2_EDA.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "pd.set_option('max_columns',1000)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Players</th>\n",
       "      <th>club</th>\n",
       "      <th>age</th>\n",
       "      <th>Position</th>\n",
       "      <th>short_passing</th>\n",
       "      <th>dribbling</th>\n",
       "      <th>long_passing</th>\n",
       "      <th>ball_control</th>\n",
       "      <th>interceptions</th>\n",
       "      <th>positioning</th>\n",
       "      <th>vision</th>\n",
       "      <th>marking</th>\n",
       "      <th>gk_handling</th>\n",
       "      <th>gk_reflexes</th>\n",
       "      <th>Goals-16/17</th>\n",
       "      <th>A-16/17</th>\n",
       "      <th>CS-16/17</th>\n",
       "      <th>Yellow-16/17</th>\n",
       "      <th>Red-16/17</th>\n",
       "      <th>Off-16/17</th>\n",
       "      <th>Pen_SV-16/17</th>\n",
       "      <th>Pen_M-16/17</th>\n",
       "      <th>Goals_conceded-16/17</th>\n",
       "      <th>OG-16/17</th>\n",
       "      <th>Apps-16/17</th>\n",
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       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Aaron Cresswell</td>\n",
       "      <td>West Ham United</td>\n",
       "      <td>27</td>\n",
       "      <td>Defender</td>\n",
       "      <td>72</td>\n",
       "      <td>76</td>\n",
       "      <td>68</td>\n",
       "      <td>75</td>\n",
       "      <td>71</td>\n",
       "      <td>63</td>\n",
       "      <td>68</td>\n",
       "      <td>76</td>\n",
       "      <td>7</td>\n",
       "      <td>12</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>5</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>37</td>\n",
       "      <td>0</td>\n",
       "      <td>26</td>\n",
       "      <td>2.269231</td>\n",
       "      <td>2</td>\n",
       "      <td>4</td>\n",
       "      <td>11</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>50</td>\n",
       "      <td>0</td>\n",
       "      <td>37</td>\n",
       "      <td>3.108108</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Aaron Hunt</td>\n",
       "      <td>Hamburger SV</td>\n",
       "      <td>30</td>\n",
       "      <td>Midfielder</td>\n",
       "      <td>78</td>\n",
       "      <td>75</td>\n",
       "      <td>68</td>\n",
       "      <td>80</td>\n",
       "      <td>55</td>\n",
       "      <td>75</td>\n",
       "      <td>74</td>\n",
       "      <td>28</td>\n",
       "      <td>12</td>\n",
       "      <td>15</td>\n",
       "      <td>4</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>7</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>22</td>\n",
       "      <td>2.590909</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>7</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>22</td>\n",
       "      <td>2.136364</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Aaron Lennon</td>\n",
       "      <td>Everton</td>\n",
       "      <td>30</td>\n",
       "      <td>Midfielder</td>\n",
       "      <td>76</td>\n",
       "      <td>82</td>\n",
       "      <td>54</td>\n",
       "      <td>80</td>\n",
       "      <td>42</td>\n",
       "      <td>71</td>\n",
       "      <td>73</td>\n",
       "      <td>40</td>\n",
       "      <td>7</td>\n",
       "      <td>11</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>5</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>11</td>\n",
       "      <td>1.818182</td>\n",
       "      <td>5</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>8</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>25</td>\n",
       "      <td>2.760000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Aaron Ramsey</td>\n",
       "      <td>Arsenal</td>\n",
       "      <td>26</td>\n",
       "      <td>Midfielder</td>\n",
       "      <td>84</td>\n",
       "      <td>81</td>\n",
       "      <td>80</td>\n",
       "      <td>82</td>\n",
       "      <td>69</td>\n",
       "      <td>82</td>\n",
       "      <td>80</td>\n",
       "      <td>60</td>\n",
       "      <td>11</td>\n",
       "      <td>8</td>\n",
       "      <td>1</td>\n",
       "      <td>4</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>23</td>\n",
       "      <td>2.173913</td>\n",
       "      <td>5</td>\n",
       "      <td>5</td>\n",
       "      <td>0</td>\n",
       "      <td>4</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>31</td>\n",
       "      <td>3.096774</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Abdoulaye Doucoure</td>\n",
