FootballGKprediction.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 117,
   "metadata": {},
   "outputs": [],
   "source": [
    "#download the packages to use\n",
    "import pandas as pd\n",
    "from datetime import datetime\n",
    "from sklearn.preprocessing import LabelEncoder\n",
    "from sklearn.impute import KNNImputer\n",
    "from sklearn.preprocessing import MinMaxScaler\n",
    "from collections import Counter\n",
    "import matplotlib.pyplot as plt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 118,
   "metadata": {},
   "outputs": [],
   "source": [
    "#load the trainings and test data\n",
    "\n",
    "train = pd.read_csv('train.csv')\n",
    "test = pd.read_csv('test.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 119,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(6895, 70)"
      ]
     },
     "execution_count": 119,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train.shape\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 120,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Features with missing values:\n",
      "['value_eur', 'club_loaned_from', 'club_joined', 'nation_jersey_number', 'release_clause_eur', 'player_tags', 'player_traits', 'pace', 'shooting', 'passing', 'dribbling', 'defending', 'physic', 'goalkeeping_speed']\n",
      "\n",
      "Count of missing values for each feature:\n",
      "value_eur                  2\n",
      "club_loaned_from        6544\n",
      "club_joined              351\n",
      "nation_jersey_number    6482\n",
      "release_clause_eur       353\n",
      "player_tags             6124\n",
      "player_traits           2589\n",
      "pace                     631\n",
      "shooting                 631\n",
      "passing                  631\n",
      "dribbling                631\n",
      "defending                631\n",
      "physic                   631\n",
      "goalkeeping_speed       6264\n",
      "dtype: int64\n"
     ]
    }
   ],
   "source": [
    "# Identify features with missing values\n",
    "features_with_missing_values = train.columns[train.isnull().any()].tolist()\n",
    "\n",
    "# Display the count of missing values for each feature\n",
    "missing_values_count = train[features_with_missing_values].isnull().sum()\n",
    "\n",
    "# Print the results\n",
    "print(\"Features with missing values:\")\n",
    "print(features_with_missing_values)\n",
    "\n",
    "print(\"\\nCount of missing values for each feature:\")\n",
    "print(missing_values_count)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 121,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(767, 69)"
      ]
     },
     "execution_count": 121,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "test.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 122,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Features with missing values:\n",
      "['value_eur', 'club_loaned_from', 'club_joined', 'nation_jersey_number', 'release_clause_eur', 'player_tags', 'player_traits', 'pace', 'shooting', 'passing', 'dribbling', 'defending', 'physic', 'goalkeeping_speed']\n",
      "\n",
      "Count of missing values for each feature:\n",
      "value_eur                 3\n",
      "club_loaned_from        722\n",
      "club_joined              45\n",
      "nation_jersey_number    716\n",
      "release_clause_eur       48\n",
      "player_tags             682\n",
      "player_traits           285\n",
      "pace                     70\n",
      "shooting                 70\n",
      "passing                  70\n",
      "dribbling                70\n",
      "defending                70\n",
      "physic                   70\n",
      "goalkeeping_speed       697\n",
      "dtype: int64\n"
     ]
    }
   ],
   "source": [
    "# Identify features with missing values\n",
    "features_with_missing_values = test.columns[test.isnull().any()].tolist()\n",
    "\n",
    "# Display the count of missing values for each feature\n",
    "missing_values_count = test[features_with_missing_values].isnull().sum()\n",
    "\n",
    "# Print the results\n",
    "print(\"Features with missing values:\")\n",
    "print(features_with_missing_values)\n",
    "\n",
    "print(\"\\nCount of missing values for each feature:\")\n",
    "print(missing_values_count)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 123,
   "metadata": {},
   "outputs": [],
   "source": [
    "#create a new column called test_train to identify after which data belongs to test and train data\n",
    "test['test_train']=0\n",
    "train['test_train']=1\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 124,
   "metadata": {},
   "outputs": [],
   "source": [
    "# concat train data with test data to have more observations, clean it all together and improve accuracy\n",
    "all_data = pd.concat([train,test]).reset_index()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 125,
   "metadata": {},
   "outputs": [],
   "source": [
    "pd.set_option('display.max_columns', None)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 1.Data Preprocessing and Quality Assurance: "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 126,
   "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>index</th>\n",
       "      <th>id</th>\n",
       "      <th>short_name</th>\n",
       "      <th>overall</th>\n",
       "      <th>potential</th>\n",
       "      <th>value_eur</th>\n",
       "      <th>wage_eur</th>\n",
       "      <th>birthday_date</th>\n",
       "      <th>height_cm</th>\n",
       "      <th>weight_kg</th>\n",
       "      <th>club_name</th>\n",
       "      <th>league_name</th>\n",
       "      <th>league_level</th>\n",
       "      <th>club_jersey_number</th>\n",
       "      <th>club_loaned_from</th>\n",
       "      <th>club_joined</th>\n",
       "      <th>club_contract_valid_until</th>\n",
       "      <th>nationality_name</th>\n",
       "      <th>nation_jersey_number</th>\n",
       "      <th>preferred_foot</th>\n",
       "      <th>weak_foot</th>\n",
       "      <th>skill_moves</th>\n",
       "      <th>international_reputation</th>\n",
       "      <th>work_rate</th>\n",
       "      <th>body_type</th>\n",
       "      <th>real_face</th>\n",
       "      <th>release_clause_eur</th>\n",
       "      <th>player_tags</th>\n",
       "      <th>player_traits</th>\n",
       "      <th>pace</th>\n",
       "      <th>shooting</th>\n",
       "      <th>passing</th>\n",
       "      <th>dribbling</th>\n",
       "      <th>defending</th>\n",
       "      <th>physic</th>\n",
       "      <th>attacking_crossing</th>\n",
       "      <th>attacking_finishing</th>\n",
       "      <th>attacking_heading_accuracy</th>\n",
       "      <th>attacking_short_passing</th>\n",
       "      <th>attacking_volleys</th>\n",
       "      <th>skill_dribbling</th>\n",
       "      <th>skill_curve</th>\n",
       "      <th>skill_fk_accuracy</th>\n",
       "      <th>skill_long_passing</th>\n",
       "      <th>skill_ball_control</th>\n",
       "      <th>movement_acceleration</th>\n",
       "      <th>movement_sprint_speed</th>\n",
       "      <th>movement_agility</th>\n",
       "      <th>movement_reactions</th>\n",
       "      <th>movement_balance</th>\n",
       "      <th>power_shot_power</th>\n",
       "      <th>power_jumping</th>\n",
       "      <th>power_stamina</th>\n",
       "      <th>power_strength</th>\n",
       "      <th>power_long_shots</th>\n",
       "      <th>mentality_aggression</th>\n",
       "      <th>mentality_interceptions</th>\n",
       "      <th>mentality_positioning</th>\n",
       "      <th>mentality_vision</th>\n",
       "      <th>mentality_penalties</th>\n",
       "      <th>mentality_composure</th>\n",
       "      <th>defending_marking_awareness</th>\n",
       "      <th>defending_standing_tackle</th>\n",
       "      <th>defending_sliding_tackle</th>\n",
       "      <th>goalkeeping_diving</th>\n",
       "      <th>goalkeeping_handling</th>\n",
       "      <th>goalkeeping_kicking</th>\n",
       "      <th>goalkeeping_positioning</th>\n",
       "      <th>goalkeeping_reflexes</th>\n",
       "      <th>goalkeeping_speed</th>\n",
       "      <th>position</th>\n",
       "      <th>test_train</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0</td>\n",
       "      <td>216302</td>\n",
       "      <td>E. García</td>\n",
       "      <td>71</td>\n",
       "      <td>71</td>\n",
       "      <td>1400000.0</td>\n",
       "      <td>10000</td>\n",
       "      <td>1989-12-28</td>\n",
       "      <td>176</td>\n",
       "      <td>73</td>\n",
       "      <td>Club Atlético de San Luis</td>\n",
       "      <td>Mexican Liga MX</td>\n",
       "      <td>1</td>\n",
       "      <td>29</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2021-07-12</td>\n",
       "      <td>2026</td>\n",
       "      <td>Mexico</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Right</td>\n",
       "      <td>5</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>Medium/High</td>\n",
       "      <td>Normal (170-185)</td>\n",
       "      <td>No</td>\n",
       "      <td>2400000.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Early Crosser</td>\n",
       "      <td>70.0</td>\n",
       "      <td>52.0</td>\n",
       "      <td>60.0</td>\n",
       "      <td>70.0</td>\n",
       "      <td>67.0</td>\n",
       "      <td>66.0</td>\n",
       "      <td>64</td>\n",
       "      <td>40</td>\n",
       "      <td>56</td>\n",
       "      <td>65</td>\n",
       "      <td>41</td>\n",
       "      <td>68</td>\n",
       "      <td>64</td>\n",
       "      <td>58</td>\n",
       "      <td>58</td>\n",
       "      <td>68</td>\n",
       "      <td>71</td>\n",
       "      <td>70</td>\n",
       "      <td>87</td>\n",
       "      <td>61</td>\n",
       "      <td>86</td>\n",
       "      <td>67</td>\n",
       "      <td>77</td>\n",
       "      <td>86</td>\n",
       "      <td>58</td>\n",
       "      <td>66</td>\n",
       "      <td>56</td>\n",
       "      <td>79</td>\n",
       "      <td>53</td>\n",
       "      <td>48</td>\n",
       "      <td>58</td>\n",
       "      <td>66</td>\n",
       "      <td>65</td>\n",
       "      <td>66</td>\n",
       "      <td>65</td>\n",
       "      <td>14</td>\n",
       "      <td>11</td>\n",
       "      <td>12</td>\n",
       "      <td>12</td>\n",
       "      <td>12</td>\n",
       "      <td>NaN</td>\n",
       "      <td>LB</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>237867</td>\n",
       "      <td>D. Cancola</td>\n",
       "      <td>65</td>\n",
       "      <td>71</td>\n",
       "      <td>1000000.0</td>\n",
       "      <td>2000</td>\n",
       "      <td>1996-10-23</td>\n",
       "      <td>183</td>\n",
       "      <td>73</td>\n",
       "      <td>Ross County FC</td>\n",
       "      <td>Scottish Premiership</td>\n",
       "      <td>1</td>\n",
       "      <td>4</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2021-07-23</td>\n",
       "      <td>2022</td>\n",
       "      <td>Austria</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Right</td>\n",
       "      <td>3</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>Medium/Medium</td>\n",
       "      <td>Lean (170-185)</td>\n",
       "      <td>No</td>\n",
       "      <td>2000000.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>65.0</td>\n",
       "      <td>38.0</td>\n",
       "      <td>58.0</td>\n",
       "      <td>60.0</td>\n",
       "      <td>63.0</td>\n",
       "      <td>67.0</td>\n",
       "      <td>50</td>\n",
       "      <td>31</td>\n",
       "      <td>59</td>\n",
       "      <td>64</td>\n",
       "      <td>31</td>\n",
       "      <td>57</td>\n",
       "      <td>36</td>\n",
       "      <td>41</td>\n",
       "      <td>64</td>\n",
       "      <td>65</td>\n",
       "      <td>64</td>\n",
       "      <td>65</td>\n",
       "      <td>57</td>\n",
       "      <td>62</td>\n",
       "      <td>67</td>\n",
       "      <td>49</td>\n",
       "      <td>62</td>\n",
       "      <td>71</td>\n",
       "      <td>66</td>\n",
       "      <td>42</td>\n",
       "      <td>66</td>\n",
       "      <td>66</td>\n",
       "      <td>48</td>\n",
       "      <td>62</td>\n",
       "      <td>46</td>\n",
       "      <td>59</td>\n",
       "      <td>65</td>\n",
       "      <td>61</td>\n",
       "      <td>58</td>\n",
       "      <td>10</td>\n",
       "      <td>13</td>\n",
       "      <td>7</td>\n",
       "      <td>6</td>\n",
       "      <td>11</td>\n",
       "      <td>NaN</td>\n",
       "      <td>LDM</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2</td>\n",
       "      <td>253472</td>\n",
       "      <td>E. Kahl</td>\n",
       "      <td>65</td>\n",
       "      <td>77</td>\n",
       "      <td>1600000.0</td>\n",
       "      <td>2000</td>\n",
       "      <td>2001-09-27</td>\n",
       "      <td>178</td>\n",
       "      <td>69</td>\n",
       "      <td>Aarhus GF</td>\n",
       "      <td>Danish Superliga</td>\n",
       "      <td>1</td>\n",
       "      <td>19</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2021-07-24</td>\n",
       "      <td>2026</td>\n",
       "      <td>Sweden</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Left</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>High/Medium</td>\n",
       "      <td>Normal (170-185)</td>\n",
       "      <td>No</td>\n",
       "      <td>2300000.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Speed Dribbler (AI)</td>\n",
       "      <td>79.0</td>\n",
       "      <td>35.0</td>\n",
       "      <td>58.0</td>\n",
       "      <td>66.0</td>\n",
       "      <td>59.0</td>\n",
       "      <td>64.0</td>\n",
       "      <td>59</td>\n",
       "      <td>30</td>\n",
       "      <td>53</td>\n",
       "      <td>60</td>\n",
       "      <td>37</td>\n",
       "      <td>65</td>\n",
       "      <td>56</td>\n",
       "      <td>35</td>\n",
       "      <td>55</td>\n",
       "      <td>64</td>\n",
       "      <td>82</td>\n",
       "      <td>77</td>\n",
       "      <td>71</td>\n",
       "      <td>62</td>\n",
       "      <td>75</td>\n",
       "      <td>50</td>\n",
       "      <td>65</td>\n",
       "      <td>67</td>\n",
       "      <td>61</td>\n",
       "      <td>28</td>\n",
       "      <td>69</td>\n",
       "      <td>62</td>\n",
       "      <td>58</td>\n",
       "      <td>60</td>\n",
       "      <td>31</td>\n",
       "      <td>65</td>\n",
       "      <td>60</td>\n",
       "      <td>58</td>\n",
       "      <td>59</td>\n",
       "      <td>10</td>\n",
       "      <td>10</td>\n",
       "      <td>8</td>\n",
       "      <td>10</td>\n",
       "      <td>11</td>\n",
       "      <td>NaN</td>\n",
       "      <td>LWB</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>3</td>\n",
       "      <td>223994</td>\n",
       "      <td>S. Mugoša</td>\n",
       "      <td>72</td>\n",
       "      <td>72</td>\n",
       "      <td>2300000.0</td>\n",
       "      <td>5000</td>\n",
       "      <td>1992-02-26</td>\n",
       "      <td>188</td>\n",
       "      <td>81</td>\n",
       "      <td>Incheon United FC</td>\n",
       "      <td>Korean K League 1</td>\n",
       "      <td>1</td>\n",
       "      <td>9</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2018-02-08</td>\n",
       "      <td>2023</td>\n",
       "      <td>Montenegro</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Right</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>High/Medium</td>\n",
       "      <td>Lean (185+)</td>\n",
       "      <td>No</td>\n",
       "      <td>2900000.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Finesse Shot</td>\n",
       "      <td>64.0</td>\n",
       "      <td>74.0</td>\n",
       "      <td>51.0</td>\n",
       "      <td>68.0</td>\n",
       "      <td>24.0</td>\n",
       "      <td>76.0</td>\n",
       "      <td>32</td>\n",
       "      <td>78</td>\n",
       "      <td>75</td>\n",
       "      <td>59</td>\n",
       "      <td>70</td>\n",
       "      <td>65</td>\n",
       "      <td>48</td>\n",
       "      <td>53</td>\n",
       "      <td>45</td>\n",
       "      <td>74</td>\n",
       "      <td>61</td>\n",
       "      <td>66</td>\n",
       "      <td>64</td>\n",
       "      <td>67</td>\n",
       "      <td>53</td>\n",
       "      <td>71</td>\n",
       "      <td>71</td>\n",
       "      <td>75</td>\n",
       "      <td>87</td>\n",
       "      <td>69</td>\n",
       "      <td>52</td>\n",
       "      <td>18</td>\n",
       "      <td>76</td>\n",
       "      <td>60</td>\n",
       "      <td>75</td>\n",
       "      <td>75</td>\n",
       "      <td>16</td>\n",
       "      <td>22</td>\n",
       "      <td>19</td>\n",
       "      <td>16</td>\n",
       "      <td>15</td>\n",
       "      <td>13</td>\n",
       "      <td>8</td>\n",
       "      <td>9</td>\n",
       "      <td>NaN</td>\n",
       "      <td>LS</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>4</td>\n",
       "      <td>251635</td>\n",
       "      <td>A. Țigănașu</td>\n",
       "      <td>65</td>\n",
       "      <td>65</td>\n",
       "      <td>525000.0</td>\n",
       "      <td>3000</td>\n",
       "      <td>1990-06-12</td>\n",
       "      <td>179</td>\n",
       "      <td>74</td>\n",
       "      <td>FC Botoşani</td>\n",
       "      <td>Romanian Liga I</td>\n",
       "      <td>1</td>\n",
       "      <td>30</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2019-07-01</td>\n",
       "      <td>2022</td>\n",
       "      <td>Romania</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Left</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>Medium/High</td>\n",
       "      <td>Normal (170-185)</td>\n",
       "      <td>No</td>\n",
       "      <td>709000.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Solid Player</td>\n",
       "      <td>74.0</td>\n",
       "      <td>53.0</td>\n",
       "      <td>59.0</td>\n",
       "      <td>53.0</td>\n",
       "      <td>61.0</td>\n",
       "      <td>69.0</td>\n",
       "      <td>64</td>\n",
       "      <td>38</td>\n",
       "      <td>49</td>\n",
       "      <td>63</td>\n",
       "      <td>46</td>\n",
       "      <td>43</td>\n",
       "      <td>53</td>\n",
       "      <td>58</td>\n",
       "      <td>55</td>\n",
       "      <td>56</td>\n",
       "      <td>71</td>\n",
       "      <td>76</td>\n",
       "      <td>70</td>\n",
       "      <td>60</td>\n",
       "      <td>71</td>\n",
       "      <td>72</td>\n",
       "      <td>80</td>\n",
       "      <td>78</td>\n",
       "      <td>70</td>\n",
       "      <td>67</td>\n",
       "      <td>53</td>\n",
       "      <td>63</td>\n",
       "      <td>60</td>\n",
       "      <td>52</td>\n",
       "      <td>52</td>\n",
       "      <td>58</td>\n",
       "      <td>64</td>\n",
       "      <td>61</td>\n",
       "      <td>58</td>\n",
       "      <td>12</td>\n",
       "      <td>5</td>\n",
       "      <td>11</td>\n",
       "      <td>12</td>\n",
       "      <td>15</td>\n",
       "      <td>NaN</td>\n",
       "      <td>LB</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   index      id   short_name  overall  potential  value_eur  wage_eur  \\\n",
       "0      0  216302    E. García       71         71  1400000.0     10000   \n",
       "1      1  237867   D. Cancola       65         71  1000000.0      2000   \n",
       "2      2  253472      E. Kahl       65         77  1600000.0      2000   \n",
       "3      3  223994    S. Mugoša       72         72  2300000.0      5000   \n",
       "4      4  251635  A. Țigănașu       65         65   525000.0      3000   \n",
       "\n",
       "  birthday_date  height_cm  weight_kg                  club_name  \\\n",
       "0    1989-12-28        176         73  Club Atlético de San Luis   \n",
       "1    1996-10-23        183         73             Ross County FC   \n",
       "2    2001-09-27        178         69                  Aarhus GF   \n",
       "3    1992-02-26        188         81          Incheon United FC   \n",
       "4    1990-06-12        179         74                FC Botoşani   \n",
       "\n",
       "            league_name  league_level  club_jersey_number club_loaned_from  \\\n",
       "0       Mexican Liga MX             1                  29              NaN   \n",
       "1  Scottish Premiership             1                   4              NaN   \n",
       "2      Danish Superliga             1                  19              NaN   \n",
       "3     Korean K League 1             1                   9              NaN   \n",
       "4       Romanian Liga I             1                  30              NaN   \n",
       "\n",
       "  club_joined  club_contract_valid_until nationality_name  \\\n",
       "0  2021-07-12                       2026           Mexico   \n",
       "1  2021-07-23                       2022          Austria   \n",
       "2  2021-07-24                       2026           Sweden   \n",
       "3  2018-02-08                       2023       Montenegro   \n",
       "4  2019-07-01                       2022          Romania   \n",
       "\n",
       "   nation_jersey_number preferred_foot  weak_foot  skill_moves  \\\n",
       "0                   NaN          Right          5            3   \n",
       "1                   NaN          Right          3            2   \n",
       "2                   NaN           Left          3            3   \n",
       "3                   NaN          Right          3            3   \n",
       "4                   NaN           Left          2            2   \n",
       "\n",
       "   international_reputation      work_rate         body_type real_face  \\\n",
       "0                         1    Medium/High  Normal (170-185)        No   \n",
       "1                         1  Medium/Medium    Lean (170-185)        No   \n",
       "2                         1    High/Medium  Normal (170-185)        No   \n",
       "3                         1    High/Medium       Lean (185+)        No   \n",
       "4                         1    Medium/High  Normal (170-185)        No   \n",
       "\n",
       "   release_clause_eur player_tags        player_traits  pace  shooting  \\\n",
       "0           2400000.0         NaN        Early Crosser  70.0      52.0   \n",
       "1           2000000.0         NaN                  NaN  65.0      38.0   \n",
       "2           2300000.0         NaN  Speed Dribbler (AI)  79.0      35.0   \n",
       "3           2900000.0         NaN         Finesse Shot  64.0      74.0   \n",
       "4            709000.0         NaN         Solid Player  74.0      53.0   \n",
       "\n",
       "   passing  dribbling  defending  physic  attacking_crossing  \\\n",
       "0     60.0       70.0       67.0    66.0                  64   \n",
       "1     58.0       60.0       63.0    67.0                  50   \n",
       "2     58.0       66.0       59.0    64.0                  59   \n",
       "3     51.0       68.0       24.0    76.0                  32   \n",
