submission.ipynb

{
 "cells": [
  {
   "cell_type": "code",
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   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/itachi/anaconda3/lib/python3.6/site-packages/sklearn/cross_validation.py:41: DeprecationWarning: This module was deprecated in version 0.18 in favor of the model_selection module into which all the refactored classes and functions are moved. Also note that the interface of the new CV iterators are different from that of this module. This module will be removed in 0.20.\n",
      "  \"This module will be removed in 0.20.\", DeprecationWarning)\n"
     ]
    }
   ],
   "source": [
    "import math\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "import seaborn as sns\n",
    "import matplotlib.pyplot as plt\n",
    "import xgboost as xgb\n",
    "from sklearn.metrics import mean_squared_error\n",
    "pd.set_option('display.max_columns', None)\n",
    "sns.set(color_codes=True)\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "test1 = pd.read_csv('test1.csv')\n",
    "test9 = pd.read_csv('test9.csv')\n",
    "train1 = pd.read_csv('train1.csv')\n",
    "train9 = pd.read_csv('train9.csv')\n",
    "hero_data = pd.read_csv('hero_data.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>hero_id</th>\n",
       "      <th>primary_attr</th>\n",
       "      <th>attack_type</th>\n",
       "      <th>roles</th>\n",
       "      <th>base_health</th>\n",
       "      <th>base_health_regen</th>\n",
       "      <th>base_mana</th>\n",
       "      <th>base_mana_regen</th>\n",
       "      <th>base_armor</th>\n",
       "      <th>base_magic_resistance</th>\n",
       "      <th>base_attack_min</th>\n",
       "      <th>base_attack_max</th>\n",
       "      <th>base_strength</th>\n",
       "      <th>base_agility</th>\n",
       "      <th>base_intelligence</th>\n",
       "      <th>strength_gain</th>\n",
       "      <th>agility_gain</th>\n",
       "      <th>intelligence_gain</th>\n",
       "      <th>attack_range</th>\n",
       "      <th>projectile_speed</th>\n",
       "      <th>attack_rate</th>\n",
       "      <th>move_speed</th>\n",
       "      <th>turn_rate</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>agi</td>\n",
       "      <td>Melee</td>\n",
       "      <td>Carry:Escape:Nuker</td>\n",
       "      <td>200</td>\n",
       "      <td>1.50</td>\n",
       "      <td>75</td>\n",
       "      <td>0.9</td>\n",
       "      <td>-1.0</td>\n",
       "      <td>25</td>\n",
       "      <td>27</td>\n",
       "      <td>31</td>\n",
       "      <td>22</td>\n",
       "      <td>22</td>\n",
       "      <td>12</td>\n",
       "      <td>1.3</td>\n",
       "      <td>2.8</td>\n",
       "      <td>1.8</td>\n",
       "      <td>150</td>\n",
       "      <td>0</td>\n",
       "      <td>1.45</td>\n",
       "      <td>310</td>\n",
       "      <td>0.5</td>\n",
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       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>str</td>\n",
       "      <td>Melee</td>\n",
       "      <td>Initiator:Durable:Disabler:Jungler</td>\n",
       "      <td>200</td>\n",
       "      <td>4.25</td>\n",
       "      <td>75</td>\n",
       "      <td>0.9</td>\n",
       "      <td>-1.0</td>\n",
       "      <td>25</td>\n",
       "      <td>24</td>\n",
       "      <td>28</td>\n",
       "      <td>25</td>\n",
       "      <td>20</td>\n",
       "      <td>18</td>\n",
       "      <td>2.8</td>\n",
       "      <td>2.2</td>\n",
       "      <td>1.6</td>\n",
       "      <td>150</td>\n",
       "      <td>900</td>\n",
       "      <td>1.70</td>\n",
       "      <td>290</td>\n",
       "      <td>0.6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "      <td>int</td>\n",
       "      <td>Ranged</td>\n",
       "      <td>Support:Disabler:Nuker:Durable</td>\n",
       "      <td>200</td>\n",
       "      <td>1.50</td>\n",
       "      <td>75</td>\n",
       "      <td>0.9</td>\n",
       "      <td>1.0</td>\n",
       "      <td>25</td>\n",
       "      <td>37</td>\n",
       "      <td>43</td>\n",
       "      <td>23</td>\n",
       "      <td>23</td>\n",
       "      <td>23</td>\n",
       "      <td>2.4</td>\n",
       "      <td>2.4</td>\n",
       "      <td>2.4</td>\n",
       "      <td>400</td>\n",
       "      <td>900</td>\n",
       "      <td>1.70</td>\n",
       "      <td>310</td>\n",
       "      <td>0.6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4</td>\n",
       "      <td>agi</td>\n",
       "      <td>Melee</td>\n",
       "      <td>Carry:Disabler:Jungler:Nuker:Initiator</td>\n",
       "      <td>200</td>\n",
       "      <td>1.50</td>\n",
       "      <td>75</td>\n",
       "      <td>0.9</td>\n",
       "      <td>0.0</td>\n",
       "      <td>25</td>\n",
       "      <td>33</td>\n",
       "      <td>39</td>\n",
       "      <td>23</td>\n",
       "      <td>24</td>\n",
       "      <td>18</td>\n",
       "      <td>2.7</td>\n",
       "      <td>3.0</td>\n",
       "      <td>1.7</td>\n",
       "      <td>150</td>\n",
       "      <td>900</td>\n",
       "      <td>1.70</td>\n",
       "      <td>285</td>\n",
       "      <td>0.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5</td>\n",
       "      <td>int</td>\n",
       "      <td>Ranged</td>\n",
       "      <td>Support:Disabler:Nuker:Jungler</td>\n",
       "      <td>200</td>\n",
       "      <td>1.50</td>\n",
       "      <td>75</td>\n",
       "      <td>0.9</td>\n",
       "      <td>-1.0</td>\n",
       "      <td>25</td>\n",
       "      <td>19</td>\n",
       "      <td>25</td>\n",
       "      <td>16</td>\n",
       "      <td>16</td>\n",
       "      <td>16</td>\n",
       "      <td>2.0</td>\n",
       "      <td>1.6</td>\n",
       "      <td>2.9</td>\n",
       "      <td>600</td>\n",
       "      <td>900</td>\n",
       "      <td>1.70</td>\n",
       "      <td>275</td>\n",
       "      <td>0.5</td>\n",
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      ],
      "text/plain": [
       "   hero_id primary_attr attack_type                                   roles  \\\n",
       "0        1          agi       Melee                      Carry:Escape:Nuker   \n",
       "1        2          str       Melee      Initiator:Durable:Disabler:Jungler   \n",
       "2        3          int      Ranged          Support:Disabler:Nuker:Durable   \n",
       "3        4          agi       Melee  Carry:Disabler:Jungler:Nuker:Initiator   \n",
       "4        5          int      Ranged          Support:Disabler:Nuker:Jungler   \n",
       "\n",
       "   base_health  base_health_regen  base_mana  base_mana_regen  base_armor  \\\n",
       "0          200               1.50         75              0.9        -1.0   \n",
       "1          200               4.25         75              0.9        -1.0   \n",
       "2          200               1.50         75              0.9         1.0   \n",
       "3          200               1.50         75              0.9         0.0   \n",
       "4          200               1.50         75              0.9        -1.0   \n",
       "\n",
       "   base_magic_resistance  base_attack_min  base_attack_max  base_strength  \\\n",
       "0                     25               27               31             22   \n",
       "1                     25               24               28             25   \n",
       "2                     25               37               43             23   \n",
       "3                     25               33               39             23   \n",
       "4                     25               19               25             16   \n",
       "\n",
       "   base_agility  base_intelligence  strength_gain  agility_gain  \\\n",
       "0            22                 12            1.3           2.8   \n",
       "1            20                 18            2.8           2.2   \n",
       "2            23                 23            2.4           2.4   \n",
       "3            24                 18            2.7           3.0   \n",
       "4            16                 16            2.0           1.6   \n",
       "\n",
       "   intelligence_gain  attack_range  projectile_speed  attack_rate  move_speed  \\\n",
       "0                1.8           150                 0         1.45         310   \n",
       "1                1.6           150               900         1.70         290   \n",
       "2                2.4           400               900         1.70         310   \n",
       "3                1.7           150               900         1.70         285   \n",
       "4                2.9           600               900         1.70         275   \n",
       "\n",
       "   turn_rate  \n",
       "0        0.5  \n",
       "1        0.6  \n",
       "2        0.6  \n",
       "3        0.5  \n",
       "4        0.5  "
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "hero_data.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Data Preprocessing"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# Combine user and hero data\n",
    "train9_data = pd.merge(train9, hero_data, on='hero_id').sort_values('user_id').reset_index(drop=True)\n",
    "train1_data = pd.merge(train1, hero_data, on='hero_id').sort_values('user_id').reset_index(drop=True)\n",
    "test1_data = pd.merge(test1, hero_data, on='hero_id').sort_values('user_id').reset_index(drop=True)\n",
    "test9_data = pd.merge(test9, hero_data, on='hero_id').sort_values('user_id').reset_index(drop=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
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       "      <th></th>\n",
       "      <th>user_id</th>\n",
       "      <th>hero_id</th>\n",
       "      <th>id</th>\n",
       "      <th>num_games</th>\n",
       "      <th>num_wins</th>\n",
       "      <th>kda_ratio</th>\n",
       "      <th>primary_attr</th>\n",
       "      <th>attack_type</th>\n",
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       "      <th>base_mana</th>\n",
       "      <th>base_mana_regen</th>\n",
       "      <th>base_armor</th>\n",
       "      <th>base_magic_resistance</th>\n",
       "      <th>base_attack_min</th>\n",
       "      <th>base_attack_max</th>\n",
       "      <th>base_strength</th>\n",
       "      <th>base_agility</th>\n",
       "      <th>base_intelligence</th>\n",
       "      <th>strength_gain</th>\n",
       "      <th>agility_gain</th>\n",
       "      <th>intelligence_gain</th>\n",
       "      <th>attack_range</th>\n",
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       "      <th>attack_rate</th>\n",
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       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>74</td>\n",
       "      <td>1_74</td>\n",
       "      <td>304</td>\n",
       "      <td>185</td>\n",
       "      <td>3743.699732</td>\n",
       "      <td>int</td>\n",
       "      <td>Ranged</td>\n",
       "      <td>Carry:Nuker:Disabler:Escape:Pusher</td>\n",
       "      <td>200</td>\n",
       "      <td>1.50</td>\n",
       "      <td>75</td>\n",
       "      <td>0.9</td>\n",
       "      <td>-1.0</td>\n",
       "      <td>25</td>\n",
       "      <td>19</td>\n",
       "      <td>25</td>\n",
       "      <td>16</td>\n",
       "      <td>14</td>\n",
       "      <td>16</td>\n",
       "      <td>2.2</td>\n",
       "      <td>1.9</td>\n",
       "      <td>4.0</td>\n",
       "      <td>600</td>\n",
       "      <td>900</td>\n",
       "      <td>1.7</td>\n",
       "      <td>280</td>\n",
       "      <td>0.5</td>\n",
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       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>39</td>\n",
       "      <td>1_39</td>\n",
       "      <td>203</td>\n",
       "      <td>135</td>\n",
       "      <td>5094.621514</td>\n",
       "      <td>int</td>\n",
       "      <td>Ranged</td>\n",
       "      <td>Carry:Nuker:Escape</td>\n",
       "      <td>200</td>\n",
       "      <td>1.50</td>\n",
       "      <td>75</td>\n",
       "      <td>0.9</td>\n",
       "      <td>-1.0</td>\n",
       "      <td>25</td>\n",
       "      <td>21</td>\n",
       "      <td>29</td>\n",
       "      <td>16</td>\n",
       "      <td>18</td>\n",
       "      <td>24</td>\n",
       "      <td>2.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>2.5</td>\n",
       "      <td>550</td>\n",
       "      <td>1500</td>\n",
       "      <td>1.5</td>\n",
       "      <td>295</td>\n",
       "      <td>0.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1</td>\n",
       "      <td>106</td>\n",
       "      <td>1_106</td>\n",
       "      <td>158</td>\n",
       "      <td>106</td>\n",
       "      <td>4619.212963</td>\n",
       "      <td>agi</td>\n",
       "      <td>Melee</td>\n",
       "      <td>Carry:Escape:Nuker:Disabler:Initiator</td>\n",
       "      <td>200</td>\n",
       "      <td>2.00</td>\n",
       "      <td>75</td>\n",
       "      <td>0.9</td>\n",
       "      <td>-2.0</td>\n",
       "      <td>25</td>\n",
       "      <td>30</td>\n",
       "      <td>34</td>\n",
       "      <td>19</td>\n",
       "      <td>22</td>\n",
       "      <td>20</td>\n",
       "      <td>2.1</td>\n",
       "      <td>1.8</td>\n",
       "      <td>1.8</td>\n",
       "      <td>150</td>\n",
       "      <td>900</td>\n",
       "      <td>1.7</td>\n",
       "      <td>305</td>\n",
       "      <td>0.5</td>\n",
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       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>1_2</td>\n",
       "      <td>157</td>\n",
       "      <td>94</td>\n",
       "      <td>2871.627907</td>\n",
       "      <td>str</td>\n",
       "      <td>Melee</td>\n",
       "      <td>Initiator:Durable:Disabler:Jungler</td>\n",
       "      <td>200</td>\n",
       "      <td>4.25</td>\n",
       "      <td>75</td>\n",
       "      <td>0.9</td>\n",
       "      <td>-1.0</td>\n",
       "      <td>25</td>\n",
       "      <td>24</td>\n",
       "      <td>28</td>\n",
       "      <td>25</td>\n",
       "      <td>20</td>\n",
       "      <td>18</td>\n",
       "      <td>2.8</td>\n",
       "      <td>2.2</td>\n",
       "      <td>1.6</td>\n",
       "      <td>150</td>\n",
       "      <td>900</td>\n",
       "      <td>1.7</td>\n",
       "      <td>290</td>\n",
       "      <td>0.6</td>\n",
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       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1</td>\n",
       "      <td>46</td>\n",
       "      <td>1_46</td>\n",
       "      <td>154</td>\n",
       "      <td>103</td>\n",
       "      <td>3533.333333</td>\n",
       "      <td>agi</td>\n",
       "      <td>Ranged</td>\n",
       "      <td>Carry:Escape</td>\n",
       "      <td>200</td>\n",
       "      <td>1.50</td>\n",
       "      <td>75</td>\n",
       "      <td>0.9</td>\n",
       "      <td>1.0</td>\n",
       "      <td>25</td>\n",
       "      <td>30</td>\n",
       "      <td>36</td>\n",
       "      <td>18</td>\n",
       "      <td>23</td>\n",
       "      <td>20</td>\n",
       "      <td>2.4</td>\n",
       "      <td>2.3</td>\n",
       "      <td>2.0</td>\n",
       "      <td>140</td>\n",
       "      <td>900</td>\n",
       "      <td>1.7</td>\n",
       "      <td>300</td>\n",