       "      <td>Watford</td>\n",
       "      <td>24</td>\n",
       "      <td>Midfielder</td>\n",
       "      <td>82</td>\n",
       "      <td>78</td>\n",
       "      <td>74</td>\n",
       "      <td>80</td>\n",
       "      <td>78</td>\n",
       "      <td>73</td>\n",
       "      <td>77</td>\n",
       "      <td>70</td>\n",
       "      <td>12</td>\n",
       "      <td>14</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>4</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>20</td>\n",
       "      <td>1.750000</td>\n",
       "      <td>2</td>\n",
       "      <td>4</td>\n",
       "      <td>0</td>\n",
       "      <td>6</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>31</td>\n",
       "      <td>2.279167</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "              Players             club  age    Position  short_passing  \\\n",
       "0     Aaron Cresswell  West Ham United   27    Defender             72   \n",
       "1          Aaron Hunt     Hamburger SV   30  Midfielder             78   \n",
       "2        Aaron Lennon          Everton   30  Midfielder             76   \n",
       "3        Aaron Ramsey          Arsenal   26  Midfielder             84   \n",
       "4  Abdoulaye Doucoure          Watford   24  Midfielder             82   \n",
       "\n",
       "   dribbling  long_passing  ball_control  interceptions  positioning  vision  \\\n",
       "0         76            68            75             71           63      68   \n",
       "1         75            68            80             55           75      74   \n",
       "2         82            54            80             42           71      73   \n",
       "3         81            80            82             69           82      80   \n",
       "4         78            74            80             78           73      77   \n",
       "\n",
       "   marking  gk_handling  gk_reflexes  Goals-16/17  A-16/17  CS-16/17  \\\n",
       "0       76            7           12            0        2         5   \n",
       "1       28           12           15            4        2         0   \n",
       "2       40            7           11            0        1         0   \n",
       "3       60           11            8            1        4         0   \n",
       "4       70           12           14            1        0         0   \n",
       "\n",
       "   Yellow-16/17  Red-16/17  Off-16/17  Pen_SV-16/17  Pen_M-16/17  \\\n",
       "0             3          0          3             0            0   \n",
       "1             3          0          7             0            1   \n",
       "2             0          0          5             0            0   \n",
       "3             3          0          3             0            0   \n",
       "4             4          0          3             0            0   \n",
       "\n",
       "   Goals_conceded-16/17  OG-16/17  Apps-16/17  Form-16/17  Goals-15/16  \\\n",
       "0                    37         0          26    2.269231            2   \n",
       "1                     0         0          22    2.590909            1   \n",
       "2                     0         0          11    1.818182            5   \n",
       "3                     0         0          23    2.173913            5   \n",
       "4                     0         0          20    1.750000            2   \n",
       "\n",
       "   A-15/16  CS-15/16  Yellow-15/16  Red-15/16  Off-15/16  Pen_SV-15/16  \\\n",
       "0        4        11             1          0          1             0   \n",
       "1        2         0             2          0          7             0   \n",
       "2        1         0             1          0          8             0   \n",
       "3        5         0             4          0          2             0   \n",
       "4        4         0             6          0          3             0   \n",
       "\n",
       "   Pen_M-15/16  Goals_conceded-15/16  OG-15/16  Apps-15/16  Form-15/16  \n",
       "0            0                    50         0          37    3.108108  \n",
       "1            0                     0         1          22    2.136364  \n",
       "2            0                     0         0          25    2.760000  \n",
       "3            0                     0         0          31    3.096774  \n",
       "4            0                     0         0          31    2.279167  "