       "4     59.0       53.0       61.0    69.0                  64   \n",
       "\n",
       "   attacking_finishing  attacking_heading_accuracy  attacking_short_passing  \\\n",
       "0                   40                          56                       65   \n",
       "1                   31                          59                       64   \n",
       "2                   30                          53                       60   \n",
       "3                   78                          75                       59   \n",
       "4                   38                          49                       63   \n",
       "\n",
       "   attacking_volleys  skill_dribbling  skill_curve  skill_fk_accuracy  \\\n",
       "0                 41               68           64                 58   \n",
       "1                 31               57           36                 41   \n",
       "2                 37               65           56                 35   \n",
       "3                 70               65           48                 53   \n",
       "4                 46               43           53                 58   \n",
       "\n",
       "   skill_long_passing  skill_ball_control  movement_acceleration  \\\n",
       "0                  58                  68                     71   \n",
       "1                  64                  65                     64   \n",
       "2                  55                  64                     82   \n",
       "3                  45                  74                     61   \n",
       "4                  55                  56                     71   \n",
       "\n",
       "   movement_sprint_speed  movement_agility  movement_reactions  \\\n",
       "0                     70                87                  61   \n",
       "1                     65                57                  62   \n",
       "2                     77                71                  62   \n",
       "3                     66                64                  67   \n",
       "4                     76                70                  60   \n",
       "\n",
       "   movement_balance  power_shot_power  power_jumping  power_stamina  \\\n",
       "0                86                67             77             86   \n",
       "1                67                49             62             71   \n",
       "2                75                50             65             67   \n",
       "3                53                71             71             75   \n",
       "4                71                72             80             78   \n",
       "\n",
       "   power_strength  power_long_shots  mentality_aggression  \\\n",
       "0              58                66                    56   \n",
       "1              66                42                    66   \n",
       "2              61                28                    69   \n",
       "3              87                69                    52   \n",
       "4              70                67                    53   \n",
       "\n",
       "   mentality_interceptions  mentality_positioning  mentality_vision  \\\n",
       "0                       79                     53                48   \n",
       "1                       66                     48                62   \n",
       "2                       62                     58                60   \n",
       "3                       18                     76                60   \n",
       "4                       63                     60                52   \n",
       "\n",
       "   mentality_penalties  mentality_composure  defending_marking_awareness  \\\n",
       "0                   58                   66                           65   \n",
       "1                   46                   59                           65   \n",
       "2                   31                   65                           60   \n",
       "3                   75                   75                           16   \n",
       "4                   52                   58                           64   \n",
       "\n",
       "   defending_standing_tackle  defending_sliding_tackle  goalkeeping_diving  \\\n",
       "0                         66                        65                  14   \n",
       "1                         61                        58                  10   \n",
       "2                         58                        59                  10   \n",
       "3                         22                        19                  16   \n",
       "4                         61                        58                  12   \n",
       "\n",
       "   goalkeeping_handling  goalkeeping_kicking  goalkeeping_positioning  \\\n",
       "0                    11                   12                       12   \n",
       "1                    13                    7                        6   \n",
       "2                    10                    8                       10   \n",
       "3                    15                   13                        8   \n",
       "4                     5                   11                       12   \n",
       "\n",
       "   goalkeeping_reflexes  goalkeeping_speed position  test_train  \n",
       "0                    12                NaN       LB           1  \n",
       "1                    11                NaN      LDM           1  \n",
       "2                    11                NaN      LWB           1  \n",
       "3                     9                NaN       LS           1  \n",
       "4                    15                NaN       LB           1  "
      ]
     },
     "execution_count": 126,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "all_data.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 127,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(7662, 72)"
      ]
     },
     "execution_count": 127,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "all_data.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 128,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "index                         0\n",
       "id                            0\n",
       "short_name                    0\n",
       "overall                       0\n",
       "potential                     0\n",
       "                           ... \n",
       "goalkeeping_positioning       0\n",
       "goalkeeping_reflexes          0\n",
       "goalkeeping_speed          6961\n",
       "position                    767\n",
       "test_train                    0\n",
       "Length: 72, dtype: int64"
      ]
     },
     "execution_count": 128,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "all_data.isna().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 129,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Features with missing values:\n",
      "['value_eur', 'club_loaned_from', 'club_joined', 'nation_jersey_number', 'release_clause_eur', 'player_tags', 'player_traits', 'pace', 'shooting', 'passing', 'dribbling', 'defending', 'physic', 'goalkeeping_speed', 'position']\n",
      "\n",
      "Count of missing values for each feature:\n",
      "value_eur                  5\n",
      "club_loaned_from        7266\n",
      "club_joined              396\n",
      "nation_jersey_number    7198\n",
      "release_clause_eur       401\n",
      "player_tags             6806\n",
      "player_traits           2874\n",
      "pace                     701\n",
      "shooting                 701\n",
      "passing                  701\n",
      "dribbling                701\n",
      "defending                701\n",
      "physic                   701\n",
      "goalkeeping_speed       6961\n",
      "position                 767\n",
      "dtype: int64\n"
     ]
    }
   ],
   "source": [
    "# Identify features with missing values\n",
    "features_with_missing_values = all_data.columns[all_data.isnull().any()].tolist()\n",
    "\n",
    "# Display the count of missing values for each feature\n",
    "missing_values_count = all_data[features_with_missing_values].isnull().sum()\n",
    "\n",
    "# Print the results\n",
    "print(\"Features with missing values:\")\n",
    "print(features_with_missing_values)\n",
    "\n",
    "print(\"\\nCount of missing values for each feature:\")\n",
    "print(missing_values_count)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 130,
   "metadata": {},
   "outputs": [],
   "source": [
    "#commenting out just to check for GK classification\n",
    "#eliminate all with over 80% missing data\n",
    "#missing_percentages = all_data.isnull().sum() / len(all_data) * 100\n",
    "#threshold = 80\n",
    "#columns_to_drop = missing_percentages[missing_percentages >= threshold].index\n",
    "#all_data = all_data.drop(columns_to_drop, axis=1)\n",
    "#all_data.isna().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 131,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Also drop the ones I don't think apport any value\n",
    "columns1 = ['short_name', 'club_name', 'league_name', 'nationality_name']\n",
    "all_data = all_data.drop(columns1, axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 132,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Features with missing values:\n",
      "['value_eur', 'club_loaned_from', 'club_joined', 'nation_jersey_number', 'release_clause_eur', 'player_tags', 'player_traits', 'pace', 'shooting', 'passing', 'dribbling', 'defending', 'physic', 'goalkeeping_speed', 'position']\n",
      "\n",
      "Count of missing values for each feature:\n",
      "value_eur                  5\n",
      "club_loaned_from        7266\n",
      "club_joined              396\n",
      "nation_jersey_number    7198\n",
      "release_clause_eur       401\n",
      "player_tags             6806\n",
      "player_traits           2874\n",
      "pace                     701\n",
      "shooting                 701\n",
      "passing                  701\n",
      "dribbling                701\n",
      "defending                701\n",
      "physic                   701\n",
      "goalkeeping_speed       6961\n",
      "position                 767\n",
      "dtype: int64\n"
     ]
    }
   ],
   "source": [
    "# Identify features with missing values\n",
    "features_with_missing_values = all_data.columns[all_data.isnull().any()].tolist()\n",
    "\n",
    "# Display the count of missing values for each feature\n",
    "missing_values_count = all_data[features_with_missing_values].isnull().sum()\n",
    "\n",
    "# Print the results\n",
    "print(\"Features with missing values:\")\n",
    "print(features_with_missing_values)\n",
    "\n",
    "print(\"\\nCount of missing values for each feature:\")\n",
    "print(missing_values_count)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 133,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(7662, 68)"
      ]
     },
     "execution_count": 133,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "all_data.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 134,
   "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>value_eur</th>\n",
       "      <th>club_loaned_from</th>\n",
       "      <th>club_joined</th>\n",
       "      <th>nation_jersey_number</th>\n",
       "      <th>release_clause_eur</th>\n",
       "      <th>player_tags</th>\n",
       "      <th>player_traits</th>\n",
       "      <th>pace</th>\n",
       "      <th>shooting</th>\n",
       "      <th>passing</th>\n",
       "      <th>dribbling</th>\n",
       "      <th>defending</th>\n",
       "      <th>physic</th>\n",
       "      <th>goalkeeping_speed</th>\n",
       "      <th>position</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1400000.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2021-07-12</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2400000.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Early Crosser</td>\n",
       "      <td>70.0</td>\n",
       "      <td>52.0</td>\n",
       "      <td>60.0</td>\n",
       "      <td>70.0</td>\n",
       "      <td>67.0</td>\n",
       "      <td>66.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>LB</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1000000.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2021-07-23</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2000000.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>65.0</td>\n",
       "      <td>38.0</td>\n",
       "      <td>58.0</td>\n",
       "      <td>60.0</td>\n",
       "      <td>63.0</td>\n",
       "      <td>67.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>LDM</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1600000.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2021-07-24</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2300000.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Speed Dribbler (AI)</td>\n",
       "      <td>79.0</td>\n",
       "      <td>35.0</td>\n",
       "      <td>58.0</td>\n",
       "      <td>66.0</td>\n",
       "      <td>59.0</td>\n",
       "      <td>64.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>LWB</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2300000.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2018-02-08</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2900000.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Finesse Shot</td>\n",
       "      <td>64.0</td>\n",
       "      <td>74.0</td>\n",
       "      <td>51.0</td>\n",
       "      <td>68.0</td>\n",
       "      <td>24.0</td>\n",
       "      <td>76.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>LS</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>525000.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2019-07-01</td>\n",
       "      <td>NaN</td>\n",
       "      <td>709000.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Solid Player</td>\n",
       "      <td>74.0</td>\n",
       "      <td>53.0</td>\n",
       "      <td>59.0</td>\n",
       "      <td>53.0</td>\n",
       "      <td>61.0</td>\n",
       "      <td>69.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>LB</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   value_eur club_loaned_from club_joined  nation_jersey_number  \\\n",
       "0  1400000.0              NaN  2021-07-12                   NaN   \n",
       "1  1000000.0              NaN  2021-07-23                   NaN   \n",
       "2  1600000.0              NaN  2021-07-24                   NaN   \n",
       "3  2300000.0              NaN  2018-02-08                   NaN   \n",
       "4   525000.0              NaN  2019-07-01                   NaN   \n",
       "\n",
       "   release_clause_eur player_tags        player_traits  pace  shooting  \\\n",
       "0           2400000.0         NaN        Early Crosser  70.0      52.0   \n",
       "1           2000000.0         NaN                  NaN  65.0      38.0   \n",
       "2           2300000.0         NaN  Speed Dribbler (AI)  79.0      35.0   \n",
       "3           2900000.0         NaN         Finesse Shot  64.0      74.0   \n",
       "4            709000.0         NaN         Solid Player  74.0      53.0   \n",
       "\n",
       "   passing  dribbling  defending  physic  goalkeeping_speed position  \n",
       "0     60.0       70.0       67.0    66.0                NaN       LB  \n",
       "1     58.0       60.0       63.0    67.0                NaN      LDM  \n",
       "2     58.0       66.0       59.0    64.0                NaN      LWB  \n",
       "3     51.0       68.0       24.0    76.0                NaN       LS  \n",
       "4     59.0       53.0       61.0    69.0                NaN       LB  "
      ]
     },
     "execution_count": 134,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "all_data[features_with_missing_values].head()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 135,
   "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>index</th>\n",
       "      <th>id</th>\n",
       "      <th>overall</th>\n",
       "      <th>potential</th>\n",
       "      <th>value_eur</th>\n",
       "      <th>wage_eur</th>\n",
       "      <th>height_cm</th>\n",
       "      <th>weight_kg</th>\n",
       "      <th>league_level</th>\n",
       "      <th>club_jersey_number</th>\n",
       "      <th>club_contract_valid_until</th>\n",
       "      <th>nation_jersey_number</th>\n",
       "      <th>weak_foot</th>\n",
       "      <th>skill_moves</th>\n",
       "      <th>international_reputation</th>\n",
       "      <th>release_clause_eur</th>\n",
       "      <th>pace</th>\n",
       "      <th>shooting</th>\n",
       "      <th>passing</th>\n",
       "      <th>dribbling</th>\n",
       "      <th>defending</th>\n",
       "      <th>physic</th>\n",
       "      <th>attacking_crossing</th>\n",
       "      <th>attacking_finishing</th>\n",
       "      <th>attacking_heading_accuracy</th>\n",
       "      <th>attacking_short_passing</th>\n",
       "      <th>attacking_volleys</th>\n",
       "      <th>skill_dribbling</th>\n",
       "      <th>skill_curve</th>\n",
       "      <th>skill_fk_accuracy</th>\n",
       "      <th>skill_long_passing</th>\n",
       "      <th>skill_ball_control</th>\n",
       "      <th>movement_acceleration</th>\n",
       "      <th>movement_sprint_speed</th>\n",
       "      <th>movement_agility</th>\n",
       "      <th>movement_reactions</th>\n",
       "      <th>movement_balance</th>\n",
       "      <th>power_shot_power</th>\n",
       "      <th>power_jumping</th>\n",
       "      <th>power_stamina</th>\n",
       "      <th>power_strength</th>\n",
       "      <th>power_long_shots</th>\n",
       "      <th>mentality_aggression</th>\n",
       "      <th>mentality_interceptions</th>\n",
       "      <th>mentality_positioning</th>\n",
       "      <th>mentality_vision</th>\n",
       "      <th>mentality_penalties</th>\n",
       "      <th>mentality_composure</th>\n",
       "      <th>defending_marking_awareness</th>\n",
       "      <th>defending_standing_tackle</th>\n",
       "      <th>defending_sliding_tackle</th>\n",
       "      <th>goalkeeping_diving</th>\n",
       "      <th>goalkeeping_handling</th>\n",
       "      <th>goalkeeping_kicking</th>\n",
       "      <th>goalkeeping_positioning</th>\n",
       "      <th>goalkeeping_reflexes</th>\n",
       "      <th>goalkeeping_speed</th>\n",
       "      <th>test_train</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>7662.000000</td>\n",
       "      <td>7662.000000</td>\n",
       "      <td>7662.000000</td>\n",
       "      <td>7662.000000</td>\n",
       "      <td>7.657000e+03</td>\n",
       "      <td>7662.000000</td>\n",
       "      <td>7662.000000</td>\n",
       "      <td>7662.000000</td>\n",
       "      <td>7662.000000</td>\n",
       "      <td>7662.000000</td>\n",
       "      <td>7662.000000</td>\n",
       "      <td>464.000000</td>\n",
       "      <td>7662.000000</td>\n",
       "      <td>7662.000000</td>\n",
       "      <td>7662.000000</td>\n",
       "      <td>7.261000e+03</td>\n",
       "      <td>6961.000000</td>\n",
       "      <td>6961.000000</td>\n",
       "      <td>6961.000000</td>\n",
       "      <td>6961.000000</td>\n",
       "      <td>6961.000000</td>\n",
       "      <td>6961.000000</td>\n",
       "      <td>7662.000000</td>\n",
       "      <td>7662.000000</td>\n",
       "      <td>7662.000000</td>\n",
       "      <td>7662.000000</td>\n",
       "      <td>7662.000000</td>\n",
       "      <td>7662.000000</td>\n",
       "      <td>7662.000000</td>\n",
       "      <td>7662.000000</td>\n",
       "      <td>7662.000000</td>\n",
       "      <td>7662.000000</td>\n",
       "      <td>7662.000000</td>\n",
       "      <td>7662.000000</td>\n",
       "      <td>7662.000000</td>\n",
       "      <td>7662.000000</td>\n",
       "      <td>7662.000000</td>\n",
       "      <td>7662.000000</td>\n",
       "      <td>7662.000000</td>\n",
       "      <td>7662.000000</td>\n",
       "      <td>7662.000000</td>\n",
       "      <td>7662.000000</td>\n",
       "      <td>7662.000000</td>\n",
       "      <td>7662.000000</td>\n",
       "      <td>7662.000000</td>\n",
       "      <td>7662.000000</td>\n",
       "      <td>7662.000000</td>\n",
       "      <td>7662.000000</td>\n",
       "      <td>7662.000000</td>\n",
       "      <td>7662.000000</td>\n",
       "      <td>7662.000000</td>\n",
       "      <td>7662.000000</td>\n",
       "      <td>7662.000000</td>\n",
       "      <td>7662.000000</td>\n",
       "      <td>7662.000000</td>\n",
       "      <td>7662.000000</td>\n",
       "      <td>701.000000</td>\n",
       "      <td>7662.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>3140.280084</td>\n",
       "      <td>223616.285435</td>\n",
       "      <td>68.842600</td>\n",
       "      <td>72.048029</td>\n",
       "      <td>4.331453e+06</td>\n",
       "      <td>12030.253198</td>\n",
       "      <td>181.484338</td>\n",
       "      <td>75.552467</td>\n",
       "      <td>1.361655</td>\n",
       "      <td>16.525320</td>\n",
       "      <td>2022.763508</td>\n",
       "      <td>12.056034</td>\n",
       "      <td>2.996346</td>\n",
       "      <td>2.451188</td>\n",
       "      <td>1.147481</td>\n",
       "      <td>8.187938e+06</td>\n",
       "      <td>68.568596</td>\n",
       "      <td>54.301250</td>\n",
       "      <td>60.085045</td>\n",
       "      <td>64.577791</td>\n",
       "      <td>55.609395</td>\n",
       "      <td>68.346215</td>\n",
       "      <td>52.883973</td>\n",
       "      <td>48.222527</td>\n",
       "      <td>55.069956</td>\n",
       "      <td>62.348865</td>\n",
       "      <td>44.775516</td>\n",
       "      <td>58.376273</td>\n",
       "      <td>50.381754</td>\n",
       "      <td>45.283738</td>\n",
       "      <td>56.815583</td>\n",
       "      <td>61.784652</td>\n",
       "      <td>65.905116</td>\n",
       "      <td>66.038110</td>\n",
       "      <td>65.287001</td>\n",
       "      <td>65.020752</td>\n",
       "      <td>64.850692</td>\n",
       "      <td>60.519838</td>\n",
       "      <td>67.255416</td>\n",
       "      <td>67.909554</td>\n",
       "      <td>68.229313</td>\n",
       "      <td>49.838684</td>\n",
       "      <td>59.815714</td>\n",
       "      <td>51.504960</td>\n",
       "      <td>53.130645</td>\n",
       "      <td>56.939441</td>\n",
       "      <td>49.895589</td>\n",
       "      <td>61.901592</td>\n",
       "      <td>51.349517</td>\n",
       "      <td>52.304098</td>\n",
       "      <td>49.800052</td>\n",
       "      <td>15.903289</td>\n",
       "      <td>15.672409</td>\n",
       "      <td>15.545680</td>\n",
       "      <td>15.813234</td>\n",
       "      <td>16.011877</td>\n",
       "      <td>40.375178</td>\n",
       "      <td>0.899896</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>2101.513688</td>\n",
       "      <td>26710.529813</td>\n",
       "      <td>5.951663</td>\n",
       "      <td>6.074939</td>\n",
       "      <td>1.045019e+07</td>\n",
       "      <td>24385.866372</td>\n",
