th>cargo_Diretor</th>\n",
       "      <th>cargo_Fundador</th>\n",
       "      <th>cargo_Gerente</th>\n",
       "      <th>cargo_Programador</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>0.177778</td>\n",
       "      <td>0.060467</td>\n",
       "      <td>0.044444</td>\n",
       "      <td>0</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>0.222222</td>\n",
       "      <td>0.405299</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>0.222222</td>\n",
       "      <td>0.326902</td>\n",
       "      <td>0.333333</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>0.266667</td>\n",
       "      <td>0.138995</td>\n",
       "      <td>0.111111</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0.044444</td>\n",
       "      <td>0.158627</td>\n",
       "      <td>0.037037</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>0.666667</td>\n",
       "      <td>0.439085</td>\n",
       "      <td>0.259259</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>10</th>\n",
       "      <td>0.333333</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.229630</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0.088889</td>\n",
       "      <td>0.357752</td>\n",
       "      <td>0.229630</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>0</th>\n",
       "      <td>0.555556</td>\n",
       "      <td>0.607359</td>\n",
       "      <td>0.333333</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>5</th>\n",
       "      <td>0.044444</td>\n",
       "      <td>0.082903</td>\n",
       "      <td>0.037037</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>0.177778</td>\n",
       "      <td>0.775634</td>\n",
       "      <td>0.407407</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": [
       "       idade   salario     bonus  cargo_Analista  cargo_Diretor  \\\n",
       "11  0.177778  0.060467  0.044444               0              0   \n",
       "2   0.222222  0.405299  0.000000               0              0   \n",
       "13  0.222222  0.326902  0.333333               0              0   \n",
       "9   0.266667  0.138995  0.111111               1              0   \n",
       "1   0.044444  0.158627  0.037037               1              0   \n",
       "7   0.666667  0.439085  0.259259               0              1   \n",
       "10  0.333333  0.000000  0.229630               0              0   \n",
       "3   0.088889  0.357752  0.229630               0              0   \n",
       "0   0.555556  0.607359  0.333333               0              1   \n",
       "5   0.044444  0.082903  0.037037               0              0   \n",
       "12  0.177778  0.775634  0.407407               0              0   \n",
       "\n",
       "    cargo_Fundador  cargo_Gerente  cargo_Programador  \n",
       "11               0              0                  1  \n",
       "2                0              0                  1  \n",
       "13               0              0                  1  \n",
       "9                0              0                  0  \n",
       "1                0              0                  0  \n",
       "7                0              0                  0  \n",
       "10               0              0                  1  \n",
       "3                0              1                  0  \n",
       "0                0              0                  0  \n",
       "5                0              0                  1  \n",
       "12               1              0                  0  "
      ]
     },
     "execution_count": 90,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X_train"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 91,
   "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>idade</th>\n",
       "      <th>salario</th>\n",
       "      <th>bonus</th>\n",
       "      <th>cargo_Analista</th>\n",
       "      <th>cargo_Diretor</th>\n",
       "      <th>cargo_Fundador</th>\n",
       "      <th>cargo_Gerente</th>\n",
       "      <th>cargo_Programador</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.405299</td>\n",
       "      <td>0.007407</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0.222222</td>\n",
       "      <td>0.915863</td>\n",
       "      <td>0.185185</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      idade   salario     bonus  cargo_Analista  cargo_Diretor  \\\n",
       "8  1.000000  1.000000  1.000000               0              0   \n",
       "6  0.000000  0.405299  0.007407               1              0   \n",
       "4  0.222222  0.915863  0.185185               0              0   \n",
       "\n",
       "   cargo_Fundador  cargo_Gerente  cargo_Programador  \n",
       "8               1              0                  0  \n",
       "6               0              0                  0  \n",
       "4               0              1                  0  "
      ]
     },
     "execution_count": 91,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X_test"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "metadata": {},
   "outputs": [],
   "source": [
    "X_new = X_minMax.values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 94,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "K = 1 - TRAIN: [ 7  8  9 10 11 12 13] TEST: [0 1 2 3 4 5 6]\n",
      "K = 2 - TRAIN: [0 1 2 3 4 5 6] TEST: [ 7  8  9 10 11 12 13]\n"
     ]
    }
   ],
   "source": [
    "from sklearn.model_selection import KFold\n",
    "kf = KFold(n_splits=2)\n",
    "time = 1\n",
    "for train_index, test_index in kf.split(X_new):\n",
    "    print(\"K = \" + str(time) +  \" - TRAIN:\", train_index, \"TEST:\", test_index)\n",
    "    X_train, X_test = X_new[train_index], X_new[test_index]\n",
    "    y_train, y_test = Y[train_index], Y[test_index]\n",
    "    time += 1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.6.7"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}