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_train = pd.read_csv('training_data/train_data.csv')\n",
    "df_targets = pd.read_csv('targets/targets.csv')\n",
    "df_train.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>age</th>\n",
       "      <th>short_passing</th>\n",
       "      <th>dribbling</th>\n",
       "      <th>long_passing</th>\n",
       "      <th>ball_control</th>\n",
       "      <th>interceptions</th>\n",
       "      <th>positioning</th>\n",
       "      <th>vision</th>\n",
       "      <th>marking</th>\n",
       "      <th>gk_handling</th>\n",
       "      <th>gk_reflexes</th>\n",
       "      <th>Goals-16/17</th>\n",
       "      <th>A-16/17</th>\n",
       "      <th>CS-16/17</th>\n",
       "      <th>Yellow-16/17</th>\n",
       "      <th>Red-16/17</th>\n",
       "      <th>Off-16/17</th>\n",
       "      <th>Pen_SV-16/17</th>\n",
       "      <th>Pen_M-16/17</th>\n",
       "      <th>Goals_conceded-16/17</th>\n",
       "      <th>OG-16/17</th>\n",
       "      <th>Apps-16/17</th>\n",
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       "      <th>Apps-15/16</th>\n",
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       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>1435.000000</td>\n",
       "      <td>1435.000000</td>\n",
       "      <td>1435.000000</td>\n",
       "      <td>1435.000000</td>\n",
       "      <td>1435.000000</td>\n",
       "      <td>1435.000000</td>\n",
       "      <td>1435.000000</td>\n",
       "      <td>1435.000000</td>\n",
       "      <td>1435.000000</td>\n",
       "      <td>1435.000000</td>\n",
       "      <td>1435.000000</td>\n",
       "      <td>1435.000000</td>\n",
       "      <td>1435.000000</td>\n",
       "      <td>1435.000000</td>\n",
       "      <td>1435.000000</td>\n",
       "      <td>1435.000000</td>\n",
       "      <td>1435.000000</td>\n",
       "      <td>1435.000000</td>\n",
       "      <td>1435.000000</td>\n",
       "      <td>1435.000000</td>\n",
       "      <td>1435.000000</td>\n",
       "      <td>1435.000000</td>\n",
       "      <td>1435.000000</td>\n",
       "      <td>1435.000000</td>\n",
       "      <td>1435.000000</td>\n",
       "      <td>1435.000000</td>\n",
       "      <td>1435.000000</td>\n",
       "      <td>1435.000000</td>\n",
       "      <td>1435.000000</td>\n",
       "      <td>1435.000000</td>\n",
       "      <td>1435.000000</td>\n",
       "      <td>1435.000000</td>\n",
       "      <td>1435.000000</td>\n",
       "      <td>1435.000000</td>\n",
       "      <td>1435.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>26.958885</td>\n",
       "      <td>69.314286</td>\n",
       "      <td>65.090592</td>\n",
       "      <td>63.509408</td>\n",
       "      <td>69.225087</td>\n",
       "      <td>57.338676</td>\n",
       "      <td>59.091986</td>\n",
       "      <td>63.041812</td>\n",
       "      <td>52.471777</td>\n",
       "      <td>16.096167</td>\n",
       "      <td>16.383972</td>\n",
       "      <td>2.460627</td>\n",
       "      <td>1.671080</td>\n",
       "      <td>1.978397</td>\n",
       "      <td>3.312892</td>\n",
       "      <td>0.093380</td>\n",
       "      <td>4.733798</td>\n",
       "      <td>0.045296</td>\n",
       "      <td>0.068990</td>\n",
       "      <td>11.429268</td>\n",
       "      <td>0.051568</td>\n",
       "      <td>22.233449</td>\n",
       "      <td>2.363065</td>\n",
       "      <td>2.388153</td>\n",
       "      <td>1.684321</td>\n",
       "      <td>2.282230</td>\n",
       "      <td>3.399303</td>\n",
       "      <td>0.088502</td>\n",
       "      <td>4.737282</td>\n",
       "      <td>0.038328</td>\n",
       "      <td>0.055052</td>\n",
       "      <td>10.951220</td>\n",
       "      <td>0.073868</td>\n",
       "      <td>22.698258</td>\n",
       "      <td>2.421104</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>3.976445</td>\n",
       "      <td>13.939780</td>\n",
       "      <td>19.397951</td>\n",
       "      <td>14.151960</td>\n",
       "      <td>16.538790</td>\n",
       "      <td>22.404756</td>\n",
       "      <td>21.139700</td>\n",
       "      <td>14.653066</td>\n",
       "      <td>24.415962</td>\n",
       "      <td>18.655781</td>\n",
       "      <td>19.829775</td>\n",
       "      <td>4.280647</td>\n",
       "      <td>2.307097</td>\n",
       "      <td>3.523020</td>\n",
       "      <td>2.904874</td>\n",
       "      <td>0.311884</td>\n",
       "      <td>4.777352</td>\n",
