XGBoost_Packt_1.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import warnings\n",
    "warnings.filterwarnings('ignore')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_bikes = pd.read_csv('bike_rentals.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>instant</th>\n",
       "      <th>dteday</th>\n",
       "      <th>season</th>\n",
       "      <th>yr</th>\n",
       "      <th>mnth</th>\n",
       "      <th>holiday</th>\n",
       "      <th>weekday</th>\n",
       "      <th>workingday</th>\n",
       "      <th>weathersit</th>\n",
       "      <th>temp</th>\n",
       "      <th>atemp</th>\n",
       "      <th>hum</th>\n",
       "      <th>windspeed</th>\n",
       "      <th>casual</th>\n",
       "      <th>registered</th>\n",
       "      <th>cnt</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>2011-01-01</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2</td>\n",
       "      <td>0.344167</td>\n",
       "      <td>0.363625</td>\n",
       "      <td>0.805833</td>\n",
       "      <td>0.160446</td>\n",
       "      <td>331</td>\n",
       "      <td>654</td>\n",
       "      <td>985</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>2011-01-02</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2</td>\n",
       "      <td>0.363478</td>\n",
       "      <td>0.353739</td>\n",
       "      <td>0.696087</td>\n",
       "      <td>0.248539</td>\n",
       "      <td>131</td>\n",
       "      <td>670</td>\n",
       "      <td>801</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "      <td>2011-01-03</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0.196364</td>\n",
       "      <td>0.189405</td>\n",
       "      <td>0.437273</td>\n",
       "      <td>0.248309</td>\n",
       "      <td>120</td>\n",
       "      <td>1229</td>\n",
       "      <td>1349</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4</td>\n",
       "      <td>2011-01-04</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0.200000</td>\n",
       "      <td>0.212122</td>\n",
       "      <td>0.590435</td>\n",
       "      <td>0.160296</td>\n",
       "      <td>108</td>\n",
       "      <td>1454</td>\n",
       "      <td>1562</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5</td>\n",
       "      <td>2011-01-05</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0.226957</td>\n",
       "      <td>0.229270</td>\n",
       "      <td>0.436957</td>\n",
       "      <td>0.186900</td>\n",
       "      <td>82</td>\n",
       "      <td>1518</td>\n",
       "      <td>1600</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   instant      dteday  season   yr  mnth  holiday  weekday  workingday  \\\n",
       "0        1  2011-01-01     1.0  0.0   1.0      0.0      6.0         0.0   \n",
       "1        2  2011-01-02     1.0  0.0   1.0      0.0      0.0         0.0   \n",
       "2        3  2011-01-03     1.0  0.0   1.0      0.0      1.0         1.0   \n",
       "3        4  2011-01-04     1.0  0.0   1.0      0.0      2.0         1.0   \n",
       "4        5  2011-01-05     1.0  0.0   1.0      0.0      3.0         1.0   \n",
       "\n",
       "   weathersit      temp     atemp       hum  windspeed  casual  registered  \\\n",
       "0           2  0.344167  0.363625  0.805833   0.160446     331         654   \n",
       "1           2  0.363478  0.353739  0.696087   0.248539     131         670   \n",
       "2           1  0.196364  0.189405  0.437273   0.248309     120        1229   \n",
       "3           1  0.200000  0.212122  0.590435   0.160296     108        1454   \n",
       "4           1  0.226957  0.229270  0.436957   0.186900      82        1518   \n",
       "\n",
       "    cnt  \n",
       "0   985  \n",
       "1   801  \n",
       "2  1349  \n",
       "3  1562  \n",
       "4  1600  "
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_bikes.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['instant', 'dteday', 'season', 'yr', 'mnth', 'holiday', 'weekday',\n",
       "       'workingday', 'weathersit', 'temp', 'atemp', 'hum', 'windspeed',\n",
       "       'casual', 'registered', 'cnt'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_bikes.columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>instant</th>\n",
       "      <th>season</th>\n",
       "      <th>yr</th>\n",
       "      <th>mnth</th>\n",
       "      <th>holiday</th>\n",
       "      <th>weekday</th>\n",
       "      <th>workingday</th>\n",
       "      <th>weathersit</th>\n",
       "      <th>temp</th>\n",
       "      <th>atemp</th>\n",
       "      <th>hum</th>\n",
       "      <th>windspeed</th>\n",
       "      <th>casual</th>\n",
       "      <th>registered</th>\n",
       "      <th>cnt</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>731.000000</td>\n",
       "      <td>730.000000</td>\n",
       "      <td>730.000000</td>\n",
       "      <td>730.000000</td>\n",
       "      <td>730.000000</td>\n",
       "      <td>730.000000</td>\n",
       "      <td>730.000000</td>\n",
       "      <td>731.000000</td>\n",
       "      <td>730.000000</td>\n",
       "      <td>730.000000</td>\n",
       "      <td>728.000000</td>\n",
       "      <td>726.000000</td>\n",
       "      <td>731.000000</td>\n",
       "      <td>731.000000</td>\n",
       "      <td>731.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>366.000000</td>\n",
       "      <td>2.498630</td>\n",
       "      <td>0.500000</td>\n",
       "      <td>6.512329</td>\n",
       "      <td>0.028767</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>0.683562</td>\n",
       "      <td>1.395349</td>\n",
       "      <td>0.495587</td>\n",
       "      <td>0.474512</td>\n",
       "      <td>0.627987</td>\n",
       "      <td>0.190476</td>\n",
       "      <td>848.176471</td>\n",
       "      <td>3656.172367</td>\n",
       "      <td>4504.348837</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>211.165812</td>\n",
       "      <td>1.110184</td>\n",
       "      <td>0.500343</td>\n",
       "      <td>3.448303</td>\n",
       "      <td>0.167266</td>\n",
       "      <td>2.004795</td>\n",
       "      <td>0.465405</td>\n",
       "      <td>0.544894</td>\n",
       "      <td>0.183094</td>\n",
       "      <td>0.163017</td>\n",
       "      <td>0.142331</td>\n",
       "      <td>0.077725</td>\n",
       "      <td>686.622488</td>\n",
       "      <td>1560.256377</td>\n",
       "      <td>1937.211452</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.059130</td>\n",
       "      <td>0.079070</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.022392</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>20.000000</td>\n",
       "      <td>22.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>183.500000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>4.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.336875</td>\n",
       "      <td>0.337794</td>\n",
       "      <td>0.521562</td>\n",
       "      <td>0.134494</td>\n",
       "      <td>315.500000</td>\n",
       "      <td>2497.000000</td>\n",
       "      <td>3152.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>366.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>0.500000</td>\n",
       "      <td>7.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.499167</td>\n",
       "      <td>0.487364</td>\n",
       "      <td>0.627083</td>\n",
       "      <td>0.180971</td>\n",
       "      <td>713.000000</td>\n",
       "      <td>3662.000000</td>\n",
       "      <td>4548.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>548.500000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>9.750000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>5.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>0.655625</td>\n",
       "      <td>0.608916</td>\n",
       "      <td>0.730104</td>\n",
       "      <td>0.233218</td>\n",
       "      <td>1096.000000</td>\n",
       "      <td>4776.500000</td>\n",
       "      <td>5956.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>731.000000</td>\n",
       "      <td>4.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>12.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>6.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>0.861667</td>\n",
       "      <td>0.840896</td>\n",
       "      <td>0.972500</td>\n",
       "      <td>0.507463</td>\n",
       "      <td>3410.000000</td>\n",
       "      <td>6946.000000</td>\n",
       "      <td>8714.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "          instant      season          yr        mnth     holiday     weekday  \\\n",
       "count  731.000000  730.000000  730.000000  730.000000  730.000000  730.000000   \n",
       "mean   366.000000    2.498630    0.500000    6.512329    0.028767    3.000000   \n",
       "std    211.165812    1.110184    0.500343    3.448303    0.167266    2.004795   \n",
       "min      1.000000    1.000000    0.000000    1.000000    0.000000    0.000000   \n",
       "25%    183.500000    2.000000    0.000000    4.000000    0.000000    1.000000   \n",
       "50%    366.000000    3.000000    0.500000    7.000000    0.000000    3.000000   \n",
       "75%    548.500000    3.000000    1.000000    9.750000    0.000000    5.000000   \n",
       "max    731.000000    4.000000    1.000000   12.000000    1.000000    6.000000   \n",
       "\n",
       "       workingday  weathersit        temp       atemp         hum   windspeed  \\\n",
       "count  730.000000  731.000000  730.000000  730.000000  728.000000  726.000000   \n",
       "mean     0.683562    1.395349    0.495587    0.474512    0.627987    0.190476   \n",
       "std      0.465405    0.544894    0.183094    0.163017    0.142331    0.077725   \n",
       "min      0.000000    1.000000    0.059130    0.079070    0.000000    0.022392   \n",
       "25%      0.000000    1.000000    0.336875    0.337794    0.521562    0.134494   \n",
       "50%      1.000000    1.000000    0.499167    0.487364    0.627083    0.180971   \n",
       "75%      1.000000    2.000000    0.655625    0.608916    0.730104    0.233218   \n",
       "max      1.000000    3.000000    0.861667    0.840896    0.972500    0.507463   \n",
       "\n",
       "            casual   registered          cnt  \n",
       "count   731.000000   731.000000   731.000000  \n",
       "mean    848.176471  3656.172367  4504.348837  \n",
       "std     686.622488  1560.256377  1937.211452  \n",
       "min       2.000000    20.000000    22.000000  \n",
       "25%     315.500000  2497.000000  3152.000000  \n",