       "      <td>0.7</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   user_id  hero_id     id  num_games  num_wins    kda_ratio primary_attr  \\\n",
       "0        1       74   1_74        304       185  3743.699732          int   \n",
       "1        1       39   1_39        203       135  5094.621514          int   \n",
       "2        1      106  1_106        158       106  4619.212963          agi   \n",
       "3        1        2    1_2        157        94  2871.627907          str   \n",
       "4        1       46   1_46        154       103  3533.333333          agi   \n",
       "\n",
       "  attack_type                                  roles  base_health  \\\n",
       "0      Ranged     Carry:Nuker:Disabler:Escape:Pusher          200   \n",
       "1      Ranged                     Carry:Nuker:Escape          200   \n",
       "2       Melee  Carry:Escape:Nuker:Disabler:Initiator          200   \n",
       "3       Melee     Initiator:Durable:Disabler:Jungler          200   \n",
       "4      Ranged                           Carry:Escape          200   \n",
       "\n",
       "   base_health_regen  base_mana  base_mana_regen  base_armor  \\\n",
       "0               1.50         75              0.9        -1.0   \n",
       "1               1.50         75              0.9        -1.0   \n",
       "2               2.00         75              0.9        -2.0   \n",
       "3               4.25         75              0.9        -1.0   \n",
       "4               1.50         75              0.9         1.0   \n",
       "\n",
       "   base_magic_resistance  base_attack_min  base_attack_max  base_strength  \\\n",
       "0                     25               19               25             16   \n",
       "1                     25               21               29             16   \n",
       "2                     25               30               34             19   \n",
       "3                     25               24               28             25   \n",
       "4                     25               30               36             18   \n",
       "\n",
       "   base_agility  base_intelligence  strength_gain  agility_gain  \\\n",
       "0            14                 16            2.2           1.9   \n",
       "1            18                 24            2.0           2.0   \n",
       "2            22                 20            2.1           1.8   \n",
       "3            20                 18            2.8           2.2   \n",
       "4            23                 20            2.4           2.3   \n",
       "\n",
       "   intelligence_gain  attack_range  projectile_speed  attack_rate  move_speed  \\\n",
       "0                4.0           600               900          1.7         280   \n",
       "1                2.5           550              1500          1.5         295   \n",
       "2                1.8           150               900          1.7         305   \n",
       "3                1.6           150               900          1.7         290   \n",
       "4                2.0           140               900          1.7         300   \n",
       "\n",
       "   turn_rate  \n",
       "0        0.5  \n",
       "1        0.5  \n",
       "2        0.5  \n",
       "3        0.6  \n",
       "4        0.7  "
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train9_data.head()"
   ]
  },
  {
   "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>user_id</th>\n",
       "      <th>hero_id</th>\n",
       "      <th>id</th>\n",
       "      <th>num_games</th>\n",
       "      <th>num_wins</th>\n",
       "      <th>kda_ratio</th>\n",
       "      <th>primary_attr</th>\n",
       "      <th>attack_type</th>\n",
       "      <th>roles</th>\n",
       "      <th>base_health</th>\n",
       "      <th>base_health_regen</th>\n",
       "      <th>base_mana</th>\n",
       "      <th>base_mana_regen</th>\n",
       "      <th>base_armor</th>\n",
       "      <th>base_magic_resistance</th>\n",
       "      <th>base_attack_min</th>\n",
       "      <th>base_attack_max</th>\n",
       "      <th>base_strength</th>\n",
       "      <th>base_agility</th>\n",
       "      <th>base_intelligence</th>\n",
       "      <th>strength_gain</th>\n",
       "      <th>agility_gain</th>\n",
       "      <th>intelligence_gain</th>\n",
       "      <th>attack_range</th>\n",
       "      <th>projectile_speed</th>\n",
       "      <th>attack_rate</th>\n",
       "      <th>move_speed</th>\n",
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       "      <th>count</th>\n",
       "      <td>18846.000000</td>\n",
       "      <td>18846.000000</td>\n",
       "      <td>18846</td>\n",
       "      <td>18846.000000</td>\n",
       "      <td>18846.000000</td>\n",
       "      <td>18846.000000</td>\n",
       "      <td>18846</td>\n",
       "      <td>18846</td>\n",
       "      <td>18846</td>\n",
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       "      <td>18846.000000</td>\n",
       "      <td>18846.0</td>\n",
       "      <td>1.884600e+04</td>\n",
       "      <td>18846.000000</td>\n",
       "      <td>18846.000000</td>\n",
       "      <td>18846.000000</td>\n",
       "      <td>18846.000000</td>\n",
       "      <td>18846.000000</td>\n",
       "      <td>18846.000000</td>\n",
       "      <td>18846.000000</td>\n",
       "      <td>18846.00000</td>\n",
       "      <td>18846.000000</td>\n",
       "      <td>18846.000000</td>\n",
       "      <td>18846.000000</td>\n",
       "      <td>18846.000000</td>\n",
       "      <td>18846.000000</td>\n",
       "      <td>18846.000000</td>\n",
       "      <td>18846.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>unique</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>18569</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>3</td>\n",
       "      <td>2</td>\n",
       "      <td>98</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>NaN</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>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>top</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2658_14</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>agi</td>\n",
       "      <td>Ranged</td>\n",
       "      <td>Carry:Nuker:Disabler:Escape:Pusher</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>NaN</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>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>freq</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>7463</td>\n",
       "      <td>9961</td>\n",
       "      <td>998</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>NaN</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>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>1488.840974</td>\n",
       "      <td>49.286957</td>\n",
       "      <td>NaN</td>\n",
       "      <td>188.120344</td>\n",
       "      <td>106.459036</td>\n",
       "      <td>3543.462003</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>200.0</td>\n",
       "      <td>1.699379</td>\n",
       "      <td>75.0</td>\n",
       "      <td>9.000000e-01</td>\n",
       "      <td>-0.156214</td>\n",
       "      <td>24.960204</td>\n",
       "      <td>24.124801</td>\n",
       "      <td>31.853231</td>\n",
       "      <td>19.540539</td>\n",
       "      <td>18.429004</td>\n",
       "      <td>19.094025</td>\n",
       "      <td>2.36815</td>\n",
       "      <td>2.009877</td>\n",
       "      <td>2.153250</td>\n",
       "      <td>345.139022</td>\n",
       "      <td>837.448265</td>\n",
       "      <td>1.674644</td>\n",
       "      <td>296.740157</td>\n",
       "      <td>0.616245</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>856.144028</td>\n",
       "      <td>34.674544</td>\n",
       "      <td>NaN</td>\n",
       "      <td>224.166666</td>\n",
       "      <td>132.167024</td>\n",
       "      <td>1081.195070</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.552141</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.986242e-13</td>\n",
       "      <td>1.583337</td>\n",
       "      <td>0.771616</td>\n",
       "      <td>7.297071</td>\n",
       "      <td>7.501991</td>\n",
       "      <td>3.338325</td>\n",
       "      <td>4.327545</td>\n",
       "      <td>4.510319</td>\n",
       "      <td>0.53086</td>\n",
       "      <td>0.660002</td>\n",
       "      <td>0.698238</td>\n",
       "      <td>199.238999</td>\n",
       "      <td>437.004559</td>\n",
       "      <td>0.095884</td>\n",
       "      <td>12.243780</td>\n",
       "      <td>0.151899</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>200.0</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>75.0</td>\n",
       "      <td>9.000000e-01</td>\n",
       "      <td>-3.000000</td>\n",
       "      <td>10.000000</td>\n",
       "      <td>4.000000</td>\n",
       "      <td>8.000000</td>\n",
       "      <td>14.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>12.000000</td>\n",
       "      <td>1.30000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>140.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.300000</td>\n",
       "      <td>270.000000</td>\n",
       "      <td>0.500000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>752.000000</td>\n",
       "      <td>14.000000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>76.000000</td>\n",
       "      <td>42.000000</td>\n",
       "      <td>2841.265936</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>200.0</td>\n",
       "      <td>1.500000</td>\n",
       "      <td>75.0</td>\n",
       "      <td>9.000000e-01</td>\n",
       "      <td>-1.000000</td>\n",
       "      <td>25.000000</td>\n",
       "      <td>20.000000</td>\n",
       "      <td>27.000000</td>\n",
       "      <td>17.000000</td>\n",
       "      <td>15.000000</td>\n",
       "      <td>16.000000</td>\n",
       "      <td>2.00000</td>\n",
       "      <td>1.500000</td>\n",
       "      <td>1.650000</td>\n",
       "      <td>150.000000</td>\n",
       "      <td>900.000000</td>\n",
       "      <td>1.700000</td>\n",
       "      <td>290.000000</td>\n",
       "      <td>0.500000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>1480.500000</td>\n",
       "      <td>46.000000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>138.000000</td>\n",
       "      <td>77.000000</td>\n",
       "      <td>3403.463637</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>200.0</td>\n",
       "      <td>1.500000</td>\n",
       "      <td>75.0</td>\n",
       "      <td>9.000000e-01</td>\n",
       "      <td>-1.000000</td>\n",
       "      <td>25.000000</td>\n",
       "      <td>24.000000</td>\n",
       "      <td>32.000000</td>\n",
       "      <td>19.000000</td>\n",
       "      <td>18.000000</td>\n",
       "      <td>18.000000</td>\n",
       "      <td>2.20000</td>\n",
       "      <td>1.900000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>330.000000</td>\n",
       "      <td>900.000000</td>\n",
       "      <td>1.700000</td>\n",
       "      <td>295.000000</td>\n",
       "      <td>0.600000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>2233.000000</td>\n",
       "      <td>76.000000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>228.000000</td>\n",
       "      <td>128.000000</td>\n",
       "      <td>4073.493596</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>200.0</td>\n",
       "      <td>1.500000</td>\n",
       "      <td>75.0</td>\n",
       "      <td>9.000000e-01</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>25.000000</td>\n",
       "      <td>28.000000</td>\n",
       "      <td>36.000000</td>\n",
       "      <td>22.000000</td>\n",
       "      <td>22.000000</td>\n",
       "      <td>23.000000</td>\n",
       "      <td>2.70000</td>\n",
       "      <td>2.400000</td>\n",
       "      <td>2.500000</td>\n",
       "      <td>550.000000</td>\n",
       "      <td>1100.000000</td>\n",
       "      <td>1.700000</td>\n",
       "      <td>305.000000</td>\n",
       "      <td>0.700000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>2992.000000</td>\n",
       "      <td>120.000000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>7280.000000</td>\n",
       "      <td>5010.000000</td>\n",
       "      <td>38000.000000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>200.0</td>\n",
       "      <td>4.750000</td>\n",
       "      <td>75.0</td>\n",
       "      <td>9.000000e-01</td>\n",
       "      <td>7.000000</td>\n",
       "      <td>25.000000</td>\n",
       "      <td>62.000000</td>\n",
       "      <td>70.000000</td>\n",
       "      <td>30.000000</td>\n",
       "      <td>30.000000</td>\n",
       "      <td>30.000000</td>\n",
       "      <td>4.30000</td>\n",
       "      <td>3.700000</td>\n",
       "      <td>4.500000</td>\n",
       "      <td>700.000000</td>\n",
       "      <td>3000.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>340.000000</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "             user_id       hero_id       id     num_games      num_wins  \\\n",
       "count   18846.000000  18846.000000    18846  18846.000000  18846.000000   \n",
       "unique           NaN           NaN    18569           NaN           NaN   \n",
       "top              NaN           NaN  2658_14           NaN           NaN   \n",
       "freq             NaN           NaN        2           NaN           NaN   \n",
       "mean     1488.840974     49.286957      NaN    188.120344    106.459036   \n",
       "std       856.144028     34.674544      NaN    224.166666    132.167024   \n",
       "min         1.000000      1.000000      NaN      1.000000      0.000000   \n",
       "25%       752.000000     14.000000      NaN     76.000000     42.000000   \n",
       "50%      1480.500000     46.000000      NaN    138.000000     77.000000   \n",
       "75%      2233.000000     76.000000      NaN    228.000000    128.000000   \n",
       "max      2992.000000    120.000000      NaN   7280.000000   5010.000000   \n",
       "\n",
       "           kda_ratio primary_attr attack_type  \\\n",
       "count   18846.000000        18846       18846   \n",
       "unique           NaN            3           2   \n",
       "top              NaN          agi      Ranged   \n",
       "freq             NaN         7463        9961   \n",
       "mean     3543.462003          NaN         NaN   \n",
       "std      1081.195070          NaN         NaN   \n",
       "min         0.000000          NaN         NaN   \n",
       "25%      2841.265936          NaN         NaN   \n",
       "50%      3403.463637          NaN         NaN   \n",
       "75%      4073.493596          NaN         NaN   \n",
       "max     38000.000000          NaN         NaN   \n",
       "\n",
       "                                     roles  base_health  base_health_regen  \\\n",
       "count                                18846      18846.0       18846.000000   \n",
       "unique                                  98          NaN                NaN   \n",
       "top     Carry:Nuker:Disabler:Escape:Pusher          NaN                NaN   \n",
       "freq                                   998          NaN                NaN   \n",
       "mean                                   NaN        200.0           1.699379   \n",