       "      <td>0.277033</td>\n",
       "      <td>0.289483</td>\n",
       "      <td>17.554251</td>\n",
       "      <td>0.236466</td>\n",
       "      <td>10.331290</td>\n",
       "      <td>1.070263</td>\n",
       "      <td>4.182450</td>\n",
       "      <td>2.436358</td>\n",
       "      <td>3.972947</td>\n",
       "      <td>2.946785</td>\n",
       "      <td>0.300813</td>\n",
       "      <td>4.863116</td>\n",
       "      <td>0.254516</td>\n",
       "      <td>0.272714</td>\n",
       "      <td>16.495606</td>\n",
       "      <td>0.282164</td>\n",
       "      <td>10.327562</td>\n",
       "      <td>1.031030</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>18.000000</td>\n",
       "      <td>12.000000</td>\n",
       "      <td>6.000000</td>\n",
       "      <td>12.000000</td>\n",
       "      <td>10.000000</td>\n",
       "      <td>10.000000</td>\n",
       "      <td>4.000000</td>\n",
       "      <td>12.000000</td>\n",
       "      <td>5.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</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",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>-2.000000</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",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>-2.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>24.000000</td>\n",
       "      <td>66.000000</td>\n",
       "      <td>60.000000</td>\n",
       "      <td>58.000000</td>\n",
       "      <td>67.000000</td>\n",
       "      <td>36.000000</td>\n",
       "      <td>49.000000</td>\n",
       "      <td>55.000000</td>\n",
       "      <td>27.000000</td>\n",
       "      <td>8.000000</td>\n",
       "      <td>8.000000</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>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>14.000000</td>\n",
       "      <td>1.714286</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>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>15.000000</td>\n",
       "      <td>1.777778</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>27.000000</td>\n",
       "      <td>73.000000</td>\n",
       "      <td>71.000000</td>\n",
       "      <td>67.000000</td>\n",
       "      <td>74.000000</td>\n",
       "      <td>68.000000</td>\n",
       "      <td>66.000000</td>\n",
       "      <td>66.000000</td>\n",
       "      <td>63.000000</td>\n",
       "      <td>11.000000</td>\n",
       "      <td>11.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>24.000000</td>\n",
       "      <td>2.212121</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>25.000000</td>\n",
       "      <td>2.281250</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>30.000000</td>\n",
       "      <td>78.000000</td>\n",
       "      <td>77.000000</td>\n",
       "      <td>74.000000</td>\n",
       "      <td>79.000000</td>\n",
       "      <td>76.000000</td>\n",
       "      <td>75.000000</td>\n",
       "      <td>74.000000</td>\n",
       "      <td>74.000000</td>\n",
       "      <td>14.000000</td>\n",
       "      <td>14.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>5.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>7.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>22.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>31.000000</td>\n",
       "      <td>2.941176</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>4.000000</td>\n",
       "      <td>5.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>7.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>22.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>31.000000</td>\n",
       "      <td>2.932184</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>39.000000</td>\n",
       "      <td>92.000000</td>\n",
       "      <td>97.000000</td>\n",
       "      <td>93.000000</td>\n",
       "      <td>95.000000</td>\n",
       "      <td>92.000000</td>\n",
       "      <td>95.000000</td>\n",
       "      <td>92.000000</td>\n",
       "      <td>92.000000</td>\n",
       "      <td>91.000000</td>\n",
       "      <td>90.000000</td>\n",
       "      <td>37.000000</td>\n",
       "      <td>18.000000</td>\n",
       "      <td>18.000000</td>\n",
       "      <td>14.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>25.000000</td>\n",
       "      <td>4.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>82.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>38.000000</td>\n",
       "      <td>8.500000</td>\n",
       "      <td>40.000000</td>\n",
