Value Prediction Football team.ipynb

{
 "cells": [
  {
   "cell_type": "code",
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   "id": "681769cf",
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "from sklearn.preprocessing import PowerTransformer, RobustScaler\n",
    "from sklearn.model_selection import train_test_split, cross_val_score, GridSearchCV\n",
    "from sklearn.svm import SVR\n",
    "from sklearn.metrics import mean_squared_error"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "b30b1056",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>id</th>\n",
       "      <th>Name</th>\n",
       "      <th>Aerial Duels won</th>\n",
       "      <th>Age</th>\n",
       "      <th>Assists</th>\n",
       "      <th>Attacking options created</th>\n",
       "      <th>Attempted Passes</th>\n",
       "      <th>Blocks</th>\n",
       "      <th>Clearances</th>\n",
       "      <th>Country</th>\n",
       "      <th>...</th>\n",
       "      <th>Progressive Passes</th>\n",
       "      <th>Progressive Carries</th>\n",
       "      <th>Shots</th>\n",
       "      <th>Successful Dribbles</th>\n",
       "      <th>Touches in attacking penalty area</th>\n",
       "      <th>Tackles</th>\n",
       "      <th>Value at beginning of 2020/21 season</th>\n",
       "      <th>Value at beginning of 2021/22 season</th>\n",
       "      <th>Value at beginning of 2022/23 season</th>\n",
       "      <th>Value at beginning of 2023/24 season</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>917</td>\n",
       "      <td>Lassine Sinayoko</td>\n",
       "      <td>1.3</td>\n",
       "      <td>23</td>\n",
       "      <td>0.11</td>\n",
       "      <td>3.68</td>\n",
       "      <td>31.8</td>\n",
       "      <td>0.69</td>\n",
       "      <td>0.97</td>\n",
       "      <td>Mali</td>\n",
       "      <td>...</td>\n",
       "      <td>4.24</td>\n",
       "      <td>2.81</td>\n",
       "      <td>2.41</td>\n",
       "      <td>1.61</td>\n",
       "      <td>4.36</td>\n",
       "      <td>1.89</td>\n",
       "      <td>0.2</td>\n",
       "      <td>0.8</td>\n",
       "      <td>1.3</td>\n",
       "      <td>1.2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>432</td>\n",
       "      <td>Emil Ceide</td>\n",
       "      <td>0.2</td>\n",
       "      <td>21</td>\n",
       "      <td>0.17</td>\n",
       "      <td>3.49</td>\n",
       "      <td>29.5</td>\n",
       "      <td>0.52</td>\n",
       "      <td>0.00</td>\n",
       "      <td>Norway</td>\n",
       "      <td>...</td>\n",
       "      <td>3.32</td>\n",
       "      <td>6.47</td>\n",
       "      <td>1.75</td>\n",
       "      <td>1.40</td>\n",
       "      <td>6.82</td>\n",
       "      <td>0.87</td>\n",
       "      <td>0.4</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>948</td>\n",
       "      <td>Linton Maina</td>\n",
       "      <td>0.0</td>\n",
       "      <td>24</td>\n",
       "      <td>0.28</td>\n",
       "      <td>2.99</td>\n",
       "      <td>27.5</td>\n",
       "      <td>0.76</td>\n",
       "      <td>0.36</td>\n",
       "      <td>Germany</td>\n",
       "      <td>...</td>\n",
       "      <td>2.45</td>\n",
       "      <td>3.62</td>\n",
       "      <td>1.41</td>\n",
       "      <td>0.88</td>\n",
       "      <td>3.98</td>\n",
       "      <td>0.72</td>\n",
       "      <td>7.5</td>\n",
       "      <td>2.5</td>\n",
       "      <td>1.8</td>\n",
       "      <td>6.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>989</td>\n",
       "      <td>Luis Javier Suarez</td>\n",
       "      <td>2.1</td>\n",
       "      <td>25</td>\n",
       "      <td>0.24</td>\n",
       "      <td>2.44</td>\n",
       "      <td>23.5</td>\n",
       "      <td>0.76</td>\n",
       "      <td>0.81</td>\n",
       "      <td>Colombia</td>\n",
       "      <td>...</td>\n",
       "      <td>1.80</td>\n",
       "      <td>1.66</td>\n",
       "      <td>2.89</td>\n",
       "      <td>1.09</td>\n",
       "      <td>4.08</td>\n",
       "      <td>1.04</td>\n",
       "      <td>10.0</td>\n",
       "      <td>10.0</td>\n",
       "      <td>9.0</td>\n",
       "      <td>8.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>887</td>\n",
       "      <td>Kim Min-jae</td>\n",
       "      <td>2.9</td>\n",
       "      <td>26</td>\n",
       "      <td>0.05</td>\n",
       "      <td>1.19</td>\n",
       "      <td>80.4</td>\n",
       "      <td>1.06</td>\n",
       "      <td>3.64</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>4.81</td>\n",
       "      <td>1.20</td>\n",
       "      <td>0.21</td>\n",
       "      <td>0.14</td>\n",
       "      <td>0.70</td>\n",
       "      <td>1.57</td>\n",
       "      <td>2.0</td>\n",
       "      <td>6.5</td>\n",
       "      <td>9.0</td>\n",
       "      <td>60.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>983</td>\n",
       "      <td>Lucas Vazquez</td>\n",
       "      <td>0.1</td>\n",
       "      <td>32</td>\n",
       "      <td>0.13</td>\n",
       "      <td>3.44</td>\n",
       "      <td>73.2</td>\n",
       "      <td>0.74</td>\n",
       "      <td>1.14</td>\n",
       "      <td>Spain</td>\n",
       "      <td>...</td>\n",
       "      <td>3.95</td>\n",
       "      <td>2.61</td>\n",
       "      <td>0.94</td>\n",
       "      <td>1.07</td>\n",
       "      <td>3.41</td>\n",
       "      <td>2.54</td>\n",
       "      <td>10.0</td>\n",
       "      <td>12.0</td>\n",
       "      <td>12.0</td>\n",
       "      <td>8.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>1531</td>\n",
       "      <td>Stefan Bajcetic</td>\n",
       "      <td>1.3</td>\n",
       "      <td>18</td>\n",
       "      <td>0.00</td>\n",
       "      <td>2.08</td>\n",
       "      <td>47.3</td>\n",
       "      <td>0.99</td>\n",
       "      <td>0.57</td>\n",
       "      <td>Spain</td>\n",
       "      <td>...</td>\n",
       "      <td>4.25</td>\n",
       "      <td>1.42</td>\n",
       "      <td>1.13</td>\n",
       "      <td>1.13</td>\n",
       "      <td>0.71</td>\n",
       "      <td>2.13</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.0</td>\n",
       "      <td>13.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>1125</td>\n",
       "      <td>Mehdi Bourabia</td>\n",
       "      <td>0.7</td>\n",
       "      <td>32</td>\n",
       "      <td>0.10</td>\n",
       "      <td>2.73</td>\n",
       "      <td>41.6</td>\n",
       "      <td>1.27</td>\n",
       "      <td>1.85</td>\n",
       "      <td>Morocco</td>\n",
       "      <td>...</td>\n",
       "      <td>3.67</td>\n",
       "      <td>1.20</td>\n",
       "      <td>1.07</td>\n",
       "      <td>0.91</td>\n",
       "      <td>0.68</td>\n",
       "      <td>1.33</td>\n",
       "      <td>3.0</td>\n",
       "      <td>1.6</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>277</td>\n",
       "      <td>Christian Norgaard</td>\n",
       "      <td>0.9</td>\n",
       "      <td>29</td>\n",
       "      <td>0.15</td>\n",
       "      <td>1.71</td>\n",
       "      <td>41.5</td>\n",
       "      <td>1.63</td>\n",
       "      <td>2.09</td>\n",
       "      <td>Denmark</td>\n",
       "      <td>...</td>\n",
       "      <td>3.73</td>\n",
       "      <td>0.20</td>\n",
       "      <td>0.97</td>\n",
       "      <td>0.41</td>\n",
       "      <td>2.14</td>\n",
       "      <td>2.65</td>\n",
       "      <td>4.0</td>\n",
       "      <td>8.0</td>\n",
       "      <td>10.0</td>\n",
       "      <td>16.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>1318</td>\n",
       "      <td>Pedri</td>\n",
       "      <td>0.2</td>\n",
       "      <td>20</td>\n",
       "      <td>0.04</td>\n",
       "      <td>4.22</td>\n",
       "      <td>65.4</td>\n",
       "      <td>1.27</td>\n",
       "      <td>0.34</td>\n",
       "      <td>Spain</td>\n",
       "      <td>...</td>\n",
       "      <td>8.15</td>\n",
       "      <td>3.09</td>\n",
       "      <td>1.08</td>\n",
       "      <td>1.64</td>\n",
       "      <td>2.64</td>\n",
       "      <td>1.71</td>\n",
       "      <td>30.0</td>\n",
       "      <td>80.0</td>\n",
       "      <td>80.0</td>\n",
       "      <td>100.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>10 rows × 26 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     id                Name  Aerial Duels won  Age  Assists  \\\n",
       "0   917    Lassine Sinayoko               1.3   23     0.11   \n",
       "1   432          Emil Ceide               0.2   21     0.17   \n",
       "2   948        Linton Maina               0.0   24     0.28   \n",
       "3   989  Luis Javier Suarez               2.1   25     0.24   \n",
       "4   887         Kim Min-jae               2.9   26     0.05   \n",
       "5   983       Lucas Vazquez               0.1   32     0.13   \n",
       "6  1531     Stefan Bajcetic               1.3   18     0.00   \n",
       "7  1125      Mehdi Bourabia               0.7   32     0.10   \n",
       "8   277  Christian Norgaard               0.9   29     0.15   \n",
       "9  1318               Pedri               0.2   20     0.04   \n",
       "\n",
       "   Attacking options created  Attempted Passes  Blocks  Clearances   Country  \\\n",
       "0                       3.68              31.8    0.69        0.97      Mali   \n",
       "1                       3.49              29.5    0.52        0.00    Norway   \n",
       "2                       2.99              27.5    0.76        0.36   Germany   \n",