       "std                                    NaN          0.0           0.552141   \n",
       "min                                    NaN        200.0           1.000000   \n",
       "25%                                    NaN        200.0           1.500000   \n",
       "50%                                    NaN        200.0           1.500000   \n",
       "75%                                    NaN        200.0           1.500000   \n",
       "max                                    NaN        200.0           4.750000   \n",
       "\n",
       "        base_mana  base_mana_regen    base_armor  base_magic_resistance  \\\n",
       "count     18846.0     1.884600e+04  18846.000000           18846.000000   \n",
       "unique        NaN              NaN           NaN                    NaN   \n",
       "top           NaN              NaN           NaN                    NaN   \n",
       "freq          NaN              NaN           NaN                    NaN   \n",
       "mean         75.0     9.000000e-01     -0.156214              24.960204   \n",
       "std           0.0     1.986242e-13      1.583337               0.771616   \n",
       "min          75.0     9.000000e-01     -3.000000              10.000000   \n",
       "25%          75.0     9.000000e-01     -1.000000              25.000000   \n",
       "50%          75.0     9.000000e-01     -1.000000              25.000000   \n",
       "75%          75.0     9.000000e-01      1.000000              25.000000   \n",
       "max          75.0     9.000000e-01      7.000000              25.000000   \n",
       "\n",
       "        base_attack_min  base_attack_max  base_strength  base_agility  \\\n",
       "count      18846.000000     18846.000000   18846.000000  18846.000000   \n",
       "unique              NaN              NaN            NaN           NaN   \n",
       "top                 NaN              NaN            NaN           NaN   \n",
       "freq                NaN              NaN            NaN           NaN   \n",
       "mean          24.124801        31.853231      19.540539     18.429004   \n",
       "std            7.297071         7.501991       3.338325      4.327545   \n",
       "min            4.000000         8.000000      14.000000      0.000000   \n",
       "25%           20.000000        27.000000      17.000000     15.000000   \n",
       "50%           24.000000        32.000000      19.000000     18.000000   \n",
       "75%           28.000000        36.000000      22.000000     22.000000   \n",
       "max           62.000000        70.000000      30.000000     30.000000   \n",
       "\n",
       "        base_intelligence  strength_gain  agility_gain  intelligence_gain  \\\n",
       "count        18846.000000    18846.00000  18846.000000       18846.000000   \n",
       "unique                NaN            NaN           NaN                NaN   \n",
       "top                   NaN            NaN           NaN                NaN   \n",
       "freq                  NaN            NaN           NaN                NaN   \n",
       "mean            19.094025        2.36815      2.009877           2.153250   \n",
       "std              4.510319        0.53086      0.660002           0.698238   \n",
       "min             12.000000        1.30000      0.000000           1.000000   \n",
       "25%             16.000000        2.00000      1.500000           1.650000   \n",
       "50%             18.000000        2.20000      1.900000           2.000000   \n",
       "75%             23.000000        2.70000      2.400000           2.500000   \n",
       "max             30.000000        4.30000      3.700000           4.500000   \n",
       "\n",
       "        attack_range  projectile_speed   attack_rate    move_speed  \\\n",
       "count   18846.000000      18846.000000  18846.000000  18846.000000   \n",
       "unique           NaN               NaN           NaN           NaN   \n",
       "top              NaN               NaN           NaN           NaN   \n",
       "freq             NaN               NaN           NaN           NaN   \n",
       "mean      345.139022        837.448265      1.674644    296.740157   \n",
       "std       199.238999        437.004559      0.095884     12.243780   \n",
       "min       140.000000          0.000000      1.300000    270.000000   \n",
       "25%       150.000000        900.000000      1.700000    290.000000   \n",
       "50%       330.000000        900.000000      1.700000    295.000000   \n",
       "75%       550.000000       1100.000000      1.700000    305.000000   \n",
       "max       700.000000       3000.000000      2.000000    340.000000   \n",
       "\n",
       "           turn_rate  \n",
       "count   18846.000000  \n",
       "unique           NaN  \n",
       "top              NaN  \n",
       "freq             NaN  \n",
       "mean        0.616245  \n",
       "std         0.151899  \n",
       "min         0.500000  \n",
       "25%         0.500000  \n",
       "50%         0.600000  \n",
       "75%         0.700000  \n",
       "max         1.000000  "
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train9_data.describe(include='all')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style>\n",
       "    .dataframe thead tr:only-child th {\n",
       "        text-align: right;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>user_id</th>\n",
       "      <th>hero_id</th>\n",
       "      <th>id</th>\n",
       "      <th>num_games</th>\n",
       "      <th>num_wins</th>\n",
       "      <th>kda_ratio</th>\n",
       "      <th>primary_attr</th>\n",
       "      <th>attack_type</th>\n",
       "      <th>roles</th>\n",
       "      <th>base_health</th>\n",
       "      <th>base_health_regen</th>\n",
       "      <th>base_mana</th>\n",
       "      <th>base_mana_regen</th>\n",
       "      <th>base_armor</th>\n",
       "      <th>base_magic_resistance</th>\n",
       "      <th>base_attack_min</th>\n",
       "      <th>base_attack_max</th>\n",
       "      <th>base_strength</th>\n",
       "      <th>base_agility</th>\n",
       "      <th>base_intelligence</th>\n",
       "      <th>strength_gain</th>\n",
       "      <th>agility_gain</th>\n",
       "      <th>intelligence_gain</th>\n",
       "      <th>attack_range</th>\n",
       "      <th>projectile_speed</th>\n",
       "      <th>attack_rate</th>\n",
       "      <th>move_speed</th>\n",
       "      <th>turn_rate</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>2094.000000</td>\n",
       "      <td>2094.000000</td>\n",
       "      <td>2094</td>\n",
       "      <td>2094.000000</td>\n",
       "      <td>2094.000000</td>\n",
       "      <td>2094.000000</td>\n",
       "      <td>2094</td>\n",
       "      <td>2094</td>\n",
       "      <td>2094</td>\n",
       "      <td>2094.0</td>\n",
       "      <td>2094.000000</td>\n",
       "      <td>2094.0</td>\n",
       "      <td>2.094000e+03</td>\n",
       "      <td>2094.000000</td>\n",
       "      <td>2094.000000</td>\n",
       "      <td>2094.000000</td>\n",
       "      <td>2094.000000</td>\n",
       "      <td>2094.000000</td>\n",
       "      <td>2094.000000</td>\n",
       "      <td>2094.000000</td>\n",
       "      <td>2094.000000</td>\n",
       "      <td>2094.000000</td>\n",
       "      <td>2094.000000</td>\n",
       "      <td>2094.000000</td>\n",
       "      <td>2094.000000</td>\n",
       "      <td>2094.000000</td>\n",
       "      <td>2094.000000</td>\n",
       "      <td>2094.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>unique</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2094</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>3</td>\n",
       "      <td>2</td>\n",
       "      <td>95</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>NaN</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>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>top</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2466_25</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>agi</td>\n",
       "      <td>Ranged</td>\n",
       "      <td>Carry:Nuker</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>NaN</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>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>freq</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>892</td>\n",
       "      <td>1127</td>\n",
       "      <td>120</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>NaN</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>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>1488.840974</td>\n",
       "      <td>48.229226</td>\n",
       "      <td>NaN</td>\n",
       "      <td>167.324737</td>\n",
       "      <td>93.691022</td>\n",
       "      <td>3549.577823</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>200.0</td>\n",
       "      <td>1.702603</td>\n",
       "      <td>75.0</td>\n",
       "      <td>9.000000e-01</td>\n",
       "      <td>-0.171203</td>\n",
       "      <td>24.957020</td>\n",
       "      <td>24.315664</td>\n",
       "      <td>32.039160</td>\n",
       "      <td>19.362464</td>\n",
       "      <td>18.578797</td>\n",
       "      <td>19.217765</td>\n",
       "      <td>2.327101</td>\n",
       "      <td>2.030683</td>\n",
       "      <td>2.142813</td>\n",
       "      <td>349.481853</td>\n",
       "      <td>844.603629</td>\n",
       "      <td>1.672206</td>\n",
       "      <td>297.187202</td>\n",
       "      <td>0.617144</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>856.325810</td>\n",
       "      <td>35.134741</td>\n",
       "      <td>NaN</td>\n",
       "      <td>114.403209</td>\n",
       "      <td>63.559871</td>\n",
       "      <td>1007.010635</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.546613</td>\n",
       "      <td>0.0</td>\n",
       "      <td>3.287045e-14</td>\n",
       "      <td>1.633730</td>\n",
       "      <td>0.801972</td>\n",
       "      <td>7.460690</td>\n",
       "      <td>7.764227</td>\n",
       "      <td>3.282516</td>\n",
       "      <td>4.342509</td>\n",
       "      <td>4.544661</td>\n",
       "      <td>0.526609</td>\n",
       "      <td>0.667481</td>\n",
       "      <td>0.655186</td>\n",
       "      <td>199.078938</td>\n",
       "      <td>444.853122</td>\n",
       "      <td>0.098417</td>\n",
       "      <td>12.217816</td>\n",
       "      <td>0.154746</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1286.968265</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>200.0</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>75.0</td>\n",
       "      <td>9.000000e-01</td>\n",
       "      <td>-3.000000</td>\n",
       "      <td>10.000000</td>\n",
       "      <td>4.000000</td>\n",
       "      <td>8.000000</td>\n",
       "      <td>14.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>12.000000</td>\n",
       "      <td>1.300000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>140.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.300000</td>\n",
       "      <td>270.000000</td>\n",
       "      <td>0.500000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>752.250000</td>\n",
       "      <td>14.000000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>84.000000</td>\n",
       "      <td>47.000000</td>\n",
       "      <td>2875.381989</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>200.0</td>\n",
       "      <td>1.500000</td>\n",
       "      <td>75.0</td>\n",
       "      <td>9.000000e-01</td>\n",
       "      <td>-1.000000</td>\n",
       "      <td>25.000000</td>\n",
       "      <td>21.000000</td>\n",
       "      <td>27.000000</td>\n",
       "      <td>17.000000</td>\n",
       "      <td>15.000000</td>\n",
       "      <td>16.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>1.500000</td>\n",
       "      <td>1.650000</td>\n",
       "      <td>150.000000</td>\n",
       "      <td>900.000000</td>\n",
       "      <td>1.700000</td>\n",
       "      <td>290.000000</td>\n",
       "      <td>0.500000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>1480.500000</td>\n",
       "      <td>41.000000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>146.000000</td>\n",
       "      <td>82.000000</td>\n",
       "      <td>3419.950213</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>200.0</td>\n",
       "      <td>1.500000</td>\n",
       "      <td>75.0</td>\n",
       "      <td>9.000000e-01</td>\n",
       "      <td>-1.000000</td>\n",
       "      <td>25.000000</td>\n",
       "      <td>24.000000</td>\n",
       "      <td>32.000000</td>\n",
       "      <td>19.000000</td>\n",
       "      <td>18.000000</td>\n",
       "      <td>18.000000</td>\n",
       "      <td>2.200000</td>\n",
       "      <td>1.900000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>350.000000</td>\n",
       "      <td>900.000000</td>\n",
       "      <td>1.700000</td>\n",
       "      <td>295.000000</td>\n",
       "      <td>0.600000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>2232.750000</td>\n",
       "      <td>77.000000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>230.000000</td>\n",
       "      <td>126.000000</td>\n",
       "      <td>4089.720280</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>200.0</td>\n",
       "      <td>1.750000</td>\n",
       "      <td>75.0</td>\n",
       "      <td>9.000000e-01</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>25.000000</td>\n",
       "      <td>28.000000</td>\n",
       "      <td>36.000000</td>\n",
       "      <td>22.000000</td>\n",
       "      <td>22.000000</td>\n",
       "      <td>23.000000</td>\n",
       "      <td>2.600000</td>\n",
       "      <td>2.500000</td>\n",
       "      <td>2.500000</td>\n",
       "      <td>550.000000</td>\n",
       "      <td>1100.000000</td>\n",
       "      <td>1.700000</td>\n",
       "      <td>305.000000</td>\n",
       "      <td>0.700000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>2992.000000</td>\n",
       "      <td>120.000000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>924.000000</td>\n",
       "      <td>505.000000</td>\n",
       "      <td>16500.000000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>200.0</td>\n",
       "      <td>4.750000</td>\n",
       "      <td>75.0</td>\n",
       "      <td>9.000000e-01</td>\n",
       "      <td>7.000000</td>\n",
       "      <td>25.000000</td>\n",
       "      <td>62.000000</td>\n",
       "      <td>70.000000</td>\n",
       "      <td>30.000000</td>\n",
       "      <td>30.000000</td>\n",
       "      <td>30.000000</td>\n",
       "      <td>4.300000</td>\n",
       "      <td>3.700000</td>\n",
       "      <td>4.000000</td>\n",
       "      <td>700.000000</td>\n",
       "      <td>3000.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>340.000000</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "            user_id      hero_id       id    num_games     num_wins  \\\n",
       "count   2094.000000  2094.000000     2094  2094.000000  2094.000000   \n",
       "unique          NaN          NaN     2094          NaN          NaN   \n",
       "top             NaN          NaN  2466_25          NaN          NaN   \n",
       "freq            NaN          NaN        1          NaN          NaN   \n",