       "      <td>19.000000</td>\n",
       "      <td>24.000000</td>\n",
       "      <td>17.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>30.000000</td>\n",
       "      <td>4.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>67.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>60.000000</td>\n",
       "      <td>9.225806</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "               age  short_passing    dribbling  long_passing  ball_control  \\\n",
       "count  1435.000000    1435.000000  1435.000000   1435.000000   1435.000000   \n",
       "mean     26.958885      69.314286    65.090592     63.509408     69.225087   \n",
       "std       3.976445      13.939780    19.397951     14.151960     16.538790   \n",
       "min      18.000000      12.000000     6.000000     12.000000     10.000000   \n",
       "25%      24.000000      66.000000    60.000000     58.000000     67.000000   \n",
       "50%      27.000000      73.000000    71.000000     67.000000     74.000000   \n",
       "75%      30.000000      78.000000    77.000000     74.000000     79.000000   \n",
       "max      39.000000      92.000000    97.000000     93.000000     95.000000   \n",
       "\n",
       "       interceptions  positioning       vision      marking  gk_handling  \\\n",
       "count    1435.000000  1435.000000  1435.000000  1435.000000  1435.000000   \n",
       "mean       57.338676    59.091986    63.041812    52.471777    16.096167   \n",
       "std        22.404756    21.139700    14.653066    24.415962    18.655781   \n",
       "min        10.000000     4.000000    12.000000     5.000000     1.000000   \n",
       "25%        36.000000    49.000000    55.000000    27.000000     8.000000   \n",
       "50%        68.000000    66.000000    66.000000    63.000000    11.000000   \n",
       "75%        76.000000    75.000000    74.000000    74.000000    14.000000   \n",
       "max        92.000000    95.000000    92.000000    92.000000    91.000000   \n",
       "\n",
       "       gk_reflexes  Goals-16/17      A-16/17     CS-16/17  Yellow-16/17  \\\n",
       "count  1435.000000  1435.000000  1435.000000  1435.000000   1435.000000   \n",
       "mean     16.383972     2.460627     1.671080     1.978397      3.312892   \n",
       "std      19.829775     4.280647     2.307097     3.523020      2.904874   \n",
       "min       1.000000     0.000000     0.000000     0.000000      0.000000   \n",
       "25%       8.000000     0.000000     0.000000     0.000000      1.000000   \n",
       "50%      11.000000     1.000000     1.000000     0.000000      3.000000   \n",
       "75%      14.000000     3.000000     2.000000     3.000000      5.000000   \n",
       "max      90.000000    37.000000    18.000000    18.000000     14.000000   \n",
       "\n",
       "         Red-16/17    Off-16/17  Pen_SV-16/17  Pen_M-16/17  \\\n",
       "count  1435.000000  1435.000000   1435.000000  1435.000000   \n",
       "mean      0.093380     4.733798      0.045296     0.068990   \n",
       "std       0.311884     4.777352      0.277033     0.289483   \n",
       "min       0.000000     0.000000      0.000000     0.000000   \n",
       "25%       0.000000     1.000000      0.000000     0.000000   \n",
       "50%       0.000000     3.000000      0.000000     0.000000   \n",
       "75%       0.000000     7.000000      0.000000     0.000000   \n",
       "max       2.000000    25.000000      4.000000     3.000000   \n",
       "\n",
       "       Goals_conceded-16/17     OG-16/17   Apps-16/17   Form-16/17  \\\n",
       "count           1435.000000  1435.000000  1435.000000  1435.000000   \n",
       "mean              11.429268     0.051568    22.233449     2.363065   \n",
       "std               17.554251     0.236466    10.331290     1.070263   \n",
       "min                0.000000     0.000000     1.000000    -2.000000   \n",
       "25%                0.000000     0.000000    14.000000     1.714286   \n",
       "50%                0.000000     0.000000    24.000000     2.212121   \n",
       "75%               22.000000     0.000000    31.000000     2.941176   \n",
       "max               82.000000     2.000000    38.000000     8.500000   \n",
       "\n",
       "       Goals-15/16      A-15/16     CS-15/16  Yellow-15/16    Red-15/16  \\\n",
       "count  1435.000000  1435.000000  1435.000000   1435.000000  1435.000000   \n",
       "mean      2.388153     1.684321     2.282230      3.399303     0.088502   \n",