       "50%     713.000000  3662.000000  4548.000000  \n",
       "75%    1096.000000  4776.500000  5956.000000  \n",
       "max    3410.000000  6946.000000  8714.000000  "
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_bikes.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 731 entries, 0 to 730\n",
      "Data columns (total 16 columns):\n",
      "instant       731 non-null int64\n",
      "dteday        731 non-null object\n",
      "season        730 non-null float64\n",
      "yr            730 non-null float64\n",
      "mnth          730 non-null float64\n",
      "holiday       730 non-null float64\n",
      "weekday       730 non-null float64\n",
      "workingday    730 non-null float64\n",
      "weathersit    731 non-null int64\n",
      "temp          730 non-null float64\n",
      "atemp         730 non-null float64\n",
      "hum           728 non-null float64\n",
      "windspeed     726 non-null float64\n",
      "casual        731 non-null int64\n",
      "registered    731 non-null int64\n",
      "cnt           731 non-null int64\n",
      "dtypes: float64(10), int64(5), object(1)\n",
      "memory usage: 91.5+ KB\n"
     ]
    }
   ],
   "source": [
    "df_bikes.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "16"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_bikes.isnull().sum().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>instant</th>\n",
       "      <th>dteday</th>\n",
       "      <th>season</th>\n",
       "      <th>yr</th>\n",
       "      <th>mnth</th>\n",
       "      <th>holiday</th>\n",
       "      <th>weekday</th>\n",
       "      <th>workingday</th>\n",
       "      <th>weathersit</th>\n",
       "      <th>temp</th>\n",
       "      <th>atemp</th>\n",
       "      <th>hum</th>\n",
       "      <th>windspeed</th>\n",
       "      <th>casual</th>\n",
       "      <th>registered</th>\n",
       "      <th>cnt</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>56</th>\n",
       "      <td>57</td>\n",
       "      <td>2011-02-26</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0.282500</td>\n",
       "      <td>0.282192</td>\n",
       "      <td>0.537917</td>\n",
       "      <td>NaN</td>\n",
       "      <td>424</td>\n",
       "      <td>1545</td>\n",
       "      <td>1969</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>81</th>\n",
       "      <td>82</td>\n",
       "      <td>2011-03-23</td>\n",
       "      <td>2.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2</td>\n",
       "      <td>0.346957</td>\n",
       "      <td>0.337939</td>\n",
       "      <td>0.839565</td>\n",
       "      <td>NaN</td>\n",
       "      <td>203</td>\n",
       "      <td>1918</td>\n",
       "      <td>2121</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>128</th>\n",
       "      <td>129</td>\n",
       "      <td>2011-05-09</td>\n",
       "      <td>2.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0.532500</td>\n",
       "      <td>0.525246</td>\n",
       "      <td>0.588750</td>\n",
       "      <td>NaN</td>\n",
       "      <td>664</td>\n",
       "      <td>3698</td>\n",
       "      <td>4362</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>129</th>\n",
       "      <td>130</td>\n",
       "      <td>2011-05-10</td>\n",
       "      <td>2.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0.532500</td>\n",
       "      <td>0.522721</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.115671</td>\n",
       "      <td>694</td>\n",
       "      <td>4109</td>\n",
       "      <td>4803</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>213</th>\n",
       "      <td>214</td>\n",
       "      <td>2011-08-02</td>\n",
       "      <td>3.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>8.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0.783333</td>\n",
       "      <td>0.707071</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.205850</td>\n",
       "      <td>801</td>\n",
       "      <td>4044</td>\n",
       "      <td>4845</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>298</th>\n",
       "      <td>299</td>\n",
       "      <td>2011-10-26</td>\n",
       "      <td>4.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>10.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2</td>\n",
       "      <td>0.484167</td>\n",
       "      <td>0.472846</td>\n",
       "      <td>0.720417</td>\n",
       "      <td>NaN</td>\n",
       "      <td>404</td>\n",
       "      <td>3490</td>\n",
       "      <td>3894</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>388</th>\n",
       "      <td>389</td>\n",
       "      <td>2012-01-24</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0.342500</td>\n",
       "      <td>0.349108</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.123767</td>\n",
       "      <td>439</td>\n",
       "      <td>3900</td>\n",
       "      <td>4339</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>528</th>\n",
       "      <td>529</td>\n",
       "      <td>2012-06-12</td>\n",
       "      <td>2.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2</td>\n",
       "      <td>0.653333</td>\n",
       "      <td>0.597875</td>\n",
       "      <td>0.833333</td>\n",
       "      <td>NaN</td>\n",
       "      <td>477</td>\n",
       "      <td>4495</td>\n",
       "      <td>4972</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>701</th>\n",
       "      <td>702</td>\n",
       "      <td>2012-12-02</td>\n",
       "      <td>4.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>12.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.823333</td>\n",
       "      <td>0.124379</td>\n",
       "      <td>892</td>\n",
       "      <td>3757</td>\n",
       "      <td>4649</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>730</th>\n",
       "      <td>731</td>\n",
       "      <td>2012-12-31</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2</td>\n",
       "      <td>0.215833</td>\n",
       "      <td>0.223487</td>\n",
       "      <td>0.577500</td>\n",
       "      <td>0.154846</td>\n",
       "      <td>439</td>\n",
       "      <td>2290</td>\n",
       "      <td>2729</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     instant      dteday  season   yr  mnth  holiday  weekday  workingday  \\\n",
       "56        57  2011-02-26     1.0  0.0   2.0      0.0      6.0         0.0   \n",
       "81        82  2011-03-23     2.0  0.0   3.0      0.0      3.0         1.0   \n",
       "128      129  2011-05-09     2.0  0.0   5.0      0.0      1.0         1.0   \n",
       "129      130  2011-05-10     2.0  0.0   5.0      0.0      2.0         1.0   \n",
       "213      214  2011-08-02     3.0  0.0   8.0      0.0      2.0         1.0   \n",
       "298      299  2011-10-26     4.0  0.0  10.0      0.0      3.0         1.0   \n",
       "388      389  2012-01-24     1.0  1.0   1.0      0.0      2.0         1.0   \n",
       "528      529  2012-06-12     2.0  1.0   6.0      0.0      2.0         1.0   \n",
       "701      702  2012-12-02     4.0  1.0  12.0      0.0      0.0         0.0   \n",
       "730      731  2012-12-31     NaN  NaN   NaN      NaN      NaN         NaN   \n",
       "\n",
       "     weathersit      temp     atemp       hum  windspeed  casual  registered  \\\n",
       "56            1  0.282500  0.282192  0.537917        NaN     424        1545   \n",
       "81            2  0.346957  0.337939  0.839565        NaN     203        1918   \n",
       "128           1  0.532500  0.525246  0.588750        NaN     664        3698   \n",
       "129           1  0.532500  0.522721       NaN   0.115671     694        4109   \n",
       "213           1  0.783333  0.707071       NaN   0.205850     801        4044   \n",
       "298           2  0.484167  0.472846  0.720417        NaN     404        3490   \n",
       "388           1  0.342500  0.349108       NaN   0.123767     439        3900   \n",
       "528           2  0.653333  0.597875  0.833333        NaN     477        4495   \n",
       "701           2       NaN       NaN  0.823333   0.124379     892        3757   \n",
       "730           2  0.215833  0.223487  0.577500   0.154846     439        2290   \n",
       "\n",
       "      cnt  \n",
       "56   1969  \n",
       "81   2121  \n",
       "128  4362  \n",
       "129  4803  \n",
       "213  4845  \n",
       "298  3894  \n",
       "388  4339  \n",
       "528  4972  \n",
       "701  4649  \n",
       "730  2729  "
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_bikes[df_bikes.isna().any(axis=1)]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "import matplotlib.pyplot as plt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_bikes['windspeed'].fillna((df_bikes['windspeed'].mean()), inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <th>instant</th>\n",
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       "      <th>windspeed</th>\n",
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       "  <tbody>\n",
       "    <tr>\n",
       "      <th>56</th>\n",
       "      <td>57</td>\n",
       "      <td>2011-02-26</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>6.0</td>\n",
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       "      <td>0.282500</td>\n",
       "      <td>0.282192</td>\n",
       "      <td>0.537917</td>\n",
       "      <td>0.190476</td>\n",
       "      <td>424</td>\n",
       "      <td>1545</td>\n",
       "      <td>1969</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>81</th>\n",
       "      <td>82</td>\n",
       "      <td>2011-03-23</td>\n",
       "      <td>2.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2</td>\n",
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       "      <td>0.337939</td>\n",
       "      <td>0.839565</td>\n",
       "      <td>0.190476</td>\n",
       "      <td>203</td>\n",
       "      <td>1918</td>\n",
       "      <td>2121</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    instant      dteday  season   yr  mnth  holiday  weekday  workingday  \\\n",
       "56       57  2011-02-26     1.0  0.0   2.0      0.0      6.0         0.0   \n",
       "81       82  2011-03-23     2.0  0.0   3.0      0.0      3.0         1.0   \n",
       "\n",
       "    weathersit      temp     atemp       hum  windspeed  casual  registered  \\\n",
       "56           1  0.282500  0.282192  0.537917   0.190476     424        1545   \n",
       "81           2  0.346957  0.337939  0.839565   0.190476     203        1918   \n",
       "\n",
       "     cnt  \n",
       "56  1969  \n",
       "81  2121  "