       "mean    1488.840974    48.229226      NaN   167.324737    93.691022   \n",
       "std      856.325810    35.134741      NaN   114.403209    63.559871   \n",
       "min        1.000000     1.000000      NaN     1.000000     0.000000   \n",
       "25%      752.250000    14.000000      NaN    84.000000    47.000000   \n",
       "50%     1480.500000    41.000000      NaN   146.000000    82.000000   \n",
       "75%     2232.750000    77.000000      NaN   230.000000   126.000000   \n",
       "max     2992.000000   120.000000      NaN   924.000000   505.000000   \n",
       "\n",
       "           kda_ratio primary_attr attack_type        roles  base_health  \\\n",
       "count    2094.000000         2094        2094         2094       2094.0   \n",
       "unique           NaN            3           2           95          NaN   \n",
       "top              NaN          agi      Ranged  Carry:Nuker          NaN   \n",
       "freq             NaN          892        1127          120          NaN   \n",
       "mean     3549.577823          NaN         NaN          NaN        200.0   \n",
       "std      1007.010635          NaN         NaN          NaN          0.0   \n",
       "min      1286.968265          NaN         NaN          NaN        200.0   \n",
       "25%      2875.381989          NaN         NaN          NaN        200.0   \n",
       "50%      3419.950213          NaN         NaN          NaN        200.0   \n",
       "75%      4089.720280          NaN         NaN          NaN        200.0   \n",
       "max     16500.000000          NaN         NaN          NaN        200.0   \n",
       "\n",
       "        base_health_regen  base_mana  base_mana_regen   base_armor  \\\n",
       "count         2094.000000     2094.0     2.094000e+03  2094.000000   \n",
       "unique                NaN        NaN              NaN          NaN   \n",
       "top                   NaN        NaN              NaN          NaN   \n",
       "freq                  NaN        NaN              NaN          NaN   \n",
       "mean             1.702603       75.0     9.000000e-01    -0.171203   \n",
       "std              0.546613        0.0     3.287045e-14     1.633730   \n",
       "min              1.000000       75.0     9.000000e-01    -3.000000   \n",
       "25%              1.500000       75.0     9.000000e-01    -1.000000   \n",
       "50%              1.500000       75.0     9.000000e-01    -1.000000   \n",
       "75%              1.750000       75.0     9.000000e-01     1.000000   \n",
       "max              4.750000       75.0     9.000000e-01     7.000000   \n",
       "\n",
       "        base_magic_resistance  base_attack_min  base_attack_max  \\\n",
       "count             2094.000000      2094.000000      2094.000000   \n",
       "unique                    NaN              NaN              NaN   \n",
       "top                       NaN              NaN              NaN   \n",
       "freq                      NaN              NaN              NaN   \n",
       "mean                24.957020        24.315664        32.039160   \n",
       "std                  0.801972         7.460690         7.764227   \n",
       "min                 10.000000         4.000000         8.000000   \n",
       "25%                 25.000000        21.000000        27.000000   \n",
       "50%                 25.000000        24.000000        32.000000   \n",
       "75%                 25.000000        28.000000        36.000000   \n",
       "max                 25.000000        62.000000        70.000000   \n",
       "\n",
       "        base_strength  base_agility  base_intelligence  strength_gain  \\\n",
       "count     2094.000000   2094.000000        2094.000000    2094.000000   \n",
       "unique            NaN           NaN                NaN            NaN   \n",
       "top               NaN           NaN                NaN            NaN   \n",
       "freq              NaN           NaN                NaN            NaN   \n",
       "mean        19.362464     18.578797          19.217765       2.327101   \n",
       "std          3.282516      4.342509           4.544661       0.526609   \n",
       "min         14.000000      0.000000          12.000000       1.300000   \n",
       "25%         17.000000     15.000000          16.000000       2.000000   \n",
       "50%         19.000000     18.000000          18.000000       2.200000   \n",
       "75%         22.000000     22.000000          23.000000       2.600000   \n",
       "max         30.000000     30.000000          30.000000       4.300000   \n",
       "\n",
       "        agility_gain  intelligence_gain  attack_range  projectile_speed  \\\n",
       "count    2094.000000        2094.000000   2094.000000       2094.000000   \n",
       "unique           NaN                NaN           NaN               NaN   \n",
       "top              NaN                NaN           NaN               NaN   \n",
       "freq             NaN                NaN           NaN               NaN   \n",
       "mean        2.030683           2.142813    349.481853        844.603629   \n",
       "std         0.667481           0.655186    199.078938        444.853122   \n",
       "min         0.000000           1.000000    140.000000          0.000000   \n",
       "25%         1.500000           1.650000    150.000000        900.000000   \n",
       "50%         1.900000           2.000000    350.000000        900.000000   \n",
       "75%         2.500000           2.500000    550.000000       1100.000000   \n",
       "max         3.700000           4.000000    700.000000       3000.000000   \n",
       "\n",
       "        attack_rate   move_speed    turn_rate  \n",
       "count   2094.000000  2094.000000  2094.000000  \n",
       "unique          NaN          NaN          NaN  \n",
       "top             NaN          NaN          NaN  \n",
       "freq            NaN          NaN          NaN  \n",
       "mean       1.672206   297.187202     0.617144  \n",
       "std        0.098417    12.217816     0.154746  \n",
       "min        1.300000   270.000000     0.500000  \n",
       "25%        1.700000   290.000000     0.500000  \n",
       "50%        1.700000   295.000000     0.600000  \n",
       "75%        1.700000   305.000000     0.700000  \n",
       "max        2.000000   340.000000     1.000000  "
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train1_data.describe(include='all')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style>\n",
       "    .dataframe thead tr:only-child th {\n",
       "        text-align: right;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: left;\n",
       "    }\n",
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>user_id</th>\n",
       "      <th>hero_id</th>\n",
       "      <th>id</th>\n",
       "      <th>num_games</th>\n",
       "      <th>num_wins</th>\n",
       "      <th>kda_ratio</th>\n",
       "      <th>primary_attr</th>\n",
       "      <th>attack_type</th>\n",
       "      <th>roles</th>\n",
       "      <th>base_health</th>\n",
       "      <th>base_health_regen</th>\n",
       "      <th>base_mana</th>\n",
       "      <th>base_mana_regen</th>\n",
       "      <th>base_armor</th>\n",
       "      <th>base_magic_resistance</th>\n",
       "      <th>base_attack_min</th>\n",
       "      <th>base_attack_max</th>\n",
       "      <th>base_strength</th>\n",
       "      <th>base_agility</th>\n",
       "      <th>base_intelligence</th>\n",
       "      <th>strength_gain</th>\n",
       "      <th>agility_gain</th>\n",
       "      <th>intelligence_gain</th>\n",
       "      <th>attack_range</th>\n",
       "      <th>projectile_speed</th>\n",
       "      <th>attack_rate</th>\n",
       "      <th>move_speed</th>\n",
       "      <th>turn_rate</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>8082.000000</td>\n",
       "      <td>8082.000000</td>\n",
       "      <td>8082</td>\n",
       "      <td>8082.000000</td>\n",
       "      <td>8082.000000</td>\n",
       "      <td>8082.000000</td>\n",
       "      <td>8082</td>\n",
       "      <td>8082</td>\n",
       "      <td>8082</td>\n",
       "      <td>8082.0</td>\n",
       "      <td>8082.000000</td>\n",
       "      <td>8082.0</td>\n",
       "      <td>8.082000e+03</td>\n",
       "      <td>8082.000000</td>\n",
       "      <td>8082.000000</td>\n",
       "      <td>8082.000000</td>\n",
       "      <td>8082.000000</td>\n",
       "      <td>8082.000000</td>\n",
       "      <td>8082.000000</td>\n",
       "      <td>8082.000000</td>\n",
       "      <td>8082.000000</td>\n",
       "      <td>8082.000000</td>\n",
       "      <td>8082.000000</td>\n",
       "      <td>8082.000000</td>\n",
       "      <td>8082.000000</td>\n",
       "      <td>8082.000000</td>\n",
       "      <td>8082.000000</td>\n",
       "      <td>8082.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>unique</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>7987</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>3</td>\n",
       "      <td>2</td>\n",
       "      <td>98</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>NaN</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>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>top</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2538_11</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>agi</td>\n",
       "      <td>Ranged</td>\n",
       "      <td>Carry:Nuker:Disabler:Escape:Pusher</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>NaN</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>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>freq</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>3158</td>\n",
       "      <td>4312</td>\n",
       "      <td>449</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>NaN</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>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>1514.359688</td>\n",
       "      <td>49.324548</td>\n",
       "      <td>NaN</td>\n",
       "      <td>192.425142</td>\n",
       "      <td>108.604801</td>\n",
       "      <td>3498.124948</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>200.0</td>\n",
       "      <td>1.707622</td>\n",
       "      <td>75.0</td>\n",
       "      <td>9.000000e-01</td>\n",
       "      <td>-0.172296</td>\n",
       "      <td>24.948033</td>\n",
       "      <td>24.005073</td>\n",
       "      <td>31.707993</td>\n",
       "      <td>19.524004</td>\n",
       "      <td>18.354863</td>\n",
       "      <td>19.138085</td>\n",
       "      <td>2.370632</td>\n",
       "      <td>2.007003</td>\n",
       "      <td>2.163864</td>\n",
       "      <td>348.811557</td>\n",
       "      <td>843.782480</td>\n",
       "      <td>1.673967</td>\n",
       "      <td>296.623361</td>\n",
       "      <td>0.618764</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>880.967122</td>\n",
       "      <td>34.788928</td>\n",
       "      <td>NaN</td>\n",
       "      <td>203.825158</td>\n",
       "      <td>117.814486</td>\n",
       "      <td>985.831478</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.564036</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.227983e-13</td>\n",
       "      <td>1.556800</td>\n",
       "      <td>0.881422</td>\n",
       "      <td>7.239318</td>\n",
       "      <td>7.514494</td>\n",
       "      <td>3.328298</td>\n",
       "      <td>4.307024</td>\n",
       "      <td>4.493088</td>\n",
       "      <td>0.530382</td>\n",
       "      <td>0.653936</td>\n",
       "      <td>0.698684</td>\n",
       "      <td>199.777785</td>\n",
       "      <td>437.797587</td>\n",
       "      <td>0.096137</td>\n",
       "      <td>12.205453</td>\n",
       "      <td>0.153861</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>3.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>142.857143</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>200.0</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>75.0</td>\n",
       "      <td>9.000000e-01</td>\n",
       "      <td>-3.000000</td>\n",
       "      <td>10.000000</td>\n",
       "      <td>4.000000</td>\n",
       "      <td>8.000000</td>\n",
       "      <td>14.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>12.000000</td>\n",
       "      <td>1.300000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>140.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.300000</td>\n",
       "      <td>270.000000</td>\n",
       "      <td>0.500000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>736.000000</td>\n",
       "      <td>14.000000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>86.000000</td>\n",
       "      <td>47.000000</td>\n",
       "      <td>2804.193865</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>200.0</td>\n",
       "      <td>1.500000</td>\n",
       "      <td>75.0</td>\n",
       "      <td>9.000000e-01</td>\n",
       "      <td>-1.000000</td>\n",
       "      <td>25.000000</td>\n",
       "      <td>19.000000</td>\n",
       "      <td>26.000000</td>\n",
       "      <td>17.000000</td>\n",
       "      <td>15.000000</td>\n",
       "      <td>16.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>1.500000</td>\n",
       "      <td>1.650000</td>\n",
       "      <td>150.000000</td>\n",
       "      <td>900.000000</td>\n",
       "      <td>1.700000</td>\n",
       "      <td>290.000000</td>\n",
       "      <td>0.500000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>1528.000000</td>\n",
       "      <td>44.000000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>146.000000</td>\n",
       "      <td>81.000000</td>\n",
       "      <td>3374.324083</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>200.0</td>\n",
       "      <td>1.500000</td>\n",
       "      <td>75.0</td>\n",
       "      <td>9.000000e-01</td>\n",
       "      <td>-1.000000</td>\n",
       "      <td>25.000000</td>\n",
       "      <td>23.000000</td>\n",
       "      <td>32.000000</td>\n",
       "      <td>19.000000</td>\n",
       "      <td>18.000000</td>\n",
       "      <td>18.000000</td>\n",
       "      <td>2.200000</td>\n",
       "      <td>1.900000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>350.000000</td>\n",
       "      <td>900.000000</td>\n",
       "      <td>1.700000</td>\n",
       "      <td>295.000000</td>\n",
       "      <td>0.600000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>2272.000000</td>\n",
       "      <td>76.000000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>234.000000</td>\n",
       "      <td>131.000000</td>\n",
       "      <td>4036.212253</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>200.0</td>\n",
       "      <td>1.500000</td>\n",
       "      <td>75.0</td>\n",
       "      <td>9.000000e-01</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>25.000000</td>\n",
       "      <td>27.000000</td>\n",
       "      <td>36.000000</td>\n",
       "      <td>22.000000</td>\n",
       "      <td>22.000000</td>\n",
       "      <td>23.000000</td>\n",
       "      <td>2.700000</td>\n",
       "      <td>2.400000</td>\n",
       "      <td>2.500000</td>\n",
       "      <td>550.000000</td>\n",
       "      <td>1100.000000</td>\n",
       "      <td>1.700000</td>\n",
       "      <td>305.000000</td>\n",