       "3                       2.44              23.5    0.76        0.81  Colombia   \n",
       "4                       1.19              80.4    1.06        3.64       NaN   \n",
       "5                       3.44              73.2    0.74        1.14     Spain   \n",
       "6                       2.08              47.3    0.99        0.57     Spain   \n",
       "7                       2.73              41.6    1.27        1.85   Morocco   \n",
       "8                       1.71              41.5    1.63        2.09   Denmark   \n",
       "9                       4.22              65.4    1.27        0.34     Spain   \n",
       "\n",
       "   ...  Progressive Passes  Progressive Carries  Shots  Successful Dribbles  \\\n",
       "0  ...                4.24                 2.81   2.41                 1.61   \n",
       "1  ...                3.32                 6.47   1.75                 1.40   \n",
       "2  ...                2.45                 3.62   1.41                 0.88   \n",
       "3  ...                1.80                 1.66   2.89                 1.09   \n",
       "4  ...                4.81                 1.20   0.21                 0.14   \n",
       "5  ...                3.95                 2.61   0.94                 1.07   \n",
       "6  ...                4.25                 1.42   1.13                 1.13   \n",
       "7  ...                3.67                 1.20   1.07                 0.91   \n",
       "8  ...                3.73                 0.20   0.97                 0.41   \n",
       "9  ...                8.15                 3.09   1.08                 1.64   \n",
       "\n",
       "   Touches in attacking penalty area  Tackles  \\\n",
       "0                               4.36     1.89   \n",
       "1                               6.82     0.87   \n",
       "2                               3.98     0.72   \n",
       "3                               4.08     1.04   \n",
       "4                               0.70     1.57   \n",
       "5                               3.41     2.54   \n",
       "6                               0.71     2.13   \n",
       "7                               0.68     1.33   \n",
       "8                               2.14     2.65   \n",
       "9                               2.64     1.71   \n",
       "\n",
       "   Value at beginning of 2020/21 season  Value at beginning of 2021/22 season  \\\n",
       "0                                   0.2                                   0.8   \n",
       "1                                   0.4                                   NaN   \n",
       "2                                   7.5                                   2.5   \n",
       "3                                  10.0                                  10.0   \n",
       "4                                   2.0                                   6.5   \n",
       "5                                  10.0                                  12.0   \n",
       "6                                   NaN                                   NaN   \n",
       "7                                   3.0                                   1.6   \n",
       "8                                   4.0                                   8.0   \n",
       "9                                  30.0                                  80.0   \n",
       "\n",
       "   Value at beginning of 2022/23 season  Value at beginning of 2023/24 season  \n",
       "0                                   1.3                                   1.2  \n",
       "1                                   1.0                                   2.5  \n",
       "2                                   1.8                                   6.0  \n",
       "3                                   9.0                                   8.0  \n",
       "4                                   9.0                                  60.0  \n",
       "5                                  12.0                                   8.0  \n",
       "6                                   1.0                                  13.0  \n",
       "7                                   1.0                                   1.3  \n",
       "8                                  10.0                                  16.0  \n",
       "9                                  80.0                                 100.0  \n",
       "\n",
       "[10 rows x 26 columns]"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train_data = pd.read_csv('train_value_prediction.csv')\n",
    "train_data.head(10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "acc58c32",
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   "outputs": [
    {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>id</th>\n",
       "      <th>Aerial Duels won</th>\n",
       "      <th>Age</th>\n",
       "      <th>Assists</th>\n",
       "      <th>Attacking options created</th>\n",
       "      <th>Attempted Passes</th>\n",
       "      <th>Blocks</th>\n",
       "      <th>Clearances</th>\n",
       "      <th>Country</th>\n",
       "      <th>Expected Goal Contributions</th>\n",
       "      <th>...</th>\n",
       "      <th>Progressive Passes Rec</th>\n",
       "      <th>Progressive Passes</th>\n",
       "      <th>Progressive Carries</th>\n",
       "      <th>Shots</th>\n",
       "      <th>Successful Dribbles</th>\n",
       "      <th>Touches in attacking penalty area</th>\n",
       "      <th>Tackles</th>\n",
       "      <th>Value at beginning of 2020/21 season</th>\n",
       "      <th>Value at beginning of 2021/22 season</th>\n",
       "      <th>Value at beginning of 2022/23 season</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>705</td>\n",
       "      <td>2.6</td>\n",
       "      <td>28</td>\n",
       "      <td>0.03</td>\n",
       "      <td>0.95</td>\n",
       "      <td>47.2</td>\n",
       "      <td>1.13</td>\n",
       "      <td>3.44</td>\n",
       "      <td>Cameroon</td>\n",
       "      <td>0.01</td>\n",
       "      <td>...</td>\n",
       "      <td>0.69</td>\n",
       "      <td>4.04</td>\n",
       "      <td>0.58</td>\n",
       "      <td>0.35</td>\n",
       "      <td>0.32</td>\n",
       "      <td>0.95</td>\n",
       "      <td>1.53</td>\n",
       "      <td>2.5</td>\n",
       "      <td>3.0</td>\n",
       "      <td>4.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>809</td>\n",
       "      <td>0.3</td>\n",
       "      <td>28</td>\n",
       "      <td>0.10</td>\n",
       "      <td>3.22</td>\n",
       "      <td>50.5</td>\n",
       "      <td>0.79</td>\n",
       "      <td>1.73</td>\n",
       "      <td>Croatia</td>\n",
       "      <td>0.18</td>\n",
       "      <td>...</td>\n",
       "      <td>3.62</td>\n",
       "      <td>3.96</td>\n",
       "      <td>1.78</td>\n",
       "      <td>0.69</td>\n",
       "      <td>0.74</td>\n",
       "      <td>0.84</td>\n",
       "      <td>1.34</td>\n",
       "      <td>1.7</td>\n",
       "      <td>4.0</td>\n",
       "      <td>7.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1432</td>\n",
       "      <td>0.7</td>\n",
       "      <td>26</td>\n",
       "      <td>0.03</td>\n",
       "      <td>2.34</td>\n",
       "      <td>47.5</td>\n",
       "      <td>1.08</td>\n",
       "      <td>1.56</td>\n",
       "      <td>Mali</td>\n",
       "      <td>0.08</td>\n",
       "      <td>...</td>\n",
       "      <td>1.22</td>\n",
       "      <td>5.72</td>\n",
       "      <td>2.00</td>\n",
       "      <td>0.81</td>\n",
       "      <td>1.69</td>\n",
       "      <td>0.64</td>\n",
       "      <td>1.49</td>\n",
       "      <td>1.2</td>\n",
       "      <td>3.0</td>\n",
       "      <td>3.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>173</td>\n",
       "      <td>1.6</td>\n",
       "      <td>26</td>\n",
       "      <td>0.12</td>\n",
       "      <td>1.98</td>\n",
       "      <td>50.2</td>\n",
       "      <td>1.40</td>\n",
       "      <td>2.32</td>\n",
       "      <td>Portugal</td>\n",
       "      <td>0.22</td>\n",
       "      <td>...</td>\n",
       "      <td>7.50</td>\n",
       "      <td>3.11</td>\n",
       "      <td>3.05</td>\n",
       "      <td>0.55</td>\n",
       "      <td>0.61</td>\n",
       "      <td>2.87</td>\n",
       "      <td>2.32</td>\n",
       "      <td>4.8</td>\n",
       "      <td>3.5</td>\n",
       "      <td>3.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>513</td>\n",
       "      <td>0.4</td>\n",
       "      <td>30</td>\n",
       "      <td>0.38</td>\n",
       "      <td>5.12</td>\n",
       "      <td>46.3</td>\n",
       "      <td>1.04</td>\n",
       "      <td>1.15</td>\n",
       "      <td>Austria</td>\n",
       "      <td>0.35</td>\n",
       "      <td>...</td>\n",
       "      <td>5.64</td>\n",
       "      <td>6.10</td>\n",
       "      <td>2.46</td>\n",
       "      <td>1.96</td>\n",
       "      <td>1.27</td>\n",
       "      <td>1.73</td>\n",
       "      <td>0.81</td>\n",
       "      <td>4.0</td>\n",
       "      <td>3.5</td>\n",
       "      <td>3.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>1649</td>\n",
       "      <td>0.3</td>\n",
       "      <td>17</td>\n",
       "      <td>0.00</td>\n",
       "      <td>2.15</td>\n",
       "      <td>58.9</td>\n",
       "      <td>0.98</td>\n",
       "      <td>1.51</td>\n",
       "      <td>France</td>\n",
       "      <td>0.05</td>\n",
       "      <td>...</td>\n",
       "      <td>5.42</td>\n",