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_bikes.iloc[[56, 81]]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <th>instant</th>\n",
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       "    <tr>\n",
       "      <th>season</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
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       "    <tr>\n",
       "      <th>1.0</th>\n",
       "      <td>365.5</td>\n",
       "      <td>0.5</td>\n",
       "      <td>2.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.286666</td>\n",
       "      <td>0.283448</td>\n",
       "      <td>0.543333</td>\n",
       "      <td>0.202933</td>\n",
       "      <td>217.5</td>\n",
       "      <td>1855.5</td>\n",
       "      <td>2200.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2.0</th>\n",
       "      <td>308.5</td>\n",
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       "      <td>0.0</td>\n",
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       "      <td>0.538212</td>\n",
       "      <td>0.646667</td>\n",
       "      <td>0.191546</td>\n",
       "      <td>867.0</td>\n",
       "      <td>3844.0</td>\n",
       "      <td>4941.5</td>\n",
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       "    <tr>\n",
       "      <th>3.0</th>\n",
       "      <td>401.5</td>\n",
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       "      <td>8.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>3.0</td>\n",
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       "      <td>0.656575</td>\n",
       "      <td>0.635833</td>\n",
       "      <td>0.165115</td>\n",
       "      <td>1050.5</td>\n",
       "      <td>4110.5</td>\n",
       "      <td>5353.5</td>\n",
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       "    <tr>\n",
       "      <th>4.0</th>\n",
       "      <td>493.0</td>\n",
       "      <td>0.5</td>\n",
       "      <td>11.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.410000</td>\n",
       "      <td>0.409708</td>\n",
       "      <td>0.661042</td>\n",
       "      <td>0.167918</td>\n",
       "      <td>544.5</td>\n",
       "      <td>3815.0</td>\n",
       "      <td>4634.5</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
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      ],
      "text/plain": [
       "        instant   yr  mnth  holiday  weekday  workingday  weathersit  \\\n",
       "season                                                                 \n",
       "1.0       365.5  0.5   2.0      0.0      3.0         1.0         1.0   \n",
       "2.0       308.5  0.5   5.0      0.0      3.0         1.0         1.0   \n",
       "3.0       401.5  0.5   8.0      0.0      3.0         1.0         1.0   \n",
       "4.0       493.0  0.5  11.0      0.0      3.0         1.0         1.0   \n",
       "\n",
       "            temp     atemp       hum  windspeed  casual  registered     cnt  \n",
       "season                                                                       \n",
       "1.0     0.286666  0.283448  0.543333   0.202933   217.5      1855.5  2200.5  \n",
       "2.0     0.562083  0.538212  0.646667   0.191546   867.0      3844.0  4941.5  \n",
       "3.0     0.714583  0.656575  0.635833   0.165115  1050.5      4110.5  5353.5  \n",
       "4.0     0.410000  0.409708  0.661042   0.167918   544.5      3815.0  4634.5  "
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_bikes.groupby(['season']).median()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_bikes['hum'] = df_bikes['hum'].fillna(df_bikes.groupby('season')['hum'].transform('median'))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <th>yr</th>\n",
       "      <th>mnth</th>\n",
       "      <th>holiday</th>\n",
       "      <th>weekday</th>\n",
       "      <th>workingday</th>\n",
       "      <th>weathersit</th>\n",
       "      <th>temp</th>\n",
       "      <th>atemp</th>\n",
       "      <th>hum</th>\n",
       "      <th>windspeed</th>\n",
       "      <th>casual</th>\n",
       "      <th>registered</th>\n",
       "      <th>cnt</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>129</th>\n",
       "      <td>130</td>\n",
       "      <td>2011-05-10</td>\n",
       "      <td>2.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0.532500</td>\n",
       "      <td>0.522721</td>\n",
       "      <td>0.646667</td>\n",
       "      <td>0.115671</td>\n",
       "      <td>694</td>\n",
       "      <td>4109</td>\n",
       "      <td>4803</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>213</th>\n",
       "      <td>214</td>\n",
       "      <td>2011-08-02</td>\n",
       "      <td>3.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>8.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0.783333</td>\n",
       "      <td>0.707071</td>\n",
       "      <td>0.635833</td>\n",
       "      <td>0.205850</td>\n",
       "      <td>801</td>\n",
       "      <td>4044</td>\n",
       "      <td>4845</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>388</th>\n",
       "      <td>389</td>\n",
       "      <td>2012-01-24</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0.342500</td>\n",
       "      <td>0.349108</td>\n",
       "      <td>0.543333</td>\n",
       "      <td>0.123767</td>\n",
       "      <td>439</td>\n",
       "      <td>3900</td>\n",
       "      <td>4339</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     instant      dteday  season   yr  mnth  holiday  weekday  workingday  \\\n",
       "129      130  2011-05-10     2.0  0.0   5.0      0.0      2.0         1.0   \n",
       "213      214  2011-08-02     3.0  0.0   8.0      0.0      2.0         1.0   \n",
       "388      389  2012-01-24     1.0  1.0   1.0      0.0      2.0         1.0   \n",
       "\n",
       "     weathersit      temp     atemp       hum  windspeed  casual  registered  \\\n",
       "129           1  0.532500  0.522721  0.646667   0.115671     694        4109   \n",
       "213           1  0.783333  0.707071  0.635833   0.205850     801        4044   \n",
       "388           1  0.342500  0.349108  0.543333   0.123767     439        3900   \n",
       "\n",
       "      cnt  \n",
       "129  4803  \n",
       "213  4845  \n",
       "388  4339  "
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_bikes.iloc[[129, 213, 388]]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>instant</th>\n",
       "      <th>dteday</th>\n",
       "      <th>season</th>\n",
       "      <th>yr</th>\n",
       "      <th>mnth</th>\n",
       "      <th>holiday</th>\n",
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       "      <th>temp</th>\n",
       "      <th>atemp</th>\n",
       "      <th>hum</th>\n",
       "      <th>windspeed</th>\n",
       "      <th>casual</th>\n",
       "      <th>registered</th>\n",
       "      <th>cnt</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>701</th>\n",
       "      <td>702</td>\n",
       "      <td>2012-12-02</td>\n",
       "      <td>4.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>12.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.823333</td>\n",
       "      <td>0.124379</td>\n",
       "      <td>892</td>\n",
       "      <td>3757</td>\n",
       "      <td>4649</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     instant      dteday  season   yr  mnth  holiday  weekday  workingday  \\\n",
       "701      702  2012-12-02     4.0  1.0  12.0      0.0      0.0         0.0   \n",
       "\n",
       "     weathersit  temp  atemp       hum  windspeed  casual  registered   cnt  \n",
       "701           2   NaN    NaN  0.823333   0.124379     892        3757  4649  "
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_bikes[df_bikes['temp'].isna()]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Take mean of temp before and af\n",
    "mean_temp = (df_bikes.iloc[700]['temp'] + df_bikes.iloc[702]['temp'])/2\n",
    "mean_atemp = (df_bikes.iloc[700]['atemp'] + df_bikes.iloc[702]['atemp'])/2\n",
    "df_bikes['temp'].fillna((mean_temp), inplace=True)\n",
    "df_bikes['atemp'].fillna((mean_atemp), inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
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       "      <th></th>\n",
       "      <th>instant</th>\n",
       "      <th>dteday</th>\n",
       "      <th>season</th>\n",
       "      <th>yr</th>\n",
       "      <th>mnth</th>\n",
       "      <th>holiday</th>\n",
       "      <th>weekday</th>\n",
       "      <th>workingday</th>\n",
       "      <th>weathersit</th>\n",
       "      <th>temp</th>\n",
       "      <th>atemp</th>\n",
       "      <th>hum</th>\n",
       "      <th>windspeed</th>\n",
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       "      <th>cnt</th>\n",
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       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>2011-01-01</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>6.0</td>\n",
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       "      <td>2</td>\n",
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       "      <td>0.363625</td>\n",
       "      <td>0.805833</td>\n",
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       "      <td>331</td>\n",
       "      <td>654</td>\n",
       "      <td>985</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>2011-01-02</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2</td>\n",
       "      <td>0.363478</td>\n",
       "      <td>0.353739</td>\n",
       "      <td>0.696087</td>\n",
       "      <td>0.248539</td>\n",
       "      <td>131</td>\n",
       "      <td>670</td>\n",
       "      <td>801</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "      <td>2011-01-03</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0.196364</td>\n",
       "      <td>0.189405</td>\n",
       "      <td>0.437273</td>\n",
       "      <td>0.248309</td>\n",
       "      <td>120</td>\n",
       "      <td>1229</td>\n",
       "      <td>1349</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4</td>\n",
       "      <td>2011-01-04</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0.200000</td>\n",
       "      <td>0.212122</td>\n",
       "      <td>0.590435</td>\n",
       "      <td>0.160296</td>\n",
       "      <td>108</td>\n",
       "      <td>1454</td>\n",
       "      <td>1562</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5</td>\n",
       "      <td>2011-01-05</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0.226957</td>\n",
       "      <td>0.229270</td>\n",