       "      <td>0.700000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>2988.000000</td>\n",
       "      <td>120.000000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>4063.000000</td>\n",
       "      <td>2292.000000</td>\n",
       "      <td>13363.636364</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>200.0</td>\n",
       "      <td>4.750000</td>\n",
       "      <td>75.0</td>\n",
       "      <td>9.000000e-01</td>\n",
       "      <td>7.000000</td>\n",
       "      <td>25.000000</td>\n",
       "      <td>62.000000</td>\n",
       "      <td>70.000000</td>\n",
       "      <td>30.000000</td>\n",
       "      <td>30.000000</td>\n",
       "      <td>30.000000</td>\n",
       "      <td>4.300000</td>\n",
       "      <td>3.700000</td>\n",
       "      <td>4.500000</td>\n",
       "      <td>700.000000</td>\n",
       "      <td>3000.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>340.000000</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "            user_id      hero_id       id    num_games     num_wins  \\\n",
       "count   8082.000000  8082.000000     8082  8082.000000  8082.000000   \n",
       "unique          NaN          NaN     7987          NaN          NaN   \n",
       "top             NaN          NaN  2538_11          NaN          NaN   \n",
       "freq            NaN          NaN        2          NaN          NaN   \n",
       "mean    1514.359688    49.324548      NaN   192.425142   108.604801   \n",
       "std      880.967122    34.788928      NaN   203.825158   117.814486   \n",
       "min        3.000000     1.000000      NaN     1.000000     0.000000   \n",
       "25%      736.000000    14.000000      NaN    86.000000    47.000000   \n",
       "50%     1528.000000    44.000000      NaN   146.000000    81.000000   \n",
       "75%     2272.000000    76.000000      NaN   234.000000   131.000000   \n",
       "max     2988.000000   120.000000      NaN  4063.000000  2292.000000   \n",
       "\n",
       "           kda_ratio primary_attr attack_type  \\\n",
       "count    8082.000000         8082        8082   \n",
       "unique           NaN            3           2   \n",
       "top              NaN          agi      Ranged   \n",
       "freq             NaN         3158        4312   \n",
       "mean     3498.124948          NaN         NaN   \n",
       "std       985.831478          NaN         NaN   \n",
       "min       142.857143          NaN         NaN   \n",
       "25%      2804.193865          NaN         NaN   \n",
       "50%      3374.324083          NaN         NaN   \n",
       "75%      4036.212253          NaN         NaN   \n",
       "max     13363.636364          NaN         NaN   \n",
       "\n",
       "                                     roles  base_health  base_health_regen  \\\n",
       "count                                 8082       8082.0        8082.000000   \n",
       "unique                                  98          NaN                NaN   \n",
       "top     Carry:Nuker:Disabler:Escape:Pusher          NaN                NaN   \n",
       "freq                                   449          NaN                NaN   \n",
       "mean                                   NaN        200.0           1.707622   \n",
       "std                                    NaN          0.0           0.564036   \n",
       "min                                    NaN        200.0           1.000000   \n",
       "25%                                    NaN        200.0           1.500000   \n",
       "50%                                    NaN        200.0           1.500000   \n",
       "75%                                    NaN        200.0           1.500000   \n",
       "max                                    NaN        200.0           4.750000   \n",
       "\n",
       "        base_mana  base_mana_regen   base_armor  base_magic_resistance  \\\n",
       "count      8082.0     8.082000e+03  8082.000000            8082.000000   \n",
       "unique        NaN              NaN          NaN                    NaN   \n",
       "top           NaN              NaN          NaN                    NaN   \n",
       "freq          NaN              NaN          NaN                    NaN   \n",
       "mean         75.0     9.000000e-01    -0.172296              24.948033   \n",
       "std           0.0     1.227983e-13     1.556800               0.881422   \n",
       "min          75.0     9.000000e-01    -3.000000              10.000000   \n",
       "25%          75.0     9.000000e-01    -1.000000              25.000000   \n",
       "50%          75.0     9.000000e-01    -1.000000              25.000000   \n",
       "75%          75.0     9.000000e-01     1.000000              25.000000   \n",
       "max          75.0     9.000000e-01     7.000000              25.000000   \n",
       "\n",
       "        base_attack_min  base_attack_max  base_strength  base_agility  \\\n",
       "count       8082.000000      8082.000000    8082.000000   8082.000000   \n",
       "unique              NaN              NaN            NaN           NaN   \n",
       "top                 NaN              NaN            NaN           NaN   \n",
       "freq                NaN              NaN            NaN           NaN   \n",
       "mean          24.005073        31.707993      19.524004     18.354863   \n",
       "std            7.239318         7.514494       3.328298      4.307024   \n",
       "min            4.000000         8.000000      14.000000      0.000000   \n",
       "25%           19.000000        26.000000      17.000000     15.000000   \n",
       "50%           23.000000        32.000000      19.000000     18.000000   \n",
       "75%           27.000000        36.000000      22.000000     22.000000   \n",
       "max           62.000000        70.000000      30.000000     30.000000   \n",
       "\n",
       "        base_intelligence  strength_gain  agility_gain  intelligence_gain  \\\n",
       "count         8082.000000    8082.000000   8082.000000        8082.000000   \n",
       "unique                NaN            NaN           NaN                NaN   \n",
       "top                   NaN            NaN           NaN                NaN   \n",
       "freq                  NaN            NaN           NaN                NaN   \n",
       "mean            19.138085       2.370632      2.007003           2.163864   \n",
       "std              4.493088       0.530382      0.653936           0.698684   \n",
       "min             12.000000       1.300000      0.000000           1.000000   \n",
       "25%             16.000000       2.000000      1.500000           1.650000   \n",
       "50%             18.000000       2.200000      1.900000           2.000000   \n",
       "75%             23.000000       2.700000      2.400000           2.500000   \n",
       "max             30.000000       4.300000      3.700000           4.500000   \n",
       "\n",
       "        attack_range  projectile_speed  attack_rate   move_speed    turn_rate  \n",
       "count    8082.000000       8082.000000  8082.000000  8082.000000  8082.000000  \n",
       "unique           NaN               NaN          NaN          NaN          NaN  \n",
       "top              NaN               NaN          NaN          NaN          NaN  \n",
       "freq             NaN               NaN          NaN          NaN          NaN  \n",
       "mean      348.811557        843.782480     1.673967   296.623361     0.618764  \n",
       "std       199.777785        437.797587     0.096137    12.205453     0.153861  \n",
       "min       140.000000          0.000000     1.300000   270.000000     0.500000  \n",
       "25%       150.000000        900.000000     1.700000   290.000000     0.500000  \n",
       "50%       350.000000        900.000000     1.700000   295.000000     0.600000  \n",
       "75%       550.000000       1100.000000     1.700000   305.000000     0.700000  \n",
       "max       700.000000       3000.000000     2.000000   340.000000     1.000000  "
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "test9_data.describe(include='all')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style>\n",
       "    .dataframe thead tr:only-child th {\n",
       "        text-align: right;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>user_id</th>\n",
       "      <th>hero_id</th>\n",
       "      <th>id</th>\n",
       "      <th>num_games</th>\n",
       "      <th>primary_attr</th>\n",
       "      <th>attack_type</th>\n",
       "      <th>roles</th>\n",
       "      <th>base_health</th>\n",
       "      <th>base_health_regen</th>\n",
       "      <th>base_mana</th>\n",
       "      <th>base_mana_regen</th>\n",
       "      <th>base_armor</th>\n",
       "      <th>base_magic_resistance</th>\n",
       "      <th>base_attack_min</th>\n",
       "      <th>base_attack_max</th>\n",
       "      <th>base_strength</th>\n",
       "      <th>base_agility</th>\n",
       "      <th>base_intelligence</th>\n",
       "      <th>strength_gain</th>\n",
       "      <th>agility_gain</th>\n",
       "      <th>intelligence_gain</th>\n",
       "      <th>attack_range</th>\n",
       "      <th>projectile_speed</th>\n",
       "      <th>attack_rate</th>\n",
       "      <th>move_speed</th>\n",
       "      <th>turn_rate</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>898.000000</td>\n",
       "      <td>898.000000</td>\n",
       "      <td>898</td>\n",
       "      <td>898.000000</td>\n",
       "      <td>898</td>\n",
       "      <td>898</td>\n",
       "      <td>898</td>\n",
       "      <td>898.0</td>\n",
       "      <td>898.000000</td>\n",
       "      <td>898.0</td>\n",
       "      <td>8.980000e+02</td>\n",
       "      <td>898.000000</td>\n",
       "      <td>898.000000</td>\n",
       "      <td>898.000000</td>\n",
       "      <td>898.000000</td>\n",
       "      <td>898.000000</td>\n",
       "      <td>898.000000</td>\n",
       "      <td>898.000000</td>\n",
       "      <td>898.000000</td>\n",
       "      <td>898.000000</td>\n",
       "      <td>898.000000</td>\n",
       "      <td>898.000000</td>\n",
       "      <td>898.000000</td>\n",
       "      <td>898.000000</td>\n",
       "      <td>898.000000</td>\n",
       "      <td>898.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>unique</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>898</td>\n",
       "      <td>NaN</td>\n",
       "      <td>3</td>\n",
       "      <td>2</td>\n",
       "      <td>89</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>NaN</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>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>top</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>120_97</td>\n",
       "      <td>NaN</td>\n",
       "      <td>agi</td>\n",
       "      <td>Ranged</td>\n",
       "      <td>Carry:Nuker</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>NaN</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>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>freq</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1</td>\n",
       "      <td>NaN</td>\n",
       "      <td>360</td>\n",
       "      <td>484</td>\n",
       "      <td>48</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>NaN</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>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>1514.359688</td>\n",
       "      <td>49.287305</td>\n",
       "      <td>NaN</td>\n",
       "      <td>189.510022</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>200.0</td>\n",
       "      <td>1.723552</td>\n",
       "      <td>75.0</td>\n",
       "      <td>9.000000e-01</td>\n",
       "      <td>-0.153675</td>\n",
       "      <td>24.933185</td>\n",
       "      <td>23.724944</td>\n",
       "      <td>31.695991</td>\n",
       "      <td>19.491091</td>\n",
       "      <td>18.384187</td>\n",
       "      <td>19.198218</td>\n",
       "      <td>2.361359</td>\n",
       "      <td>2.008241</td>\n",
       "      <td>2.150612</td>\n",
       "      <td>349.938753</td>\n",
       "      <td>847.271715</td>\n",
       "      <td>1.680067</td>\n",
       "      <td>296.787305</td>\n",
       "      <td>0.617428</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>881.403514</td>\n",
       "      <td>34.488262</td>\n",
       "      <td>NaN</td>\n",
       "      <td>202.581116</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.589824</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.432986e-14</td>\n",
       "      <td>1.568322</td>\n",
       "      <td>0.999437</td>\n",
       "      <td>6.912663</td>\n",
       "      <td>7.344221</td>\n",
       "      <td>3.330617</td>\n",
       "      <td>4.301934</td>\n",
       "      <td>4.602447</td>\n",
       "      <td>0.513952</td>\n",
       "      <td>0.660153</td>\n",
       "      <td>0.695580</td>\n",
       "      <td>198.063286</td>\n",
       "      <td>438.277343</td>\n",
       "      <td>0.094605</td>\n",
       "      <td>12.062009</td>\n",
       "      <td>0.156590</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>3.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>200.0</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>75.0</td>\n",
       "      <td>9.000000e-01</td>\n",
       "      <td>-3.000000</td>\n",
       "      <td>10.000000</td>\n",
       "      <td>4.000000</td>\n",
       "      <td>8.000000</td>\n",
       "      <td>14.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>12.000000</td>\n",
       "      <td>1.300000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>140.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.400000</td>\n",
       "      <td>270.000000</td>\n",
       "      <td>0.500000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>736.250000</td>\n",
       "      <td>14.000000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>89.000000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>200.0</td>\n",
       "      <td>1.500000</td>\n",
       "      <td>75.0</td>\n",
       "      <td>9.000000e-01</td>\n",
       "      <td>-1.000000</td>\n",
       "      <td>25.000000</td>\n",
       "      <td>19.000000</td>\n",
       "      <td>27.000000</td>\n",
       "      <td>17.000000</td>\n",
       "      <td>14.000000</td>\n",
       "      <td>16.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>1.500000</td>\n",
       "      <td>1.650000</td>\n",
       "      <td>150.000000</td>\n",
       "      <td>900.000000</td>\n",
       "      <td>1.700000</td>\n",
       "      <td>290.000000</td>\n",
       "      <td>0.500000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>1528.000000</td>\n",
       "      <td>44.000000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>147.000000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>200.0</td>\n",
       "      <td>1.500000</td>\n",
       "      <td>75.0</td>\n",
       "      <td>9.000000e-01</td>\n",
       "      <td>-1.000000</td>\n",
       "      <td>25.000000</td>\n",
       "      <td>23.000000</td>\n",
       "      <td>32.000000</td>\n",
       "      <td>19.000000</td>\n",
       "      <td>18.000000</td>\n",
       "      <td>18.000000</td>\n",
       "      <td>2.200000</td>\n",
       "      <td>1.900000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>350.000000</td>\n",