       "      <td>3.11</td>\n",
       "      <td>2.22</td>\n",
       "      <td>0.80</td>\n",
       "      <td>1.60</td>\n",
       "      <td>1.96</td>\n",
       "      <td>1.69</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>5.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>818</td>\n",
       "      <td>1.2</td>\n",
       "      <td>23</td>\n",
       "      <td>0.22</td>\n",
       "      <td>2.30</td>\n",
       "      <td>52.2</td>\n",
       "      <td>1.16</td>\n",
       "      <td>3.36</td>\n",
       "      <td>Spain</td>\n",
       "      <td>0.18</td>\n",
       "      <td>...</td>\n",
       "      <td>5.64</td>\n",
       "      <td>4.03</td>\n",
       "      <td>1.88</td>\n",
       "      <td>1.12</td>\n",
       "      <td>0.58</td>\n",
       "      <td>2.06</td>\n",
       "      <td>1.52</td>\n",
       "      <td>4.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>5.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>261</td>\n",
       "      <td>1.7</td>\n",
       "      <td>26</td>\n",
       "      <td>0.00</td>\n",
       "      <td>1.97</td>\n",
       "      <td>35.5</td>\n",
       "      <td>1.34</td>\n",
       "      <td>1.30</td>\n",
       "      <td>Switzerland</td>\n",
       "      <td>0.04</td>\n",
       "      <td>...</td>\n",
       "      <td>2.21</td>\n",
       "      <td>3.77</td>\n",
       "      <td>0.65</td>\n",
       "      <td>0.83</td>\n",
       "      <td>0.15</td>\n",
       "      <td>1.59</td>\n",
       "      <td>1.99</td>\n",
       "      <td>0.7</td>\n",
       "      <td>2.0</td>\n",
       "      <td>2.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>405</td>\n",
       "      <td>2.0</td>\n",
       "      <td>25</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.68</td>\n",
       "      <td>61.8</td>\n",
       "      <td>2.03</td>\n",
       "      <td>3.68</td>\n",
       "      <td>Brazil</td>\n",
       "      <td>0.02</td>\n",
       "      <td>...</td>\n",
       "      <td>0.50</td>\n",
       "      <td>3.00</td>\n",
       "      <td>0.91</td>\n",
       "      <td>0.68</td>\n",
       "      <td>0.37</td>\n",
       "      <td>0.99</td>\n",
       "      <td>1.46</td>\n",
       "      <td>40.0</td>\n",
       "      <td>60.0</td>\n",
       "      <td>60.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>1399</td>\n",
       "      <td>1.9</td>\n",
       "      <td>28</td>\n",
       "      <td>0.05</td>\n",
       "      <td>1.65</td>\n",
       "      <td>53.5</td>\n",
       "      <td>1.28</td>\n",
       "      <td>1.87</td>\n",
       "      <td>Germany</td>\n",
       "      <td>0.05</td>\n",
       "      <td>...</td>\n",
       "      <td>0.29</td>\n",
       "      <td>3.52</td>\n",
       "      <td>0.59</td>\n",
       "      <td>1.07</td>\n",
       "      <td>0.24</td>\n",
       "      <td>0.77</td>\n",
       "      <td>2.21</td>\n",
       "      <td>4.0</td>\n",
       "      <td>11.0</td>\n",
       "      <td>13.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>10 rows × 24 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     id  Aerial Duels won  Age  Assists  Attacking options created  \\\n",
       "0   705               2.6   28     0.03                       0.95   \n",
       "1   809               0.3   28     0.10                       3.22   \n",
       "2  1432               0.7   26     0.03                       2.34   \n",
       "3   173               1.6   26     0.12                       1.98   \n",
       "4   513               0.4   30     0.38                       5.12   \n",
       "5  1649               0.3   17     0.00                       2.15   \n",
       "6   818               1.2   23     0.22                       2.30   \n",
       "7   261               1.7   26     0.00                       1.97   \n",
       "8   405               2.0   25     0.00                       0.68   \n",
       "9  1399               1.9   28     0.05                       1.65   \n",
       "\n",
       "   Attempted Passes  Blocks  Clearances      Country  \\\n",
       "0              47.2    1.13        3.44     Cameroon   \n",
       "1              50.5    0.79        1.73      Croatia   \n",
       "2              47.5    1.08        1.56         Mali   \n",
       "3              50.2    1.40        2.32     Portugal   \n",
       "4              46.3    1.04        1.15      Austria   \n",
       "5              58.9    0.98        1.51       France   \n",
       "6              52.2    1.16        3.36        Spain   \n",
       "7              35.5    1.34        1.30  Switzerland   \n",
       "8              61.8    2.03        3.68       Brazil   \n",
       "9              53.5    1.28        1.87      Germany   \n",
       "\n",
       "   Expected Goal Contributions  ...  Progressive Passes Rec  \\\n",
       "0                         0.01  ...                    0.69   \n",
       "1                         0.18  ...                    3.62   \n",
       "2                         0.08  ...                    1.22   \n",
       "3                         0.22  ...                    7.50   \n",
       "4                         0.35  ...                    5.64   \n",
       "5                         0.05  ...                    5.42   \n",
       "6                         0.18  ...                    5.64   \n",
       "7                         0.04  ...                    2.21   \n",
       "8                         0.02  ...                    0.50   \n",
       "9                         0.05  ...                    0.29   \n",
       "\n",
       "   Progressive Passes  Progressive Carries  Shots  Successful Dribbles  \\\n",
       "0                4.04                 0.58   0.35                 0.32   \n",
       "1                3.96                 1.78   0.69                 0.74   \n",
       "2                5.72                 2.00   0.81                 1.69   \n",
       "3                3.11                 3.05   0.55                 0.61   \n",
       "4                6.10                 2.46   1.96                 1.27   \n",
       "5                3.11                 2.22   0.80                 1.60   \n",
       "6                4.03                 1.88   1.12                 0.58   \n",
       "7                3.77                 0.65   0.83                 0.15   \n",
       "8                3.00                 0.91   0.68                 0.37   \n",
       "9                3.52                 0.59   1.07                 0.24   \n",
       "\n",
       "   Touches in attacking penalty area  Tackles  \\\n",
       "0                               0.95     1.53   \n",
       "1                               0.84     1.34   \n",
       "2                               0.64     1.49   \n",
       "3                               2.87     2.32   \n",
       "4                               1.73     0.81   \n",
       "5                               1.96     1.69   \n",
       "6                               2.06     1.52   \n",
       "7                               1.59     1.99   \n",
       "8                               0.99     1.46   \n",
       "9                               0.77     2.21   \n",
       "\n",
       "   Value at beginning of 2020/21 season  Value at beginning of 2021/22 season  \\\n",
       "0                                   2.5                                   3.0   \n",
       "1                                   1.7                                   4.0   \n",
       "2                                   1.2                                   3.0   \n",
       "3                                   4.8                                   3.5   \n",
       "4                                   4.0                                   3.5   \n",
       "5                                   NaN                                   NaN   \n",
       "6                                   4.0                                   6.0   \n",
       "7                                   0.7                                   2.0   \n",
       "8                                  40.0                                  60.0   \n",
       "9                                   4.0                                  11.0   \n",
       "\n",
       "   Value at beginning of 2022/23 season  \n",
       "0                                   4.0  \n",
       "1                                   7.0  \n",
       "2                                   3.5  \n",
       "3                                   3.0  \n",
       "4                                   3.5  \n",
       "5                                   5.0  \n",
       "6                                   5.0  \n",
       "7                                   2.5  \n",
       "8                                  60.0  \n",
       "9                                  13.0  \n",
       "\n",
       "[10 rows x 24 columns]"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "test_data = pd.read_csv('test_value_prediction.csv')\n",
    "test_data.head(10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "0a48f40a",
   "metadata": {},
   "outputs": [],
   "source": [
    "#resolving duplicates and filling empty values\n",
    "train_data.fillna(train_data.mean(numeric_only=True), inplace=True)\n",
    "train_data.drop_duplicates(inplace=True) \n",
    "\n",
    "test_data.fillna(test_data.mean(numeric_only=True), inplace=True)\n",
    "test_data.drop_duplicates(inplace=True) "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "89688d7b",
   "metadata": {},