       "      <td>0.436957</td>\n",
       "      <td>0.186900</td>\n",
       "      <td>82</td>\n",
       "      <td>1518</td>\n",
       "      <td>1600</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   instant      dteday  season   yr  mnth  holiday  weekday  workingday  \\\n",
       "0        1  2011-01-01     1.0  0.0   1.0      0.0      6.0         0.0   \n",
       "1        2  2011-01-02     1.0  0.0   1.0      0.0      0.0         0.0   \n",
       "2        3  2011-01-03     1.0  0.0   1.0      0.0      1.0         1.0   \n",
       "3        4  2011-01-04     1.0  0.0   1.0      0.0      2.0         1.0   \n",
       "4        5  2011-01-05     1.0  0.0   1.0      0.0      3.0         1.0   \n",
       "\n",
       "   weathersit      temp     atemp       hum  windspeed  casual  registered  \\\n",
       "0           2  0.344167  0.363625  0.805833   0.160446     331         654   \n",
       "1           2  0.363478  0.353739  0.696087   0.248539     131         670   \n",
       "2           1  0.196364  0.189405  0.437273   0.248309     120        1229   \n",
       "3           1  0.200000  0.212122  0.590435   0.160296     108        1454   \n",
       "4           1  0.226957  0.229270  0.436957   0.186900      82        1518   \n",
       "\n",
       "    cnt  \n",
       "0   985  \n",
       "1   801  \n",
       "2  1349  \n",
       "3  1562  \n",
       "4  1600  "
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_bikes.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_bikes['dteday'] = pd.to_datetime(df_bikes['dteday'],infer_datetime_format=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 731 entries, 0 to 730\n",
      "Data columns (total 16 columns):\n",
      "instant       731 non-null int64\n",
      "dteday        731 non-null datetime64[ns]\n",
      "season        730 non-null float64\n",
      "yr            730 non-null float64\n",
      "mnth          730 non-null float64\n",
      "holiday       730 non-null float64\n",
      "weekday       730 non-null float64\n",
      "workingday    730 non-null float64\n",
      "weathersit    731 non-null int64\n",
      "temp          731 non-null float64\n",
      "atemp         731 non-null float64\n",
      "hum           731 non-null float64\n",
      "windspeed     731 non-null float64\n",
      "casual        731 non-null int64\n",
      "registered    731 non-null int64\n",
      "cnt           731 non-null int64\n",
      "dtypes: datetime64[ns](1), float64(10), int64(5)\n",
      "memory usage: 91.5 KB\n"
     ]
    }
   ],
   "source": [
    "df_bikes.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <th>casual</th>\n",
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       "      <th>cnt</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>730</th>\n",
       "      <td>731</td>\n",
       "      <td>2012-12-31</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2</td>\n",
       "      <td>0.215833</td>\n",
       "      <td>0.223487</td>\n",
       "      <td>0.5775</td>\n",
       "      <td>0.154846</td>\n",
       "      <td>439</td>\n",
       "      <td>2290</td>\n",
       "      <td>2729</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     instant     dteday  season  yr  mnth  holiday  weekday  workingday  \\\n",
       "730      731 2012-12-31     NaN NaN   NaN      NaN      NaN         NaN   \n",
       "\n",
       "     weathersit      temp     atemp     hum  windspeed  casual  registered  \\\n",
       "730           2  0.215833  0.223487  0.5775   0.154846     439        2290   \n",
       "\n",
       "      cnt  \n",
       "730  2729  "
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_bikes[df_bikes.isna().any(axis=1)]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [],
   "source": [
    "import datetime as dt\n",
    "df_bikes.at[730, 'mnth'] = df_bikes.loc[730, ['dteday']].dt.month\n",
    "df_bikes.at[730, 'yr'] = df_bikes.loc[730, ['dteday']].dt.year\n",
    "#df_bikes.at[730, 'weekday'] = df_bikes.loc[730, ['dteday']].dt.dayofweek"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <th>dteday</th>\n",
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       "      <th>casual</th>\n",
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       "      <th>cnt</th>\n",
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       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>730</th>\n",
       "      <td>731</td>\n",
       "      <td>2012-12-31</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2012.0</td>\n",
       "      <td>12.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
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       "      <td>2</td>\n",
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       "      <td>439</td>\n",
       "      <td>2290</td>\n",
       "      <td>2729</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     instant     dteday  season      yr  mnth  holiday  weekday  workingday  \\\n",
       "730      731 2012-12-31     NaN  2012.0  12.0      NaN      NaN         NaN   \n",
       "\n",
       "     weathersit      temp     atemp     hum  windspeed  casual  registered  \\\n",
       "730           2  0.215833  0.223487  0.5775   0.154846     439        2290   \n",
       "\n",
       "      cnt  \n",
       "730  2729  "
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_bikes[df_bikes.isna().any(axis=1)]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "    .dataframe tbody tr th {\n",
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       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
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       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>instant</th>\n",
       "      <th>dteday</th>\n",
       "      <th>season</th>\n",
       "      <th>yr</th>\n",
       "      <th>mnth</th>\n",
       "      <th>holiday</th>\n",
       "      <th>weekday</th>\n",
       "      <th>workingday</th>\n",
       "      <th>weathersit</th>\n",
       "      <th>temp</th>\n",
       "      <th>atemp</th>\n",
       "      <th>hum</th>\n",
       "      <th>windspeed</th>\n",
       "      <th>casual</th>\n",
       "      <th>registered</th>\n",
       "      <th>cnt</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>726</th>\n",
       "      <td>727</td>\n",
       "      <td>2012-12-27</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>12.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2</td>\n",
       "      <td>0.254167</td>\n",
       "      <td>0.226642</td>\n",
       "      <td>0.652917</td>\n",
       "      <td>0.350133</td>\n",
       "      <td>247</td>\n",
       "      <td>1867</td>\n",
       "      <td>2114</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>727</th>\n",
       "      <td>728</td>\n",
       "      <td>2012-12-28</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>12.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2</td>\n",
       "      <td>0.253333</td>\n",
       "      <td>0.255046</td>\n",
       "      <td>0.590000</td>\n",
       "      <td>0.155471</td>\n",
       "      <td>644</td>\n",
       "      <td>2451</td>\n",
       "      <td>3095</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>728</th>\n",
       "      <td>729</td>\n",
       "      <td>2012-12-29</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>12.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2</td>\n",
       "      <td>0.253333</td>\n",
       "      <td>0.242400</td>\n",
       "      <td>0.752917</td>\n",
       "      <td>0.124383</td>\n",
       "      <td>159</td>\n",
       "      <td>1182</td>\n",
       "      <td>1341</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>729</th>\n",
       "      <td>730</td>\n",
       "      <td>2012-12-30</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>12.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0.255833</td>\n",
       "      <td>0.231700</td>\n",
       "      <td>0.483333</td>\n",
       "      <td>0.350754</td>\n",
       "      <td>364</td>\n",
       "      <td>1432</td>\n",
       "      <td>1796</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>730</th>\n",
       "      <td>731</td>\n",
       "      <td>2012-12-31</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2012.0</td>\n",
       "      <td>12.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2</td>\n",
       "      <td>0.215833</td>\n",
       "      <td>0.223487</td>\n",
       "      <td>0.577500</td>\n",
       "      <td>0.154846</td>\n",
       "      <td>439</td>\n",
       "      <td>2290</td>\n",
       "      <td>2729</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     instant     dteday  season      yr  mnth  holiday  weekday  workingday  \\\n",
       "726      727 2012-12-27     1.0     1.0  12.0      0.0      4.0         1.0   \n",
       "727      728 2012-12-28     1.0     1.0  12.0      0.0      5.0         1.0   \n",
       "728      729 2012-12-29     1.0     1.0  12.0      0.0      6.0         0.0   \n",
       "729      730 2012-12-30     1.0     1.0  12.0      0.0      0.0         0.0   \n",
       "730      731 2012-12-31     NaN  2012.0  12.0      NaN      NaN         NaN   \n",
       "\n",
       "     weathersit      temp     atemp       hum  windspeed  casual  registered  \\\n",
       "726           2  0.254167  0.226642  0.652917   0.350133     247        1867   \n",
       "727           2  0.253333  0.255046  0.590000   0.155471     644        2451   \n",
       "728           2  0.253333  0.242400  0.752917   0.124383     159        1182   \n",
       "729           1  0.255833  0.231700  0.483333   0.350754     364        1432   \n",
       "730           2  0.215833  0.223487  0.577500   0.154846     439        2290   \n",
       "\n",
       "      cnt  \n",
       "726  2114  \n",
       "727  3095  \n",
       "728  1341  \n",
       "729  1796  \n",
       "730  2729  "
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_bikes.tail()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_bikes.at[730, 'weekday'] = 1.0\n",
    "df_bikes.at[730, 'season'] = 1.0\n",
    "df_bikes.at[730, 'holiday'] = 0.0\n",
    "df_bikes.at[730, 'workingday'] = 0.0\n",
    "df_bikes.at[730, 'yr'] = 1.0"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [],
   "source": [
    "#from pandas.tseries.holiday import USFederalHolidayCalendar as calendar\n",
    "#dr = pd.date_range(start='2011-01-01', end='2012-12-31')\n",
    "#cal = calendar()\n",
    "#holidays = cal.holidays(start=dr.min(), end=dr.max())\n",