       "      <td>900.000000</td>\n",
       "      <td>1.700000</td>\n",
       "      <td>295.000000</td>\n",
       "      <td>0.600000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>2271.250000</td>\n",
       "      <td>76.000000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>222.000000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>200.0</td>\n",
       "      <td>1.750000</td>\n",
       "      <td>75.0</td>\n",
       "      <td>9.000000e-01</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>25.000000</td>\n",
       "      <td>27.000000</td>\n",
       "      <td>36.000000</td>\n",
       "      <td>22.000000</td>\n",
       "      <td>22.000000</td>\n",
       "      <td>24.000000</td>\n",
       "      <td>2.700000</td>\n",
       "      <td>2.400000</td>\n",
       "      <td>2.500000</td>\n",
       "      <td>550.000000</td>\n",
       "      <td>1100.000000</td>\n",
       "      <td>1.700000</td>\n",
       "      <td>305.000000</td>\n",
       "      <td>0.700000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>2988.000000</td>\n",
       "      <td>120.000000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>3176.000000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>200.0</td>\n",
       "      <td>4.750000</td>\n",
       "      <td>75.0</td>\n",
       "      <td>9.000000e-01</td>\n",
       "      <td>7.000000</td>\n",
       "      <td>25.000000</td>\n",
       "      <td>62.000000</td>\n",
       "      <td>70.000000</td>\n",
       "      <td>30.000000</td>\n",
       "      <td>30.000000</td>\n",
       "      <td>30.000000</td>\n",
       "      <td>3.600000</td>\n",
       "      <td>3.700000</td>\n",
       "      <td>4.500000</td>\n",
       "      <td>700.000000</td>\n",
       "      <td>3000.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>340.000000</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "            user_id     hero_id      id    num_games primary_attr attack_type  \\\n",
       "count    898.000000  898.000000     898   898.000000          898         898   \n",
       "unique          NaN         NaN     898          NaN            3           2   \n",
       "top             NaN         NaN  120_97          NaN          agi      Ranged   \n",
       "freq            NaN         NaN       1          NaN          360         484   \n",
       "mean    1514.359688   49.287305     NaN   189.510022          NaN         NaN   \n",
       "std      881.403514   34.488262     NaN   202.581116          NaN         NaN   \n",
       "min        3.000000    1.000000     NaN     2.000000          NaN         NaN   \n",
       "25%      736.250000   14.000000     NaN    89.000000          NaN         NaN   \n",
       "50%     1528.000000   44.000000     NaN   147.000000          NaN         NaN   \n",
       "75%     2271.250000   76.000000     NaN   222.000000          NaN         NaN   \n",
       "max     2988.000000  120.000000     NaN  3176.000000          NaN         NaN   \n",
       "\n",
       "              roles  base_health  base_health_regen  base_mana  \\\n",
       "count           898        898.0         898.000000      898.0   \n",
       "unique           89          NaN                NaN        NaN   \n",
       "top     Carry:Nuker          NaN                NaN        NaN   \n",
       "freq             48          NaN                NaN        NaN   \n",
       "mean            NaN        200.0           1.723552       75.0   \n",
       "std             NaN          0.0           0.589824        0.0   \n",
       "min             NaN        200.0           1.000000       75.0   \n",
       "25%             NaN        200.0           1.500000       75.0   \n",
       "50%             NaN        200.0           1.500000       75.0   \n",
       "75%             NaN        200.0           1.750000       75.0   \n",
       "max             NaN        200.0           4.750000       75.0   \n",
       "\n",
       "        base_mana_regen  base_armor  base_magic_resistance  base_attack_min  \\\n",
       "count      8.980000e+02  898.000000             898.000000       898.000000   \n",
       "unique              NaN         NaN                    NaN              NaN   \n",
       "top                 NaN         NaN                    NaN              NaN   \n",
       "freq                NaN         NaN                    NaN              NaN   \n",
       "mean       9.000000e-01   -0.153675              24.933185        23.724944   \n",
       "std        1.432986e-14    1.568322               0.999437         6.912663   \n",
       "min        9.000000e-01   -3.000000              10.000000         4.000000   \n",
       "25%        9.000000e-01   -1.000000              25.000000        19.000000   \n",
       "50%        9.000000e-01   -1.000000              25.000000        23.000000   \n",
       "75%        9.000000e-01    1.000000              25.000000        27.000000   \n",
       "max        9.000000e-01    7.000000              25.000000        62.000000   \n",
       "\n",
       "        base_attack_max  base_strength  base_agility  base_intelligence  \\\n",
       "count        898.000000     898.000000    898.000000         898.000000   \n",
       "unique              NaN            NaN           NaN                NaN   \n",
       "top                 NaN            NaN           NaN                NaN   \n",
       "freq                NaN            NaN           NaN                NaN   \n",
       "mean          31.695991      19.491091     18.384187          19.198218   \n",
       "std            7.344221       3.330617      4.301934           4.602447   \n",
       "min            8.000000      14.000000      0.000000          12.000000   \n",
       "25%           27.000000      17.000000     14.000000          16.000000   \n",
       "50%           32.000000      19.000000     18.000000          18.000000   \n",
       "75%           36.000000      22.000000     22.000000          24.000000   \n",
       "max           70.000000      30.000000     30.000000          30.000000   \n",
       "\n",
       "        strength_gain  agility_gain  intelligence_gain  attack_range  \\\n",
       "count      898.000000    898.000000         898.000000    898.000000   \n",
       "unique            NaN           NaN                NaN           NaN   \n",
       "top               NaN           NaN                NaN           NaN   \n",
       "freq              NaN           NaN                NaN           NaN   \n",
       "mean         2.361359      2.008241           2.150612    349.938753   \n",
       "std          0.513952      0.660153           0.695580    198.063286   \n",
       "min          1.300000      0.000000           1.000000    140.000000   \n",
       "25%          2.000000      1.500000           1.650000    150.000000   \n",
       "50%          2.200000      1.900000           2.000000    350.000000   \n",
       "75%          2.700000      2.400000           2.500000    550.000000   \n",
       "max          3.600000      3.700000           4.500000    700.000000   \n",
       "\n",
       "        projectile_speed  attack_rate  move_speed   turn_rate  \n",
       "count         898.000000   898.000000  898.000000  898.000000  \n",
       "unique               NaN          NaN         NaN         NaN  \n",
       "top                  NaN          NaN         NaN         NaN  \n",
       "freq                 NaN          NaN         NaN         NaN  \n",
       "mean          847.271715     1.680067  296.787305    0.617428  \n",
       "std           438.277343     0.094605   12.062009    0.156590  \n",
       "min             0.000000     1.400000  270.000000    0.500000  \n",
       "25%           900.000000     1.700000  290.000000    0.500000  \n",
       "50%           900.000000     1.700000  295.000000    0.600000  \n",
       "75%          1100.000000     1.700000  305.000000    0.700000  \n",
       "max          3000.000000     2.000000  340.000000    1.000000  "
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "test1_data.describe(include='all')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# remove features\n",
    "train9_data.drop(['id', 'num_wins', 'base_health', 'base_mana'], axis=1, inplace=True)\n",
    "train1_data.drop(['id', 'num_wins', 'base_health', 'base_mana'], axis=1, inplace=True)\n",
    "\n",
    "test9_data.drop(['id', 'num_wins', 'base_health', 'base_mana'], axis=1, inplace=True)\n",
    "test1_data.drop(['id', 'base_health', 'base_mana'], axis=1, inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# label encode\n",
    "train9_data.primary_attr.replace(['int', 'agi', 'str'], [1, 2, 3], inplace=True)\n",
    "train9_data.attack_type.replace(['Ranged', 'Melee'], [1, 0], inplace=True)\n",
    "train1_data.primary_attr.replace(['int', 'agi', 'str'], [1, 2, 3], inplace=True)\n",
    "train1_data.attack_type.replace(['Ranged', 'Melee'], [1, 0], inplace=True)\n",
    "\n",
    "test9_data.primary_attr.replace(['int', 'agi', 'str'], [1, 2, 3], inplace=True)\n",
    "test9_data.attack_type.replace(['Ranged', 'Melee'], [1, 0], inplace=True)\n",
    "test1_data.primary_attr.replace(['int', 'agi', 'str'], [1, 2, 3], inplace=True)\n",
    "test1_data.attack_type.replace(['Ranged', 'Melee'], [1, 0], inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "collapsed": true,
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "# spliting roles column-values into dummy variables\n",
    "data = train9_data['roles'].str.get_dummies(sep=':')\n",
    "train9_f = pd.concat([train9_data, data], axis=1)\n",
    "train9_f.drop('roles', axis=1, inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "data = train1_data['roles'].str.get_dummies(sep=':')\n",
    "train1_f = pd.concat([train1_data, data], axis=1)\n",
    "train1_f.drop('roles', axis=1, inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "data = test9_data['roles'].str.get_dummies(sep=':')\n",
    "test9_f = pd.concat([test9_data, data], axis=1)\n",
    "test9_f.drop('roles', axis=1, inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "data = test1_data['roles'].str.get_dummies(sep=':')\n",
    "test1_f = pd.concat([test1_data, data], axis=1)\n",
    "test1_f.drop('roles', axis=1, inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style>\n",
       "    .dataframe thead tr:only-child th {\n",
       "        text-align: right;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>user_id</th>\n",
       "      <th>hero_id</th>\n",
       "      <th>num_games</th>\n",
       "      <th>kda_ratio</th>\n",
       "      <th>primary_attr</th>\n",
       "      <th>attack_type</th>\n",
       "      <th>base_health_regen</th>\n",
       "      <th>base_mana_regen</th>\n",
       "      <th>base_armor</th>\n",
       "      <th>base_magic_resistance</th>\n",
       "      <th>base_attack_min</th>\n",
       "      <th>base_attack_max</th>\n",
       "      <th>base_strength</th>\n",
       "      <th>base_agility</th>\n",
       "      <th>base_intelligence</th>\n",
       "      <th>strength_gain</th>\n",
       "      <th>agility_gain</th>\n",
       "      <th>intelligence_gain</th>\n",
       "      <th>attack_range</th>\n",
       "      <th>projectile_speed</th>\n",
       "      <th>attack_rate</th>\n",
       "      <th>move_speed</th>\n",
       "      <th>turn_rate</th>\n",
       "      <th>Carry</th>\n",
       "      <th>Disabler</th>\n",
       "      <th>Durable</th>\n",
       "      <th>Escape</th>\n",
       "      <th>Initiator</th>\n",
       "      <th>Jungler</th>\n",
       "      <th>Nuker</th>\n",
       "      <th>Pusher</th>\n",
       "      <th>Support</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>41</td>\n",
       "      <td>201</td>\n",
       "      <td>3731.707317</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>2.00</td>\n",
       "      <td>0.9</td>\n",
       "      <td>0.0</td>\n",
       "      <td>25</td>\n",
       "      <td>33</td>\n",
       "      <td>39</td>\n",
       "      <td>23</td>\n",
       "      <td>23</td>\n",
       "      <td>15</td>\n",
       "      <td>2.1</td>\n",
       "      <td>2.8</td>\n",
       "      <td>1.5</td>\n",
       "      <td>150</td>\n",
       "      <td>900</td>\n",
       "      <td>1.7</td>\n",
       "      <td>295</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>17</td>\n",
       "      <td>427</td>\n",
       "      <td>2922.058381</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1.50</td>\n",
       "      <td>0.9</td>\n",
       "      <td>2.0</td>\n",
       "      <td>25</td>\n",
       "      <td>22</td>\n",
       "      <td>32</td>\n",
       "      <td>19</td>\n",
       "      <td>22</td>\n",
       "      <td>24</td>\n",
       "      <td>1.8</td>\n",
       "      <td>1.5</td>\n",
       "      <td>3.0</td>\n",
       "      <td>480</td>\n",
       "      <td>1100</td>\n",
       "      <td>1.7</td>\n",
       "      <td>285</td>\n",
       "      <td>0.8</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>5</td>\n",
       "      <td>106</td>\n",
       "      <td>240</td>\n",
       "      <td>5116.682739</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>2.00</td>\n",
       "      <td>0.9</td>\n",
       "      <td>-2.0</td>\n",
       "      <td>25</td>\n",
       "      <td>30</td>\n",
       "      <td>34</td>\n",
       "      <td>19</td>\n",
       "      <td>22</td>\n",
       "      <td>20</td>\n",
       "      <td>2.1</td>\n",
       "      <td>1.8</td>\n",
       "      <td>1.8</td>\n",
       "      <td>150</td>\n",
       "      <td>900</td>\n",
       "      <td>1.7</td>\n",
       "      <td>305</td>\n",
       "      <td>0.5</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>6</td>\n",
       "      <td>65</td>\n",
       "      <td>206</td>\n",
       "      <td>4131.553861</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>3.25</td>\n",
       "      <td>0.9</td>\n",
       "      <td>0.0</td>\n",
       "      <td>25</td>\n",
       "      <td>14</td>\n",
       "      <td>18</td>\n",
       "      <td>23</td>\n",
       "      <td>15</td>\n",
       "      <td>24</td>\n",
       "      <td>2.7</td>\n",
       "      <td>1.5</td>\n",
       "      <td>2.5</td>\n",
       "      <td>375</td>\n",
       "      <td>900</td>\n",
       "      <td>1.7</td>\n",
       "      <td>290</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>7</td>\n",
       "      <td>69</td>\n",
       "      <td>697</td>\n",
       "      <td>2474.469306</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>1.50</td>\n",
       "      <td>0.9</td>\n",
       "      <td>-1.0</td>\n",
       "      <td>25</td>\n",
       "      <td>27</td>\n",
       "      <td>43</td>\n",
       "      <td>26</td>\n",
       "      <td>11</td>\n",
       "      <td>13</td>\n",
       "      <td>3.5</td>\n",
       "      <td>0.9</td>\n",
       "      <td>2.1</td>\n",
       "      <td>150</td>\n",
       "      <td>900</td>\n",
       "      <td>2.0</td>\n",
       "      <td>290</td>\n",
       "      <td>0.5</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   user_id  hero_id  num_games    kda_ratio  primary_attr  attack_type  \\\n",
       "0        1       41        201  3731.707317             2            0   \n",
       "1        2       17        427  2922.058381             1            1   \n",