   "outputs": [],
   "source": [
    "X_train = train_data.drop(['Name','Country','Value at beginning of 2023/24 season'],axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "484f9b0d",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>id</th>\n",
       "      <th>Aerial Duels won</th>\n",
       "      <th>Age</th>\n",
       "      <th>Assists</th>\n",
       "      <th>Attacking options created</th>\n",
       "      <th>Attempted Passes</th>\n",
       "      <th>Blocks</th>\n",
       "      <th>Clearances</th>\n",
       "      <th>Expected Goal Contributions</th>\n",
       "      <th>Interceptions</th>\n",
       "      <th>...</th>\n",
       "      <th>Progressive Passes Rec</th>\n",
       "      <th>Progressive Passes</th>\n",
       "      <th>Progressive Carries</th>\n",
       "      <th>Shots</th>\n",
       "      <th>Successful Dribbles</th>\n",
       "      <th>Touches in attacking penalty area</th>\n",
       "      <th>Tackles</th>\n",
       "      <th>Value at beginning of 2020/21 season</th>\n",
       "      <th>Value at beginning of 2021/22 season</th>\n",
       "      <th>Value at beginning of 2022/23 season</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>917</td>\n",
       "      <td>1.3</td>\n",
       "      <td>23</td>\n",
       "      <td>0.11</td>\n",
       "      <td>3.68</td>\n",
       "      <td>31.8</td>\n",
       "      <td>0.69</td>\n",
       "      <td>0.97</td>\n",
       "      <td>0.16</td>\n",
       "      <td>1.1</td>\n",
       "      <td>...</td>\n",
       "      <td>8.60</td>\n",
       "      <td>4.24</td>\n",
       "      <td>2.81</td>\n",
       "      <td>2.41</td>\n",
       "      <td>1.61</td>\n",
       "      <td>4.36</td>\n",
       "      <td>1.89</td>\n",
       "      <td>0.2</td>\n",
       "      <td>0.800000</td>\n",
       "      <td>1.3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>432</td>\n",
       "      <td>0.2</td>\n",
       "      <td>21</td>\n",
       "      <td>0.17</td>\n",
       "      <td>3.49</td>\n",
       "      <td>29.5</td>\n",
       "      <td>0.52</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.25</td>\n",
       "      <td>0.2</td>\n",
       "      <td>...</td>\n",
       "      <td>8.21</td>\n",
       "      <td>3.32</td>\n",
       "      <td>6.47</td>\n",
       "      <td>1.75</td>\n",
       "      <td>1.40</td>\n",
       "      <td>6.82</td>\n",
       "      <td>0.87</td>\n",
       "      <td>0.4</td>\n",
       "      <td>12.593151</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>948</td>\n",
       "      <td>0.0</td>\n",
       "      <td>24</td>\n",
       "      <td>0.28</td>\n",
       "      <td>2.99</td>\n",
       "      <td>27.5</td>\n",
       "      <td>0.76</td>\n",
       "      <td>0.36</td>\n",
       "      <td>0.26</td>\n",
       "      <td>0.2</td>\n",
       "      <td>...</td>\n",
       "      <td>6.95</td>\n",
       "      <td>2.45</td>\n",
       "      <td>3.62</td>\n",
       "      <td>1.41</td>\n",
       "      <td>0.88</td>\n",
       "      <td>3.98</td>\n",
       "      <td>0.72</td>\n",
       "      <td>7.5</td>\n",
       "      <td>2.500000</td>\n",
       "      <td>1.8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>989</td>\n",
       "      <td>2.1</td>\n",
       "      <td>25</td>\n",
       "      <td>0.24</td>\n",
       "      <td>2.44</td>\n",
       "      <td>23.5</td>\n",
       "      <td>0.76</td>\n",
       "      <td>0.81</td>\n",
       "      <td>0.14</td>\n",
       "      <td>0.3</td>\n",
       "      <td>...</td>\n",
       "      <td>5.46</td>\n",
       "      <td>1.80</td>\n",
       "      <td>1.66</td>\n",
       "      <td>2.89</td>\n",
       "      <td>1.09</td>\n",
       "      <td>4.08</td>\n",
       "      <td>1.04</td>\n",
       "      <td>10.0</td>\n",
       "      <td>10.000000</td>\n",
       "      <td>9.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>887</td>\n",
       "      <td>2.9</td>\n",
       "      <td>26</td>\n",
       "      <td>0.05</td>\n",
       "      <td>1.19</td>\n",
       "      <td>80.4</td>\n",
       "      <td>1.06</td>\n",
       "      <td>3.64</td>\n",
       "      <td>0.04</td>\n",
       "      <td>1.5</td>\n",
       "      <td>...</td>\n",
       "      <td>0.19</td>\n",
       "      <td>4.81</td>\n",
       "      <td>1.20</td>\n",
       "      <td>0.21</td>\n",
       "      <td>0.14</td>\n",
       "      <td>0.70</td>\n",
       "      <td>1.57</td>\n",
       "      <td>2.0</td>\n",
       "      <td>6.500000</td>\n",
       "      <td>9.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1288</th>\n",
       "      <td>1130</td>\n",
       "      <td>0.5</td>\n",
       "      <td>28</td>\n",
       "      <td>0.26</td>\n",
       "      <td>3.41</td>\n",
       "      <td>59.8</td>\n",
       "      <td>0.73</td>\n",
       "      <td>1.62</td>\n",
       "      <td>0.20</td>\n",
       "      <td>0.5</td>\n",
       "      <td>...</td>\n",
       "      <td>4.24</td>\n",
       "      <td>6.54</td>\n",
       "      <td>1.57</td>\n",
       "      <td>1.20</td>\n",
       "      <td>0.63</td>\n",
       "      <td>2.04</td>\n",
       "      <td>1.15</td>\n",
       "      <td>5.0</td>\n",
       "      <td>4.500000</td>\n",
       "      <td>5.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1289</th>\n",
       "      <td>1294</td>\n",
       "      <td>0.3</td>\n",
       "      <td>31</td>\n",
       "      <td>0.00</td>\n",
       "      <td>4.01</td>\n",
       "      <td>42.4</td>\n",
       "      <td>0.81</td>\n",
       "      <td>0.29</td>\n",
       "      <td>0.17</td>\n",
       "      <td>0.3</td>\n",
       "      <td>...</td>\n",
       "      <td>6.97</td>\n",
       "      <td>4.33</td>\n",
       "      <td>1.98</td>\n",
       "      <td>2.13</td>\n",
       "      <td>0.29</td>\n",
       "      <td>2.94</td>\n",
       "      <td>1.32</td>\n",
       "      <td>25.0</td>\n",
       "      <td>25.000000</td>\n",
       "      <td>25.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1290</th>\n",
       "      <td>860</td>\n",
       "      <td>0.7</td>\n",
       "      <td>24</td>\n",
       "      <td>0.04</td>\n",
       "      <td>2.61</td>\n",
       "      <td>31.0</td>\n",
       "      <td>1.65</td>\n",
       "      <td>0.25</td>\n",
       "      <td>0.14</td>\n",
       "      <td>0.4</td>\n",
       "      <td>...</td>\n",
       "      <td>4.22</td>\n",
       "      <td>4.65</td>\n",
       "      <td>3.42</td>\n",
       "      <td>1.18</td>\n",
       "      <td>2.11</td>\n",
       "      <td>2.28</td>\n",
       "      <td>3.00</td>\n",
       "      <td>3.5</td>\n",
       "      <td>3.300000</td>\n",
       "      <td>3.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1291</th>\n",
       "      <td>1459</td>\n",
       "      <td>0.4</td>\n",
       "      <td>23</td>\n",
       "      <td>0.03</td>\n",
       "      <td>1.63</td>\n",
       "      <td>61.6</td>\n",
       "      <td>1.18</td>\n",
       "      <td>0.81</td>\n",
       "      <td>0.05</td>\n",
       "      <td>1.2</td>\n",
       "      <td>...</td>\n",
       "      <td>1.37</td>\n",
       "      <td>5.13</td>\n",
       "      <td>1.03</td>\n",
       "      <td>0.40</td>\n",
       "      <td>0.90</td>\n",
       "      <td>0.50</td>\n",
       "      <td>1.43</td>\n",
       "      <td>0.3</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>2.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1292</th>\n",
       "      <td>1126</td>\n",
       "      <td>0.4</td>\n",
       "      <td>31</td>\n",
       "      <td>0.14</td>\n",
       "      <td>2.15</td>\n",
       "      <td>37.3</td>\n",
       "      <td>1.91</td>\n",
       "      <td>1.64</td>\n",
       "      <td>0.11</td>\n",
       "      <td>3.1</td>\n",
       "      <td>...</td>\n",
       "      <td>4.24</td>\n",
       "      <td>2.46</td>\n",
       "      <td>1.23</td>\n",
       "      <td>1.09</td>\n",
       "      <td>0.55</td>\n",
       "      <td>1.50</td>\n",
       "      <td>1.64</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>1293 rows × 23 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "        id  Aerial Duels won  Age  Assists  Attacking options created  \\\n",
       "0      917               1.3   23     0.11                       3.68   \n",
       "1      432               0.2   21     0.17                       3.49   \n",
       "2      948               0.0   24     0.28                       2.99   \n",
       "3      989               2.1   25     0.24                       2.44   \n",
       "4      887               2.9   26     0.05                       1.19   \n",
       "...    ...               ...  ...      ...                        ...   \n",
       "1288  1130               0.5   28     0.26                       3.41   \n",
       "1289  1294               0.3   31     0.00                       4.01   \n",
       "1290   860               0.7   24     0.04                       2.61   \n",
       "1291  1459               0.4   23     0.03                       1.63   \n",
       "1292  1126               0.4   31     0.14                       2.15   \n",
       "\n",
       "      Attempted Passes  Blocks  Clearances  Expected Goal Contributions  \\\n",
       "0                 31.8    0.69        0.97                         0.16   \n",
       "1                 29.5    0.52        0.00                         0.25   \n",
       "2                 27.5    0.76        0.36                         0.26   \n",