    "#df_bikes['holidays'] = df_bikes['dteday'].dt.date.astype('datetime64').isin(holidays)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 731 entries, 0 to 730\n",
      "Data columns (total 16 columns):\n",
      "instant       731 non-null int64\n",
      "dteday        731 non-null datetime64[ns]\n",
      "season        731 non-null float64\n",
      "yr            731 non-null float64\n",
      "mnth          731 non-null float64\n",
      "holiday       731 non-null float64\n",
      "weekday       731 non-null float64\n",
      "workingday    731 non-null float64\n",
      "weathersit    731 non-null int64\n",
      "temp          731 non-null float64\n",
      "atemp         731 non-null float64\n",
      "hum           731 non-null float64\n",
      "windspeed     731 non-null float64\n",
      "casual        731 non-null int64\n",
      "registered    731 non-null int64\n",
      "cnt           731 non-null int64\n",
      "dtypes: datetime64[ns](1), float64(10), int64(5)\n",
      "memory usage: 91.5 KB\n"
     ]
    }
   ],
   "source": [
    "df_bikes.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "  <thead>\n",
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       "      <th></th>\n",
       "      <th>instant</th>\n",
       "      <th>dteday</th>\n",
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       "      <th>yr</th>\n",
       "      <th>mnth</th>\n",
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       "      <th>weekday</th>\n",
       "      <th>workingday</th>\n",
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       "      <th>atemp</th>\n",
       "      <th>hum</th>\n",
       "      <th>windspeed</th>\n",
       "      <th>casual</th>\n",
       "      <th>registered</th>\n",
       "      <th>cnt</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>726</th>\n",
       "      <td>727</td>\n",
       "      <td>2012-12-27</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
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       "    <tr>\n",
       "      <th>727</th>\n",
       "      <td>728</td>\n",
       "      <td>2012-12-28</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>12.0</td>\n",
       "      <td>0.0</td>\n",
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       "      <td>0.253333</td>\n",
       "      <td>0.255046</td>\n",
       "      <td>0.590000</td>\n",
       "      <td>0.155471</td>\n",
       "      <td>644</td>\n",
       "      <td>2451</td>\n",
       "      <td>3095</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>728</th>\n",
       "      <td>729</td>\n",
       "      <td>2012-12-29</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>12.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2</td>\n",
       "      <td>0.253333</td>\n",
       "      <td>0.242400</td>\n",
       "      <td>0.752917</td>\n",
       "      <td>0.124383</td>\n",
       "      <td>159</td>\n",
       "      <td>1182</td>\n",
       "      <td>1341</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>729</th>\n",
       "      <td>730</td>\n",
       "      <td>2012-12-30</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>12.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0.255833</td>\n",
       "      <td>0.231700</td>\n",
       "      <td>0.483333</td>\n",
       "      <td>0.350754</td>\n",
       "      <td>364</td>\n",
       "      <td>1432</td>\n",
       "      <td>1796</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>730</th>\n",
       "      <td>731</td>\n",
       "      <td>2012-12-31</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>12.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2</td>\n",
       "      <td>0.215833</td>\n",
       "      <td>0.223487</td>\n",
       "      <td>0.577500</td>\n",
       "      <td>0.154846</td>\n",
       "      <td>439</td>\n",
       "      <td>2290</td>\n",
       "      <td>2729</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     instant     dteday  season   yr  mnth  holiday  weekday  workingday  \\\n",
       "726      727 2012-12-27     1.0  1.0  12.0      0.0      4.0         1.0   \n",
       "727      728 2012-12-28     1.0  1.0  12.0      0.0      5.0         1.0   \n",
       "728      729 2012-12-29     1.0  1.0  12.0      0.0      6.0         0.0   \n",
       "729      730 2012-12-30     1.0  1.0  12.0      0.0      0.0         0.0   \n",
       "730      731 2012-12-31     1.0  1.0  12.0      0.0      1.0         0.0   \n",
       "\n",
       "     weathersit      temp     atemp       hum  windspeed  casual  registered  \\\n",
       "726           2  0.254167  0.226642  0.652917   0.350133     247        1867   \n",
       "727           2  0.253333  0.255046  0.590000   0.155471     644        2451   \n",
       "728           2  0.253333  0.242400  0.752917   0.124383     159        1182   \n",
       "729           1  0.255833  0.231700  0.483333   0.350754     364        1432   \n",
       "730           2  0.215833  0.223487  0.577500   0.154846     439        2290   \n",
       "\n",
       "      cnt  \n",
       "726  2114  \n",
       "727  3095  \n",
       "728  1341  \n",
       "729  1796  \n",
       "730  2729  "
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_bikes.tail()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_bikes = df_bikes.drop(['dteday', 'casual', 'registered'], axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [],
   "source": [
    "# split data into X and y\n",
    "X = df_bikes.iloc[:,:-1]\n",
    "y = df_bikes.iloc[:,-1]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Import train_test_split\n",
    "from sklearn.model_selection import train_test_split\n",
    "\n",
    "# Import Linear Regression\n",
    "from sklearn.linear_model import LinearRegression\n",
    "\n",
    "# Split data into train and test sets\n",
    "X_train, X_test, y_train, y_test = train_test_split(X, y, random_state=2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "RMSE: 898.30\n"
     ]
    }
   ],
   "source": [
    "# Instantial LinearRegression model\n",
    "lin_reg = LinearRegression()\n",
    "\n",
    "# Fit model on training data\n",
    "lin_reg.fit(X_train, y_train)\n",
    "\n",
    "# Make predictions for test data\n",
    "y_pred = lin_reg.predict(X_test)\n",
    "\n",
    "# Import the mean_squared_error\n",
    "from sklearn.metrics import mean_squared_error\n",
    "\n",
    "# Import numpy for fast computations\n",
    "import numpy as np\n",
    "\n",
    "# Compute the mean_squared_error, mse\n",
    "mse = mean_squared_error(y_test, y_pred)\n",
    "\n",
    "# Compute the root mean squared error, rmse\n",
    "rmse = np.sqrt(mse)\n",
    "\n",
    "# Display the root mean squared error\n",
    "print(\"RMSE: %0.2f\" % (rmse))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "count     731.000000\n",
       "mean     4504.348837\n",
       "std      1937.211452\n",
       "min        22.000000\n",
       "25%      3152.000000\n",
       "50%      4548.000000\n",
       "75%      5956.000000\n",
       "max      8714.000000\n",
       "Name: cnt, dtype: float64"
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Display bike rental stats\n",
    "df_bikes['cnt'].describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[23:07:55] WARNING: src/objective/regression_obj.cu:152: reg:linear is now deprecated in favor of reg:squarederror.\n",
      "RMSE: 637.57\n"
     ]
    }
   ],
   "source": [
    "# Import XGBRegressor\n",
    "from xgboost import XGBRegressor\n",
    "\n",
    "# Instantiate the XGBRegressor, xg_reg\n",
    "xg_reg = XGBRegressor()\n",
    "\n",
    "# Fit xg_reg to training set\n",
    "xg_reg.fit(X_train, y_train)\n",
    "\n",
    "# Predict labels of test set, y_pred\n",
    "y_pred = xg_reg.predict(X_test)\n",
    "\n",
    "# Compute the mean_squared_error, mse\n",
    "mse = mean_squared_error(y_test, y_pred)\n",
    "\n",
    "# Compute the root mean squared error, rmse\n",
    "rmse = np.sqrt(mse)\n",
    "\n",
    "# Display the root mean squared error\n",
    "print(\"RMSE: %0.2f\" % (rmse))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Reg rmse: [ 503.82  840.79 1141.08  728.45  640.34  970.22 1133.38 1252.6  1084.62\n",
      " 1425.34]\n",
      "RMSE mean: 972.06\n"
     ]
    }
   ],
   "source": [
    "# Import cross_val_score\n",
    "from sklearn.model_selection import cross_val_score\n",
    "\n",
    "# Instantiate Linear Regression\n",
    "model = LinearRegression()\n",
    "\n",
    "# Obtain scores of cross-validation using 10 splits and mean squared error\n",
    "scores = cross_val_score(model, X, y, scoring='neg_mean_squared_error', cv=10)\n",
    "\n",
    "# Take square root of the scores\n",
    "rmse = np.sqrt(-scores)\n",
    "\n",
    "# Display root mean squared error\n",
    "print('Reg rmse:', np.round(rmse, 2))\n",
    "\n",
    "# Display mean score\n",
    "print('RMSE mean: %0.2f' % (rmse.mean()))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Reg rmse: [ 734.4   437.14  447.01  629.79  813.36  920.45  870.41  691.53  756.61\n",
      " 1665.  ]\n",
      "RMSE mean: 796.57\n"
     ]
    }
   ],
   "source": [
    "# Instantiate XGBRegressor\n",
    "model = XGBRegressor(objective=\"reg:squarederror\")\n",
    "\n",
    "# Obtain scores of cross-validation using 10 splits and mean squared error\n",
    "scores = cross_val_score(model, X, y, scoring='neg_mean_squared_error', cv=10)\n",
    "\n",
    "# Take square root of the scores\n",
    "rmse = np.sqrt(-scores)\n",
    "\n",
    "# Display root mean squared error\n",
    "print('Reg rmse:', np.round(rmse, 2))\n",
    "\n",
    "# Display mean score\n",
    "print('RMSE mean: %0.2f' % (rmse.mean()))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [
    {
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       "      <th>39</th>\n",
       "      <th>State-gov</th>\n",
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       "      <th>1</th>\n",
       "      <td>38</td>\n",
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       "      <td>HS-grad</td>\n",
       "      <td>9</td>\n",
       "      <td>Divorced</td>\n",
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       "      <td>United-States</td>\n",
       "      <td>&lt;=50K</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>53</td>\n",
       "      <td>Private</td>\n",
       "      <td>234721</td>\n",
       "      <td>11th</td>\n",
       "      <td>7</td>\n",
       "      <td>Married-civ-spouse</td>\n",
       "      <td>Handlers-cleaners</td>\n",
       "      <td>Husband</td>\n",
       "      <td>Black</td>\n",