       "2        5      106        240  5116.682739             2            0   \n",
       "3        6       65        206  4131.553861             1            1   \n",
       "4        7       69        697  2474.469306             3            0   \n",
       "\n",
       "   base_health_regen  base_mana_regen  base_armor  base_magic_resistance  \\\n",
       "0               2.00              0.9         0.0                     25   \n",
       "1               1.50              0.9         2.0                     25   \n",
       "2               2.00              0.9        -2.0                     25   \n",
       "3               3.25              0.9         0.0                     25   \n",
       "4               1.50              0.9        -1.0                     25   \n",
       "\n",
       "   base_attack_min  base_attack_max  base_strength  base_agility  \\\n",
       "0               33               39             23            23   \n",
       "1               22               32             19            22   \n",
       "2               30               34             19            22   \n",
       "3               14               18             23            15   \n",
       "4               27               43             26            11   \n",
       "\n",
       "   base_intelligence  strength_gain  agility_gain  intelligence_gain  \\\n",
       "0                 15            2.1           2.8                1.5   \n",
       "1                 24            1.8           1.5                3.0   \n",
       "2                 20            2.1           1.8                1.8   \n",
       "3                 24            2.7           1.5                2.5   \n",
       "4                 13            3.5           0.9                2.1   \n",
       "\n",
       "   attack_range  projectile_speed  attack_rate  move_speed  turn_rate  Carry  \\\n",
       "0           150               900          1.7         295        1.0      1   \n",
       "1           480              1100          1.7         285        0.8      1   \n",
       "2           150               900          1.7         305        0.5      1   \n",
       "3           375               900          1.7         290        1.0      0   \n",
       "4           150               900          2.0         290        0.5      1   \n",
       "\n",
       "   Disabler  Durable  Escape  Initiator  Jungler  Nuker  Pusher  Support  \n",
       "0         1        1       1          1        0      0       0        0  \n",
       "1         1        0       1          1        0      1       0        0  \n",
       "2         1        0       1          1        0      1       0        0  \n",
       "3         1        0       1          1        1      0       0        0  \n",
       "4         1        1       0          1        0      1       0        0  "
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train1_f.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# EDA"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x7f3dc96c80f0>"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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CpZucO3+dufkm9dkqZ88c57HTR7Z93ImHZ7j62gJXbjRYarZZbnVIkpRyKeDUsRpf8+iR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\n07s/XNPfPN7UR59sqd3ZyQoFu++CDklV19Nfr23uFASB3GZ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      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f3dc9658f98>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Scatter plot\n",
    "plt.figure(figsize=(20,10))\n",
    "sns.regplot(x=\"kda_ratio\", y=\"base_attack_min\", data=train9_data, fit_reg=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x7f3dc966f240>"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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0IzYBAAAA0I3YBAAAAEA3YhMAAAAA3YhNAAAAAHQjNgEAAADQjdgEAAAAQDdiEwAAAADd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\nSZIkqWsMmyRJkiRJktQ1/wP/NG+LONJPjwAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f3dc9658f28>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# boxplot\n",
    "# as we can see there are 5 (kda_ratio > 15000) outliers, we can remove them. It may improve our model\n",
    "plt.figure(figsize=(20,10))\n",
    "sns.boxplot(x=\"base_magic_resistance\", y=\"kda_ratio\", data=train9_data)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f3dc9543da0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# log transformation on kde_ratio\n",
    "plt.figure(figsize=(20,10))\n",
    "plt.hist(np.log(1+train9_data['kda_ratio']), bins=500)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x7f3dc9596860>"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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JBGa+PIoBPRp+M+frX4/wbWoOVhIpfbrK8R/zLNdu3mL+hh1oKiq5frOayc+7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rQaQtYH8tlX09j16y/s/fMPrl6RadGyFa3atGXyzLkm0wGoqerXu3z1OqJOnUZhpmDqFxO4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\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8wZvfLKTbFz48zcwgTfOYvNxcHlwOKbGQ6L9gM+9MXso7k5fiWLch3b+cSlUrFcE/buSl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\nDD4kSZIkNWHwIUmSJKmJ3wNDA1900cq5uAAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f3dc9620d30>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "corr = train9_data.corr()\n",
    "f, ax = plt.subplots(figsize=(13, 11))\n",
    "sns.heatmap(corr, annot=True, fmt=\".2f\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style>\n",
       "    .dataframe thead tr:only-child th {\n",
       "        text-align: right;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>user_id</th>\n",
       "      <th>hero_id</th>\n",
       "      <th>num_games</th>\n",
       "      <th>primary_attr</th>\n",
       "      <th>attack_type</th>\n",
       "      <th>base_health_regen</th>\n",
       "      <th>base_mana_regen</th>\n",
       "      <th>base_armor</th>\n",
       "      <th>base_magic_resistance</th>\n",
       "      <th>base_attack_min</th>\n",
       "      <th>base_attack_max</th>\n",
       "      <th>base_strength</th>\n",
       "      <th>base_agility</th>\n",
       "      <th>base_intelligence</th>\n",
       "      <th>strength_gain</th>\n",
       "      <th>agility_gain</th>\n",
       "      <th>intelligence_gain</th>\n",
       "      <th>attack_range</th>\n",
       "      <th>projectile_speed</th>\n",
       "      <th>attack_rate</th>\n",
       "      <th>move_speed</th>\n",
       "      <th>turn_rate</th>\n",
       "      <th>Carry</th>\n",
       "      <th>Disabler</th>\n",
       "      <th>Durable</th>\n",
       "      <th>Escape</th>\n",
       "      <th>Initiator</th>\n",
       "      <th>Jungler</th>\n",
       "      <th>Nuker</th>\n",
       "      <th>Pusher</th>\n",
       "      <th>Support</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>898.000000</td>\n",
       "      <td>898.000000</td>\n",
       "      <td>898.000000</td>\n",
       "      <td>898.000000</td>\n",
       "      <td>898.000000</td>\n",
       "      <td>898.000000</td>\n",
       "      <td>8.980000e+02</td>\n",
       "      <td>898.000000</td>\n",
       "      <td>898.000000</td>\n",
       "      <td>898.000000</td>\n",
       "      <td>898.000000</td>\n",
       "      <td>898.000000</td>\n",
       "      <td>898.000000</td>\n",
       "      <td>898.000000</td>\n",
       "      <td>898.000000</td>\n",
       "      <td>898.000000</td>\n",
       "      <td>898.000000</td>\n",
       "      <td>898.000000</td>\n",
       "      <td>898.000000</td>\n",
       "      <td>898.000000</td>\n",
       "      <td>898.000000</td>\n",
       "      <td>898.000000</td>\n",
       "      <td>898.000000</td>\n",
       "      <td>898.000000</td>\n",
       "      <td>898.000000</td>\n",
       "      <td>898.000000</td>\n",
       "      <td>898.000000</td>\n",
       "      <td>898.000000</td>\n",
       "      <td>898.000000</td>\n",
       "      <td>898.000000</td>\n",
       "      <td>898.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>1514.359688</td>\n",
       "      <td>49.287305</td>\n",
       "      <td>189.510022</td>\n",
       "      <td>1.870824</td>\n",
       "      <td>0.538976</td>\n",
       "      <td>1.723552</td>\n",
       "      <td>9.000000e-01</td>\n",
       "      <td>-0.153675</td>\n",
       "      <td>24.933185</td>\n",
       "      <td>23.724944</td>\n",
       "      <td>31.695991</td>\n",
       "      <td>19.491091</td>\n",
       "      <td>18.384187</td>\n",
       "      <td>19.198218</td>\n",
       "      <td>2.361359</td>\n",
       "      <td>2.008241</td>\n",
       "      <td>2.150612</td>\n",
       "      <td>349.938753</td>\n",
       "      <td>847.271715</td>\n",
       "      <td>1.680067</td>\n",
       "      <td>296.787305</td>\n",
       "      <td>0.617428</td>\n",
       "      <td>0.695991</td>\n",
       "      <td>0.701559</td>\n",
       "      <td>0.269488</td>\n",
       "      <td>0.543430</td>\n",
       "      <td>0.413140</td>\n",
       "      <td>0.086860</td>\n",
       "      <td>0.791759</td>\n",
       "      <td>0.218263</td>\n",
       "      <td>0.204900</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>881.403514</td>\n",
       "      <td>34.488262</td>\n",
       "      <td>202.581116</td>\n",
       "      <td>0.763592</td>\n",
       "      <td>0.498756</td>\n",
       "      <td>0.589824</td>\n",
       "      <td>1.432986e-14</td>\n",
       "      <td>1.568322</td>\n",
       "      <td>0.999437</td>\n",
       "      <td>6.912663</td>\n",
       "      <td>7.344221</td>\n",
       "      <td>3.330617</td>\n",
       "      <td>4.301934</td>\n",
       "      <td>4.602447</td>\n",
       "      <td>0.513952</td>\n",
       "      <td>0.660153</td>\n",
       "      <td>0.695580</td>\n",
       "      <td>198.063286</td>\n",
       "      <td>438.277343</td>\n",
       "      <td>0.094605</td>\n",
       "      <td>12.062009</td>\n",
       "      <td>0.156590</td>\n",
       "      <td>0.460243</td>\n",
       "      <td>0.457829</td>\n",
       "      <td>0.443941</td>\n",
       "      <td>0.498388</td>\n",
       "      <td>0.492672</td>\n",
       "      <td>0.281786</td>\n",
       "      <td>0.406276</td>\n",
       "      <td>0.413297</td>\n",
       "      <td>0.403853</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>3.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>9.000000e-01</td>\n",
       "      <td>-3.000000</td>\n",
       "      <td>10.000000</td>\n",
       "      <td>4.000000</td>\n",
       "      <td>8.000000</td>\n",
       "      <td>14.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>12.000000</td>\n",
       "      <td>1.300000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>140.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.400000</td>\n",
       "      <td>270.000000</td>\n",
       "      <td>0.500000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>736.250000</td>\n",
       "      <td>14.000000</td>\n",
       "      <td>89.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.500000</td>\n",
       "      <td>9.000000e-01</td>\n",
       "      <td>-1.000000</td>\n",
       "      <td>25.000000</td>\n",
       "      <td>19.000000</td>\n",
       "      <td>27.000000</td>\n",
       "      <td>17.000000</td>\n",
       "      <td>14.000000</td>\n",
       "      <td>16.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>1.500000</td>\n",
       "      <td>1.650000</td>\n",
       "      <td>150.000000</td>\n",
       "      <td>900.000000</td>\n",
       "      <td>1.700000</td>\n",
       "      <td>290.000000</td>\n",
       "      <td>0.500000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>1528.000000</td>\n",
       "      <td>44.000000</td>\n",
       "      <td>147.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.500000</td>\n",
       "      <td>9.000000e-01</td>\n",
       "      <td>-1.000000</td>\n",
       "      <td>25.000000</td>\n",
       "      <td>23.000000</td>\n",
       "      <td>32.000000</td>\n",
       "      <td>19.000000</td>\n",
       "      <td>18.000000</td>\n",
       "      <td>18.000000</td>\n",
       "      <td>2.200000</td>\n",
       "      <td>1.900000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>350.000000</td>\n",
       "      <td>900.000000</td>\n",
       "      <td>1.700000</td>\n",
       "      <td>295.000000</td>\n",
       "      <td>0.600000</td>\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>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>2271.250000</td>\n",
       "      <td>76.000000</td>\n",
       "      <td>222.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.750000</td>\n",
       "      <td>9.000000e-01</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>25.000000</td>\n",
       "      <td>27.000000</td>\n",
       "      <td>36.000000</td>\n",
       "      <td>22.000000</td>\n",
       "      <td>22.000000</td>\n",
       "      <td>24.000000</td>\n",
       "      <td>2.700000</td>\n",
       "      <td>2.400000</td>\n",
       "      <td>2.500000</td>\n",
       "      <td>550.000000</td>\n",
       "      <td>1100.000000</td>\n",
       "      <td>1.700000</td>\n",
       "      <td>305.000000</td>\n",
       "      <td>0.700000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\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",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>2988.000000</td>\n",
       "      <td>120.000000</td>\n",
       "      <td>3176.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>4.750000</td>\n",
       "      <td>9.000000e-01</td>\n",
       "      <td>7.000000</td>\n",
       "      <td>25.000000</td>\n",
       "      <td>62.000000</td>\n",
       "      <td>70.000000</td>\n",
       "      <td>30.000000</td>\n",
       "      <td>30.000000</td>\n",
       "      <td>30.000000</td>\n",
       "      <td>3.600000</td>\n",
       "      <td>3.700000</td>\n",
       "      <td>4.500000</td>\n",
       "      <td>700.000000</td>\n",
       "      <td>3000.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>340.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "           user_id     hero_id    num_games  primary_attr  attack_type  \\\n",
       "count   898.000000  898.000000   898.000000    898.000000   898.000000   \n",
       "mean   1514.359688   49.287305   189.510022      1.870824     0.538976   \n",
       "std     881.403514   34.488262   202.581116      0.763592     0.498756   \n",
       "min       3.000000    1.000000     2.000000      1.000000     0.000000   \n",
       "25%     736.250000   14.000000    89.000000      1.000000     0.000000   \n",
       "50%    1528.000000   44.000000   147.000000      2.000000     1.000000   \n",
       "75%    2271.250000   76.000000   222.000000      2.000000     1.000000   \n",
       "max    2988.000000  120.000000  3176.000000      3.000000     1.000000   \n",
       "\n",
       "       base_health_regen  base_mana_regen  base_armor  base_magic_resistance  \\\n",
       "count         898.000000     8.980000e+02  898.000000             898.000000   \n",
       "mean            1.723552     9.000000e-01   -0.153675              24.933185   \n",
       "std             0.589824     1.432986e-14    1.568322               0.999437   \n",