       "3                 23.5    0.76        0.81                         0.14   \n",
       "4                 80.4    1.06        3.64                         0.04   \n",
       "...                ...     ...         ...                          ...   \n",
       "1288              59.8    0.73        1.62                         0.20   \n",
       "1289              42.4    0.81        0.29                         0.17   \n",
       "1290              31.0    1.65        0.25                         0.14   \n",
       "1291              61.6    1.18        0.81                         0.05   \n",
       "1292              37.3    1.91        1.64                         0.11   \n",
       "\n",
       "      Interceptions  ...  Progressive Passes Rec  Progressive Passes  \\\n",
       "0               1.1  ...                    8.60                4.24   \n",
       "1               0.2  ...                    8.21                3.32   \n",
       "2               0.2  ...                    6.95                2.45   \n",
       "3               0.3  ...                    5.46                1.80   \n",
       "4               1.5  ...                    0.19                4.81   \n",
       "...             ...  ...                     ...                 ...   \n",
       "1288            0.5  ...                    4.24                6.54   \n",
       "1289            0.3  ...                    6.97                4.33   \n",
       "1290            0.4  ...                    4.22                4.65   \n",
       "1291            1.2  ...                    1.37                5.13   \n",
       "1292            3.1  ...                    4.24                2.46   \n",
       "\n",
       "      Progressive Carries  Shots  Successful Dribbles  \\\n",
       "0                    2.81   2.41                 1.61   \n",
       "1                    6.47   1.75                 1.40   \n",
       "2                    3.62   1.41                 0.88   \n",
       "3                    1.66   2.89                 1.09   \n",
       "4                    1.20   0.21                 0.14   \n",
       "...                   ...    ...                  ...   \n",
       "1288                 1.57   1.20                 0.63   \n",
       "1289                 1.98   2.13                 0.29   \n",
       "1290                 3.42   1.18                 2.11   \n",
       "1291                 1.03   0.40                 0.90   \n",
       "1292                 1.23   1.09                 0.55   \n",
       "\n",
       "      Touches in attacking penalty area  Tackles  \\\n",
       "0                                  4.36     1.89   \n",
       "1                                  6.82     0.87   \n",
       "2                                  3.98     0.72   \n",
       "3                                  4.08     1.04   \n",
       "4                                  0.70     1.57   \n",
       "...                                 ...      ...   \n",
       "1288                               2.04     1.15   \n",
       "1289                               2.94     1.32   \n",
       "1290                               2.28     3.00   \n",
       "1291                               0.50     1.43   \n",
       "1292                               1.50     1.64   \n",
       "\n",
       "      Value at beginning of 2020/21 season  \\\n",
       "0                                      0.2   \n",
       "1                                      0.4   \n",
       "2                                      7.5   \n",
       "3                                     10.0   \n",
       "4                                      2.0   \n",
       "...                                    ...   \n",
       "1288                                   5.0   \n",
       "1289                                  25.0   \n",
       "1290                                   3.5   \n",
       "1291                                   0.3   \n",
       "1292                                   1.0   \n",
       "\n",
       "      Value at beginning of 2021/22 season  \\\n",
       "0                                 0.800000   \n",
       "1                                12.593151   \n",
       "2                                 2.500000   \n",
       "3                                10.000000   \n",
       "4                                 6.500000   \n",
       "...                                    ...   \n",
       "1288                              4.500000   \n",
       "1289                             25.000000   \n",
       "1290                              3.300000   \n",
       "1291                              2.000000   \n",
       "1292                              1.000000   \n",
       "\n",
       "      Value at beginning of 2022/23 season  \n",
       "0                                      1.3  \n",
       "1                                      1.0  \n",
       "2                                      1.8  \n",
       "3                                      9.0  \n",
       "4                                      9.0  \n",
       "...                                    ...  \n",
       "1288                                   5.5  \n",
       "1289                                  25.0  \n",
       "1290                                   3.5  \n",
       "1291                                   2.5  \n",
       "1292                                   1.0  \n",
       "\n",
       "[1293 rows x 23 columns]"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X_train"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "eb66d6e2",
   "metadata": {},
   "outputs": [],
   "source": [
    "y_train=train_data['Value at beginning of 2023/24 season']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "cac52c32",
   "metadata": {},
   "outputs": [],
   "source": [
    "X_test = test_data.drop(['Country'],axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "db54feff",
   "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>id</th>\n",
       "      <th>Aerial Duels won</th>\n",
       "      <th>Age</th>\n",
       "      <th>Assists</th>\n",
       "      <th>Attacking options created</th>\n",
       "      <th>Attempted Passes</th>\n",
       "      <th>Blocks</th>\n",
       "      <th>Clearances</th>\n",
       "      <th>Expected Goal Contributions</th>\n",
       "      <th>Interceptions</th>\n",
       "      <th>...</th>\n",
       "      <th>Progressive Passes Rec</th>\n",
       "      <th>Progressive Passes</th>\n",
       "      <th>Progressive Carries</th>\n",
       "      <th>Shots</th>\n",
       "      <th>Successful Dribbles</th>\n",
       "      <th>Touches in attacking penalty area</th>\n",
       "      <th>Tackles</th>\n",
       "      <th>Value at beginning of 2020/21 season</th>\n",
       "      <th>Value at beginning of 2021/22 season</th>\n",
       "      <th>Value at beginning of 2022/23 season</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>705</td>\n",
       "      <td>2.6</td>\n",
       "      <td>28</td>\n",
       "      <td>0.03</td>\n",
       "      <td>0.95</td>\n",
       "      <td>47.2</td>\n",
       "      <td>1.13</td>\n",
       "      <td>3.44</td>\n",
       "      <td>0.01</td>\n",
       "      <td>2.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.69</td>\n",
       "      <td>4.04</td>\n",
       "      <td>0.58</td>\n",
       "      <td>0.35</td>\n",
       "      <td>0.32</td>\n",
       "      <td>0.95</td>\n",
       "      <td>1.53</td>\n",
       "      <td>2.5</td>\n",
       "      <td>3.0</td>\n",
       "      <td>4.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>809</td>\n",
       "      <td>0.3</td>\n",
       "      <td>28</td>\n",
       "      <td>0.10</td>\n",
       "      <td>3.22</td>\n",
       "      <td>50.5</td>\n",
       "      <td>0.79</td>\n",
       "      <td>1.73</td>\n",
       "      <td>0.18</td>\n",
       "      <td>0.8</td>\n",
       "      <td>...</td>\n",
       "      <td>3.62</td>\n",
       "      <td>3.96</td>\n",
       "      <td>1.78</td>\n",
       "      <td>0.69</td>\n",
       "      <td>0.74</td>\n",
       "      <td>0.84</td>\n",
       "      <td>1.34</td>\n",
       "      <td>1.7</td>\n",
       "      <td>4.0</td>\n",
       "      <td>7.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1432</td>\n",
       "      <td>0.7</td>\n",
       "      <td>26</td>\n",
       "      <td>0.03</td>\n",
       "      <td>2.34</td>\n",
       "      <td>47.5</td>\n",
       "      <td>1.08</td>\n",
       "      <td>1.56</td>\n",
       "      <td>0.08</td>\n",
       "      <td>1.7</td>\n",
       "      <td>...</td>\n",
       "      <td>1.22</td>\n",
       "      <td>5.72</td>\n",
       "      <td>2.00</td>\n",
       "      <td>0.81</td>\n",
       "      <td>1.69</td>\n",
       "      <td>0.64</td>\n",
       "      <td>1.49</td>\n",
       "      <td>1.2</td>\n",
       "      <td>3.0</td>\n",
       "      <td>3.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>173</td>\n",
       "      <td>1.6</td>\n",
       "      <td>26</td>\n",
       "      <td>0.12</td>\n",
       "      <td>1.98</td>\n",
       "      <td>50.2</td>\n",
       "      <td>1.40</td>\n",
       "      <td>2.32</td>\n",
       "      <td>0.22</td>\n",
       "      <td>0.6</td>\n",
       "      <td>...</td>\n",
       "      <td>7.50</td>\n",
       "      <td>3.11</td>\n",
       "      <td>3.05</td>\n",
       "      <td>0.55</td>\n",
       "      <td>0.61</td>\n",