       "      <td>Male</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>40</td>\n",
       "      <td>United-States</td>\n",
       "      <td>&lt;=50K</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>28</td>\n",
       "      <td>Private</td>\n",
       "      <td>338409</td>\n",
       "      <td>Bachelors</td>\n",
       "      <td>13</td>\n",
       "      <td>Married-civ-spouse</td>\n",
       "      <td>Prof-specialty</td>\n",
       "      <td>Wife</td>\n",
       "      <td>Black</td>\n",
       "      <td>Female</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>40</td>\n",
       "      <td>Cuba</td>\n",
       "      <td>&lt;=50K</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>37</td>\n",
       "      <td>Private</td>\n",
       "      <td>284582</td>\n",
       "      <td>Masters</td>\n",
       "      <td>14</td>\n",
       "      <td>Married-civ-spouse</td>\n",
       "      <td>Exec-managerial</td>\n",
       "      <td>Wife</td>\n",
       "      <td>White</td>\n",
       "      <td>Female</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",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   39          State-gov   77516   Bachelors   13        Never-married  \\\n",
       "0  50   Self-emp-not-inc   83311   Bachelors   13   Married-civ-spouse   \n",
       "1  38            Private  215646     HS-grad    9             Divorced   \n",
       "2  53            Private  234721        11th    7   Married-civ-spouse   \n",
       "3  28            Private  338409   Bachelors   13   Married-civ-spouse   \n",
       "4  37            Private  284582     Masters   14   Married-civ-spouse   \n",
       "\n",
       "         Adm-clerical   Not-in-family   White     Male   2174   0   40  \\\n",
       "0     Exec-managerial         Husband   White     Male      0   0   13   \n",
       "1   Handlers-cleaners   Not-in-family   White     Male      0   0   40   \n",
       "2   Handlers-cleaners         Husband   Black     Male      0   0   40   \n",
       "3      Prof-specialty            Wife   Black   Female      0   0   40   \n",
       "4     Exec-managerial            Wife   White   Female      0   0   40   \n",
       "\n",
       "    United-States   <=50K  \n",
       "0   United-States   <=50K  \n",
       "1   United-States   <=50K  \n",
       "2   United-States   <=50K  \n",
       "3            Cuba   <=50K  \n",
       "4   United-States   <=50K  "
      ]
     },
     "execution_count": 37,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import pandas as pd\n",
    "df_census = pd.read_csv('https://archive.ics.uci.edu/ml/machine-learning-databases/adult/adult.data')\n",
    "df_census.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <td>38</td>\n",
       "      <td>Private</td>\n",
       "      <td>215646</td>\n",
       "      <td>HS-grad</td>\n",
       "      <td>9</td>\n",
       "      <td>Divorced</td>\n",
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       "      <td>Not-in-family</td>\n",
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       "      <td>0</td>\n",
       "      <td>40</td>\n",
       "      <td>United-States</td>\n",
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       "      <th>3</th>\n",
       "      <td>53</td>\n",
       "      <td>Private</td>\n",
       "      <td>234721</td>\n",
       "      <td>11th</td>\n",
       "      <td>7</td>\n",
       "      <td>Married-civ-spouse</td>\n",
       "      <td>Handlers-cleaners</td>\n",
       "      <td>Husband</td>\n",
       "      <td>Black</td>\n",
       "      <td>Male</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>40</td>\n",
       "      <td>United-States</td>\n",
       "      <td>&lt;=50K</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>28</td>\n",
       "      <td>Private</td>\n",
       "      <td>338409</td>\n",
       "      <td>Bachelors</td>\n",
       "      <td>13</td>\n",
       "      <td>Married-civ-spouse</td>\n",
       "      <td>Prof-specialty</td>\n",
       "      <td>Wife</td>\n",
       "      <td>Black</td>\n",
       "      <td>Female</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>40</td>\n",
       "      <td>Cuba</td>\n",
       "      <td>&lt;=50K</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   0                  1       2           3   4                    5   \\\n",
       "0  39          State-gov   77516   Bachelors  13        Never-married   \n",
       "1  50   Self-emp-not-inc   83311   Bachelors  13   Married-civ-spouse   \n",
       "2  38            Private  215646     HS-grad   9             Divorced   \n",
       "3  53            Private  234721        11th   7   Married-civ-spouse   \n",
       "4  28            Private  338409   Bachelors  13   Married-civ-spouse   \n",
       "\n",
       "                   6               7       8        9     10  11  12  \\\n",
       "0        Adm-clerical   Not-in-family   White     Male  2174   0  40   \n",
       "1     Exec-managerial         Husband   White     Male     0   0  13   \n",
       "2   Handlers-cleaners   Not-in-family   White     Male     0   0  40   \n",
       "3   Handlers-cleaners         Husband   Black     Male     0   0  40   \n",
       "4      Prof-specialty            Wife   Black   Female     0   0  40   \n",
       "\n",
       "               13      14  \n",
       "0   United-States   <=50K  \n",
       "1   United-States   <=50K  \n",
       "2   United-States   <=50K  \n",
       "3   United-States   <=50K  \n",
       "4            Cuba   <=50K  "
      ]
     },
     "execution_count": 38,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_census = pd.read_csv('https://archive.ics.uci.edu/ml/machine-learning-databases/adult/adult.data', header=None)\n",
    "df_census.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>38</td>\n",
       "      <td>Private</td>\n",
       "      <td>215646</td>\n",
       "      <td>HS-grad</td>\n",
       "      <td>9</td>\n",
       "      <td>Divorced</td>\n",
       "      <td>Handlers-cleaners</td>\n",
       "      <td>Not-in-family</td>\n",
       "      <td>White</td>\n",
       "      <td>Male</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>40</td>\n",
       "      <td>United-States</td>\n",
       "      <td>&lt;=50K</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>53</td>\n",
       "      <td>Private</td>\n",
       "      <td>234721</td>\n",
       "      <td>11th</td>\n",
       "      <td>7</td>\n",
       "      <td>Married-civ-spouse</td>\n",
       "      <td>Handlers-cleaners</td>\n",
       "      <td>Husband</td>\n",
       "      <td>Black</td>\n",
       "      <td>Male</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>40</td>\n",
       "      <td>United-States</td>\n",
       "      <td>&lt;=50K</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>28</td>\n",
       "      <td>Private</td>\n",
       "      <td>338409</td>\n",
       "      <td>Bachelors</td>\n",
       "      <td>13</td>\n",
       "      <td>Married-civ-spouse</td>\n",
       "      <td>Prof-specialty</td>\n",
       "      <td>Wife</td>\n",
       "      <td>Black</td>\n",
       "      <td>Female</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>40</td>\n",
       "      <td>Cuba</td>\n",
       "      <td>&lt;=50K</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   age          workclass  fnlwgt   education  education-num  \\\n",
       "0   39          State-gov   77516   Bachelors             13   \n",
       "1   50   Self-emp-not-inc   83311   Bachelors             13   \n",
       "2   38            Private  215646     HS-grad              9   \n",
       "3   53            Private  234721        11th              7   \n",
       "4   28            Private  338409   Bachelors             13   \n",
       "\n",
       "        marital-status          occupation    relationship    race      sex  \\\n",
       "0        Never-married        Adm-clerical   Not-in-family   White     Male   \n",
       "1   Married-civ-spouse     Exec-managerial         Husband   White     Male   \n",
       "2             Divorced   Handlers-cleaners   Not-in-family   White     Male   \n",
       "3   Married-civ-spouse   Handlers-cleaners         Husband   Black     Male   \n",
       "4   Married-civ-spouse      Prof-specialty            Wife   Black   Female   \n",
       "\n",
       "   capital-gain  capital-loss  hours-per-week  native-country  income  \n",
       "0          2174             0              40   United-States   <=50K  \n",
       "1             0             0              13   United-States   <=50K  \n",
       "2             0             0              40   United-States   <=50K  \n",
       "3             0             0              40   United-States   <=50K  \n",
       "4             0             0              40            Cuba   <=50K  "
      ]
     },
     "execution_count": 39,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_census.columns=['age', 'workclass', 'fnlwgt', 'education', 'education-num', 'marital-status', 'occupation',\n",
    "                  'relationship', 'race', 'sex', 'capital-gain', 'capital-loss', 'hours-per-week', 'native-country', 'income']\n",
    "df_census.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 32561 entries, 0 to 32560\n",
      "Data columns (total 15 columns):\n",
      "age               32561 non-null int64\n",
      "workclass         32561 non-null object\n",
      "fnlwgt            32561 non-null int64\n",
      "education         32561 non-null object\n",
      "education-num     32561 non-null int64\n",
      "marital-status    32561 non-null object\n",
      "occupation        32561 non-null object\n",
      "relationship      32561 non-null object\n",
      "race              32561 non-null object\n",
      "sex               32561 non-null object\n",
      "capital-gain      32561 non-null int64\n",
      "capital-loss      32561 non-null int64\n",
      "hours-per-week    32561 non-null int64\n",
      "native-country    32561 non-null object\n",
      "income            32561 non-null object\n",
      "dtypes: int64(6), object(9)\n",
      "memory usage: 3.7+ MB\n"
     ]
    }
   ],
   "source": [
    "df_census.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>age</th>\n",
       "      <th>workclass</th>\n",
       "      <th>fnlwgt</th>\n",
       "      <th>education</th>\n",
       "      <th>education-num</th>\n",
       "      <th>marital-status</th>\n",
       "      <th>occupation</th>\n",
       "      <th>relationship</th>\n",
       "      <th>race</th>\n",