       "      <td>6.860952</td>\n",
       "      <td>7.042419</td>\n",
       "      <td>0.761018</td>\n",
       "      <td>15.487419</td>\n",
       "      <td>1.207030</td>\n",
       "      <td>6.902991</td>\n",
       "      <td>0.685301</td>\n",
       "      <td>0.780693</td>\n",
       "      <td>0.470143</td>\n",
       "      <td>2.044656e+07</td>\n",
       "      <td>11.708722</td>\n",
       "      <td>14.354508</td>\n",
       "      <td>9.574193</td>\n",
       "      <td>9.348321</td>\n",
       "      <td>15.624753</td>\n",
       "      <td>8.509289</td>\n",
       "      <td>17.850296</td>\n",
       "      <td>19.569581</td>\n",
       "      <td>16.905886</td>\n",
       "      <td>13.281468</td>\n",
       "      <td>17.879807</td>\n",
       "      <td>17.897941</td>\n",
       "      <td>18.364759</td>\n",
       "      <td>17.835926</td>\n",
       "      <td>14.149990</td>\n",
       "      <td>15.398347</td>\n",
       "      <td>14.501858</td>\n",
       "      <td>14.401695</td>\n",
       "      <td>14.472439</td>\n",
       "      <td>7.930761</td>\n",
       "      <td>14.235664</td>\n",
       "      <td>13.076355</td>\n",
       "      <td>12.171742</td>\n",
       "      <td>15.096912</td>\n",
       "      <td>11.786433</td>\n",
       "      <td>19.300841</td>\n",
       "      <td>16.272640</td>\n",
       "      <td>20.123523</td>\n",
       "      <td>19.313219</td>\n",
       "      <td>13.559154</td>\n",
       "      <td>15.668827</td>\n",
       "      <td>10.886174</td>\n",
       "      <td>19.658360</td>\n",
       "      <td>20.918691</td>\n",
       "      <td>20.638862</td>\n",
       "      <td>17.466642</td>\n",
       "      <td>16.767293</td>\n",
       "      <td>16.319634</td>\n",
       "      <td>17.191808</td>\n",
       "      <td>17.877237</td>\n",
       "      <td>10.194699</td>\n",
       "      <td>0.300159</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>41.000000</td>\n",
       "      <td>48.000000</td>\n",
       "      <td>53.000000</td>\n",
       "      <td>2.500000e+04</td>\n",
       "      <td>500.000000</td>\n",
       "      <td>156.000000</td>\n",
       "      <td>53.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>2021.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>3.900000e+04</td>\n",
       "      <td>28.000000</td>\n",
       "      <td>18.000000</td>\n",
       "      <td>25.000000</td>\n",
       "      <td>29.000000</td>\n",
       "      <td>16.000000</td>\n",
       "      <td>31.000000</td>\n",
       "      <td>7.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>5.000000</td>\n",
       "      <td>7.000000</td>\n",
       "      <td>4.000000</td>\n",
       "      <td>4.000000</td>\n",
       "      <td>7.000000</td>\n",
       "      <td>6.000000</td>\n",
       "      <td>9.000000</td>\n",
       "      <td>10.000000</td>\n",
       "      <td>15.000000</td>\n",
       "      <td>15.000000</td>\n",
       "      <td>19.000000</td>\n",
       "      <td>32.000000</td>\n",
       "      <td>19.000000</td>\n",
       "      <td>20.000000</td>\n",
       "      <td>27.000000</td>\n",
       "      <td>14.000000</td>\n",
       "      <td>25.000000</td>\n",
       "      <td>4.000000</td>\n",
       "      <td>11.000000</td>\n",
       "      <td>6.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>10.000000</td>\n",
       "      <td>8.000000</td>\n",
       "      <td>12.000000</td>\n",
       "      <td>4.000000</td>\n",
       "      <td>5.000000</td>\n",
       "      <td>6.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>15.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>1148.250000</td>\n",
       "      <td>207642.250000</td>\n",
       "      <td>65.000000</td>\n",
       "      <td>68.000000</td>\n",
       "      <td>7.250000e+05</td>\n",
       "      <td>2000.000000</td>\n",
       "      <td>177.000000</td>\n",
       "      <td>70.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>6.000000</td>\n",
       "      <td>2022.000000</td>\n",
       "      <td>6.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.100000e+06</td>\n",
       "      <td>62.000000</td>\n",
       "      <td>43.000000</td>\n",
       "      <td>54.000000</td>\n",
       "      <td>59.000000</td>\n",
       "      <td>43.000000</td>\n",
       "      <td>63.000000</td>\n",
       "      <td>42.000000</td>\n",
       "      <td>33.000000</td>\n",
       "      <td>48.000000</td>\n",
       "      <td>58.000000</td>\n",
       "      <td>31.000000</td>\n",
       "      <td>53.000000</td>\n",
       "      <td>37.000000</td>\n",
       "      <td>32.000000</td>\n",
       "      <td>50.000000</td>\n",
       "      <td>58.000000</td>\n",
       "      <td>58.000000</td>\n",
       "      <td>59.000000</td>\n",
       "      <td>57.000000</td>\n",
       "      <td>60.000000</td>\n",
       "      <td>56.000000</td>\n",
       "      <td>51.000000</td>\n",
       "      <td>60.000000</td>\n",
       "      <td>63.000000</td>\n",
       "      <td>61.000000</td>\n",
       "      <td>35.000000</td>\n",
       "      <td>51.000000</td>\n",
       "      <td>33.000000</td>\n",
       "      <td>42.000000</td>\n",
       "      <td>48.000000</td>\n",
       "      <td>40.000000</td>\n",
       "      <td>56.000000</td>\n",
       "      <td>35.000000</td>\n",
       "      <td>34.000000</td>\n",
       "      <td>31.000000</td>\n",
       "      <td>8.000000</td>\n",
       "      <td>8.000000</td>\n",
       "      <td>8.000000</td>\n",
       "      <td>8.000000</td>\n",
       "      <td>8.000000</td>\n",
       "      <td>33.000000</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>3063.500000</td>\n",
       "      <td>228152.500000</td>\n",
       "      <td>68.000000</td>\n",
       "      <td>72.000000</td>\n",
       "      <td>1.400000e+06</td>\n",
       "      <td>4000.000000</td>\n",
       "      <td>182.000000</td>\n",
       "      <td>75.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>13.000000</td>\n",
       "      <td>2023.000000</td>\n",
       "      <td>12.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>2.300000e+06</td>\n",
       "      <td>70.000000</td>\n",
       "      <td>57.000000</td>\n",
       "      <td>60.000000</td>\n",
       "      <td>65.000000</td>\n",
       "      <td>61.000000</td>\n",
       "      <td>69.000000</td>\n",
       "      <td>58.000000</td>\n",
       "      <td>52.000000</td>\n",
       "      <td>58.000000</td>\n",
       "      <td>65.000000</td>\n",
       "      <td>46.000000</td>\n",
       "      <td>63.000000</td>\n",
       "      <td>53.000000</td>\n",
       "      <td>44.000000</td>\n",
       "      <td>60.000000</td>\n",
       "      <td>65.000000</td>\n",
       "      <td>68.000000</td>\n",
       "      <td>69.000000</td>\n",
       "      <td>67.000000</td>\n",
       "      <td>65.000000</td>\n",
       "      <td>67.000000</td>\n",
       "      <td>62.000000</td>\n",
       "      <td>69.000000</td>\n",
       "      <td>71.000000</td>\n",
       "      <td>69.000000</td>\n",
       "      <td>55.000000</td>\n",
       "      <td>63.000000</td>\n",
       "      <td>59.000000</td>\n",
       "      <td>59.000000</td>\n",
       "      <td>59.000000</td>\n",
       "      <td>50.000000</td>\n",
       "      <td>63.000000</td>\n",
       "      <td>58.000000</td>\n",
       "      <td>61.000000</td>\n",
       "      <td>58.000000</td>\n",
       "      <td>11.000000</td>\n",
       "      <td>11.000000</td>\n",
       "      <td>11.000000</td>\n",
       "      <td>11.000000</td>\n",
       "      <td>11.000000</td>\n",
       "      <td>42.000000</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>4978.750000</td>\n",
       "      <td>242513.500000</td>\n",
       "      <td>72.000000</td>\n",
       "      <td>76.000000</td>\n",
       "      <td>3.000000e+06</td>\n",
       "      <td>12000.000000</td>\n",
       "      <td>186.000000</td>\n",
       "      <td>80.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>22.000000</td>\n",
       "      <td>2024.000000</td>\n",
       "      <td>18.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>5.400000e+06</td>\n",
       "      <td>77.000000</td>\n",
       "      <td>65.000000</td>\n",
       "      <td>67.000000</td>\n",
       "      <td>71.000000</td>\n",
       "      <td>67.000000</td>\n",
       "      <td>74.000000</td>\n",
       "      <td>66.000000</td>\n",
       "      <td>64.000000</td>\n",
       "      <td>67.000000</td>\n",
       "      <td>70.000000</td>\n",
       "      <td>59.000000</td>\n",
       "      <td>70.000000</td>\n",
       "      <td>64.000000</td>\n",
       "      <td>60.000000</td>\n",
       "      <td>66.000000</td>\n",
       "      <td>71.000000</td>\n",
       "      <td>76.000000</td>\n",
       "      <td>76.000000</td>\n",
       "      <td>75.000000</td>\n",
       "      <td>70.000000</td>\n",
       "      <td>75.000000</td>\n",
       "      <td>70.000000</td>\n",
       "      <td>76.000000</td>\n",
       "      <td>77.000000</td>\n",
       "      <td>76.000000</td>\n",
       "      <td>65.000000</td>\n",
       "      <td>72.000000</td>\n",
       "      <td>67.000000</td>\n",
       "      <td>67.000000</td>\n",
       "      <td>67.000000</td>\n",
       "      <td>62.000000</td>\n",
       "      <td>69.000000</td>\n",
       "      <td>66.000000</td>\n",
       "      <td>68.000000</td>\n",
       "      <td>66.000000</td>\n",
       "      <td>14.000000</td>\n",
       "      <td>14.000000</td>\n",
       "      <td>14.000000</td>\n",
       "      <td>14.000000</td>\n",
       "      <td>14.000000</td>\n",
       "      <td>47.000000</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>6894.000000</td>\n",
       "      <td>264481.000000</td>\n",
       "      <td>93.000000</td>\n",
       "      <td>95.000000</td>\n",
       "      <td>1.940000e+08</td>\n",
       "      <td>350000.000000</td>\n",
       "      <td>203.000000</td>\n",
       "      <td>103.000000</td>\n",
       "      <td>5.000000</td>\n",
       "      <td>99.000000</td>\n",
       "      <td>2031.000000</td>\n",
       "      <td>28.000000</td>\n",
       "      <td>5.000000</td>\n",
       "      <td>5.000000</td>\n",
       "      <td>5.000000</td>\n",
       "      <td>3.735000e+08</td>\n",
       "      <td>97.000000</td>\n",
       "      <td>94.000000</td>\n",
       "      <td>93.000000</td>\n",
       "      <td>95.000000</td>\n",
       "      <td>91.000000</td>\n",
       "      <td>90.000000</td>\n",
       "      <td>94.000000</td>\n",
       "      <td>95.000000</td>\n",
       "      <td>93.000000</td>\n",
       "      <td>94.000000</td>\n",
       "      <td>90.000000</td>\n",
       "      <td>96.000000</td>\n",
       "      <td>94.000000</td>\n",
       "      <td>94.000000</td>\n",
       "      <td>93.000000</td>\n",
       "      <td>96.000000</td>\n",
       "      <td>97.000000</td>\n",
       "      <td>97.000000</td>\n",
       "      <td>96.000000</td>\n",
       "      <td>94.000000</td>\n",
       "      <td>95.000000</td>\n",
       "      <td>94.000000</td>\n",
       "      <td>95.000000</td>\n",
       "      <td>97.000000</td>\n",
       "      <td>95.000000</td>\n",
       "      <td>94.000000</td>\n",
       "      <td>95.000000</td>\n",
       "      <td>91.000000</td>\n",
       "      <td>96.000000</td>\n",
       "      <td>95.000000</td>\n",
       "      <td>93.000000</td>\n",
       "      <td>96.000000</td>\n",
       "      <td>92.000000</td>\n",
       "      <td>93.000000</td>\n",
       "      <td>92.000000</td>\n",
       "      <td>91.000000</td>\n",
       "      <td>92.000000</td>\n",
       "      <td>93.000000</td>\n",
       "      <td>92.000000</td>\n",
       "      <td>90.000000</td>\n",
       "      <td>65.000000</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "             index             id      overall    potential     value_eur  \\\n",
       "count  7662.000000    7662.000000  7662.000000  7662.000000  7.657000e+03   \n",
       "mean   3140.280084  223616.285435    68.842600    72.048029  4.331453e+06   \n",
       "std    2101.513688   26710.529813     5.951663     6.074939  1.045019e+07   \n",
       "min       0.000000      41.000000    48.000000    53.000000  2.500000e+04   \n",
       "25%    1148.250000  207642.250000    65.000000    68.000000  7.250000e+05   \n",
       "50%    3063.500000  228152.500000    68.000000    72.000000  1.400000e+06   \n",
       "75%    4978.750000  242513.500000    72.000000    76.000000  3.000000e+06   \n",
       "max    6894.000000  264481.000000    93.000000    95.000000  1.940000e+08   \n",
       "\n",
       "            wage_eur    height_cm    weight_kg  league_level  \\\n",
       "count    7662.000000  7662.000000  7662.000000   7662.000000   \n",
       "mean    12030.253198   181.484338    75.552467      1.361655   \n",
       "std     24385.866372     6.860952     7.042419      0.761018   \n",
       "min       500.000000   156.000000    53.000000      1.000000   \n",
       "25%      2000.000000   177.000000    70.000000      1.000000   \n",
       "50%      4000.000000   182.000000    75.000000      1.000000   \n",
       "75%     12000.000000   186.000000    80.000000      1.000000   \n",
       "max    350000.000000   203.000000   103.000000      5.000000   \n",
       "\n",
       "       club_jersey_number  club_contract_valid_until  nation_jersey_number  \\\n",
       "count         7662.000000                7662.000000            464.000000   \n",
       "mean            16.525320                2022.763508             12.056034   \n",
       "std             15.487419                   1.207030              6.902991   \n",
       "min              1.000000                2021.000000              1.000000   \n",
       "25%              6.000000                2022.000000              6.000000   \n",
       "50%             13.000000                2023.000000             12.000000   \n",
       "75%             22.000000                2024.000000             18.000000   \n",
       "max             99.000000                2031.000000             28.000000   \n",
       "\n",
       "         weak_foot  skill_moves  international_reputation  release_clause_eur  \\\n",
       "count  7662.000000  7662.000000               7662.000000        7.261000e+03   \n",
       "mean      2.996346     2.451188                  1.147481        8.187938e+06   \n",
       "std       0.685301     0.780693                  0.470143        2.044656e+07   \n",
       "min       1.000000     1.000000                  1.000000        3.900000e+04   \n",
       "25%       3.000000     2.000000                  1.000000        1.100000e+06   \n",
       "50%       3.000000     2.000000                  1.000000        2.300000e+06   \n",
       "75%       3.000000     3.000000                  1.000000        5.400000e+06   \n",
       "max       5.000000     5.000000                  5.000000        3.735000e+08   \n",
       "\n",
       "              pace     shooting      passing    dribbling    defending  \\\n",
       "count  6961.000000  6961.000000  6961.000000  6961.000000  6961.000000   \n",
       "mean     68.568596    54.301250    60.085045    64.577791    55.609395   \n",
       "std      11.708722    14.354508     9.574193     9.348321    15.624753   \n",
       "min      28.000000    18.000000    25.000000    29.000000    16.000000   \n",
       "25%      62.000000    43.000000    54.000000    59.000000    43.000000   \n",
       "50%      70.000000    57.000000    60.000000    65.000000    61.000000   \n",
       "75%      77.000000    65.000000    67.000000    71.000000    67.000000   \n",
       "max      97.000000    94.000000    93.000000    95.000000    91.000000   \n",
       "\n",
       "            physic  attacking_crossing  attacking_finishing  \\\n",
       "count  6961.000000         7662.000000          7662.000000   \n",
       "mean     68.346215           52.883973            48.222527   \n",
       "std       8.509289           17.850296            19.569581   \n",
       "min      31.000000            7.000000             2.000000   \n",
       "25%      63.000000           42.000000            33.000000   \n",
       "50%      69.000000           58.000000            52.000000   \n",
       "75%      74.000000           66.000000            64.000000   \n",
       "max      90.000000           94.000000            95.000000   \n",
       "\n",
       "       attacking_heading_accuracy  attacking_short_passing  attacking_volleys  \\\n",
       "count                 7662.000000              7662.000000        7662.000000   \n",
       "mean                    55.069956                62.348865          44.775516   \n",
       "std                     16.905886                13.281468          17.879807   \n",
       "min                      5.000000                 7.000000           4.000000   \n",
       "25%                     48.000000                58.000000          31.000000   \n",
       "50%                     58.000000                65.000000          46.000000   \n",
       "75%                     67.000000                70.000000          59.000000   \n",
       "max                     93.000000                94.000000          90.000000   \n",
       "\n",
       "       skill_dribbling  skill_curve  skill_fk_accuracy  skill_long_passing  \\\n",
       "count      7662.000000  7662.000000        7662.000000         7662.000000   \n",
       "mean         58.376273    50.381754          45.283738           56.815583   \n",
       "std          17.897941    18.364759          17.835926           14.149990   \n",
       "min           4.000000     7.000000           6.000000            9.000000   \n",
       "25%          53.000000    37.000000          32.000000           50.000000   \n",
       "50%          63.000000    53.000000          44.000000           60.000000   \n",
       "75%          70.000000    64.000000          60.000000           66.000000   \n",
       "max          96.000000    94.000000          94.000000           93.000000   \n",
       "\n",
       "       skill_ball_control  movement_acceleration  movement_sprint_speed  \\\n",
       "count         7662.000000            7662.000000            7662.000000   \n",
       "mean            61.784652              65.905116              66.038110   \n",
       "std             15.398347              14.501858              14.401695   \n",
       "min             10.000000              15.000000              15.000000   \n",
       "25%             58.000000              58.000000              59.000000   \n",
       "50%             65.000000              68.000000              69.000000   \n",
       "75%             71.000000              76.000000              76.000000   \n",
       "max             96.000000              97.000000              97.000000   \n",
       "\n",
       "       movement_agility  movement_reactions  movement_balance  \\\n",
       "count       7662.000000         7662.000000       7662.000000   \n",
       "mean          65.287001           65.020752         64.850692   \n",
       "std           14.472439            7.930761         14.235664   \n",
       "min           19.000000           32.000000         19.000000   \n",
       "25%           57.000000           60.000000         56.000000   \n",
       "50%           67.000000           65.000000         67.000000   \n",
       "75%           75.000000           70.000000         75.000000   \n",
       "max           96.000000           94.000000         95.000000   \n",
       "\n",
       "       power_shot_power  power_jumping  power_stamina  power_strength  \\\n",
       "count       7662.000000    7662.000000    7662.000000     7662.000000   \n",
       "mean          60.519838      67.255416      67.909554       68.229313   \n",
       "std           13.076355      12.171742      15.096912       11.786433   \n",
       "min           20.000000      27.000000      14.000000       25.000000   \n",
       "25%           51.000000      60.000000      63.000000       61.000000   \n",
       "50%           62.000000      69.000000      71.000000       69.000000   \n",
       "75%           70.000000      76.000000      77.000000       76.000000   \n",
       "max           94.000000      95.000000      97.000000       95.000000   \n",
       "\n",
       "       power_long_shots  mentality_aggression  mentality_interceptions  \\\n",
       "count       7662.000000           7662.000000              7662.000000   \n",
       "mean          49.838684             59.815714                51.504960   \n",
       "std           19.300841             16.272640                20.123523   \n",
       "min            4.000000             11.000000                 6.000000   \n",
       "25%           35.000000             51.000000                33.000000   \n",
       "50%           55.000000             63.000000                59.000000   \n",
       "75%           65.000000             72.000000                67.000000   \n",
       "max           94.000000             95.000000                91.000000   \n",
       "\n",
       "       mentality_positioning  mentality_vision  mentality_penalties  \\\n",
       "count            7662.000000       7662.000000          7662.000000   \n",
       "mean               53.130645         56.939441            49.895589   \n",
       "std                19.313219         13.559154            15.668827   \n",
       "min                 2.000000         10.000000             8.000000   \n",
       "25%                42.000000         48.000000            40.000000   \n",
       "50%                59.000000         59.000000            50.000000   \n",
       "75%                67.000000         67.000000            62.000000   \n",
       "max                96.000000         95.000000            93.000000   \n",
       "\n",
       "       mentality_composure  defending_marking_awareness  \\\n",
       "count          7662.000000                  7662.000000   \n",
       "mean             61.901592                    51.349517   \n",
       "std              10.886174                    19.658360   \n",
       "min              12.000000                     4.000000   \n",
       "25%              56.000000                    35.000000   \n",
       "50%              63.000000                    58.000000   \n",
       "75%              69.000000                    66.000000   \n",
       "max              96.000000                    92.000000   \n",
       "\n",
       "       defending_standing_tackle  defending_sliding_tackle  \\\n",
       "count                7662.000000               7662.000000   \n",
       "mean                   52.304098                 49.800052   \n",
       "std                    20.918691                 20.638862   \n",
       "min                     5.000000                  6.000000   \n",
       "25%                    34.000000                 31.000000   \n",
       "50%                    61.000000                 58.000000   \n",
       "75%                    68.000000                 66.000000   \n",