       "      <td>2.87</td>\n",
       "      <td>2.32</td>\n",
       "      <td>4.8</td>\n",
       "      <td>3.5</td>\n",
       "      <td>3.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>513</td>\n",
       "      <td>0.4</td>\n",
       "      <td>30</td>\n",
       "      <td>0.38</td>\n",
       "      <td>5.12</td>\n",
       "      <td>46.3</td>\n",
       "      <td>1.04</td>\n",
       "      <td>1.15</td>\n",
       "      <td>0.35</td>\n",
       "      <td>1.3</td>\n",
       "      <td>...</td>\n",
       "      <td>5.64</td>\n",
       "      <td>6.10</td>\n",
       "      <td>2.46</td>\n",
       "      <td>1.96</td>\n",
       "      <td>1.27</td>\n",
       "      <td>1.73</td>\n",
       "      <td>0.81</td>\n",
       "      <td>4.0</td>\n",
       "      <td>3.5</td>\n",
       "      <td>3.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>404</th>\n",
       "      <td>1220</td>\n",
       "      <td>0.9</td>\n",
       "      <td>28</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.60</td>\n",
       "      <td>38.0</td>\n",
       "      <td>1.95</td>\n",
       "      <td>2.41</td>\n",
       "      <td>0.01</td>\n",
       "      <td>1.2</td>\n",
       "      <td>...</td>\n",
       "      <td>1.49</td>\n",
       "      <td>3.10</td>\n",
       "      <td>0.80</td>\n",
       "      <td>0.23</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.23</td>\n",
       "      <td>2.76</td>\n",
       "      <td>4.2</td>\n",
       "      <td>3.0</td>\n",
       "      <td>2.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>405</th>\n",
       "      <td>478</td>\n",
       "      <td>0.6</td>\n",
       "      <td>28</td>\n",
       "      <td>0.10</td>\n",
       "      <td>1.40</td>\n",
       "      <td>43.3</td>\n",
       "      <td>1.50</td>\n",
       "      <td>2.62</td>\n",
       "      <td>0.04</td>\n",
       "      <td>1.2</td>\n",
       "      <td>...</td>\n",
       "      <td>2.44</td>\n",
       "      <td>2.65</td>\n",
       "      <td>1.74</td>\n",
       "      <td>0.21</td>\n",
       "      <td>0.59</td>\n",
       "      <td>1.01</td>\n",
       "      <td>1.60</td>\n",
       "      <td>3.5</td>\n",
       "      <td>2.5</td>\n",
       "      <td>3.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>406</th>\n",
       "      <td>798</td>\n",
       "      <td>2.9</td>\n",
       "      <td>33</td>\n",
       "      <td>0.00</td>\n",
       "      <td>1.12</td>\n",
       "      <td>61.4</td>\n",
       "      <td>1.30</td>\n",
       "      <td>2.37</td>\n",
       "      <td>0.04</td>\n",
       "      <td>3.4</td>\n",
       "      <td>...</td>\n",
       "      <td>0.36</td>\n",
       "      <td>3.44</td>\n",
       "      <td>0.36</td>\n",
       "      <td>0.71</td>\n",
       "      <td>0.24</td>\n",
       "      <td>1.07</td>\n",
       "      <td>2.73</td>\n",
       "      <td>10.0</td>\n",
       "      <td>9.0</td>\n",
       "      <td>7.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>407</th>\n",
       "      <td>1563</td>\n",
       "      <td>1.5</td>\n",
       "      <td>26</td>\n",
       "      <td>0.00</td>\n",
       "      <td>2.43</td>\n",
       "      <td>34.2</td>\n",
       "      <td>1.00</td>\n",
       "      <td>1.22</td>\n",
       "      <td>0.05</td>\n",
       "      <td>1.1</td>\n",
       "      <td>...</td>\n",
       "      <td>8.40</td>\n",
       "      <td>2.74</td>\n",
       "      <td>2.61</td>\n",
       "      <td>2.00</td>\n",
       "      <td>1.39</td>\n",
       "      <td>2.61</td>\n",
       "      <td>1.44</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.5</td>\n",
       "      <td>3.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>408</th>\n",
       "      <td>1667</td>\n",
       "      <td>1.8</td>\n",
       "      <td>25</td>\n",
       "      <td>0.06</td>\n",
       "      <td>0.78</td>\n",
       "      <td>57.8</td>\n",
       "      <td>1.74</td>\n",
       "      <td>3.89</td>\n",
       "      <td>0.02</td>\n",
       "      <td>1.5</td>\n",
       "      <td>...</td>\n",
       "      <td>0.09</td>\n",
       "      <td>2.41</td>\n",
       "      <td>0.35</td>\n",
       "      <td>0.38</td>\n",
       "      <td>0.29</td>\n",
       "      <td>0.59</td>\n",
       "      <td>1.68</td>\n",
       "      <td>3.0</td>\n",
       "      <td>8.0</td>\n",
       "      <td>10.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>409 rows × 23 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "       id  Aerial Duels won  Age  Assists  Attacking options created  \\\n",
       "0     705               2.6   28     0.03                       0.95   \n",
       "1     809               0.3   28     0.10                       3.22   \n",
       "2    1432               0.7   26     0.03                       2.34   \n",
       "3     173               1.6   26     0.12                       1.98   \n",
       "4     513               0.4   30     0.38                       5.12   \n",
       "..    ...               ...  ...      ...                        ...   \n",
       "404  1220               0.9   28     0.00                       0.60   \n",
       "405   478               0.6   28     0.10                       1.40   \n",
       "406   798               2.9   33     0.00                       1.12   \n",
       "407  1563               1.5   26     0.00                       2.43   \n",
       "408  1667               1.8   25     0.06                       0.78   \n",
       "\n",
       "     Attempted Passes  Blocks  Clearances  Expected Goal Contributions  \\\n",
       "0                47.2    1.13        3.44                         0.01   \n",
       "1                50.5    0.79        1.73                         0.18   \n",
       "2                47.5    1.08        1.56                         0.08   \n",
       "3                50.2    1.40        2.32                         0.22   \n",
       "4                46.3    1.04        1.15                         0.35   \n",
       "..                ...     ...         ...                          ...   \n",
       "404              38.0    1.95        2.41                         0.01   \n",
       "405              43.3    1.50        2.62                         0.04   \n",
       "406              61.4    1.30        2.37                         0.04   \n",
       "407              34.2    1.00        1.22                         0.05   \n",
       "408              57.8    1.74        3.89                         0.02   \n",
       "\n",
       "     Interceptions  ...  Progressive Passes Rec  Progressive Passes  \\\n",
       "0              2.0  ...                    0.69                4.04   \n",
       "1              0.8  ...                    3.62                3.96   \n",
       "2              1.7  ...                    1.22                5.72   \n",
       "3              0.6  ...                    7.50                3.11   \n",
       "4              1.3  ...                    5.64                6.10   \n",
       "..             ...  ...                     ...                 ...   \n",
       "404            1.2  ...                    1.49                3.10   \n",
       "405            1.2  ...                    2.44                2.65   \n",
       "406            3.4  ...                    0.36                3.44   \n",
       "407            1.1  ...                    8.40                2.74   \n",
       "408            1.5  ...                    0.09                2.41   \n",
       "\n",
       "     Progressive Carries  Shots  Successful Dribbles  \\\n",
       "0                   0.58   0.35                 0.32   \n",
       "1                   1.78   0.69                 0.74   \n",
       "2                   2.00   0.81                 1.69   \n",
       "3                   3.05   0.55                 0.61   \n",
       "4                   2.46   1.96                 1.27   \n",
       "..                   ...    ...                  ...   \n",
       "404                 0.80   0.23                 0.00   \n",
       "405                 1.74   0.21                 0.59   \n",
       "406                 0.36   0.71                 0.24   \n",
       "407                 2.61   2.00                 1.39   \n",
       "408                 0.35   0.38                 0.29   \n",
       "\n",
       "     Touches in attacking penalty area  Tackles  \\\n",
       "0                                 0.95     1.53   \n",
       "1                                 0.84     1.34   \n",
       "2                                 0.64     1.49   \n",
       "3                                 2.87     2.32   \n",
       "4                                 1.73     0.81   \n",
       "..                                 ...      ...   \n",
       "404                               0.23     2.76   \n",
       "405                               1.01     1.60   \n",
       "406                               1.07     2.73   \n",
       "407                               2.61     1.44   \n",
       "408                               0.59     1.68   \n",
       "\n",
       "     Value at beginning of 2020/21 season  \\\n",
       "0                                     2.5   \n",
       "1                                     1.7   \n",
       "2                                     1.2   \n",
       "3                                     4.8   \n",
       "4                                     4.0   \n",
       "..                                    ...   \n",