       "std       4.182450     2.436358     3.972947      2.946785     0.300813   \n",
       "min       0.000000     0.000000     0.000000      0.000000     0.000000   \n",
       "25%       0.000000     0.000000     0.000000      1.000000     0.000000   \n",
       "50%       1.000000     1.000000     0.000000      3.000000     0.000000   \n",
       "75%       3.000000     2.000000     4.000000      5.000000     0.000000   \n",
       "max      40.000000    19.000000    24.000000     17.000000     2.000000   \n",
       "\n",
       "         Off-15/16  Pen_SV-15/16  Pen_M-15/16  Goals_conceded-15/16  \\\n",
       "count  1435.000000   1435.000000  1435.000000           1435.000000   \n",
       "mean      4.737282      0.038328     0.055052             10.951220   \n",
       "std       4.863116      0.254516     0.272714             16.495606   \n",
       "min       0.000000      0.000000     0.000000              0.000000   \n",
       "25%       1.000000      0.000000     0.000000              0.000000   \n",
       "50%       3.000000      0.000000     0.000000              0.000000   \n",
       "75%       7.000000      0.000000     0.000000             22.000000   \n",
       "max      30.000000      4.000000     3.000000             67.000000   \n",
       "\n",
       "          OG-15/16   Apps-15/16   Form-15/16  \n",
       "count  1435.000000  1435.000000  1435.000000  \n",
       "mean      0.073868    22.698258     2.421104  \n",
       "std       0.282164    10.327562     1.031030  \n",
       "min       0.000000     1.000000    -2.000000  \n",
       "25%       0.000000    15.000000     1.777778  \n",
       "50%       0.000000    25.000000     2.281250  \n",
       "75%       0.000000    31.000000     2.932184  \n",
       "max       2.000000    60.000000     9.225806  "
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_train.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Players                 False\n",
       "club                    False\n",
       "age                     False\n",
       "Position                False\n",
       "short_passing           False\n",
       "dribbling               False\n",
       "long_passing            False\n",
       "ball_control            False\n",
       "interceptions           False\n",
       "positioning             False\n",
       "vision                  False\n",
       "marking                 False\n",
       "gk_handling             False\n",
       "gk_reflexes             False\n",
       "Goals-16/17             False\n",
       "A-16/17                 False\n",
       "CS-16/17                False\n",
       "Yellow-16/17            False\n",
       "Red-16/17               False\n",
       "Off-16/17               False\n",
       "Pen_SV-16/17            False\n",
       "Pen_M-16/17             False\n",
       "Goals_conceded-16/17    False\n",
       "OG-16/17                False\n",
       "Apps-16/17              False\n",
       "Form-16/17              False\n",
       "Goals-15/16             False\n",
       "A-15/16                 False\n",
       "CS-15/16                False\n",
       "Yellow-15/16            False\n",
       "Red-15/16               False\n",
       "Off-15/16               False\n",
       "Pen_SV-15/16            False\n",
       "Pen_M-15/16             False\n",
       "Goals_conceded-15/16    False\n",
       "OG-15/16                False\n",
       "Apps-15/16              False\n",
       "Form-15/16              False\n",
       "dtype: bool"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Checking for null values in feature data\n",
    "df_train.isnull().any()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "False"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Checking for null values in targeet data\n",
    "df_targets['overall'].isnull().any()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x7ffbd0b7f290>"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 792x648 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Checking feature correlation using correlation matrix\n",
    "df_train_num = df_train.select_dtypes(include = ['number'])\n",
    "df_train_corr = df_train_num.corr()\n",
    "mask = np.triu(np.ones_like(df_train_corr, dtype = np.bool))\n",
    "f,ax = plt.subplots(figsize = (11,9))\n",
    "sns.heatmap(df_train_corr, mask = mask, square = True, vmin = 0, vmax = 1, cmap = 'YlGnBu')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.7.6"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}