       "max                    93.000000                 92.000000   \n",
       "\n",
       "       goalkeeping_diving  goalkeeping_handling  goalkeeping_kicking  \\\n",
       "count         7662.000000           7662.000000          7662.000000   \n",
       "mean            15.903289             15.672409            15.545680   \n",
       "std             17.466642             16.767293            16.319634   \n",
       "min              2.000000              2.000000             2.000000   \n",
       "25%              8.000000              8.000000             8.000000   \n",
       "50%             11.000000             11.000000            11.000000   \n",
       "75%             14.000000             14.000000            14.000000   \n",
       "max             91.000000             92.000000            93.000000   \n",
       "\n",
       "       goalkeeping_positioning  goalkeeping_reflexes  goalkeeping_speed  \\\n",
       "count              7662.000000           7662.000000         701.000000   \n",
       "mean                 15.813234             16.011877          40.375178   \n",
       "std                  17.191808             17.877237          10.194699   \n",
       "min                   2.000000              2.000000          15.000000   \n",
       "25%                   8.000000              8.000000          33.000000   \n",
       "50%                  11.000000             11.000000          42.000000   \n",
       "75%                  14.000000             14.000000          47.000000   \n",
       "max                  92.000000             90.000000          65.000000   \n",
       "\n",
       "        test_train  \n",
       "count  7662.000000  \n",
       "mean      0.899896  \n",
       "std       0.300159  \n",
       "min       0.000000  \n",
       "25%       1.000000  \n",
       "50%       1.000000  \n",
       "75%       1.000000  \n",
       "max       1.000000  "
      ]
     },
     "execution_count": 135,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "all_data.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 136,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 7662 entries, 0 to 7661\n",
      "Data columns (total 68 columns):\n",
      " #   Column                       Non-Null Count  Dtype  \n",
      "---  ------                       --------------  -----  \n",
      " 0   index                        7662 non-null   int64  \n",
      " 1   id                           7662 non-null   int64  \n",
      " 2   overall                      7662 non-null   int64  \n",
      " 3   potential                    7662 non-null   int64  \n",
      " 4   value_eur                    7657 non-null   float64\n",
      " 5   wage_eur                     7662 non-null   int64  \n",
      " 6   birthday_date                7662 non-null   object \n",
      " 7   height_cm                    7662 non-null   int64  \n",
      " 8   weight_kg                    7662 non-null   int64  \n",
      " 9   league_level                 7662 non-null   int64  \n",
      " 10  club_jersey_number           7662 non-null   int64  \n",
      " 11  club_loaned_from             396 non-null    object \n",
      " 12  club_joined                  7266 non-null   object \n",
      " 13  club_contract_valid_until    7662 non-null   int64  \n",
      " 14  nation_jersey_number         464 non-null    float64\n",
      " 15  preferred_foot               7662 non-null   object \n",
      " 16  weak_foot                    7662 non-null   int64  \n",
      " 17  skill_moves                  7662 non-null   int64  \n",
      " 18  international_reputation     7662 non-null   int64  \n",
      " 19  work_rate                    7662 non-null   object \n",
      " 20  body_type                    7662 non-null   object \n",
      " 21  real_face                    7662 non-null   object \n",
      " 22  release_clause_eur           7261 non-null   float64\n",
      " 23  player_tags                  856 non-null    object \n",
      " 24  player_traits                4788 non-null   object \n",
      " 25  pace                         6961 non-null   float64\n",
      " 26  shooting                     6961 non-null   float64\n",
      " 27  passing                      6961 non-null   float64\n",
      " 28  dribbling                    6961 non-null   float64\n",
      " 29  defending                    6961 non-null   float64\n",
      " 30  physic                       6961 non-null   float64\n",
      " 31  attacking_crossing           7662 non-null   int64  \n",
      " 32  attacking_finishing          7662 non-null   int64  \n",
      " 33  attacking_heading_accuracy   7662 non-null   int64  \n",
      " 34  attacking_short_passing      7662 non-null   int64  \n",
      " 35  attacking_volleys            7662 non-null   int64  \n",
      " 36  skill_dribbling              7662 non-null   int64  \n",
      " 37  skill_curve                  7662 non-null   int64  \n",
      " 38  skill_fk_accuracy            7662 non-null   int64  \n",
      " 39  skill_long_passing           7662 non-null   int64  \n",
      " 40  skill_ball_control           7662 non-null   int64  \n",
      " 41  movement_acceleration        7662 non-null   int64  \n",
      " 42  movement_sprint_speed        7662 non-null   int64  \n",
      " 43  movement_agility             7662 non-null   int64  \n",
      " 44  movement_reactions           7662 non-null   int64  \n",
      " 45  movement_balance             7662 non-null   int64  \n",
      " 46  power_shot_power             7662 non-null   int64  \n",
      " 47  power_jumping                7662 non-null   int64  \n",
      " 48  power_stamina                7662 non-null   int64  \n",
      " 49  power_strength               7662 non-null   int64  \n",
      " 50  power_long_shots             7662 non-null   int64  \n",
      " 51  mentality_aggression         7662 non-null   int64  \n",
      " 52  mentality_interceptions      7662 non-null   int64  \n",
      " 53  mentality_positioning        7662 non-null   int64  \n",
      " 54  mentality_vision             7662 non-null   int64  \n",
      " 55  mentality_penalties          7662 non-null   int64  \n",
      " 56  mentality_composure          7662 non-null   int64  \n",
      " 57  defending_marking_awareness  7662 non-null   int64  \n",
      " 58  defending_standing_tackle    7662 non-null   int64  \n",
      " 59  defending_sliding_tackle     7662 non-null   int64  \n",
      " 60  goalkeeping_diving           7662 non-null   int64  \n",
      " 61  goalkeeping_handling         7662 non-null   int64  \n",
      " 62  goalkeeping_kicking          7662 non-null   int64  \n",
      " 63  goalkeeping_positioning      7662 non-null   int64  \n",
      " 64  goalkeeping_reflexes         7662 non-null   int64  \n",
      " 65  goalkeeping_speed            701 non-null    float64\n",
      " 66  position                     6895 non-null   object \n",
      " 67  test_train                   7662 non-null   int64  \n",
      "dtypes: float64(10), int64(48), object(10)\n",
      "memory usage: 4.0+ MB\n"
     ]
    }
   ],
   "source": [
    "all_data.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 137,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The number of unique traits is 48\n",
      "Early Crosser: 216\n",
      "Speed Dribbler (AI): 358\n",
      "Finesse Shot: 272\n",
      "Solid Player: 359\n",
      "Cautious With Crosses: 54\n",
      "Leadership: 371\n",
      " Long Passer (AI): 314\n",
      " Long Shot Taker (AI): 513\n",
      "Long Throw-in: 270\n",
      " Comes For Crosses: 78\n",
      "Injury Prone: 452\n",
      " Playmaker (AI): 365\n",
      " Outside Foot Shot: 317\n",
      " Technical Dribbler (AI): 620\n",
      " Leadership: 192\n",
      " Team Player: 396\n",
      " Dives Into Tackles (AI): 124\n",
      " Speed Dribbler (AI): 337\n",
      " Power Header: 230\n",
      "Playmaker (AI): 116\n",
      "Dives Into Tackles (AI): 378\n",
      "Long Shot Taker (AI): 329\n",
      "Flair: 416\n",
      " Flair: 225\n",
      "Long Passer (AI): 300\n",
      " Giant Throw-in: 24\n",
      "Power Header: 216\n",
      "Chip Shot (AI): 13\n",
      "Power Free-Kick: 126\n",
      " Solid Player: 32\n",
      "Technical Dribbler (AI): 164\n",
      "Rushes Out Of Goal: 49\n",
      " Saves with Feet: 67\n",
      "Comes For Crosses: 72\n",
      " Finesse Shot: 88\n",
      "Saves with Feet: 23\n",
      "Outside Foot Shot: 39\n",
      "Team Player: 121\n",
      " Injury Prone: 31\n",
      " Early Crosser: 123\n",
      " Chip Shot (AI): 65\n",
      " Cautious With Crosses: 43\n",
      " GK Long Throw: 19\n",
      " Rushes Out Of Goal: 38\n",
      "GK Long Throw: 64\n",
      " One Club Player: 1\n",
      "Giant Throw-in: 10\n",
      " Power Free-Kick: 9\n"
     ]
    }
   ],
   "source": [
    "# Cleaning player_traits\n",
    "\n",
    "# Function to convert the string list to a list\n",
    "def clean_traits(x):\n",
    "    if isinstance(x, str):\n",
    "        return x.split(',')\n",
    "    else:\n",
    "        return x\n",
    "\n",
    "# Apply function to column\n",
    "all_data['player_traits'] = all_data['player_traits'].apply(clean_traits)\n",
    "\n",
    "# Look at number of unique player traits\n",
    "traits = [trait for sublist in all_data[~all_data['player_traits'].isna()]['player_traits'] for trait in sublist]\n",
    "\n",
    "# Count the occurrences of each string\n",
    "trait_counts = Counter(traits)\n",
    "\n",
    "# Get the unique strings and their counts\n",
    "print('The number of unique traits is ' + str(len(trait_counts.keys())))\n",
    "\n",
    "for key, count in trait_counts.items():\n",
    "    print(f\"{key}: {count}\")\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 138,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 700x1000 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Plot chart with traits\n",
    "keys, counts = zip(*trait_counts.items())\n",
    "\n",
    "fig, ax = plt.subplots(figsize=(7, 10))\n",
    "ax.barh(keys, counts)\n",
    "ax.set_xlabel('Keys')\n",
    "ax.set_ylabel('Counts')\n",
    "ax.set_title('Counts of traits')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 139,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Commenting out the below based on new trait info above\n",
    "\n",
    "# Calculate the frequency of each trait\n",
    "#trait_frequency = all_data['player_traits'].value_counts(normalize=True)\n",
    "\n",
    "# Map the traits to their frequencies\n",
    "#all_data['player_traits_encoded'] = all_data['player_traits'].map(trait_frequency)\n",
    "\n",
    "# Print the result\n",
    "#print(all_data[['player_traits', 'player_traits_encoded']].head())\n",
    "#test = all_data[['player_traits', 'player_traits_encoded']]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 140,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "   yearinclub  age  years_until_contract_expires\n",
      "0         2.0   33                             3\n",
      "1         2.0   27                            -1\n",
      "2         2.0   22                             3\n",
      "3         5.0   31                             0\n",
      "4         4.0   33                            -1\n"
     ]
    }
   ],
   "source": [
    "#encoding dates\n",
    "\n",
    "# Convert 'birthdate' to a datetime object\n",
    "all_data['club_joined'] = pd.to_datetime(all_data['club_joined'])\n",
    "all_data['birthday_date'] = pd.to_datetime(all_data['birthday_date'])\n",
    "\n",
    "# Define the reference date (e.g., the current date)\n",
    "reference_date = datetime.now()\n",
    "\n",
    "# Calculate the age by subtracting the birthdate from the reference date\n",
    "all_data['yearinclub'] = (reference_date - all_data['club_joined']).dt.days // 365\n",
    "all_data['age'] = (reference_date - all_data['birthday_date']).dt.days // 365\n",
    "all_data['years_until_contract_expires'] =  all_data.club_contract_valid_until - datetime.today().year\n",
    "\n",
    "# Print the result\n",
    "print(all_data[['yearinclub', 'age', 'years_until_contract_expires']].head())\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 141,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "['Normal' 'Lean' 'Stocky' 'Unique']\n"
     ]
    }
   ],
   "source": [
    "#extracting just the bodytype since it's accompanied by height of the player and there's already a column for it\n",
    "def extract_bodytype(bodytype_string):\n",
    "    return bodytype_string.split()[0]\n",
    "\n",
    "# Apply the function to the 'bodytype' column\n",
    "all_data['body_type'] = all_data['body_type'].apply(extract_bodytype)\n",
    "\n",
    "print(all_data['body_type'].unique())\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 142,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "['Medium/High' 'Medium/Medium' 'High/Medium' 'High/Low' 'High/High'\n",
      " 'Medium/Low' 'Low/Medium' 'Low/High' 'Low/Low']\n"
     ]
    }
   ],
   "source": [
    "#work_rate\n",
    "print(all_data['work_rate'].unique())\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 143,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Encoding all small unique values categorical data\n",
    "encoder = LabelEncoder()\n",
    "all_data['body_type_encoded'] = encoder.fit_transform(all_data['body_type'])\n",
    "all_data['work_rate_encoded'] = encoder.fit_transform(all_data['work_rate'])\n",
    "all_data['preferred_foot_encoded'] = encoder.fit_transform(all_data['preferred_foot'])\n",
    "all_data['real_face_encoded'] = encoder.fit_transform(all_data['real_face'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 144,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Features with missing values:\n",
      "['value_eur', 'club_loaned_from', 'club_joined', 'nation_jersey_number', 'release_clause_eur', 'player_tags', 'player_traits', 'pace', 'shooting', 'passing', 'dribbling', 'defending', 'physic', 'goalkeeping_speed', 'position', 'yearinclub']\n",
      "\n",
      "Count of missing values for each feature:\n",
      "value_eur                  5\n",
      "club_loaned_from        7266\n",
      "club_joined              396\n",
      "nation_jersey_number    7198\n",
      "release_clause_eur       401\n",
      "player_tags             6806\n",
      "player_traits           2874\n",
      "pace                     701\n",
      "shooting                 701\n",
      "passing                  701\n",
      "dribbling                701\n",
      "defending                701\n",
      "physic                   701\n",
      "goalkeeping_speed       6961\n",
      "position                 767\n",
      "yearinclub               396\n",
      "dtype: int64\n"
     ]
    }
   ],
   "source": [
    "# Identify features with missing values\n",
    "features_with_missing_values = all_data.columns[all_data.isnull().any()].tolist()\n",
    "\n",
    "# Display the count of missing values for each feature\n",
    "missing_values_count = all_data[features_with_missing_values].isnull().sum()\n",
    "\n",
    "# Print the results\n",
    "print(\"Features with missing values:\")\n",
    "print(features_with_missing_values)\n",
    "\n",
    "print(\"\\nCount of missing values for each feature:\")\n",
    "print(missing_values_count)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 145,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "index                           0\n",
       "id                              0\n",
       "overall                         0\n",
       "potential                       0\n",
       "value_eur                       0\n",
       "                               ..\n",
       "years_until_contract_expires    0\n",
       "body_type_encoded               0\n",
       "work_rate_encoded               0\n",
       "preferred_foot_encoded          0\n",
       "real_face_encoded               0\n",
       "Length: 75, dtype: int64"
      ]
     },
     "execution_count": 145,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#we use the KNNimputer to imputate the missing values in the numeric variables\n",
    "def impute(data,columns):\n",
    "    # Create KNNImputer object with k=8\n",
    "    imputer = KNNImputer(n_neighbors=8)\n",
    "    # Perform KNN imputation\n",
    "    data[columns] = imputer.fit_transform(data[columns])\n",
    "    return data[columns]\n",
    "\n",
    "imputer_columns=['value_eur', 'release_clause_eur', 'pace', 'shooting', 'passing', 'dribbling', 'defending', 'physic', 'yearinclub']\n",
    "\n",
    "impute(all_data,imputer_columns)\n",
    "all_data.isna().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 146,
   "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>index</th>\n",
       "      <th>id</th>\n",
       "      <th>overall</th>\n",
       "      <th>potential</th>\n",
       "      <th>value_eur</th>\n",
       "      <th>wage_eur</th>\n",
       "      <th>birthday_date</th>\n",
       "      <th>height_cm</th>\n",
       "      <th>weight_kg</th>\n",
       "      <th>league_level</th>\n",
       "      <th>club_jersey_number</th>\n",
       "      <th>club_loaned_from</th>\n",
       "      <th>club_joined</th>\n",
       "      <th>club_contract_valid_until</th>\n",
       "      <th>nation_jersey_number</th>\n",
       "      <th>preferred_foot</th>\n",
       "      <th>weak_foot</th>\n",
       "      <th>skill_moves</th>\n",
       "      <th>international_reputation</th>\n",
       "      <th>work_rate</th>\n",
       "      <th>body_type</th>\n",
       "      <th>real_face</th>\n",
       "      <th>release_clause_eur</th>\n",
       "      <th>player_tags</th>\n",
       "      <th>player_traits</th>\n",
       "      <th>pace</th>\n",
       "      <th>shooting</th>\n",
       "      <th>passing</th>\n",
       "      <th>dribbling</th>\n",
       "      <th>defending</th>\n",
       "      <th>physic</th>\n",
       "      <th>attacking_crossing</th>\n",
       "      <th>attacking_finishing</th>\n",
       "      <th>attacking_heading_accuracy</th>\n",
       "      <th>attacking_short_passing</th>\n",
       "      <th>attacking_volleys</th>\n",
       "      <th>skill_dribbling</th>\n",
       "      <th>skill_curve</th>\n",
       "      <th>skill_fk_accuracy</th>\n",
       "      <th>skill_long_passing</th>\n",
       "      <th>skill_ball_control</th>\n",
       "      <th>movement_acceleration</th>\n",
       "      <th>movement_sprint_speed</th>\n",
       "      <th>movement_agility</th>\n",
       "      <th>movement_reactions</th>\n",
       "      <th>movement_balance</th>\n",
       "      <th>power_shot_power</th>\n",
       "      <th>power_jumping</th>\n",
       "      <th>power_stamina</th>\n",
       "      <th>power_strength</th>\n",
       "      <th>power_long_shots</th>\n",
       "      <th>mentality_aggression</th>\n",
       "      <th>mentality_interceptions</th>\n",
       "      <th>mentality_positioning</th>\n",
       "      <th>mentality_vision</th>\n",
       "      <th>mentality_penalties</th>\n",
       "      <th>mentality_composure</th>\n",
       "      <th>defending_marking_awareness</th>\n",
       "      <th>defending_standing_tackle</th>\n",
       "      <th>defending_sliding_tackle</th>\n",
       "      <th>goalkeeping_diving</th>\n",
       "      <th>goalkeeping_handling</th>\n",
       "      <th>goalkeeping_kicking</th>\n",
       "      <th>goalkeeping_positioning</th>\n",
       "      <th>goalkeeping_reflexes</th>\n",
       "      <th>goalkeeping_speed</th>\n",
       "      <th>position</th>\n",
       "      <th>test_train</th>\n",
       "      <th>yearinclub</th>\n",
       "      <th>age</th>\n",
       "      <th>years_until_contract_expires</th>\n",
       "      <th>body_type_encoded</th>\n",
       "      <th>work_rate_encoded</th>\n",
       "      <th>preferred_foot_encoded</th>\n",
       "      <th>real_face_encoded</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0</td>\n",
       "      <td>216302</td>\n",
       "      <td>71</td>\n",
       "      <td>71</td>\n",
       "      <td>1400000.0</td>\n",
       "      <td>10000</td>\n",
       "      <td>1989-12-28</td>\n",
       "      <td>176</td>\n",
       "      <td>73</td>\n",
       "      <td>1</td>\n",
       "      <td>29</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2021-07-12</td>\n",
       "      <td>2026</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Right</td>\n",
       "      <td>5</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>Medium/High</td>\n",
       "      <td>Normal</td>\n",
       "      <td>No</td>\n",
       "      <td>2400000.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>[Early Crosser]</td>\n",
       "      <td>70.0</td>\n",
       "      <td>52.0</td>\n",
       "      <td>60.0</td>\n",
       "      <td>70.0</td>\n",
       "      <td>67.0</td>\n",
       "      <td>66.0</td>\n",
       "      <td>64</td>\n",
       "      <td>40</td>\n",
       "      <td>56</td>\n",
       "      <td>65</td>\n",
       "      <td>41</td>\n",
       "      <td>68</td>\n",
       "      <td>64</td>\n",
       "      <td>58</td>\n",
       "      <td>58</td>\n",
       "      <td>68</td>\n",
       "      <td>71</td>\n",
       "      <td>70</td>\n",
       "      <td>87</td>\n",
       "      <td>61</td>\n",
       "      <td>86</td>\n",
       "      <td>67</td>\n",
       "      <td>77</td>\n",
       "      <td>86</td>\n",
       "      <td>58</td>\n",
       "      <td>66</td>\n",
       "      <td>56</td>\n",
       "      <td>79</td>\n",
       "      <td>53</td>\n",
       "      <td>48</td>\n",
       "      <td>58</td>\n",
       "      <td>66</td>\n",
       "      <td>65</td>\n",
       "      <td>66</td>\n",
       "      <td>65</td>\n",
       "      <td>14</td>\n",
       "      <td>11</td>\n",
       "      <td>12</td>\n",
       "      <td>12</td>\n",
       "      <td>12</td>\n",
       "      <td>NaN</td>\n",
       "      <td>LB</td>\n",
       "      <td>1</td>\n",
       "      <td>2.0</td>\n",
       "      <td>33</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>6</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>237867</td>\n",
       "      <td>65</td>\n",
       "      <td>71</td>\n",
       "      <td>1000000.0</td>\n",
       "      <td>2000</td>\n",
       "      <td>1996-10-23</td>\n",
       "      <td>183</td>\n",
       "      <td>73</td>\n",
       "      <td>1</td>\n",
       "      <td>4</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2021-07-23</td>\n",
       "      <td>2022</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Right</td>\n",
       "      <td>3</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>Medium/Medium</td>\n",
       "      <td>Lean</td>\n",
       "      <td>No</td>\n",
       "      <td>2000000.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>65.0</td>\n",
       "      <td>38.0</td>\n",
       "      <td>58.0</td>\n",
       "      <td>60.0</td>\n",
       "      <td>63.0</td>\n",
       "      <td>67.0</td>\n",
       "      <td>50</td>\n",
       "      <td>31</td>\n",