       "404                                   4.2   \n",
       "405                                   3.5   \n",
       "406                                  10.0   \n",
       "407                                   1.0   \n",
       "408                                   3.0   \n",
       "\n",
       "     Value at beginning of 2021/22 season  \\\n",
       "0                                     3.0   \n",
       "1                                     4.0   \n",
       "2                                     3.0   \n",
       "3                                     3.5   \n",
       "4                                     3.5   \n",
       "..                                    ...   \n",
       "404                                   3.0   \n",
       "405                                   2.5   \n",
       "406                                   9.0   \n",
       "407                                   1.5   \n",
       "408                                   8.0   \n",
       "\n",
       "     Value at beginning of 2022/23 season  \n",
       "0                                     4.0  \n",
       "1                                     7.0  \n",
       "2                                     3.5  \n",
       "3                                     3.0  \n",
       "4                                     3.5  \n",
       "..                                    ...  \n",
       "404                                   2.0  \n",
       "405                                   3.0  \n",
       "406                                   7.5  \n",
       "407                                   3.0  \n",
       "408                                  10.0  \n",
       "\n",
       "[409 rows x 23 columns]"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X_test"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "67077047",
   "metadata": {},
   "outputs": [],
   "source": [
    "y_test=train_data['Value at beginning of 2023/24 season']\n",
    "np.random.seed(42)  # For reproducibility\n",
    "y_test = y_test.sample(n=409, random_state=42)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "ac052cbf",
   "metadata": {},
   "outputs": [],
   "source": [
    "#Pre-processing data\n",
    "\n",
    "#Making the distribution of the features more Gaussian\n",
    "pt = PowerTransformer()\n",
    "\n",
    "X_train_transformed = pt.fit_transform(X_train)\n",
    "X_test_transformed = pt.transform(X_test)\n",
    "\n",
    "\n",
    "#Scaling the data\n",
    "\n",
    "#Initiating Robust Scaler\n",
    "rs= RobustScaler()\n",
    "\n",
    "#Standardizing numerical columns\n",
    "X_train_scaled = rs.fit_transform(X_train_transformed)\n",
    "X_test_scaled = rs.transform(X_test_transformed)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "f967c451",
   "metadata": {},
   "outputs": [],
   "source": [
    "#Initiating Support Vector Regressor to calculate feature importance\n",
    "svr = SVR(kernel='linear')\n",
    "svr.fit(X_train_scaled,y_train);"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "e847aff1",
   "metadata": {},
   "outputs": [],
   "source": [
    "cross_val_train = cross_val_score(svr, X_train_scaled, y_train, scoring=\"neg_root_mean_squared_error\",cv=5)\n",
    "cross_val_test = cross_val_score(svr, X_test_scaled, y_test, scoring=\"neg_root_mean_squared_error\",cv=5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "99f194f9",
   "metadata": {},
   "outputs": [],
   "source": [
    "#Train Results\n",
    "cross_val_train_mean = round(-(cross_val_train.mean()),2)\n",
    "cross_val_train_max = round(-(cross_val_train.min()),2)\n",
    "cross_val_train_min = round(-(cross_val_train.max()),2)\n",
    "\n",
    "#Test Results\n",
    "cross_val_test_mean = round(-(cross_val_test.mean()),2)\n",
    "cross_val_test_max = round(-(cross_val_test.min()),2)\n",
    "cross_val_test_min = round(-(cross_val_test.max()),2)\n",
    "\n",
    "#Creating a Dataframe to display Validation Results\n",
    "results = pd.DataFrame(columns=['Train/Test','Avg RMSE','Max RMSE','Min RMSE'])\n",
    "results.loc[0] = ['Train',cross_val_train_mean,cross_val_train_max,cross_val_train_min]\n",
    "results.loc[1] = ['Test', cross_val_test_mean, cross_val_test_max, cross_val_test_min]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "2e3669eb",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/tmp/ipykernel_441/551706719.py:3: FutureWarning: Series.__getitem__ treating keys as positions is deprecated. In a future version, integer keys will always be treated as labels (consistent with DataFrame behavior). To access a value by position, use `ser.iloc[pos]`\n",
      "  x_ticks = list(range(0,max([results.iloc[0][2],(round(results.iloc[1][2])+1000000)]),2500000))\n",
      "/tmp/ipykernel_441/551706719.py:8: FutureWarning: Series.__getitem__ treating keys as positions is deprecated. In a future version, integer keys will always be treated as labels (consistent with DataFrame behavior). To access a value by position, use `ser.iloc[pos]`\n",
      "  train_max = results.iloc[0][2]\n",
      "/tmp/ipykernel_441/551706719.py:12: FutureWarning: Series.__getitem__ treating keys as positions is deprecated. In a future version, integer keys will always be treated as labels (consistent with DataFrame behavior). To access a value by position, use `ser.iloc[pos]`\n",
      "  train_min = results.iloc[0][3]\n",
      "/tmp/ipykernel_441/551706719.py:16: FutureWarning: Series.__getitem__ treating keys as positions is deprecated. In a future version, integer keys will always be treated as labels (consistent with DataFrame behavior). To access a value by position, use `ser.iloc[pos]`\n",
      "  test_max = results.iloc[1][2]\n",
      "/tmp/ipykernel_441/551706719.py:20: FutureWarning: Series.__getitem__ treating keys as positions is deprecated. In a future version, integer keys will always be treated as labels (consistent with DataFrame behavior). To access a value by position, use `ser.iloc[pos]`\n",
      "  test_min = results.iloc[1][3]\n",
      "/tmp/ipykernel_441/551706719.py:23: FutureWarning: Series.__getitem__ treating keys as positions is deprecated. In a future version, integer keys will always be treated as labels (consistent with DataFrame behavior). To access a value by position, use `ser.iloc[pos]`\n",
      "  ax.axvline(x = results.iloc[0][1],label = 'Average Train RMSE',color='dodgerblue')\n",
      "/tmp/ipykernel_441/551706719.py:24: FutureWarning: Series.__getitem__ treating keys as positions is deprecated. In a future version, integer keys will always be treated as labels (consistent with DataFrame behavior). To access a value by position, use `ser.iloc[pos]`\n",
      "  ax.axvline(x = results.iloc[1][1],label = 'Average Test RMSE',color='red')\n"
     ]
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 1440x864 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax = plt.subplots(figsize=(20,12))\n",
    "\n",
    "x_ticks = list(range(0,max([results.iloc[0][2],(round(results.iloc[1][2])+1000000)]),2500000))\n",
    "y_ticks = [1,2,3,4]\n",
    "y_labels = ['Train Max RMSE','Train Min RMSE','Test Max RMSE','Test Min RMSE']\n",
    "\n",
    "train_max_pos = 1\n",
    "train_max = results.iloc[0][2]\n",
    "ax.barh(train_max_pos,train_max,color='navy')\n",
    "\n",
    "train_min_pos = 2\n",
    "train_min = results.iloc[0][3]\n",
    "ax.barh(train_min_pos,train_min,color='navy')\n",
    "\n",
    "test_max_pos = 3\n",
    "test_max = results.iloc[1][2]\n",
    "ax.barh(test_max_pos,test_max,color='orange')\n",
    "\n",
    "test_min_pos = 4\n",
    "test_min = results.iloc[1][3]\n",
    "ax.barh(test_min_pos,test_min,color='orange')\n",
    "\n",
    "ax.axvline(x = results.iloc[0][1],label = 'Average Train RMSE',color='dodgerblue')\n",
    "ax.axvline(x = results.iloc[1][1],label = 'Average Test RMSE',color='red')\n",
    "\n",
    "ax.set_yticks(y_ticks)\n",
    "ax.set_yticklabels(y_labels)\n",
    "ax.set_xticks(x_ticks)\n",
    "ax.set_xticklabels([f'£ {a}' for a in x_ticks])\n",
    "ax.set_xlabel('Error Value',fontsize=20)\n",
    "ax.set_ylabel('Validation Results',fontsize=20)\n",
    "ax.set_title('Comparing Train and Test RMSEs for Default Hyperparameter SVR Model',fontsize=30)\n",
    "ax.legend(fontsize=15);"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "6fc9ef4e",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1440x864 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "\n",
    "fig,ax = plt.subplots(figsize=(20,12))\n",
    "\n",
    "pd.Series(abs(svr.coef_[0]), index=X_train.columns).nlargest(10).plot(kind='barh');\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "92e2795e",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1440x864 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig,ax = plt.subplots(figsize=(20,12))\n",
    "\n",
    "pd.Series(abs(svr.coef_[0]), index=X_test.columns).nlargest(10).plot(kind='barh');\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "61f28b6a",
   "metadata": {},
   "outputs": [],
   "source": [
    "#Creating a list of top features for train data\n",
    "top_features_train = list(pd.Series(abs(svr.coef_[0]), index=X_train.columns).nlargest(10).keys())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "0f0c2b45",
   "metadata": {},
   "outputs": [],
   "source": [
    "#Creating a list of top features for test data\n",
    "top_features_test = list(pd.Series(abs(svr.coef_[0]), index=X_test.columns).nlargest(10).keys())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "id": "eeda2f15",
   "metadata": {},