       "min             1.000000     9.000000e-01   -3.000000              10.000000   \n",
       "25%             1.500000     9.000000e-01   -1.000000              25.000000   \n",
       "50%             1.500000     9.000000e-01   -1.000000              25.000000   \n",
       "75%             1.750000     9.000000e-01    1.000000              25.000000   \n",
       "max             4.750000     9.000000e-01    7.000000              25.000000   \n",
       "\n",
       "       base_attack_min  base_attack_max  base_strength  base_agility  \\\n",
       "count       898.000000       898.000000     898.000000    898.000000   \n",
       "mean         23.724944        31.695991      19.491091     18.384187   \n",
       "std           6.912663         7.344221       3.330617      4.301934   \n",
       "min           4.000000         8.000000      14.000000      0.000000   \n",
       "25%          19.000000        27.000000      17.000000     14.000000   \n",
       "50%          23.000000        32.000000      19.000000     18.000000   \n",
       "75%          27.000000        36.000000      22.000000     22.000000   \n",
       "max          62.000000        70.000000      30.000000     30.000000   \n",
       "\n",
       "       base_intelligence  strength_gain  agility_gain  intelligence_gain  \\\n",
       "count         898.000000     898.000000    898.000000         898.000000   \n",
       "mean           19.198218       2.361359      2.008241           2.150612   \n",
       "std             4.602447       0.513952      0.660153           0.695580   \n",
       "min            12.000000       1.300000      0.000000           1.000000   \n",
       "25%            16.000000       2.000000      1.500000           1.650000   \n",
       "50%            18.000000       2.200000      1.900000           2.000000   \n",
       "75%            24.000000       2.700000      2.400000           2.500000   \n",
       "max            30.000000       3.600000      3.700000           4.500000   \n",
       "\n",
       "       attack_range  projectile_speed  attack_rate  move_speed   turn_rate  \\\n",
       "count    898.000000        898.000000   898.000000  898.000000  898.000000   \n",
       "mean     349.938753        847.271715     1.680067  296.787305    0.617428   \n",
       "std      198.063286        438.277343     0.094605   12.062009    0.156590   \n",
       "min      140.000000          0.000000     1.400000  270.000000    0.500000   \n",
       "25%      150.000000        900.000000     1.700000  290.000000    0.500000   \n",
       "50%      350.000000        900.000000     1.700000  295.000000    0.600000   \n",
       "75%      550.000000       1100.000000     1.700000  305.000000    0.700000   \n",
       "max      700.000000       3000.000000     2.000000  340.000000    1.000000   \n",
       "\n",
       "            Carry    Disabler     Durable      Escape   Initiator     Jungler  \\\n",
       "count  898.000000  898.000000  898.000000  898.000000  898.000000  898.000000   \n",
       "mean     0.695991    0.701559    0.269488    0.543430    0.413140    0.086860   \n",
       "std      0.460243    0.457829    0.443941    0.498388    0.492672    0.281786   \n",
       "min      0.000000    0.000000    0.000000    0.000000    0.000000    0.000000   \n",
       "25%      0.000000    0.000000    0.000000    0.000000    0.000000    0.000000   \n",
       "50%      1.000000    1.000000    0.000000    1.000000    0.000000    0.000000   \n",
       "75%      1.000000    1.000000    1.000000    1.000000    1.000000    0.000000   \n",
       "max      1.000000    1.000000    1.000000    1.000000    1.000000    1.000000   \n",
       "\n",
       "            Nuker      Pusher     Support  \n",
       "count  898.000000  898.000000  898.000000  \n",
       "mean     0.791759    0.218263    0.204900  \n",
       "std      0.406276    0.413297    0.403853  \n",
       "min      0.000000    0.000000    0.000000  \n",
       "25%      1.000000    0.000000    0.000000  \n",
       "50%      1.000000    0.000000    0.000000  \n",
       "75%      1.000000    0.000000    0.000000  \n",
       "max      1.000000    1.000000    1.000000  "
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "test1_f.describe(include='all')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(2094, 32)\n",
      "(18846, 32)\n",
      "(898, 31)\n",
      "(8082, 32)\n"
     ]
    }
   ],
   "source": [
    "print(train1_f.shape)\n",
    "print(train9_f.shape)\n",
    "print(test1_f.shape)\n",
    "print(test9_f.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['user_id', 'hero_id', 'num_games', 'primary_attr', 'attack_type',\n",
       "       'base_health_regen', 'base_mana_regen', 'base_armor',\n",
       "       'base_magic_resistance', 'base_attack_min', 'base_attack_max',\n",
       "       'base_strength', 'base_agility', 'base_intelligence', 'strength_gain',\n",
       "       'agility_gain', 'intelligence_gain', 'attack_range', 'projectile_speed',\n",
       "       'attack_rate', 'move_speed', 'turn_rate', 'Carry', 'Disabler',\n",
       "       'Durable', 'Escape', 'Initiator', 'Jungler', 'Nuker', 'Pusher',\n",
       "       'Support'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "test1_f.columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['user_id', 'hero_id', 'num_games', 'kda_ratio', 'primary_attr',\n",
       "       'attack_type', 'base_health_regen', 'base_mana_regen', 'base_armor',\n",
       "       'base_magic_resistance', 'base_attack_min', 'base_attack_max',\n",
       "       'base_strength', 'base_agility', 'base_intelligence', 'strength_gain',\n",
       "       'agility_gain', 'intelligence_gain', 'attack_range', 'projectile_speed',\n",
       "       'attack_rate', 'move_speed', 'turn_rate', 'Carry', 'Disabler',\n",
       "       'Durable', 'Escape', 'Initiator', 'Jungler', 'Nuker', 'Pusher',\n",
       "       'Support'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "test9_f.columns"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Model\n",
    "we used two xgb models for ensembling and it improved our result."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "train = pd.concat([train9_f, test9_f, train1_f], axis=0).sort_values('user_id').reset_index(drop=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "\"\"\"\n",
    "Features : \"avg\" - average from user_id\n",
    "           \"hero_avg\" - average from hero_id\n",
    "\"\"\"\n",
    "\n",
    "train['avg'] = train['kda_ratio'].groupby(train['user_id']).transform('mean')\n",
    "train['hero_avg'] = train['kda_ratio'].groupby(train['hero_id']).transform('mean')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {
    "collapsed": true,
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "test1_hero = pd.DataFrame(test9_f['kda_ratio'].groupby(test9_f['hero_id']).mean().reset_index())\n",
    "test1_hero.columns = ['hero_id', 'hero_avg']\n",
    "\n",
    "train1_hero = pd.DataFrame(train9_f['kda_ratio'].groupby(train9_f['hero_id']).mean().reset_index())\n",
    "train1_hero.columns = ['hero_id', 'hero_avg']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {
    "collapsed": true,
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "test1_f['avg'] = pd.DataFrame(test9_f['kda_ratio'].groupby(test9_f['user_id']).mean().reset_index(drop=True))['kda_ratio']\n",
    "train1_f['avg'] = pd.DataFrame(train9_f['kda_ratio'].groupby(train9_f['user_id']).mean().reset_index(drop=True))['kda_ratio']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "test1_f = pd.merge(test1_f, test1_hero,how=\"left\",on=\"hero_id\")\n",
    "train1_f = pd.merge(train1_f, train1_hero,how=\"left\",on=\"hero_id\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "y_train = train['kda_ratio']\n",
    "X_train = train.drop(['kda_ratio', 'base_mana_regen'], axis=1)\n",
    "y_valid = train1_f['kda_ratio']\n",
    "X_valid = train1_f.drop(['kda_ratio', 'base_mana_regen'], axis=1)\n",
    "X_test = test1_f.drop(['base_mana_regen'], axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "((29022, 32), (2094, 32), (898, 32))"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X_train.shape, X_valid.shape, X_test.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Index(['user_id', 'hero_id', 'num_games', 'primary_attr', 'attack_type',\n",
      "       'base_health_regen', 'base_armor', 'base_magic_resistance',\n",
      "       'base_attack_min', 'base_attack_max', 'base_strength', 'base_agility',\n",
      "       'base_intelligence', 'strength_gain', 'agility_gain',\n",
      "       'intelligence_gain', 'attack_range', 'projectile_speed', 'attack_rate',\n",
      "       'move_speed', 'turn_rate', 'Carry', 'Disabler', 'Durable', 'Escape',\n",
      "       'Initiator', 'Jungler', 'Nuker', 'Pusher', 'Support', 'avg',\n",
      "       'hero_avg'],\n",
      "      dtype='object')\n",
      "Index(['user_id', 'hero_id', 'num_games', 'primary_attr', 'attack_type',\n",
      "       'base_health_regen', 'base_armor', 'base_magic_resistance',\n",
      "       'base_attack_min', 'base_attack_max', 'base_strength', 'base_agility',\n",
      "       'base_intelligence', 'strength_gain', 'agility_gain',\n",
      "       'intelligence_gain', 'attack_range', 'projectile_speed', 'attack_rate',\n",
      "       'move_speed', 'turn_rate', 'Carry', 'Disabler', 'Durable', 'Escape',\n",
      "       'Initiator', 'Jungler', 'Nuker', 'Pusher', 'Support', 'avg',\n",
      "       'hero_avg'],\n",
      "      dtype='object')\n",
      "Index(['user_id', 'hero_id', 'num_games', 'primary_attr', 'attack_type',\n",
      "       'base_health_regen', 'base_armor', 'base_magic_resistance',\n",
      "       'base_attack_min', 'base_attack_max', 'base_strength', 'base_agility',\n",
      "       'base_intelligence', 'strength_gain', 'agility_gain',\n",
      "       'intelligence_gain', 'attack_range', 'projectile_speed', 'attack_rate',\n",
      "       'move_speed', 'turn_rate', 'Carry', 'Disabler', 'Durable', 'Escape',\n",
      "       'Initiator', 'Jungler', 'Nuker', 'Pusher', 'Support', 'avg',\n",
      "       'hero_avg'],\n",
      "      dtype='object')\n"
     ]
    }
   ],
   "source": [
    "print(X_train.columns)\n",
    "print(X_valid.columns)\n",
    "print(X_test.columns)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Training error : 283.18425763666363\n",
      "Validation error : 416.4476880973586\n"
     ]
    }
   ],
   "source": [
    "from xgboost import XGBRegressor\n",
    "\n",
    "xgb_model = XGBRegressor(n_estimators=1200,\n",
    "                   max_depth=8,\n",
    "                   learning_rate=0.2,\n",
    "                   subsample=0.85,\n",
    "                   colsample_bytree=0.85,\n",
    "                   gamma=0.05,\n",
    "                   seed=7\n",
    "                  )\n",
    "\n",
    "xgb_model.fit(X_train, np.log(1+y_train))\n",
    "\n",
    "pred1 = xgb_model.predict(X_train)\n",
    "pred2 = xgb_model.predict(X_valid)\n",
    "print('Training error :',math.sqrt(mean_squared_error(y_train, np.exp(pred1))))\n",
    "print('Validation error :',math.sqrt(mean_squared_error(y_valid, np.exp(pred2))))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x7f3dc511a978>"
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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EZCkmyAcA/VR1kYi0AV5S1W4iMg+zVP0w8HJCgTaliEuJ3KRJI4KDgwkLC2PU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\ngBmHJgAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f3dc92e5080>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "xgb.plot_importance(xgb_model,height=0.5, xlabel='Importance', ylabel='Features')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Training error : 364.3244615893699\n",
      "Validation error : 453.72497151335745\n"
     ]
    }
   ],
   "source": [
    "xgb_model1 = XGBRegressor(n_estimators=1200,\n",
    "                   max_depth=7,\n",
    "                   learning_rate=0.2,\n",
    "                   subsample=0.75,\n",
    "                   colsample_bytree=0.83,\n",
    "                   gamma=0.09,\n",
    "                   seed=7\n",
    "                  )\n",
    "\n",
    "xgb_model1.fit(X_train, np.log(1+y_train))\n",
    "\n",
    "pred1 = xgb_model1.predict(X_train)\n",
    "pred2 = xgb_model1.predict(X_valid)\n",
    "print('Training error :',math.sqrt(mean_squared_error(y_train, np.exp(pred1))))\n",
    "print('Validation error :',math.sqrt(mean_squared_error(y_valid, np.exp(pred2))))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x7f3dc910aa20>"
      ]
     },
     "execution_count": 36,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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ah/5M7V/Pqxq5bdsOlxQQUP2DXLlyMXToQPbt20Pdug1o1eqJNLXX4XA4rpTM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\nOI5TwniwdRzHcZwS5v8BZqDrj94uaGwAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f3dc912d668>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "xgb.plot_importance(xgb_model1,height=0.5, xlabel='Importance', ylabel='Features')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "submission = pd.read_csv('sample_submission_CKEH6IJ.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "pred1 = xgb_model.predict(X_test)\n",
    "pred2 = xgb_model1.predict(X_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "validation error : 425.55354987345896\n"
     ]
    }
   ],
   "source": [
    "pred3 = xgb_model.predict(X_valid)\n",
    "pred4 = xgb_model1.predict(X_valid)\n",
    "print(\"validation error :\" , math.sqrt(mean_squared_error(y_valid, (0.5*np.exp(pred3) + 0.5*np.exp(pred4)))))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {
    "collapsed": true,
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "submission['kda_ratio'] = (np.exp(pred1) + np.exp(pred2))/2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style>\n",
       "    .dataframe thead tr:only-child th {\n",
       "        text-align: right;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>id</th>\n",
       "      <th>kda_ratio</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>3_53</td>\n",
       "      <td>2518.925293</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>4_17</td>\n",
       "      <td>3310.660645</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>9_51</td>\n",
       "      <td>2798.226562</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>10_54</td>\n",
       "      <td>4633.034180</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>15_74</td>\n",
       "      <td>4874.923828</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      id    kda_ratio\n",
       "0   3_53  2518.925293\n",
       "1   4_17  3310.660645\n",
       "2   9_51  2798.226562\n",
       "3  10_54  4633.034180\n",
       "4  15_74  4874.923828"
      ]
     },
     "execution_count": 41,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "submission.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "submission.to_csv('submission.csv', index=False)"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 2",
   "language": "python",
   "name": "python2"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 2
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython2",
   "version": "2.7.13"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
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