       "      <td>59</td>\n",
       "      <td>64</td>\n",
       "      <td>31</td>\n",
       "      <td>57</td>\n",
       "      <td>36</td>\n",
       "      <td>41</td>\n",
       "      <td>64</td>\n",
       "      <td>65</td>\n",
       "      <td>64</td>\n",
       "      <td>65</td>\n",
       "      <td>57</td>\n",
       "      <td>62</td>\n",
       "      <td>67</td>\n",
       "      <td>49</td>\n",
       "      <td>62</td>\n",
       "      <td>71</td>\n",
       "      <td>66</td>\n",
       "      <td>42</td>\n",
       "      <td>66</td>\n",
       "      <td>66</td>\n",
       "      <td>48</td>\n",
       "      <td>62</td>\n",
       "      <td>46</td>\n",
       "      <td>59</td>\n",
       "      <td>65</td>\n",
       "      <td>61</td>\n",
       "      <td>58</td>\n",
       "      <td>10</td>\n",
       "      <td>13</td>\n",
       "      <td>7</td>\n",
       "      <td>6</td>\n",
       "      <td>11</td>\n",
       "      <td>NaN</td>\n",
       "      <td>LDM</td>\n",
       "      <td>1</td>\n",
       "      <td>2.0</td>\n",
       "      <td>27</td>\n",
       "      <td>-1</td>\n",
       "      <td>0</td>\n",
       "      <td>8</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2</td>\n",
       "      <td>253472</td>\n",
       "      <td>65</td>\n",
       "      <td>77</td>\n",
       "      <td>1600000.0</td>\n",
       "      <td>2000</td>\n",
       "      <td>2001-09-27</td>\n",
       "      <td>178</td>\n",
       "      <td>69</td>\n",
       "      <td>1</td>\n",
       "      <td>19</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2021-07-24</td>\n",
       "      <td>2026</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Left</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>High/Medium</td>\n",
       "      <td>Normal</td>\n",
       "      <td>No</td>\n",
       "      <td>2300000.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>[Speed Dribbler (AI)]</td>\n",
       "      <td>79.0</td>\n",
       "      <td>35.0</td>\n",
       "      <td>58.0</td>\n",
       "      <td>66.0</td>\n",
       "      <td>59.0</td>\n",
       "      <td>64.0</td>\n",
       "      <td>59</td>\n",
       "      <td>30</td>\n",
       "      <td>53</td>\n",
       "      <td>60</td>\n",
       "      <td>37</td>\n",
       "      <td>65</td>\n",
       "      <td>56</td>\n",
       "      <td>35</td>\n",
       "      <td>55</td>\n",
       "      <td>64</td>\n",
       "      <td>82</td>\n",
       "      <td>77</td>\n",
       "      <td>71</td>\n",
       "      <td>62</td>\n",
       "      <td>75</td>\n",
       "      <td>50</td>\n",
       "      <td>65</td>\n",
       "      <td>67</td>\n",
       "      <td>61</td>\n",
       "      <td>28</td>\n",
       "      <td>69</td>\n",
       "      <td>62</td>\n",
       "      <td>58</td>\n",
       "      <td>60</td>\n",
       "      <td>31</td>\n",
       "      <td>65</td>\n",
       "      <td>60</td>\n",
       "      <td>58</td>\n",
       "      <td>59</td>\n",
       "      <td>10</td>\n",
       "      <td>10</td>\n",
       "      <td>8</td>\n",
       "      <td>10</td>\n",
       "      <td>11</td>\n",
       "      <td>NaN</td>\n",
       "      <td>LWB</td>\n",
       "      <td>1</td>\n",
       "      <td>2.0</td>\n",
       "      <td>22</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>3</td>\n",
       "      <td>223994</td>\n",
       "      <td>72</td>\n",
       "      <td>72</td>\n",
       "      <td>2300000.0</td>\n",
       "      <td>5000</td>\n",
       "      <td>1992-02-26</td>\n",
       "      <td>188</td>\n",
       "      <td>81</td>\n",
       "      <td>1</td>\n",
       "      <td>9</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2018-02-08</td>\n",
       "      <td>2023</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Right</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>High/Medium</td>\n",
       "      <td>Lean</td>\n",
       "      <td>No</td>\n",
       "      <td>2900000.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>[Finesse Shot]</td>\n",
       "      <td>64.0</td>\n",
       "      <td>74.0</td>\n",
       "      <td>51.0</td>\n",
       "      <td>68.0</td>\n",
       "      <td>24.0</td>\n",
       "      <td>76.0</td>\n",
       "      <td>32</td>\n",
       "      <td>78</td>\n",
       "      <td>75</td>\n",
       "      <td>59</td>\n",
       "      <td>70</td>\n",
       "      <td>65</td>\n",
       "      <td>48</td>\n",
       "      <td>53</td>\n",
       "      <td>45</td>\n",
       "      <td>74</td>\n",
       "      <td>61</td>\n",
       "      <td>66</td>\n",
       "      <td>64</td>\n",
       "      <td>67</td>\n",
       "      <td>53</td>\n",
       "      <td>71</td>\n",
       "      <td>71</td>\n",
       "      <td>75</td>\n",
       "      <td>87</td>\n",
       "      <td>69</td>\n",
       "      <td>52</td>\n",
       "      <td>18</td>\n",
       "      <td>76</td>\n",
       "      <td>60</td>\n",
       "      <td>75</td>\n",
       "      <td>75</td>\n",
       "      <td>16</td>\n",
       "      <td>22</td>\n",
       "      <td>19</td>\n",
       "      <td>16</td>\n",
       "      <td>15</td>\n",
       "      <td>13</td>\n",
       "      <td>8</td>\n",
       "      <td>9</td>\n",
       "      <td>NaN</td>\n",
       "      <td>LS</td>\n",
       "      <td>1</td>\n",
       "      <td>5.0</td>\n",
       "      <td>31</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>4</td>\n",
       "      <td>251635</td>\n",
       "      <td>65</td>\n",
       "      <td>65</td>\n",
       "      <td>525000.0</td>\n",
       "      <td>3000</td>\n",
       "      <td>1990-06-12</td>\n",
       "      <td>179</td>\n",
       "      <td>74</td>\n",
       "      <td>1</td>\n",
       "      <td>30</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2019-07-01</td>\n",
       "      <td>2022</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Left</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>Medium/High</td>\n",
       "      <td>Normal</td>\n",
       "      <td>No</td>\n",
       "      <td>709000.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>[Solid Player]</td>\n",
       "      <td>74.0</td>\n",
       "      <td>53.0</td>\n",
       "      <td>59.0</td>\n",
       "      <td>53.0</td>\n",
       "      <td>61.0</td>\n",
       "      <td>69.0</td>\n",
       "      <td>64</td>\n",
       "      <td>38</td>\n",
       "      <td>49</td>\n",
       "      <td>63</td>\n",
       "      <td>46</td>\n",
       "      <td>43</td>\n",
       "      <td>53</td>\n",
       "      <td>58</td>\n",
       "      <td>55</td>\n",
       "      <td>56</td>\n",
       "      <td>71</td>\n",
       "      <td>76</td>\n",
       "      <td>70</td>\n",
       "      <td>60</td>\n",
       "      <td>71</td>\n",
       "      <td>72</td>\n",
       "      <td>80</td>\n",
       "      <td>78</td>\n",
       "      <td>70</td>\n",
       "      <td>67</td>\n",
       "      <td>53</td>\n",
       "      <td>63</td>\n",
       "      <td>60</td>\n",
       "      <td>52</td>\n",
       "      <td>52</td>\n",
       "      <td>58</td>\n",
       "      <td>64</td>\n",
       "      <td>61</td>\n",
       "      <td>58</td>\n",
       "      <td>12</td>\n",
       "      <td>5</td>\n",
       "      <td>11</td>\n",
       "      <td>12</td>\n",
       "      <td>15</td>\n",
       "      <td>NaN</td>\n",
       "      <td>LB</td>\n",
       "      <td>1</td>\n",
       "      <td>4.0</td>\n",
       "      <td>33</td>\n",
       "      <td>-1</td>\n",
       "      <td>1</td>\n",
       "      <td>6</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   index      id  overall  potential  value_eur  wage_eur birthday_date  \\\n",
       "0      0  216302       71         71  1400000.0     10000    1989-12-28   \n",
       "1      1  237867       65         71  1000000.0      2000    1996-10-23   \n",
       "2      2  253472       65         77  1600000.0      2000    2001-09-27   \n",
       "3      3  223994       72         72  2300000.0      5000    1992-02-26   \n",
       "4      4  251635       65         65   525000.0      3000    1990-06-12   \n",
       "\n",
       "   height_cm  weight_kg  league_level  club_jersey_number club_loaned_from  \\\n",
       "0        176         73             1                  29              NaN   \n",
       "1        183         73             1                   4              NaN   \n",
       "2        178         69             1                  19              NaN   \n",
       "3        188         81             1                   9              NaN   \n",
       "4        179         74             1                  30              NaN   \n",
       "\n",
       "  club_joined  club_contract_valid_until  nation_jersey_number preferred_foot  \\\n",
       "0  2021-07-12                       2026                   NaN          Right   \n",
       "1  2021-07-23                       2022                   NaN          Right   \n",
       "2  2021-07-24                       2026                   NaN           Left   \n",
       "3  2018-02-08                       2023                   NaN          Right   \n",
       "4  2019-07-01                       2022                   NaN           Left   \n",
       "\n",
       "   weak_foot  skill_moves  international_reputation      work_rate body_type  \\\n",
       "0          5            3                         1    Medium/High    Normal   \n",
       "1          3            2                         1  Medium/Medium      Lean   \n",
       "2          3            3                         1    High/Medium    Normal   \n",
       "3          3            3                         1    High/Medium      Lean   \n",
       "4          2            2                         1    Medium/High    Normal   \n",
       "\n",
       "  real_face  release_clause_eur player_tags          player_traits  pace  \\\n",
       "0        No           2400000.0         NaN        [Early Crosser]  70.0   \n",
       "1        No           2000000.0         NaN                    NaN  65.0   \n",
       "2        No           2300000.0         NaN  [Speed Dribbler (AI)]  79.0   \n",
       "3        No           2900000.0         NaN         [Finesse Shot]  64.0   \n",
       "4        No            709000.0         NaN         [Solid Player]  74.0   \n",
       "\n",
       "   shooting  passing  dribbling  defending  physic  attacking_crossing  \\\n",
       "0      52.0     60.0       70.0       67.0    66.0                  64   \n",
       "1      38.0     58.0       60.0       63.0    67.0                  50   \n",
       "2      35.0     58.0       66.0       59.0    64.0                  59   \n",
       "3      74.0     51.0       68.0       24.0    76.0                  32   \n",
       "4      53.0     59.0       53.0       61.0    69.0                  64   \n",
       "\n",
       "   attacking_finishing  attacking_heading_accuracy  attacking_short_passing  \\\n",
       "0                   40                          56                       65   \n",
       "1                   31                          59                       64   \n",
       "2                   30                          53                       60   \n",
       "3                   78                          75                       59   \n",
       "4                   38                          49                       63   \n",
       "\n",
       "   attacking_volleys  skill_dribbling  skill_curve  skill_fk_accuracy  \\\n",
       "0                 41               68           64                 58   \n",
       "1                 31               57           36                 41   \n",
       "2                 37               65           56                 35   \n",
       "3                 70               65           48                 53   \n",
       "4                 46               43           53                 58   \n",
       "\n",
       "   skill_long_passing  skill_ball_control  movement_acceleration  \\\n",
       "0                  58                  68                     71   \n",
       "1                  64                  65                     64   \n",
       "2                  55                  64                     82   \n",
       "3                  45                  74                     61   \n",
       "4                  55                  56                     71   \n",
       "\n",
       "   movement_sprint_speed  movement_agility  movement_reactions  \\\n",
       "0                     70                87                  61   \n",
       "1                     65                57                  62   \n",
       "2                     77                71                  62   \n",
       "3                     66                64                  67   \n",
       "4                     76                70                  60   \n",
       "\n",
       "   movement_balance  power_shot_power  power_jumping  power_stamina  \\\n",
       "0                86                67             77             86   \n",
       "1                67                49             62             71   \n",
       "2                75                50             65             67   \n",
       "3                53                71             71             75   \n",
       "4                71                72             80             78   \n",
       "\n",
       "   power_strength  power_long_shots  mentality_aggression  \\\n",
       "0              58                66                    56   \n",
       "1              66                42                    66   \n",
       "2              61                28                    69   \n",
       "3              87                69                    52   \n",
       "4              70                67                    53   \n",
       "\n",
       "   mentality_interceptions  mentality_positioning  mentality_vision  \\\n",
       "0                       79                     53                48   \n",
       "1                       66                     48                62   \n",
       "2                       62                     58                60   \n",
       "3                       18                     76                60   \n",
       "4                       63                     60                52   \n",
       "\n",
       "   mentality_penalties  mentality_composure  defending_marking_awareness  \\\n",
       "0                   58                   66                           65   \n",
       "1                   46                   59                           65   \n",
       "2                   31                   65                           60   \n",
       "3                   75                   75                           16   \n",
       "4                   52                   58                           64   \n",
       "\n",
       "   defending_standing_tackle  defending_sliding_tackle  goalkeeping_diving  \\\n",
       "0                         66                        65                  14   \n",
       "1                         61                        58                  10   \n",
       "2                         58                        59                  10   \n",
       "3                         22                        19                  16   \n",
       "4                         61                        58                  12   \n",
       "\n",
       "   goalkeeping_handling  goalkeeping_kicking  goalkeeping_positioning  \\\n",
       "0                    11                   12                       12   \n",
       "1                    13                    7                        6   \n",
       "2                    10                    8                       10   \n",
       "3                    15                   13                        8   \n",
       "4                     5                   11                       12   \n",
       "\n",
       "   goalkeeping_reflexes  goalkeeping_speed position  test_train  yearinclub  \\\n",
       "0                    12                NaN       LB           1         2.0   \n",
       "1                    11                NaN      LDM           1         2.0   \n",
       "2                    11                NaN      LWB           1         2.0   \n",
       "3                     9                NaN       LS           1         5.0   \n",
       "4                    15                NaN       LB           1         4.0   \n",
       "\n",
       "   age  years_until_contract_expires  body_type_encoded  work_rate_encoded  \\\n",
       "0   33                             3                  1                  6   \n",
       "1   27                            -1                  0                  8   \n",
       "2   22                             3                  1                  2   \n",
       "3   31                             0                  0                  2   \n",
       "4   33                            -1                  1                  6   \n",
       "\n",
       "   preferred_foot_encoded  real_face_encoded  \n",
       "0                       1                  0  \n",
       "1                       1                  0  \n",
       "2                       0                  0  \n",
       "3                       1                  0  \n",
       "4                       0                  0  "
      ]
     },
     "execution_count": 146,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "all_data.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 147,
   "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>index</th>\n",
       "      <th>id</th>\n",
       "      <th>overall</th>\n",
       "      <th>potential</th>\n",
       "      <th>value_eur</th>\n",
       "      <th>wage_eur</th>\n",
       "      <th>birthday_date</th>\n",
       "      <th>height_cm</th>\n",
       "      <th>weight_kg</th>\n",
       "      <th>league_level</th>\n",
       "      <th>club_jersey_number</th>\n",
       "      <th>club_loaned_from</th>\n",
       "      <th>club_joined</th>\n",
       "      <th>club_contract_valid_until</th>\n",
       "      <th>nation_jersey_number</th>\n",
       "      <th>preferred_foot</th>\n",
       "      <th>weak_foot</th>\n",
       "      <th>skill_moves</th>\n",
       "      <th>international_reputation</th>\n",
       "      <th>work_rate</th>\n",
       "      <th>body_type</th>\n",
       "      <th>real_face</th>\n",
       "      <th>release_clause_eur</th>\n",
       "      <th>player_tags</th>\n",
       "      <th>player_traits</th>\n",
       "      <th>pace</th>\n",
       "      <th>shooting</th>\n",
       "      <th>passing</th>\n",
       "      <th>dribbling</th>\n",
       "      <th>defending</th>\n",
       "      <th>physic</th>\n",
       "      <th>attacking_crossing</th>\n",
       "      <th>attacking_finishing</th>\n",
       "      <th>attacking_heading_accuracy</th>\n",
       "      <th>attacking_short_passing</th>\n",
       "      <th>attacking_volleys</th>\n",
       "      <th>skill_dribbling</th>\n",
       "      <th>skill_curve</th>\n",
       "      <th>skill_fk_accuracy</th>\n",
       "      <th>skill_long_passing</th>\n",
       "      <th>skill_ball_control</th>\n",
       "      <th>movement_acceleration</th>\n",
       "      <th>movement_sprint_speed</th>\n",
       "      <th>movement_agility</th>\n",
       "      <th>movement_reactions</th>\n",
       "      <th>movement_balance</th>\n",
       "      <th>power_shot_power</th>\n",
       "      <th>power_jumping</th>\n",
       "      <th>power_stamina</th>\n",
       "      <th>power_strength</th>\n",
       "      <th>power_long_shots</th>\n",
       "      <th>mentality_aggression</th>\n",
       "      <th>mentality_interceptions</th>\n",
       "      <th>mentality_positioning</th>\n",
       "      <th>mentality_vision</th>\n",
       "      <th>mentality_penalties</th>\n",
       "      <th>mentality_composure</th>\n",
       "      <th>defending_marking_awareness</th>\n",
       "      <th>defending_standing_tackle</th>\n",
       "      <th>defending_sliding_tackle</th>\n",
       "      <th>goalkeeping_diving</th>\n",
       "      <th>goalkeeping_handling</th>\n",
       "      <th>goalkeeping_kicking</th>\n",
       "      <th>goalkeeping_positioning</th>\n",
       "      <th>goalkeeping_reflexes</th>\n",
       "      <th>goalkeeping_speed</th>\n",
       "      <th>position</th>\n",
       "      <th>test_train</th>\n",
       "      <th>yearinclub</th>\n",
       "      <th>age</th>\n",
       "      <th>years_until_contract_expires</th>\n",
       "      <th>body_type_encoded</th>\n",
       "      <th>work_rate_encoded</th>\n",
       "      <th>preferred_foot_encoded</th>\n",
       "      <th>real_face_encoded</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0</td>\n",
       "      <td>216302</td>\n",
       "      <td>0.511111</td>\n",
       "      <td>0.428571</td>\n",
       "      <td>0.007089</td>\n",
       "      <td>0.027182</td>\n",
       "      <td>1989-12-28</td>\n",
       "      <td>0.425532</td>\n",
       "      <td>0.40</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.285714</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2021-07-12</td>\n",
       "      <td>2026</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Right</td>\n",
       "      <td>1.00</td>\n",
       "      <td>0.50</td>\n",
       "      <td>0.0</td>\n",
       "      <td>Medium/High</td>\n",
       "      <td>Normal</td>\n",
       "      <td>No</td>\n",
       "      <td>0.006322</td>\n",
       "      <td>NaN</td>\n",
       "      <td>[Early Crosser]</td>\n",
       "      <td>0.608696</td>\n",
       "      <td>0.447368</td>\n",
       "      <td>0.514706</td>\n",
       "      <td>0.621212</td>\n",
       "      <td>0.680000</td>\n",
       "      <td>0.593220</td>\n",
       "      <td>0.655172</td>\n",
       "      <td>0.408602</td>\n",
       "      <td>0.579545</td>\n",
       "      <td>65</td>\n",
       "      <td>41</td>\n",
       "      <td>68</td>\n",
       "      <td>64</td>\n",
       "      <td>58</td>\n",
       "      <td>58</td>\n",
       "      <td>68</td>\n",
       "      <td>71</td>\n",
       "      <td>70</td>\n",
       "      <td>87</td>\n",
       "      <td>61</td>\n",
       "      <td>86</td>\n",
       "      <td>67</td>\n",
       "      <td>77</td>\n",
       "      <td>86</td>\n",
       "      <td>0.471429</td>\n",
       "      <td>66</td>\n",
       "      <td>56</td>\n",
       "      <td>0.858824</td>\n",
       "      <td>0.542553</td>\n",
       "      <td>48</td>\n",
       "      <td>58</td>\n",
       "      <td>66</td>\n",
       "      <td>65</td>\n",
       "      <td>66</td>\n",
       "      <td>0.686047</td>\n",
       "      <td>14</td>\n",
       "      <td>11</td>\n",
       "      <td>12</td>\n",
       "      <td>12</td>\n",
       "      <td>0.113636</td>\n",
       "      <td>NaN</td>\n",
       "      <td>LB</td>\n",
       "      <td>1</td>\n",