       "      <th>sex</th>\n",
       "      <th>capital-gain</th>\n",
       "      <th>capital-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>32541</th>\n",
       "      <td>41</td>\n",
       "      <td>?</td>\n",
       "      <td>202822</td>\n",
       "      <td>HS-grad</td>\n",
       "      <td>9</td>\n",
       "      <td>Separated</td>\n",
       "      <td>?</td>\n",
       "      <td>Not-in-family</td>\n",
       "      <td>Black</td>\n",
       "      <td>Female</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>32</td>\n",
       "      <td>United-States</td>\n",
       "      <td>&lt;=50K</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>32542</th>\n",
       "      <td>72</td>\n",
       "      <td>?</td>\n",
       "      <td>129912</td>\n",
       "      <td>HS-grad</td>\n",
       "      <td>9</td>\n",
       "      <td>Married-civ-spouse</td>\n",
       "      <td>?</td>\n",
       "      <td>Husband</td>\n",
       "      <td>White</td>\n",
       "      <td>Male</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>25</td>\n",
       "      <td>United-States</td>\n",
       "      <td>&lt;=50K</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>32543</th>\n",
       "      <td>45</td>\n",
       "      <td>Local-gov</td>\n",
       "      <td>119199</td>\n",
       "      <td>Assoc-acdm</td>\n",
       "      <td>12</td>\n",
       "      <td>Divorced</td>\n",
       "      <td>Prof-specialty</td>\n",
       "      <td>Unmarried</td>\n",
       "      <td>White</td>\n",
       "      <td>Female</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>48</td>\n",
       "      <td>United-States</td>\n",
       "      <td>&lt;=50K</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>32544</th>\n",
       "      <td>31</td>\n",
       "      <td>Private</td>\n",
       "      <td>199655</td>\n",
       "      <td>Masters</td>\n",
       "      <td>14</td>\n",
       "      <td>Divorced</td>\n",
       "      <td>Other-service</td>\n",
       "      <td>Not-in-family</td>\n",
       "      <td>Other</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",
       "    <tr>\n",
       "      <th>32545</th>\n",
       "      <td>39</td>\n",
       "      <td>Local-gov</td>\n",
       "      <td>111499</td>\n",
       "      <td>Assoc-acdm</td>\n",
       "      <td>12</td>\n",
       "      <td>Married-civ-spouse</td>\n",
       "      <td>Adm-clerical</td>\n",
       "      <td>Wife</td>\n",
       "      <td>White</td>\n",
       "      <td>Female</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>20</td>\n",
       "      <td>United-States</td>\n",
       "      <td>&gt;50K</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>32546</th>\n",
       "      <td>37</td>\n",
       "      <td>Private</td>\n",
       "      <td>198216</td>\n",
       "      <td>Assoc-acdm</td>\n",
       "      <td>12</td>\n",
       "      <td>Divorced</td>\n",
       "      <td>Tech-support</td>\n",
       "      <td>Not-in-family</td>\n",
       "      <td>White</td>\n",
       "      <td>Female</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>32547</th>\n",
       "      <td>43</td>\n",
       "      <td>Private</td>\n",
       "      <td>260761</td>\n",
       "      <td>HS-grad</td>\n",
       "      <td>9</td>\n",
       "      <td>Married-civ-spouse</td>\n",
       "      <td>Machine-op-inspct</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>Mexico</td>\n",
       "      <td>&lt;=50K</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>32548</th>\n",
       "      <td>65</td>\n",
       "      <td>Self-emp-not-inc</td>\n",
       "      <td>99359</td>\n",
       "      <td>Prof-school</td>\n",
       "      <td>15</td>\n",
       "      <td>Never-married</td>\n",
       "      <td>Prof-specialty</td>\n",
       "      <td>Not-in-family</td>\n",
       "      <td>White</td>\n",
       "      <td>Male</td>\n",
       "      <td>1086</td>\n",
       "      <td>0</td>\n",
       "      <td>60</td>\n",
       "      <td>United-States</td>\n",
       "      <td>&lt;=50K</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>32549</th>\n",
       "      <td>43</td>\n",
       "      <td>State-gov</td>\n",
       "      <td>255835</td>\n",
       "      <td>Some-college</td>\n",
       "      <td>10</td>\n",
       "      <td>Divorced</td>\n",
       "      <td>Adm-clerical</td>\n",
       "      <td>Other-relative</td>\n",
       "      <td>White</td>\n",
       "      <td>Female</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>32550</th>\n",
       "      <td>43</td>\n",
       "      <td>Self-emp-not-inc</td>\n",
       "      <td>27242</td>\n",
       "      <td>Some-college</td>\n",
       "      <td>10</td>\n",
       "      <td>Married-civ-spouse</td>\n",
       "      <td>Craft-repair</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>32551</th>\n",
       "      <td>32</td>\n",
       "      <td>Private</td>\n",
       "      <td>34066</td>\n",
       "      <td>10th</td>\n",
       "      <td>6</td>\n",
       "      <td>Married-civ-spouse</td>\n",
       "      <td>Handlers-cleaners</td>\n",
       "      <td>Husband</td>\n",
       "      <td>Amer-Indian-Eskimo</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>32552</th>\n",
       "      <td>43</td>\n",
       "      <td>Private</td>\n",
       "      <td>84661</td>\n",
       "      <td>Assoc-voc</td>\n",
       "      <td>11</td>\n",
       "      <td>Married-civ-spouse</td>\n",
       "      <td>Sales</td>\n",
       "      <td>Husband</td>\n",
       "      <td>White</td>\n",
       "      <td>Male</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>45</td>\n",
       "      <td>United-States</td>\n",
       "      <td>&lt;=50K</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>32553</th>\n",
       "      <td>32</td>\n",
       "      <td>Private</td>\n",
       "      <td>116138</td>\n",
       "      <td>Masters</td>\n",
       "      <td>14</td>\n",
       "      <td>Never-married</td>\n",
       "      <td>Tech-support</td>\n",
       "      <td>Not-in-family</td>\n",
       "      <td>Asian-Pac-Islander</td>\n",
       "      <td>Male</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>11</td>\n",
       "      <td>Taiwan</td>\n",
       "      <td>&lt;=50K</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>32554</th>\n",
       "      <td>53</td>\n",
       "      <td>Private</td>\n",
       "      <td>321865</td>\n",
       "      <td>Masters</td>\n",
       "      <td>14</td>\n",
       "      <td>Married-civ-spouse</td>\n",
       "      <td>Exec-managerial</td>\n",
       "      <td>Husband</td>\n",
       "      <td>White</td>\n",
       "      <td>Male</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>40</td>\n",
       "      <td>United-States</td>\n",
       "      <td>&gt;50K</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>32555</th>\n",
       "      <td>22</td>\n",
       "      <td>Private</td>\n",
       "      <td>310152</td>\n",
       "      <td>Some-college</td>\n",
       "      <td>10</td>\n",
       "      <td>Never-married</td>\n",
       "      <td>Protective-serv</td>\n",
       "      <td>Not-in-family</td>\n",
       "      <td>White</td>\n",
       "      <td>Male</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>40</td>\n",
       "      <td>United-States</td>\n",
       "      <td>&lt;=50K</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>32556</th>\n",
       "      <td>27</td>\n",
       "      <td>Private</td>\n",
       "      <td>257302</td>\n",
       "      <td>Assoc-acdm</td>\n",
       "      <td>12</td>\n",
       "      <td>Married-civ-spouse</td>\n",
       "      <td>Tech-support</td>\n",
       "      <td>Wife</td>\n",
       "      <td>White</td>\n",
       "      <td>Female</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>38</td>\n",
       "      <td>United-States</td>\n",
       "      <td>&lt;=50K</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>32557</th>\n",
       "      <td>40</td>\n",
       "      <td>Private</td>\n",
       "      <td>154374</td>\n",
       "      <td>HS-grad</td>\n",
       "      <td>9</td>\n",
       "      <td>Married-civ-spouse</td>\n",
       "      <td>Machine-op-inspct</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>32558</th>\n",
       "      <td>58</td>\n",
       "      <td>Private</td>\n",
       "      <td>151910</td>\n",
       "      <td>HS-grad</td>\n",
       "      <td>9</td>\n",
       "      <td>Widowed</td>\n",
       "      <td>Adm-clerical</td>\n",
       "      <td>Unmarried</td>\n",
       "      <td>White</td>\n",
       "      <td>Female</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>32559</th>\n",
       "      <td>22</td>\n",
       "      <td>Private</td>\n",
       "      <td>201490</td>\n",
       "      <td>HS-grad</td>\n",
       "      <td>9</td>\n",
       "      <td>Never-married</td>\n",
       "      <td>Adm-clerical</td>\n",
       "      <td>Own-child</td>\n",
       "      <td>White</td>\n",
       "      <td>Male</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>20</td>\n",
       "      <td>United-States</td>\n",
       "      <td>&lt;=50K</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>32560</th>\n",
       "      <td>52</td>\n",
       "      <td>Self-emp-inc</td>\n",
       "      <td>287927</td>\n",
       "      <td>HS-grad</td>\n",
       "      <td>9</td>\n",
       "      <td>Married-civ-spouse</td>\n",
       "      <td>Exec-managerial</td>\n",
       "      <td>Wife</td>\n",
       "      <td>White</td>\n",
       "      <td>Female</td>\n",
       "      <td>15024</td>\n",
       "      <td>0</td>\n",
       "      <td>40</td>\n",
       "      <td>United-States</td>\n",
       "      <td>&gt;50K</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       age          workclass  fnlwgt      education  education-num  \\\n",
       "32541   41                  ?  202822        HS-grad              9   \n",
       "32542   72                  ?  129912        HS-grad              9   \n",
       "32543   45          Local-gov  119199     Assoc-acdm             12   \n",
       "32544   31            Private  199655        Masters             14   \n",
       "32545   39          Local-gov  111499     Assoc-acdm             12   \n",
       "32546   37            Private  198216     Assoc-acdm             12   \n",
       "32547   43            Private  260761        HS-grad              9   \n",
       "32548   65   Self-emp-not-inc   99359    Prof-school             15   \n",