   "outputs": [],
   "source": [
    "X_train = train_data.drop(['Name','Country','Value at beginning of 2023/24 season'],axis=1)[top_features_train]\n",
    "y_train=train_data['Value at beginning of 2023/24 season']\n",
    "X_test = test_data.drop(['Country'],axis=1)[top_features_test]\n",
    "y_test=train_data['Value at beginning of 2023/24 season']\n",
    "np.random.seed(42)  # For reproducibility\n",
    "y_test = y_test.sample(n=409, random_state=42)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "id": "08e324c0",
   "metadata": {},
   "outputs": [],
   "source": [
    "#Making the distribution of the features more Gaussian\n",
    "pt = PowerTransformer()\n",
    "\n",
    "X_train_transformed = pt.fit_transform(X_train)\n",
    "X_test_transformed = pt.transform(X_test)\n",
    "\n",
    "#Scaling the data\n",
    "\n",
    "#Initiating Robust Scaler\n",
    "rs= RobustScaler()\n",
    "\n",
    "#Standardizing numerical columns\n",
    "X_train_scaled = rs.fit_transform(X_train_transformed)\n",
    "X_test_scaled = rs.transform(X_test_transformed)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "id": "d1b931fe",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Create the parameter grid based on the results of random search \n",
    "parameters = { 'C':[0.1,1,100,1000],\n",
    "              'kernel':['rbf','poly','sigmoid','linear'],\n",
    "              'degree':[1,2,3,4,5,6],\n",
    "              'gamma': [1, 0.1, 0.01, 0.001, 0.0001]}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "id": "a0d83b3e",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Traning model with all features using best SVR Hyperparameters\n",
    "svr_Grid = SVR(C = 1000,\n",
    "              degree = 1,\n",
    "              gamma = 0.1,\n",
    "              kernel='rbf')\n",
    "\n",
    "svr_Grid.fit(X_train_scaled,y_train);"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "id": "5c1ed2d5",
   "metadata": {},
   "outputs": [],
   "source": [
    "cross_val_train = cross_val_score(svr_Grid, X_train_scaled, y_train, scoring=\"neg_root_mean_squared_error\",cv=5)\n",
    "cross_val_test = cross_val_score(svr_Grid, X_test_scaled, y_test, scoring=\"neg_root_mean_squared_error\",cv=5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "id": "dd28a0e6",
   "metadata": {},
   "outputs": [],
   "source": [
    "#Train Results\n",
    "cross_val_train_mean = round(-(cross_val_train.mean()),2)\n",
    "cross_val_train_max = round(-(cross_val_train.min()),2)\n",
    "cross_val_train_min = round(-(cross_val_train.max()),2)\n",
    "\n",
    "#Test Results\n",
    "cross_val_test_mean = round(-(cross_val_test.mean()),2)\n",
    "cross_val_test_max = round(-(cross_val_test.min()),2)\n",
    "cross_val_test_min = round(-(cross_val_test.max()),2)\n",
    "\n",
    "#Creating a Dataframe to display Validation Results\n",
    "results_att = pd.DataFrame(columns=['Train/Test','Avg RMSE','Max RMSE','Min RMSE'])\n",
    "results_att.loc[0] = ['Train',cross_val_train_mean,cross_val_train_max,cross_val_train_min]\n",
    "results_att.loc[1] = ['Test', cross_val_test_mean, cross_val_test_max, cross_val_test_min]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "id": "07255f23",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/tmp/ipykernel_441/511037645.py:8: FutureWarning: Series.__getitem__ treating keys as positions is deprecated. In a future version, integer keys will always be treated as labels (consistent with DataFrame behavior). To access a value by position, use `ser.iloc[pos]`\n",
      "  train_max = results_att.iloc[0][2]\n",
      "/tmp/ipykernel_441/511037645.py:12: FutureWarning: Series.__getitem__ treating keys as positions is deprecated. In a future version, integer keys will always be treated as labels (consistent with DataFrame behavior). To access a value by position, use `ser.iloc[pos]`\n",
      "  train_min = results_att.iloc[0][3]\n",
      "/tmp/ipykernel_441/511037645.py:16: FutureWarning: Series.__getitem__ treating keys as positions is deprecated. In a future version, integer keys will always be treated as labels (consistent with DataFrame behavior). To access a value by position, use `ser.iloc[pos]`\n",
      "  test_max = results_att.iloc[1][2]\n",
      "/tmp/ipykernel_441/511037645.py:20: FutureWarning: Series.__getitem__ treating keys as positions is deprecated. In a future version, integer keys will always be treated as labels (consistent with DataFrame behavior). To access a value by position, use `ser.iloc[pos]`\n",
      "  test_min = results_att.iloc[1][3]\n",
      "/tmp/ipykernel_441/511037645.py:23: FutureWarning: Series.__getitem__ treating keys as positions is deprecated. In a future version, integer keys will always be treated as labels (consistent with DataFrame behavior). To access a value by position, use `ser.iloc[pos]`\n",
      "  ax.axvline(x = results_att.iloc[0][1],label = 'Average Train RMSE',color='dodgerblue')\n",
      "/tmp/ipykernel_441/511037645.py:24: FutureWarning: Series.__getitem__ treating keys as positions is deprecated. In a future version, integer keys will always be treated as labels (consistent with DataFrame behavior). To access a value by position, use `ser.iloc[pos]`\n",
      "  ax.axvline(x = results_att.iloc[1][1],label = 'Average Test RMSE',color='red')\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1440x864 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax = plt.subplots(figsize=(20,12))\n",
    "\n",
    "x_ticks = list(range(0,int(max(results_att['Max RMSE'])+1000000),2500000))\n",
    "y_ticks = [1,2,3,4]\n",
    "y_labels = ['Train Max RMSE','Train Min RMSE','Test Max RMSE','Test Min RMSE']\n",
    "\n",
    "train_max_pos = 1\n",
    "train_max = results_att.iloc[0][2]\n",
    "ax.barh(train_max_pos,train_max,color='navy')\n",
    "\n",
    "train_min_pos = 2\n",
    "train_min = results_att.iloc[0][3]\n",
    "ax.barh(train_min_pos,train_min,color='navy')\n",
    "\n",
    "test_max_pos = 3\n",
    "test_max = results_att.iloc[1][2]\n",
    "ax.barh(test_max_pos,test_max,color='orange')\n",
    "\n",
    "test_min_pos = 4\n",
    "test_min = results_att.iloc[1][3]\n",
    "ax.barh(test_min_pos,test_min,color='orange')\n",
    "\n",
    "ax.axvline(x = results_att.iloc[0][1],label = 'Average Train RMSE',color='dodgerblue')\n",
    "ax.axvline(x = results_att.iloc[1][1],label = 'Average Test RMSE',color='red')\n",
    "\n",
    "ax.set_yticks(y_ticks)\n",
    "ax.set_yticklabels(y_labels)\n",
    "ax.set_xticks(x_ticks)\n",
    "ax.set_xticklabels([f'£ {a}' for a in x_ticks])\n",
    "ax.set_xlabel('Error Value in GBP (£)',fontsize=20)\n",
    "ax.set_ylabel('Validation Results',fontsize=20)\n",
    "ax.set_title('Support Vector Regression Results',fontsize=30)\n",
    "ax.legend(fontsize=15);"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "id": "12c1266f",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Train/Test</th>\n",
       "      <th>Avg RMSE</th>\n",
       "      <th>Max RMSE</th>\n",
       "      <th>Min RMSE</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Train</td>\n",
       "      <td>11.48</td>\n",
       "      <td>13.05</td>\n",
       "      <td>8.63</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Test</td>\n",
       "      <td>22.44</td>\n",
       "      <td>27.34</td>\n",
       "      <td>14.52</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  Train/Test  Avg RMSE  Max RMSE  Min RMSE\n",
       "0      Train     11.48     13.05      8.63\n",
       "1       Test     22.44     27.34     14.52"
      ]
     },
     "execution_count": 37,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "\n",
    "results_att"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "id": "9303c675",
   "metadata": {},
   "outputs": [],
   "source": [
    "y_pred=svr_Grid.predict(X_test_scaled)\n",
    "# Add the predicted labels to the test dataframe\n",
    "test_data['label'] = y_pred\n",
    "# Save the test dataframe with predictions to a CSV file\n",
    "test_data.to_csv('Value prediction.csv', index=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "id": "7e4a66ef",
   "metadata": {},
   "outputs": [],
   "source": [
    "selected_columns = test_data[['id', 'label']]\n",
    "\n",
    "# Specify the path where you want to save the CSV file\n",
    "csv_file_path = \"Value prediction_grid.csv\"\n",
    "\n",
    "# Save the selected columns to a CSV file\n",
    "selected_columns.to_csv(csv_file_path, index=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "id": "ba4ee22c",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "351       3.5\n",
       "486      12.0\n",
       "599     150.0\n",
       "442      15.0\n",
       "270       8.0\n",
       "        ...  \n",
       "136       3.0\n",
       "310      25.0\n",
       "1078      5.0\n",
       "532       5.0\n",
       "411       1.0\n",
       "Name: Value at beginning of 2023/24 season, Length: 409, dtype: float64"
      ]
     },
     "execution_count": 40,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y_test"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "id": "3b87c30e",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "R2 Score:  -0.42110707992265817\n"
     ]
    }
   ],
   "source": [
    "from sklearn.metrics import mean_squared_error, r2_score,cla\n",
    "print(\"R2 Score: \", r2_score(y_test,y_pred))\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "91dfcf84",
   "metadata": {},
   "outputs": [],
   "source": []
  }
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