       "      <td>2.0</td>\n",
       "      <td>33</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>6</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>237867</td>\n",
       "      <td>0.377778</td>\n",
       "      <td>0.428571</td>\n",
       "      <td>0.005026</td>\n",
       "      <td>0.004292</td>\n",
       "      <td>1996-10-23</td>\n",
       "      <td>0.574468</td>\n",
       "      <td>0.40</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.030612</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2021-07-23</td>\n",
       "      <td>2022</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Right</td>\n",
       "      <td>0.50</td>\n",
       "      <td>0.25</td>\n",
       "      <td>0.0</td>\n",
       "      <td>Medium/Medium</td>\n",
       "      <td>Lean</td>\n",
       "      <td>No</td>\n",
       "      <td>0.005251</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.536232</td>\n",
       "      <td>0.263158</td>\n",
       "      <td>0.485294</td>\n",
       "      <td>0.469697</td>\n",
       "      <td>0.626667</td>\n",
       "      <td>0.610169</td>\n",
       "      <td>0.494253</td>\n",
       "      <td>0.311828</td>\n",
       "      <td>0.613636</td>\n",
       "      <td>64</td>\n",
       "      <td>31</td>\n",
       "      <td>57</td>\n",
       "      <td>36</td>\n",
       "      <td>41</td>\n",
       "      <td>64</td>\n",
       "      <td>65</td>\n",
       "      <td>64</td>\n",
       "      <td>65</td>\n",
       "      <td>57</td>\n",
       "      <td>62</td>\n",
       "      <td>67</td>\n",
       "      <td>49</td>\n",
       "      <td>62</td>\n",
       "      <td>71</td>\n",
       "      <td>0.585714</td>\n",
       "      <td>42</td>\n",
       "      <td>66</td>\n",
       "      <td>0.705882</td>\n",
       "      <td>0.489362</td>\n",
       "      <td>62</td>\n",
       "      <td>46</td>\n",
       "      <td>59</td>\n",
       "      <td>65</td>\n",
       "      <td>61</td>\n",
       "      <td>0.604651</td>\n",
       "      <td>10</td>\n",
       "      <td>13</td>\n",
       "      <td>7</td>\n",
       "      <td>6</td>\n",
       "      <td>0.102273</td>\n",
       "      <td>NaN</td>\n",
       "      <td>LDM</td>\n",
       "      <td>1</td>\n",
       "      <td>2.0</td>\n",
       "      <td>27</td>\n",
       "      <td>-1</td>\n",
       "      <td>0</td>\n",
       "      <td>8</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2</td>\n",
       "      <td>253472</td>\n",
       "      <td>0.377778</td>\n",
       "      <td>0.571429</td>\n",
       "      <td>0.008120</td>\n",
       "      <td>0.004292</td>\n",
       "      <td>2001-09-27</td>\n",
       "      <td>0.468085</td>\n",
       "      <td>0.32</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.183673</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2021-07-24</td>\n",
       "      <td>2026</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Left</td>\n",
       "      <td>0.50</td>\n",
       "      <td>0.50</td>\n",
       "      <td>0.0</td>\n",
       "      <td>High/Medium</td>\n",
       "      <td>Normal</td>\n",
       "      <td>No</td>\n",
       "      <td>0.006054</td>\n",
       "      <td>NaN</td>\n",
       "      <td>[Speed Dribbler (AI)]</td>\n",
       "      <td>0.739130</td>\n",
       "      <td>0.223684</td>\n",
       "      <td>0.485294</td>\n",
       "      <td>0.560606</td>\n",
       "      <td>0.573333</td>\n",
       "      <td>0.559322</td>\n",
       "      <td>0.597701</td>\n",
       "      <td>0.301075</td>\n",
       "      <td>0.545455</td>\n",
       "      <td>60</td>\n",
       "      <td>37</td>\n",
       "      <td>65</td>\n",
       "      <td>56</td>\n",
       "      <td>35</td>\n",
       "      <td>55</td>\n",
       "      <td>64</td>\n",
       "      <td>82</td>\n",
       "      <td>77</td>\n",
       "      <td>71</td>\n",
       "      <td>62</td>\n",
       "      <td>75</td>\n",
       "      <td>50</td>\n",
       "      <td>65</td>\n",
       "      <td>67</td>\n",
       "      <td>0.514286</td>\n",
       "      <td>28</td>\n",
       "      <td>69</td>\n",
       "      <td>0.658824</td>\n",
       "      <td>0.595745</td>\n",
       "      <td>60</td>\n",
       "      <td>31</td>\n",
       "      <td>65</td>\n",
       "      <td>60</td>\n",
       "      <td>58</td>\n",
       "      <td>0.616279</td>\n",
       "      <td>10</td>\n",
       "      <td>10</td>\n",
       "      <td>8</td>\n",
       "      <td>10</td>\n",
       "      <td>0.102273</td>\n",
       "      <td>NaN</td>\n",
       "      <td>LWB</td>\n",
       "      <td>1</td>\n",
       "      <td>2.0</td>\n",
       "      <td>22</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>3</td>\n",
       "      <td>223994</td>\n",
       "      <td>0.533333</td>\n",
       "      <td>0.452381</td>\n",
       "      <td>0.011728</td>\n",
       "      <td>0.012876</td>\n",
       "      <td>1992-02-26</td>\n",
       "      <td>0.680851</td>\n",
       "      <td>0.56</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.081633</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2018-02-08</td>\n",
       "      <td>2023</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Right</td>\n",
       "      <td>0.50</td>\n",
       "      <td>0.50</td>\n",
       "      <td>0.0</td>\n",
       "      <td>High/Medium</td>\n",
       "      <td>Lean</td>\n",
       "      <td>No</td>\n",
       "      <td>0.007661</td>\n",
       "      <td>NaN</td>\n",
       "      <td>[Finesse Shot]</td>\n",
       "      <td>0.521739</td>\n",
       "      <td>0.736842</td>\n",
       "      <td>0.382353</td>\n",
       "      <td>0.590909</td>\n",
       "      <td>0.106667</td>\n",
       "      <td>0.762712</td>\n",
       "      <td>0.287356</td>\n",
       "      <td>0.817204</td>\n",
       "      <td>0.795455</td>\n",
       "      <td>59</td>\n",
       "      <td>70</td>\n",
       "      <td>65</td>\n",
       "      <td>48</td>\n",
       "      <td>53</td>\n",
       "      <td>45</td>\n",
       "      <td>74</td>\n",
       "      <td>61</td>\n",
       "      <td>66</td>\n",
       "      <td>64</td>\n",
       "      <td>67</td>\n",
       "      <td>53</td>\n",
       "      <td>71</td>\n",
       "      <td>71</td>\n",
       "      <td>75</td>\n",
       "      <td>0.885714</td>\n",
       "      <td>69</td>\n",
       "      <td>52</td>\n",
       "      <td>0.141176</td>\n",
       "      <td>0.787234</td>\n",
       "      <td>60</td>\n",
       "      <td>75</td>\n",
       "      <td>75</td>\n",
       "      <td>16</td>\n",
       "      <td>22</td>\n",
       "      <td>0.151163</td>\n",
       "      <td>16</td>\n",
       "      <td>15</td>\n",
       "      <td>13</td>\n",
       "      <td>8</td>\n",
       "      <td>0.079545</td>\n",
       "      <td>NaN</td>\n",
       "      <td>LS</td>\n",
       "      <td>1</td>\n",
       "      <td>5.0</td>\n",
       "      <td>31</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>4</td>\n",
       "      <td>251635</td>\n",
       "      <td>0.377778</td>\n",
       "      <td>0.285714</td>\n",
       "      <td>0.002578</td>\n",
       "      <td>0.007153</td>\n",
       "      <td>1990-06-12</td>\n",
       "      <td>0.489362</td>\n",
       "      <td>0.42</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.295918</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2019-07-01</td>\n",
       "      <td>2022</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Left</td>\n",
       "      <td>0.25</td>\n",
       "      <td>0.25</td>\n",
       "      <td>0.0</td>\n",
       "      <td>Medium/High</td>\n",
       "      <td>Normal</td>\n",
       "      <td>No</td>\n",
       "      <td>0.001794</td>\n",
       "      <td>NaN</td>\n",
       "      <td>[Solid Player]</td>\n",
       "      <td>0.666667</td>\n",
       "      <td>0.460526</td>\n",
       "      <td>0.500000</td>\n",
       "      <td>0.363636</td>\n",
       "      <td>0.600000</td>\n",
       "      <td>0.644068</td>\n",
       "      <td>0.655172</td>\n",
       "      <td>0.387097</td>\n",
       "      <td>0.500000</td>\n",
       "      <td>63</td>\n",
       "      <td>46</td>\n",
       "      <td>43</td>\n",
       "      <td>53</td>\n",
       "      <td>58</td>\n",
       "      <td>55</td>\n",
       "      <td>56</td>\n",
       "      <td>71</td>\n",
       "      <td>76</td>\n",
       "      <td>70</td>\n",
       "      <td>60</td>\n",
       "      <td>71</td>\n",
       "      <td>72</td>\n",
       "      <td>80</td>\n",
       "      <td>78</td>\n",
       "      <td>0.642857</td>\n",
       "      <td>67</td>\n",
       "      <td>53</td>\n",
       "      <td>0.670588</td>\n",
       "      <td>0.617021</td>\n",
       "      <td>52</td>\n",
       "      <td>52</td>\n",
       "      <td>58</td>\n",
       "      <td>64</td>\n",
       "      <td>61</td>\n",
       "      <td>0.604651</td>\n",
       "      <td>12</td>\n",
       "      <td>5</td>\n",
       "      <td>11</td>\n",
       "      <td>12</td>\n",
       "      <td>0.147727</td>\n",
       "      <td>NaN</td>\n",
       "      <td>LB</td>\n",
       "      <td>1</td>\n",
       "      <td>4.0</td>\n",
       "      <td>33</td>\n",
       "      <td>-1</td>\n",
       "      <td>1</td>\n",
       "      <td>6</td>\n",
       "      <td>0.0</td>\n",
       "      <td>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",
       "      <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",
       "      <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",
       "      <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>7657</th>\n",
       "      <td>762</td>\n",
       "      <td>205601</td>\n",
       "      <td>0.533333</td>\n",
       "      <td>0.452381</td>\n",
       "      <td>0.011728</td>\n",
       "      <td>0.047210</td>\n",
       "      <td>1992-01-10</td>\n",
       "      <td>0.191489</td>\n",
       "      <td>0.24</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.020408</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2021-07-17</td>\n",
       "      <td>2025</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Left</td>\n",
       "      <td>0.50</td>\n",
       "      <td>0.50</td>\n",
       "      <td>0.0</td>\n",
       "      <td>Medium/Low</td>\n",
       "      <td>Lean</td>\n",
       "      <td>Yes</td>\n",
       "      <td>0.010071</td>\n",
       "      <td>NaN</td>\n",
       "      <td>[Speed Dribbler (AI)]</td>\n",
       "      <td>0.782609</td>\n",
       "      <td>0.631579</td>\n",
       "      <td>0.632353</td>\n",
       "      <td>0.696970</td>\n",
       "      <td>0.240000</td>\n",
       "      <td>0.152542</td>\n",
       "      <td>0.701149</td>\n",
       "      <td>0.655914</td>\n",
       "      <td>0.477273</td>\n",
       "      <td>70</td>\n",
       "      <td>58</td>\n",
       "      <td>76</td>\n",
       "      <td>70</td>\n",
       "      <td>58</td>\n",
       "      <td>61</td>\n",
       "      <td>72</td>\n",
       "      <td>86</td>\n",
       "      <td>78</td>\n",
       "      <td>86</td>\n",
       "      <td>65</td>\n",
       "      <td>87</td>\n",
       "      <td>72</td>\n",
       "      <td>65</td>\n",
       "      <td>50</td>\n",
       "      <td>0.085714</td>\n",
       "      <td>66</td>\n",
       "      <td>43</td>\n",
       "      <td>0.341176</td>\n",
       "      <td>0.755319</td>\n",
       "      <td>71</td>\n",
       "      <td>70</td>\n",
       "      <td>64</td>\n",
       "      <td>30</td>\n",
       "      <td>33</td>\n",
       "      <td>0.302326</td>\n",
       "      <td>13</td>\n",
       "      <td>12</td>\n",
       "      <td>12</td>\n",
       "      <td>10</td>\n",
       "      <td>0.079545</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>31</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>7</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7658</th>\n",
       "      <td>763</td>\n",
       "      <td>223752</td>\n",
       "      <td>0.444444</td>\n",
       "      <td>0.500000</td>\n",
       "      <td>0.008635</td>\n",
       "      <td>0.004292</td>\n",
       "      <td>1996-04-17</td>\n",
       "      <td>0.510638</td>\n",
       "      <td>0.34</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.010204</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2019-01-01</td>\n",
       "      <td>2022</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Right</td>\n",
       "      <td>0.25</td>\n",
       "      <td>0.25</td>\n",
       "      <td>0.0</td>\n",
       "      <td>Low/High</td>\n",
       "      <td>Stocky</td>\n",
       "      <td>No</td>\n",
       "      <td>0.006857</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.579710</td>\n",
       "      <td>0.342105</td>\n",
       "      <td>0.367647</td>\n",
       "      <td>0.303030</td>\n",
       "      <td>0.653333</td>\n",
       "      <td>0.847458</td>\n",
       "      <td>0.517241</td>\n",
       "      <td>0.397849</td>\n",
       "      <td>0.750000</td>\n",
       "      <td>59</td>\n",
       "      <td>46</td>\n",
       "      <td>34</td>\n",
       "      <td>35</td>\n",
       "      <td>29</td>\n",
       "      <td>53</td>\n",
       "      <td>60</td>\n",
       "      <td>66</td>\n",
       "      <td>69</td>\n",
       "      <td>65</td>\n",
       "      <td>63</td>\n",
       "      <td>57</td>\n",
       "      <td>52</td>\n",
       "      <td>91</td>\n",
       "      <td>78</td>\n",
       "      <td>0.857143</td>\n",
       "      <td>45</td>\n",
       "      <td>70</td>\n",
       "      <td>0.705882</td>\n",
       "      <td>0.489362</td>\n",
       "      <td>41</td>\n",
       "      <td>37</td>\n",
       "      <td>57</td>\n",
       "      <td>64</td>\n",
       "      <td>65</td>\n",
       "      <td>0.662791</td>\n",
       "      <td>8</td>\n",
       "      <td>10</td>\n",
       "      <td>8</td>\n",
       "      <td>15</td>\n",
       "      <td>0.079545</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>27</td>\n",
       "      <td>-1</td>\n",
       "      <td>2</td>\n",
       "      <td>3</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7659</th>\n",
       "      <td>764</td>\n",
       "      <td>192450</td>\n",
       "      <td>0.377778</td>\n",
       "      <td>0.285714</td>\n",
       "      <td>0.003480</td>\n",
       "      <td>0.015737</td>\n",
       "      <td>1990-10-18</td>\n",
       "      <td>0.617021</td>\n",
       "      <td>0.54</td>\n",
       "      <td>0.75</td>\n",
       "      <td>0.081633</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2021-07-01</td>\n",
       "      <td>2023</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Right</td>\n",
       "      <td>0.75</td>\n",
       "      <td>0.50</td>\n",
       "      <td>0.0</td>\n",
       "      <td>Low/Low</td>\n",
       "      <td>Stocky</td>\n",
       "      <td>No</td>\n",
       "      <td>0.003109</td>\n",
       "      <td>#Strength</td>\n",
       "      <td>[Power Header]</td>\n",
       "      <td>0.536232</td>\n",
       "      <td>0.605263</td>\n",
       "      <td>0.397059</td>\n",
       "      <td>0.484848</td>\n",
       "      <td>0.160000</td>\n",
       "      <td>0.898305</td>\n",
       "      <td>0.379310</td>\n",
       "      <td>0.677419</td>\n",
       "      <td>0.681818</td>\n",
       "      <td>58</td>\n",
       "      <td>61</td>\n",
       "      <td>59</td>\n",
       "      <td>58</td>\n",
       "      <td>40</td>\n",
       "      <td>53</td>\n",
       "      <td>62</td>\n",
       "      <td>66</td>\n",
       "      <td>65</td>\n",
       "      <td>64</td>\n",
       "      <td>63</td>\n",
       "      <td>66</td>\n",
       "      <td>68</td>\n",
       "      <td>83</td>\n",
       "      <td>68</td>\n",
       "      <td>0.928571</td>\n",
       "      <td>60</td>\n",
       "      <td>87</td>\n",
       "      <td>0.200000</td>\n",
       "      <td>0.670213</td>\n",
       "      <td>56</td>\n",
       "      <td>56</td>\n",
       "      <td>61</td>\n",
       "      <td>22</td>\n",
       "      <td>26</td>\n",
       "      <td>0.220930</td>\n",
       "      <td>11</td>\n",
       "      <td>11</td>\n",
       "      <td>10</td>\n",
       "      <td>7</td>\n",
       "      <td>0.090909</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>33</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>4</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7660</th>\n",
       "      <td>765</td>\n",
       "      <td>192366</td>\n",
       "      <td>0.733333</td>\n",
       "      <td>0.666667</td>\n",
       "      <td>0.064312</td>\n",
       "      <td>0.050072</td>\n",
       "      <td>1988-02-12</td>\n",
       "      <td>0.574468</td>\n",
       "      <td>0.46</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.295918</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2020-09-29</td>\n",
       "      <td>2023</td>\n",
       "      <td>19.0</td>\n",
       "      <td>Right</td>\n",
       "      <td>0.50</td>\n",
       "      <td>0.25</td>\n",
       "      <td>0.5</td>\n",
       "      <td>Medium/High</td>\n",
       "      <td>Normal</td>\n",
       "      <td>Yes</td>\n",
       "      <td>0.066837</td>\n",
       "      <td>NaN</td>\n",
       "      <td>[Solid Player,  Dives Into Tackles (AI),  Lead...</td>\n",
       "      <td>0.507246</td>\n",
       "      <td>0.513158</td>\n",
       "      <td>0.544118</td>\n",
       "      <td>0.454545</td>\n",
       "      <td>0.893333</td>\n",
       "      <td>0.847458</td>\n",
       "      <td>0.517241</td>\n",
       "      <td>0.559140</td>\n",
       "      <td>0.840909</td>\n",
       "      <td>73</td>\n",
       "      <td>57</td>\n",
       "      <td>51</td>\n",
       "      <td>50</td>\n",
       "      <td>39</td>\n",
       "      <td>70</td>\n",
       "      <td>66</td>\n",
       "      <td>60</td>\n",
       "      <td>65</td>\n",
       "      <td>58</td>\n",
       "      <td>79</td>\n",
       "      <td>58</td>\n",
       "      <td>69</td>\n",
       "      <td>88</td>\n",
       "      <td>75</td>\n",
       "      <td>0.785714</td>\n",
       "      <td>56</td>\n",
       "      <td>90</td>\n",
       "      <td>0.917647</td>\n",
       "      <td>0.489362</td>\n",
       "      <td>53</td>\n",
       "      <td>45</td>\n",
       "      <td>79</td>\n",
       "      <td>84</td>\n",
       "      <td>83</td>\n",
       "      <td>0.860465</td>\n",
       "      <td>12</td>\n",
       "      <td>5</td>\n",
       "      <td>8</td>\n",
       "      <td>11</td>\n",
       "      <td>0.113636</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>35</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>6</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7661</th>\n",
       "      <td>766</td>\n",
       "      <td>232228</td>\n",
       "      <td>0.466667</td>\n",
       "      <td>0.500000</td>\n",
       "      <td>0.010697</td>\n",
       "      <td>0.010014</td>\n",
       "      <td>1996-01-21</td>\n",
       "      <td>0.574468</td>\n",
       "      <td>0.40</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.285714</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2021-07-13</td>\n",
       "      <td>2024</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Right</td>\n",
       "      <td>0.50</td>\n",
       "      <td>0.25</td>\n",
       "      <td>0.0</td>\n",
       "      <td>Medium/Medium</td>\n",
       "      <td>Normal</td>\n",
       "      <td>No</td>\n",
       "      <td>0.012213</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.536232</td>\n",
       "      <td>0.684211</td>\n",
       "      <td>0.367647</td>\n",
       "      <td>0.575758</td>\n",
       "      <td>0.080000</td>\n",
       "      <td>0.525424</td>\n",
       "      <td>0.390805</td>\n",
       "      <td>0.774194</td>\n",
       "      <td>0.715909</td>\n",
       "      <td>58</td>\n",
       "      <td>64</td>\n",
       "      <td>68</td>\n",
       "      <td>54</td>\n",
       "      <td>42</td>\n",
       "      <td>50</td>\n",
       "      <td>69</td>\n",
       "      <td>63</td>\n",
       "      <td>67</td>\n",
       "      <td>57</td>\n",
       "      <td>72</td>\n",
       "      <td>60</td>\n",
       "      <td>69</td>\n",
       "      <td>63</td>\n",
       "      <td>64</td>\n",
       "      <td>0.542857</td>\n",
       "      <td>61</td>\n",
       "      <td>57</td>\n",
       "      <td>0.141176</td>\n",
       "      <td>0.723404</td>\n",
       "      <td>47</td>\n",
       "      <td>71</td>\n",
       "      <td>59</td>\n",
       "      <td>17</td>\n",
       "      <td>15</td>\n",
       "      <td>0.139535</td>\n",
       "      <td>12</td>\n",
       "      <td>13</td>\n",
       "      <td>12</td>\n",
       "      <td>7</td>\n",
       "      <td>0.113636</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>27</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>8</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>7662 rows × 75 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "      index      id   overall  potential  value_eur  wage_eur birthday_date  \\\n",
       "0         0  216302  0.511111   0.428571   0.007089  0.027182    1989-12-28   \n",
       "1         1  237867  0.377778   0.428571   0.005026  0.004292    1996-10-23   \n",
       "2         2  253472  0.377778   0.571429   0.008120  0.004292    2001-09-27   \n",
       "3         3  223994  0.533333   0.452381   0.011728  0.012876    1992-02-26   \n",
       "4         4  251635  0.377778   0.285714   0.002578  0.007153    1990-06-12   \n",
       "...     ...     ...       ...        ...        ...       ...           ...   \n",
       "7657    762  205601  0.533333   0.452381   0.011728  0.047210    1992-01-10   \n",
       "7658    763  223752  0.444444   0.500000   0.008635  0.004292    1996-04-17   \n",
       "7659    764  192450  0.377778   0.285714   0.003480  0.015737    1990-10-18   \n",
       "7660    765  192366  0.733333   0.666667   0.064312  0.050072    1988-02-12   \n",
       "7661    766  232228  0.466667   0.500000   0.010697  0.010014    1996-01-21   \n",
       "\n",
       "      height_cm  weight_kg  league_level  club_jersey_number club_loaned_from  \\\n",
       "0      0.425532       0.40          0.00            0.285714              NaN   \n",
       "1      0.574468       0.40          0.00            0.030612              NaN   \n",
       "2      0.468085       0.32          0.00            0.183673              NaN   \n",
       "3      0.680851       0.56          0.00            0.081633              NaN   \n",
       "4      0.489362       0.42          0.00            0.295918              NaN   \n",
       "...         ...        ...           ...                 ...              ...   \n",
       "7657   0.191489       0.24          0.00            0.020408              NaN   \n",
       "7658   0.510638       0.34          0.00            0.010204              NaN   \n",
       "7659   0.617021       0.54          0.75            0.081633              NaN   \n",