       "32549   43          State-gov  255835   Some-college             10   \n",
       "32550   43   Self-emp-not-inc   27242   Some-college             10   \n",
       "32551   32            Private   34066           10th              6   \n",
       "32552   43            Private   84661      Assoc-voc             11   \n",
       "32553   32            Private  116138        Masters             14   \n",
       "32554   53            Private  321865        Masters             14   \n",
       "32555   22            Private  310152   Some-college             10   \n",
       "32556   27            Private  257302     Assoc-acdm             12   \n",
       "32557   40            Private  154374        HS-grad              9   \n",
       "32558   58            Private  151910        HS-grad              9   \n",
       "32559   22            Private  201490        HS-grad              9   \n",
       "32560   52       Self-emp-inc  287927        HS-grad              9   \n",
       "\n",
       "            marital-status          occupation     relationship  \\\n",
       "32541            Separated                   ?    Not-in-family   \n",
       "32542   Married-civ-spouse                   ?          Husband   \n",
       "32543             Divorced      Prof-specialty        Unmarried   \n",
       "32544             Divorced       Other-service    Not-in-family   \n",
       "32545   Married-civ-spouse        Adm-clerical             Wife   \n",
       "32546             Divorced        Tech-support    Not-in-family   \n",
       "32547   Married-civ-spouse   Machine-op-inspct          Husband   \n",
       "32548        Never-married      Prof-specialty    Not-in-family   \n",
       "32549             Divorced        Adm-clerical   Other-relative   \n",
       "32550   Married-civ-spouse        Craft-repair          Husband   \n",
       "32551   Married-civ-spouse   Handlers-cleaners          Husband   \n",
       "32552   Married-civ-spouse               Sales          Husband   \n",
       "32553        Never-married        Tech-support    Not-in-family   \n",
       "32554   Married-civ-spouse     Exec-managerial          Husband   \n",
       "32555        Never-married     Protective-serv    Not-in-family   \n",
       "32556   Married-civ-spouse        Tech-support             Wife   \n",
       "32557   Married-civ-spouse   Machine-op-inspct          Husband   \n",
       "32558              Widowed        Adm-clerical        Unmarried   \n",
       "32559        Never-married        Adm-clerical        Own-child   \n",
       "32560   Married-civ-spouse     Exec-managerial             Wife   \n",
       "\n",
       "                      race      sex  capital-gain  capital-loss  \\\n",
       "32541                Black   Female             0             0   \n",
       "32542                White     Male             0             0   \n",
       "32543                White   Female             0             0   \n",
       "32544                Other   Female             0             0   \n",
       "32545                White   Female             0             0   \n",
       "32546                White   Female             0             0   \n",
       "32547                White     Male             0             0   \n",
       "32548                White     Male          1086             0   \n",
       "32549                White   Female             0             0   \n",
       "32550                White     Male             0             0   \n",
       "32551   Amer-Indian-Eskimo     Male             0             0   \n",
       "32552                White     Male             0             0   \n",
       "32553   Asian-Pac-Islander     Male             0             0   \n",
       "32554                White     Male             0             0   \n",
       "32555                White     Male             0             0   \n",
       "32556                White   Female             0             0   \n",
       "32557                White     Male             0             0   \n",
       "32558                White   Female             0             0   \n",
       "32559                White     Male             0             0   \n",
       "32560                White   Female         15024             0   \n",
       "\n",
       "       hours-per-week  native-country  income  \n",
       "32541              32   United-States   <=50K  \n",
       "32542              25   United-States   <=50K  \n",
       "32543              48   United-States   <=50K  \n",
       "32544              30   United-States   <=50K  \n",
       "32545              20   United-States    >50K  \n",
       "32546              40   United-States   <=50K  \n",
       "32547              40          Mexico   <=50K  \n",
       "32548              60   United-States   <=50K  \n",
       "32549              40   United-States   <=50K  \n",
       "32550              50   United-States   <=50K  \n",
       "32551              40   United-States   <=50K  \n",
       "32552              45   United-States   <=50K  \n",
       "32553              11          Taiwan   <=50K  \n",
       "32554              40   United-States    >50K  \n",
       "32555              40   United-States   <=50K  \n",
       "32556              38   United-States   <=50K  \n",
       "32557              40   United-States    >50K  \n",
       "32558              40   United-States   <=50K  \n",
       "32559              20   United-States   <=50K  \n",
       "32560              40   United-States    >50K  "
      ]
     },
     "execution_count": 41,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_census.tail(20)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Drop education column\n",
    "df_census = df_census.drop(['education'], axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "      <th></th>\n",
       "      <th>age</th>\n",
       "      <th>fnlwgt</th>\n",
       "      <th>education-num</th>\n",
       "      <th>capital-gain</th>\n",
       "      <th>capital-loss</th>\n",
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       "      <td>39</td>\n",
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       "<p>5 rows × 94 columns</p>\n",
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      ],
      "text/plain": [
       "   age  fnlwgt  education-num  capital-gain  capital-loss  hours-per-week  \\\n",
       "0   39   77516             13          2174             0              40   \n",
       "1   50   83311             13             0             0              13   \n",
       "2   38  215646              9             0             0              40   \n",
       "3   53  234721              7             0             0              40   \n",
       "4   28  338409             13             0             0              40   \n",
       "\n",
       "   workclass_ ?  workclass_ Federal-gov  workclass_ Local-gov  \\\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",
       "   workclass_ Never-worked  ...  native-country_ Scotland  \\\n",
       "0                        0  ...                         0   \n",
       "1                        0  ...                         0   \n",
       "2                        0  ...                         0   \n",
       "3                        0  ...                         0   \n",
       "4                        0  ...                         0   \n",
       "\n",
       "   native-country_ South  native-country_ Taiwan  native-country_ Thailand  \\\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_ Trinadad&Tobago  native-country_ United-States  \\\n",
       "0                                0                              1   \n",
       "1                                0                              1   \n",
       "2                                0                              1   \n",
       "3                                0                              1   \n",
       "4                                0                              0   \n",
       "\n",
       "   native-country_ Vietnam  native-country_ Yugoslavia  income_ <=50K  \\\n",
       "0                        0                           0              1   \n",
       "1                        0                           0              1   \n",
       "2                        0                           0              1   \n",
       "3                        0                           0              1   \n",
       "4                        0                           0              1   \n",
       "\n",
       "   income_ >50K  \n",
       "0             0  \n",
       "1             0  \n",
       "2             0  \n",
       "3             0  \n",
       "4             0  \n",
       "\n",
       "[5 rows x 94 columns]"
      ]
     },
     "execution_count": 43,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Convert non-numeric columns using get_dummies\n",
    "df_census = pd.get_dummies(df_census)\n",
    "\n",
    "# Display first five rows\n",
    "df_census.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "outputs": [],
   "source": [
    "# split data into X and y\n",
    "X = df_census.iloc[:,:-2]\n",
    "y = df_census.iloc[:,-2]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.linear_model import LogisticRegression"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Import cross_val_score\n",
    "from sklearn.model_selection import cross_val_score\n",
    "\n",
    "# Define cross_val function with classifer and num_splits as input\n",
    "def cross_val(classifier, num_splits=10):\n",
    "    \n",
    "    # Instantiate the classifier\n",
    "    model = classifier\n",
    "\n",
    "    # Obtain scores of cross-validation\n",
    "    scores = cross_val_score(model, X, y, cv=num_splits)\n",
    "\n",
    "    # Display accuracy\n",
    "    print('Accuracy:', np.round(scores, 2))\n",
    "\n",
    "    # Display mean accuracy\n",
    "    print('Accuracy mean: %0.2f' % (scores.mean()))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Accuracy: [0.8  0.8  0.79 0.79 0.79 0.81 0.79 0.8  0.8  0.8 ]\n",
      "Accuracy mean: 0.80\n"
     ]
    }
   ],
   "source": [
    "cross_val(LogisticRegression())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Accuracy: [0.83 0.84 0.85 0.83 0.84 0.85 0.85 0.85 0.85 0.84]\n",
      "Accuracy mean: 0.84\n"
     ]
    }
   ],
   "source": [
    "from xgboost import XGBClassifier\n",
    "cross_val(XGBClassifier(n_estimators=5))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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