       "7660   0.574468       0.46          0.00            0.295918              NaN   \n",
       "7661   0.574468       0.40          0.00            0.285714              NaN   \n",
       "\n",
       "     club_joined  club_contract_valid_until  nation_jersey_number  \\\n",
       "0     2021-07-12                       2026                   NaN   \n",
       "1     2021-07-23                       2022                   NaN   \n",
       "2     2021-07-24                       2026                   NaN   \n",
       "3     2018-02-08                       2023                   NaN   \n",
       "4     2019-07-01                       2022                   NaN   \n",
       "...          ...                        ...                   ...   \n",
       "7657  2021-07-17                       2025                   NaN   \n",
       "7658  2019-01-01                       2022                   NaN   \n",
       "7659  2021-07-01                       2023                   NaN   \n",
       "7660  2020-09-29                       2023                  19.0   \n",
       "7661  2021-07-13                       2024                   NaN   \n",
       "\n",
       "     preferred_foot  weak_foot  skill_moves  international_reputation  \\\n",
       "0             Right       1.00         0.50                       0.0   \n",
       "1             Right       0.50         0.25                       0.0   \n",
       "2              Left       0.50         0.50                       0.0   \n",
       "3             Right       0.50         0.50                       0.0   \n",
       "4              Left       0.25         0.25                       0.0   \n",
       "...             ...        ...          ...                       ...   \n",
       "7657           Left       0.50         0.50                       0.0   \n",
       "7658          Right       0.25         0.25                       0.0   \n",
       "7659          Right       0.75         0.50                       0.0   \n",
       "7660          Right       0.50         0.25                       0.5   \n",
       "7661          Right       0.50         0.25                       0.0   \n",
       "\n",
       "          work_rate body_type real_face  release_clause_eur player_tags  \\\n",
       "0       Medium/High    Normal        No            0.006322         NaN   \n",
       "1     Medium/Medium      Lean        No            0.005251         NaN   \n",
       "2       High/Medium    Normal        No            0.006054         NaN   \n",
       "3       High/Medium      Lean        No            0.007661         NaN   \n",
       "4       Medium/High    Normal        No            0.001794         NaN   \n",
       "...             ...       ...       ...                 ...         ...   \n",
       "7657     Medium/Low      Lean       Yes            0.010071         NaN   \n",
       "7658       Low/High    Stocky        No            0.006857         NaN   \n",
       "7659        Low/Low    Stocky        No            0.003109   #Strength   \n",
       "7660    Medium/High    Normal       Yes            0.066837         NaN   \n",
       "7661  Medium/Medium    Normal        No            0.012213         NaN   \n",
       "\n",
       "                                          player_traits      pace  shooting  \\\n",
       "0                                       [Early Crosser]  0.608696  0.447368   \n",
       "1                                                   NaN  0.536232  0.263158   \n",
       "2                                 [Speed Dribbler (AI)]  0.739130  0.223684   \n",
       "3                                        [Finesse Shot]  0.521739  0.736842   \n",
       "4                                        [Solid Player]  0.666667  0.460526   \n",
       "...                                                 ...       ...       ...   \n",
       "7657                              [Speed Dribbler (AI)]  0.782609  0.631579   \n",
       "7658                                                NaN  0.579710  0.342105   \n",
       "7659                                     [Power Header]  0.536232  0.605263   \n",
       "7660  [Solid Player,  Dives Into Tackles (AI),  Lead...  0.507246  0.513158   \n",
       "7661                                                NaN  0.536232  0.684211   \n",
       "\n",
       "       passing  dribbling  defending    physic  attacking_crossing  \\\n",
       "0     0.514706   0.621212   0.680000  0.593220            0.655172   \n",
       "1     0.485294   0.469697   0.626667  0.610169            0.494253   \n",
       "2     0.485294   0.560606   0.573333  0.559322            0.597701   \n",
       "3     0.382353   0.590909   0.106667  0.762712            0.287356   \n",
       "4     0.500000   0.363636   0.600000  0.644068            0.655172   \n",
       "...        ...        ...        ...       ...                 ...   \n",
       "7657  0.632353   0.696970   0.240000  0.152542            0.701149   \n",
       "7658  0.367647   0.303030   0.653333  0.847458            0.517241   \n",
       "7659  0.397059   0.484848   0.160000  0.898305            0.379310   \n",
       "7660  0.544118   0.454545   0.893333  0.847458            0.517241   \n",
       "7661  0.367647   0.575758   0.080000  0.525424            0.390805   \n",
       "\n",
       "      attacking_finishing  attacking_heading_accuracy  \\\n",
       "0                0.408602                    0.579545   \n",
       "1                0.311828                    0.613636   \n",
       "2                0.301075                    0.545455   \n",
       "3                0.817204                    0.795455   \n",
       "4                0.387097                    0.500000   \n",
       "...                   ...                         ...   \n",
       "7657             0.655914                    0.477273   \n",
       "7658             0.397849                    0.750000   \n",
       "7659             0.677419                    0.681818   \n",
       "7660             0.559140                    0.840909   \n",
       "7661             0.774194                    0.715909   \n",
       "\n",
       "      attacking_short_passing  attacking_volleys  skill_dribbling  \\\n",
       "0                          65                 41               68   \n",
       "1                          64                 31               57   \n",
       "2                          60                 37               65   \n",
       "3                          59                 70               65   \n",
       "4                          63                 46               43   \n",
       "...                       ...                ...              ...   \n",
       "7657                       70                 58               76   \n",
       "7658                       59                 46               34   \n",
       "7659                       58                 61               59   \n",
       "7660                       73                 57               51   \n",
       "7661                       58                 64               68   \n",
       "\n",
       "      skill_curve  skill_fk_accuracy  skill_long_passing  skill_ball_control  \\\n",
       "0              64                 58                  58                  68   \n",
       "1              36                 41                  64                  65   \n",
       "2              56                 35                  55                  64   \n",
       "3              48                 53                  45                  74   \n",
       "4              53                 58                  55                  56   \n",
       "...           ...                ...                 ...                 ...   \n",
       "7657           70                 58                  61                  72   \n",
       "7658           35                 29                  53                  60   \n",
       "7659           58                 40                  53                  62   \n",
       "7660           50                 39                  70                  66   \n",
       "7661           54                 42                  50                  69   \n",
       "\n",
       "      movement_acceleration  movement_sprint_speed  movement_agility  \\\n",
       "0                        71                     70                87   \n",
       "1                        64                     65                57   \n",
       "2                        82                     77                71   \n",
       "3                        61                     66                64   \n",
       "4                        71                     76                70   \n",
       "...                     ...                    ...               ...   \n",
       "7657                     86                     78                86   \n",
       "7658                     66                     69                65   \n",
       "7659                     66                     65                64   \n",
       "7660                     60                     65                58   \n",
       "7661                     63                     67                57   \n",
       "\n",
       "      movement_reactions  movement_balance  power_shot_power  power_jumping  \\\n",
       "0                     61                86                67             77   \n",
       "1                     62                67                49             62   \n",
       "2                     62                75                50             65   \n",
       "3                     67                53                71             71   \n",
       "4                     60                71                72             80   \n",
       "...                  ...               ...               ...            ...   \n",
       "7657                  65                87                72             65   \n",
       "7658                  63                57                52             91   \n",
       "7659                  63                66                68             83   \n",
       "7660                  79                58                69             88   \n",
       "7661                  72                60                69             63   \n",
       "\n",
       "      power_stamina  power_strength  power_long_shots  mentality_aggression  \\\n",
       "0                86        0.471429                66                    56   \n",
       "1                71        0.585714                42                    66   \n",
       "2                67        0.514286                28                    69   \n",
       "3                75        0.885714                69                    52   \n",
       "4                78        0.642857                67                    53   \n",
       "...             ...             ...               ...                   ...   \n",
       "7657             50        0.085714                66                    43   \n",
       "7658             78        0.857143                45                    70   \n",
       "7659             68        0.928571                60                    87   \n",
       "7660             75        0.785714                56                    90   \n",
       "7661             64        0.542857                61                    57   \n",
       "\n",
       "      mentality_interceptions  mentality_positioning  mentality_vision  \\\n",
       "0                    0.858824               0.542553                48   \n",
       "1                    0.705882               0.489362                62   \n",
       "2                    0.658824               0.595745                60   \n",
       "3                    0.141176               0.787234                60   \n",
       "4                    0.670588               0.617021                52   \n",
       "...                       ...                    ...               ...   \n",
       "7657                 0.341176               0.755319                71   \n",
       "7658                 0.705882               0.489362                41   \n",
       "7659                 0.200000               0.670213                56   \n",
       "7660                 0.917647               0.489362                53   \n",
       "7661                 0.141176               0.723404                47   \n",
       "\n",
       "      mentality_penalties  mentality_composure  defending_marking_awareness  \\\n",
       "0                      58                   66                           65   \n",
       "1                      46                   59                           65   \n",
       "2                      31                   65                           60   \n",
       "3                      75                   75                           16   \n",
       "4                      52                   58                           64   \n",
       "...                   ...                  ...                          ...   \n",
       "7657                   70                   64                           30   \n",
       "7658                   37                   57                           64   \n",
       "7659                   56                   61                           22   \n",
       "7660                   45                   79                           84   \n",
       "7661                   71                   59                           17   \n",
       "\n",
       "      defending_standing_tackle  defending_sliding_tackle  goalkeeping_diving  \\\n",
       "0                            66                  0.686047                  14   \n",
       "1                            61                  0.604651                  10   \n",
       "2                            58                  0.616279                  10   \n",
       "3                            22                  0.151163                  16   \n",
       "4                            61                  0.604651                  12   \n",
       "...                         ...                       ...                 ...   \n",
       "7657                         33                  0.302326                  13   \n",
       "7658                         65                  0.662791                   8   \n",
       "7659                         26                  0.220930                  11   \n",
       "7660                         83                  0.860465                  12   \n",
       "7661                         15                  0.139535                  12   \n",
       "\n",
       "      goalkeeping_handling  goalkeeping_kicking  goalkeeping_positioning  \\\n",
       "0                       11                   12                       12   \n",
       "1                       13                    7                        6   \n",
       "2                       10                    8                       10   \n",
       "3                       15                   13                        8   \n",
       "4                        5                   11                       12   \n",
       "...                    ...                  ...                      ...   \n",
       "7657                    12                   12                       10   \n",
       "7658                    10                    8                       15   \n",
       "7659                    11                   10                        7   \n",
       "7660                     5                    8                       11   \n",
       "7661                    13                   12                        7   \n",
       "\n",
       "      goalkeeping_reflexes  goalkeeping_speed position  test_train  \\\n",
       "0                 0.113636                NaN       LB           1   \n",
       "1                 0.102273                NaN      LDM           1   \n",
       "2                 0.102273                NaN      LWB           1   \n",
       "3                 0.079545                NaN       LS           1   \n",
       "4                 0.147727                NaN       LB           1   \n",
       "...                    ...                ...      ...         ...   \n",
       "7657              0.079545                NaN      NaN           0   \n",
       "7658              0.079545                NaN      NaN           0   \n",
       "7659              0.090909                NaN      NaN           0   \n",
       "7660              0.113636                NaN      NaN           0   \n",
       "7661              0.113636                NaN      NaN           0   \n",
       "\n",
       "      yearinclub  age  years_until_contract_expires  body_type_encoded  \\\n",
       "0            2.0   33                             3                  1   \n",
       "1            2.0   27                            -1                  0   \n",
       "2            2.0   22                             3                  1   \n",
       "3            5.0   31                             0                  0   \n",
       "4            4.0   33                            -1                  1   \n",
       "...          ...  ...                           ...                ...   \n",
       "7657         2.0   31                             2                  0   \n",
       "7658         4.0   27                            -1                  2   \n",
       "7659         2.0   33                             0                  2   \n",
       "7660         3.0   35                             0                  1   \n",
       "7661         2.0   27                             1                  1   \n",
       "\n",
       "      work_rate_encoded  preferred_foot_encoded  real_face_encoded  \n",
       "0                     6                     1.0                  0  \n",
       "1                     8                     1.0                  0  \n",
       "2                     2                     0.0                  0  \n",
       "3                     2                     1.0                  0  \n",
       "4                     6                     0.0                  0  \n",
       "...                 ...                     ...                ...  \n",
       "7657                  7                     0.0                  1  \n",
       "7658                  3                     1.0                  0  \n",
       "7659                  4                     1.0                  0  \n",
       "7660                  6                     1.0                  1  \n",
       "7661                  8                     1.0                  0  \n",
       "\n",
       "[7662 rows x 75 columns]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#we start by normalizing the numerical data\n",
    "def normalize_data(data):\n",
    "    min_value = min(data)\n",
    "    max_value = max(data)\n",
    "    normalized_data = []\n",
    "\n",
    "    for value in data:\n",
    "        normalized_value = (value - min_value) / (max_value - min_value)\n",
    "        normalized_data.append(normalized_value)\n",
    "\n",
    "    return normalized_data\n",
    "\n",
    "all_data['overall'] = normalize_data(all_data['overall'])\n",
    "all_data['potential'] = normalize_data(all_data['potential'])\n",
    "all_data['value_eur'] = normalize_data(all_data['value_eur'])\n",
    "all_data['wage_eur'] = normalize_data(all_data['wage_eur'])\n",
    "all_data['height_cm'] = normalize_data(all_data['height_cm'])\n",
    "all_data['weight_kg'] = normalize_data(all_data['weight_kg'])\n",
    "all_data['league_level'] = normalize_data(all_data['league_level'])\n",
    "all_data['club_jersey_number'] = normalize_data(all_data['club_jersey_number'])\n",
    "all_data['weak_foot'] = normalize_data(all_data['weak_foot'])\n",
    "all_data['skill_moves'] = normalize_data(all_data['skill_moves'])\n",
    "all_data['international_reputation'] = normalize_data(all_data['international_reputation'])\n",
    "all_data['release_clause_eur'] = normalize_data(all_data['release_clause_eur'])\n",
    "all_data['pace'] = normalize_data(all_data['pace'])\n",
    "all_data['shooting'] = normalize_data(all_data['shooting'])\n",
    "all_data['passing'] = normalize_data(all_data['passing'])\n",
    "all_data['dribbling'] = normalize_data(all_data['dribbling'])\n",
    "all_data['defending'] = normalize_data(all_data['defending'])\n",
    "all_data['physic'] = normalize_data(all_data['physic'])\n",
    "all_data['attacking_crossing'] = normalize_data(all_data['attacking_crossing'])\n",
    "all_data['attacking_heading_accuracy'] = normalize_data(all_data['attacking_heading_accuracy'])\n",
    "all_data['preferred_foot_encoded'] = normalize_data(all_data['preferred_foot_encoded'])\n",
    "all_data['mentality_positioning'] = normalize_data(all_data['mentality_positioning'])\n",
    "all_data['attacking_finishing'] = normalize_data(all_data['attacking_finishing'])\n",
    "all_data['defending_sliding_tackle'] = normalize_data(all_data['defending_sliding_tackle'])\n",
    "all_data['goalkeeping_reflexes'] = normalize_data(all_data['goalkeeping_reflexes'])\n",
    "all_data['mentality_interceptions'] = normalize_data(all_data['mentality_interceptions'])\n",
    "all_data['power_strength'] = normalize_data(all_data['power_strength'])\n",
    "\n",
    "display(all_data)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 148,
   "metadata": {},
   "outputs": [],
   "source": [
    "#we deconcatenate again the test data and train data from the all_data\n",
    "train_data = all_data.loc[all_data.test_train == 1]\n",
    "test_data = all_data.loc[all_data.test_train == 0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 149,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "((6895, 75), (767, 75))"
      ]
     },
     "execution_count": 149,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#We check that it deconcatenated properly and we are working with the correct dataframes\n",
    "train_data.shape,test_data.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 150,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "All column names: ['index', 'id', 'overall', 'potential', 'value_eur', 'wage_eur', 'birthday_date', 'height_cm', 'weight_kg', 'league_level', 'club_jersey_number', 'club_loaned_from', 'club_joined', 'club_contract_valid_until', 'nation_jersey_number', 'preferred_foot', 'weak_foot', 'skill_moves', 'international_reputation', 'work_rate', 'body_type', 'real_face', 'release_clause_eur', 'player_tags', 'player_traits', 'pace', 'shooting', 'passing', 'dribbling', 'defending', 'physic', 'attacking_crossing', 'attacking_finishing', 'attacking_heading_accuracy', 'attacking_short_passing', 'attacking_volleys', 'skill_dribbling', 'skill_curve', 'skill_fk_accuracy', 'skill_long_passing', 'skill_ball_control', 'movement_acceleration', 'movement_sprint_speed', 'movement_agility', 'movement_reactions', 'movement_balance', 'power_shot_power', 'power_jumping', 'power_stamina', 'power_strength', 'power_long_shots', 'mentality_aggression', 'mentality_interceptions', 'mentality_positioning', 'mentality_vision', 'mentality_penalties', 'mentality_composure', 'defending_marking_awareness', 'defending_standing_tackle', 'defending_sliding_tackle', 'goalkeeping_diving', 'goalkeeping_handling', 'goalkeeping_kicking', 'goalkeeping_positioning', 'goalkeeping_reflexes', 'goalkeeping_speed', 'position', 'test_train', 'yearinclub', 'age', 'years_until_contract_expires', 'body_type_encoded', 'work_rate_encoded', 'preferred_foot_encoded', 'real_face_encoded']\n"
     ]
    }
   ],
   "source": [
    "#We get all the columns names by a list\n",
    "all_columns = all_data.columns.tolist()\n",
    "print(\"All column names:\", all_columns)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 152,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.preprocessing import OneHotEncoder\n",
    "\n",
    "encoder = OneHotEncoder()\n",
    "encoded_data = encoder.fit_transform(train_data[['position']]).toarray()\n",
    "# Convert the encoded data back to a dataframe\n",
    "encoded_df = pd.DataFrame(encoded_data, columns=encoder.get_feature_names_out (['position']))\n",
    "# Concatenate the encoded dataframe with the 'id' column\n",
    "encoded_df = pd.concat([train_data['id'], encoded_df], axis=1)\n",
    "train_data = pd.concat([train_data, encoded_df], axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 153,
   "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>index</th>\n",
       "      <th>id</th>\n",
       "      <th>overall</th>\n",
       "      <th>potential</th>\n",
       "      <th>value_eur</th>\n",
       "      <th>wage_eur</th>\n",
       "      <th>birthday_date</th>\n",
       "      <th>height_cm</th>\n",
       "      <th>weight_kg</th>\n",
       "      <th>league_level</th>\n",
       "      <th>club_jersey_number</th>\n",
       "      <th>club_loaned_from</th>\n",
       "      <th>club_joined</th>\n",
       "      <th>club_contract_valid_until</th>\n",
       "      <th>nation_jersey_number</th>\n",
       "      <th>preferred_foot</th>\n",
       "      <th>weak_foot</th>\n",
       "      <th>skill_moves</th>\n",
       "      <th>international_reputation</th>\n",
       "      <th>work_rate</th>\n",
       "      <th>body_type</th>\n",
       "      <th>real_face</th>\n",
       "      <th>release_clause_eur</th>\n",
       "      <th>player_tags</th>\n",
       "      <th>player_traits</th>\n",
       "      <th>pace</th>\n",
       "      <th>shooting</th>\n",
       "      <th>passing</th>\n",
       "      <th>dribbling</th>\n",
       "      <th>defending</th>\n",
       "      <th>physic</th>\n",
       "      <th>attacking_crossing</th>\n",
       "      <th>attacking_finishing</th>\n",
       "      <th>attacking_heading_accuracy</th>\n",
       "      <th>attacking_short_passing</th>\n",
       "      <th>attacking_volleys</th>\n",
       "      <th>skill_dribbling</th>\n",
       "      <th>skill_curve</th>\n",
       "      <th>skill_fk_accuracy</th>\n",
       "      <th>skill_long_passing</th>\n",
       "      <th>skill_ball_control</th>\n",
       "      <th>movement_acceleration</th>\n",
       "      <th>movement_sprint_speed</th>\n",
       "      <th>movement_agility</th>\n",
       "      <th>movement_reactions</th>\n",
       "      <th>movement_balance</th>\n",
       "      <th>power_shot_power</th>\n",
       "      <th>power_jumping</th>\n",
       "      <th>power_stamina</th>\n",
       "      <th>power_strength</th>\n",
       "      <th>power_long_shots</th>\n",
       "      <th>mentality_aggression</th>\n",
       "      <th>mentality_interceptions</th>\n",
       "      <th>mentality_positioning</th>\n",
       "      <th>mentality_vision</th>\n",
       "      <th>mentality_penalties</th>\n",
       "      <th>mentality_composure</th>\n",
       "      <th>defending_marking_awareness</th>\n",
       "      <th>defending_standing_tackle</th>\n",
       "      <th>defending_sliding_tackle</th>\n",
       "      <th>goalkeeping_diving</th>\n",
       "      <th>goalkeeping_handling</th>\n",
       "      <th>goalkeeping_kicking</th>\n",
       "      <th>goalkeeping_positioning</th>\n",
       "      <th>goalkeeping_reflexes</th>\n",
       "      <th>goalkeeping_speed</th>\n",
       "      <th>position</th>\n",
       "      <th>test_train</th>\n",
       "      <th>yearinclub</th>\n",
       "      <th>age</th>\n",
       "      <th>years_until_contract_expires</th>\n",
       "      <th>body_type_encoded</th>\n",
       "      <th>work_rate_encoded</th>\n",
       "      <th>preferred_foot_encoded</th>\n",
       "      <th>real_face_encoded</th>\n",
       "      <th>id</th>\n",
       "      <th>position_CAM</th>\n",
       "      <th>position_CB</th>\n",
       "      <th>position_CDM</th>\n",
       "      <th>position_CM</th>\n",
       "      <th>position_GK</th>\n",
       "      <th>position_LB</th>\n",
       "      <th>position_LCB</th>\n",
       "      <th>position_LCM</th>\n",
       "      <th>position_LDM</th>\n",
       "      <th>position_LF</th>\n",
       "      <th>position_LM</th>\n",
       "      <th>position_LS</th>\n",
       "      <th>position_LW</th>\n",
       "      <th>position_LWB</th>\n",
       "      <th>position_RB</th>\n",
       "      <th>position_RCB</th>\n",
       "      <th>position_RCM</th>\n",
       "      <th>position_RDM</th>\n",
       "      <th>position_RF</th>\n",
       "      <th>position_RM</th>\n",
       "      <th>position_RS</th>\n",
       "      <th>position_RW</th>\n",
       "      <th>position_RWB</th>\n",
       "      <th>position_ST</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0</td>\n",
       "      <td>216302</td>\n",
       "      <td>0.511111</td>\n",
       "      <td>0.428571</td>\n",
       "      <td>0.007089</td>\n",
       "      <td>0.027182</td>\n",
       "      <td>1989-12-28</td>\n",
       "      <td>0.425532</td>\n",
       "      <td>0.40</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.285714</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2021-07-12</td>\n",
       "      <td>2026</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Right</td>\n",
       "      <td>1.00</td>\n",
       "      <td>0.50</td>\n",
       "      <td>0.0</td>\n",
       "      <td>Medium/High</td>\n",
       "      <td>Normal</td>\n",
       "      <td>No</td>\n",
       "      <td>0.006322</td>\n",
       "      <td>NaN</td>\n",
       "      <td>[Early Crosser]</td>\n",
       "      <td>0.608696</td>\n",
       "      <td>0.447368</td>\n",
       "      <td>0.514706</td>\n",
       "      <td>0.621212</td>\n",
       "      <td>0.680000</td>\n",
       "      <td>0.593220</td>\n",
       "      <td>0.655172</td>\n",
       "      <td>0.408602</td>\n",
       "      <td>0.579545</td>\n",
       "      <td>65</td>\n",
       "      <td>41</td>\n",
       "      <td>68</td>\n",
       "      <td>64</td>\n",
       "      <td>58</td>\n",
       "      <td>58</td>\n",
       "      <td>68</td>\n",
       "      <td>71</td>\n",
       "      <td>70</td>\n",
       "      <td>87</td>\n",
       "      <td>61</td>\n",
       "      <td>86</td>\n",
       "      <td>67</td>\n",
       "      <td>77</td>\n",
       "      <td>86</td>\n",
       "      <td>0.471429</td>\n",
       "      <td>66</td>\n",
       "      <td>56</td>\n",
       "      <td>0.858824</td>\n",
       "      <td>0.542553</td>\n",
       "      <td>48</td>\n",
       "      <td>58</td>\n",
       "      <td>66</td>\n",
       "      <td>65</td>\n",
       "      <td>66</td>\n",
       "      <td>0.686047</td>\n",
       "      <td>14</td>\n",
       "      <td>11</td>\n",
       "      <td>12</td>\n",
       "      <td>12</td>\n",
       "      <td>0.113636</td>\n",
       "      <td>NaN</td>\n",
       "      <td>LB</td>\n",
       "      <td>1</td>\n",
       "      <td>2.0</td>\n",
       "      <td>33</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>6</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0</td>\n",
       "      <td>216302</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>237867</td>\n",
       "      <td>0.377778</td>\n",
       "      <td>0.428571</td>\n",
       "      <td>0.005026</td>\n",
       "      <td>0.004292</td>\n",
       "      <td>1996-10-23</td>\n",
       "      <td>0.574468</td>\n",
       "      <td>0.40</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.030612</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2021-07-23</td>\n",
       "      <td>2022</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Right</td>\n",
       "      <td>0.50</td>\n",
       "      <td>0.25</td>\n",
       "      <td>0.0</td>\n",
       "      <td>Medium/Medium</td>\n",
       "      <td>Lean</td>\n",
       "      <td>No</td>\n",
       "      <td>0.005251</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.536232</td>\n",
       "      <td>0.263158</td>\n",
       "      <td>0.485294</td>\n",
       "      <td>0.469697</td>\n",
       "      <td>0.626667</td>\n",
       "      <td>0.610169</td>\n",
       "      <td>0.494253</td>\n",
       "      <td>0.311828</td>\n",
       "      <td>0.613636</td>\n",
       "      <td>64</td>\n",
       "      <td>31</td>\n",
       "      <td>57</td>\n",
       "      <td>36</td>\n",
       "      <td>41</td>\n",
       "      <td>64</td>\n",
       "      <td>65</td>\n",
       "      <td>64</td>\n",
       "      <td>65</td>\n",
       "      <td>57</td>\n",
       "      <td>62</td>\n",
       "      <td>67</td>\n",
       "      <td>49</td>\n",
       "      <td>62</td>\n",
       "      <td>71</td>\n",
       "      <td>0.585714</td>\n",
       "      <td>42</td>\n",
       "      <td>66</td>\n",
       "      <td>0.705882</td>\n",
       "      <td>0.489362</td>\n",
       "      <td>62</td>\n",
       "      <td>46</td>\n",
       "      <td>59</td>\n",
       "      <td>65</td>\n",
       "      <td>61</td>\n",
       "      <td>0.604651</td>\n",
       "      <td>10</td>\n",
       "      <td>13</td>\n",
       "      <td>7</td>\n",
       "      <td>6</td>\n",
       "      <td>0.102273</td>\n",
       "      <td>NaN</td>\n",
       "      <td>LDM</td>\n",
       "      <td>1</td>\n",
       "      <td>2.0</td>\n",
       "      <td>27</td>\n",
       "      <td>-1</td>\n",
       "      <td>0</td>\n",
       "      <td>8</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0</td>\n",
       "      <td>237867</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2</td>\n",
       "      <td>253472</td>\n",
       "      <td>0.377778</td>\n",
       "      <td>0.571429</td>\n",
       "      <td>0.008120</td>\n",
       "      <td>0.004292</td>\n",
       "      <td>2001-09-27</td>\n",
       "      <td>0.468085</td>\n",
       "      <td>0.32</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.183673</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2021-07-24</td>\n",
       "      <td>2026</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Left</td>\n",
       "      <td>0.50</td>\n",
       "      <td>0.50</td>\n",
       "      <td>0.0</td>\n",
       "      <td>High/Medium</td>\n",
       "      <td>Normal</td>\n",
       "      <td>No</td>\n",
       "      <td>0.006054</td>\n",
       "      <td>NaN</td>\n",
       "      <td>[Speed Dribbler (AI)]</td>\n",
       "      <td>0.739130</td>\n",
       "      <td>0.223684</td>\n",
       "      <td>0.485294</td>\n",
       "      <td>0.560606</td>\n",
       "      <td>0.573333</td>\n",
       "      <td>0.559322</td>\n",
       "      <td>0.597701</td>\n",
       "      <td>0.301075</td>\n",
       "      <td>0.545455</td>\n",
       "      <td>60</td>\n",
       "      <td>37</td>\n",
       "      <td>65</td>\n",
       "      <td>56</td>\n",
       "      <td>35</td>\n",
       "      <td>55</td>\n",
       "      <td>64</td>\n",
       "      <td>82</td>\n",
       "      <td>77</td>\n",
       "      <td>71</td>\n",
       "      <td>62</td>\n",
       "      <td>75</td>\n",
       "      <td>50</td>\n",
       "      <td>65</td>\n",
       "      <td>67</td>\n",
       "      <td>0.514286</td>\n",
       "      <td>28</td>\n",
       "      <td>69</td>\n",
       "      <td>0.658824</td>\n",
       "      <td>0.595745</td>\n",
       "      <td>60</td>\n",
       "      <td>31</td>\n",
       "      <td>65</td>\n",
       "      <td>60</td>\n",
       "      <td>58</td>\n",
       "      <td>0.616279</td>\n",
       "      <td>10</td>\n",
       "      <td>10</td>\n",
       "      <td>8</td>\n",
       "      <td>10</td>\n",
       "      <td>0.102273</td>\n",
       "      <td>NaN</td>\n",
       "      <td>LWB</td>\n",
       "      <td>1</td>\n",
       "      <td>2.0</td>\n",
       "      <td>22</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>253472</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>3</td>\n",
       "      <td>223994</td>\n",
       "      <td>0.533333</td>\n",
       "      <td>0.452381</td>\n",
       "      <td>0.011728</td>\n",
       "      <td>0.012876</td>\n",
       "      <td>1992-02-26</td>\n",
       "      <td>0.680851</td>\n",
       "      <td>0.56</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.081633</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2018-02-08</td>\n",
       "      <td>2023</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Right</td>\n",
       "      <td>0.50</td>\n",
       "      <td>0.50</td>\n",
       "      <td>0.0</td>\n",
       "      <td>High/Medium</td>\n",
       "      <td>Lean</td>\n",
       "      <td>No</td>\n",
       "      <td>0.007661</td>\n",
       "      <td>NaN</td>\n",
       "      <td>[Finesse Shot]</td>\n",
       "      <td>0.521739</td>\n",
       "      <td>0.736842</td>\n",
       "      <td>0.382353</td>\n",
       "      <td>0.590909</td>\n",
       "      <td>0.106667</td>\n",
       "      <td>0.762712</td>\n",
       "      <td>0.287356</td>\n",
       "      <td>0.817204</td>\n",
       "      <td>0.795455</td>\n",
       "      <td>59</td>\n",
       "      <td>70</td>\n",
       "      <td>65</td>\n",
       "      <td>48</td>\n",
       "      <td>53</td>\n",
       "      <td>45</td>\n",
       "      <td>74</td>\n",
       "      <td>61</td>\n",
       "      <td>66</td>\n",
       "      <td>64</td>\n",
       "      <td>67</td>\n",
       "      <td>53</td>\n",
       "      <td>71</td>\n",
       "      <td>71</td>\n",
       "      <td>75</td>\n",
       "      <td>0.885714</td>\n",
       "      <td>69</td>\n",
       "      <td>52</td>\n",
       "      <td>0.141176</td>\n",
       "      <td>0.787234</td>\n",
       "      <td>60</td>\n",
       "      <td>75</td>\n",
       "      <td>75</td>\n",
       "      <td>16</td>\n",
       "      <td>22</td>\n",
       "      <td>0.151163</td>\n",
       "      <td>16</td>\n",
       "      <td>15</td>\n",
       "      <td>13</td>\n",
       "      <td>8</td>\n",
       "      <td>0.079545</td>\n",
       "      <td>NaN</td>\n",
       "      <td>LS</td>\n",
       "      <td>1</td>\n",
       "      <td>5.0</td>\n",
       "      <td>31</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0</td>\n",
       "      <td>223994</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>4</td>\n",
       "      <td>251635</td>\n",
       "      <td>0.377778</td>\n",
       "      <td>0.285714</td>\n",
       "      <td>0.002578</td>\n",
       "      <td>0.007153</td>\n",
       "      <td>1990-06-12</td>\n",
       "      <td>0.489362</td>\n",
       "      <td>0.42</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.295918</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2019-07-01</td>\n",
       "      <td>2022</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Left</td>\n",
       "      <td>0.25</td>\n",
       "      <td>0.25</td>\n",
       "      <td>0.0</td>\n",
       "      <td>Medium/High</td>\n",
       "      <td>Normal</td>\n",
       "      <td>No</td>\n",
       "      <td>0.001794</td>\n",
       "      <td>NaN</td>\n",
       "      <td>[Solid Player]</td>\n",
       "      <td>0.666667</td>\n",
       "      <td>0.460526</td>\n",
       "      <td>0.500000</td>\n",
       "      <td>0.363636</td>\n",
       "      <td>0.600000</td>\n",
       "      <td>0.644068</td>\n",
       "      <td>0.655172</td>\n",
       "      <td>0.387097</td>\n",
       "      <td>0.500000</td>\n",
       "      <td>63</td>\n",
       "      <td>46</td>\n",
       "      <td>43</td>\n",
       "      <td>53</td>\n",
       "      <td>58</td>\n",
       "      <td>55</td>\n",
       "      <td>56</td>\n",
       "      <td>71</td>\n",
       "      <td>76</td>\n",
       "      <td>70</td>\n",
       "      <td>60</td>\n",
       "      <td>71</td>\n",
       "      <td>72</td>\n",
       "      <td>80</td>\n",
       "      <td>78</td>\n",
       "      <td>0.642857</td>\n",
       "      <td>67</td>\n",
       "      <td>53</td>\n",
       "      <td>0.670588</td>\n",
       "      <td>0.617021</td>\n",
       "      <td>52</td>\n",
       "      <td>52</td>\n",
       "      <td>58</td>\n",
       "      <td>64</td>\n",
       "      <td>61</td>\n",
       "      <td>0.604651</td>\n",
       "      <td>12</td>\n",
       "      <td>5</td>\n",
       "      <td>11</td>\n",
       "      <td>12</td>\n",
       "      <td>0.147727</td>\n",
       "      <td>NaN</td>\n",
       "      <td>LB</td>\n",
       "      <td>1</td>\n",
       "      <td>4.0</td>\n",
       "      <td>33</td>\n",
       "      <td>-1</td>\n",
       "      <td>1</td>\n",
       "      <td>6</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>251635</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   index      id   overall  potential  value_eur  wage_eur birthday_date  \\\n",
       "0      0  216302  0.511111   0.428571   0.007089  0.027182    1989-12-28   \n",
       "1      1  237867  0.377778   0.428571   0.005026  0.004292    1996-10-23   \n",
       "2      2  253472  0.377778   0.571429   0.008120  0.004292    2001-09-27   \n",
       "3      3  223994  0.533333   0.452381   0.011728  0.012876    1992-02-26   \n",
       "4      4  251635  0.377778   0.285714   0.002578  0.007153    1990-06-12   \n",
       "\n",
       "   height_cm  weight_kg  league_level  club_jersey_number club_loaned_from  \\\n",
       "0   0.425532       0.40           0.0            0.285714              NaN   \n",
       "1   0.574468       0.40           0.0            0.030612              NaN   \n",
       "2   0.468085       0.32           0.0            0.183673              NaN   \n",
       "3   0.680851       0.56           0.0            0.081633              NaN   \n",
       "4   0.489362       0.42           0.0            0.295918              NaN   \n",
       "\n",
       "  club_joined  club_contract_valid_until  nation_jersey_number preferred_foot  \\\n",
       "0  2021-07-12                       2026                   NaN          Right   \n",
       "1  2021-07-23                       2022                   NaN          Right   \n",
       "2  2021-07-24                       2026                   NaN           Left   \n",
       "3  2018-02-08                       2023                   NaN          Right   \n",
       "4  2019-07-01                       2022                   NaN           Left   \n",
       "\n",
       "   weak_foot  skill_moves  international_reputation      work_rate body_type  \\\n",
       "0       1.00         0.50                       0.0    Medium/High    Normal   \n",
       "1       0.50         0.25                       0.0  Medium/Medium      Lean   \n",
       "2       0.50         0.50                       0.0    High/Medium    Normal   \n",
       "3       0.50         0.50                       0.0    High/Medium      Lean   \n",
       "4       0.25         0.25                       0.0    Medium/High    Normal   \n",
       "\n",
       "  real_face  release_clause_eur player_tags          player_traits      pace  \\\n",
       "0        No            0.006322         NaN        [Early Crosser]  0.608696   \n",
       "1        No            0.005251         NaN                    NaN  0.536232   \n",
       "2        No            0.006054         NaN  [Speed Dribbler (AI)]  0.739130   \n",
       "3        No            0.007661         NaN         [Finesse Shot]  0.521739   \n",
       "4        No            0.001794         NaN         [Solid Player]  0.666667   \n",
       "\n",
       "   shooting   passing  dribbling  defending    physic  attacking_crossing  \\\n",
       "0  0.447368  0.514706   0.621212   0.680000  0.593220            0.655172   \n",
       "1  0.263158  0.485294   0.469697   0.626667  0.610169            0.494253   \n",
       "2  0.223684  0.485294   0.560606   0.573333  0.559322            0.597701   \n",
       "3  0.736842  0.382353   0.590909   0.106667  0.762712            0.287356   \n",
       "4  0.460526  0.500000   0.363636   0.600000  0.644068            0.655172   \n",
       "\n",
       "   attacking_finishing  attacking_heading_accuracy  attacking_short_passing  \\\n",
       "0             0.408602                    0.579545                       65   \n",
       "1             0.311828                    0.613636                       64   \n",
       "2             0.301075                    0.545455                       60   \n",
       "3             0.817204                    0.795455                       59   \n",
       "4             0.387097                    0.500000                       63   \n",
       "\n",
       "   attacking_volleys  skill_dribbling  skill_curve  skill_fk_accuracy  \\\n",
       "0                 41               68           64                 58   \n",
       "1                 31               57           36                 41   \n",
       "2                 37               65           56                 35   \n",
       "3                 70               65           48                 53   \n",
       "4                 46               43           53                 58   \n",
       "\n",
       "   skill_long_passing  skill_ball_control  movement_acceleration  \\\n",
       "0                  58                  68                     71   \n",
       "1                  64                  65                     64   \n",
       "2                  55                  64                     82   \n",
       "3                  45                  74                     61   \n",
       "4                  55                  56                     71   \n",
       "\n",
       "   movement_sprint_speed  movement_agility  movement_reactions  \\\n",
       "0                     70                87                  61   \n",
       "1                     65                57                  62   \n",
       "2                     77                71                  62   \n",
       "3                     66                64                  67   \n",
       "4                     76                70                  60   \n",
       "\n",
       "   movement_balance  power_shot_power  power_jumping  power_stamina  \\\n",
       "0                86                67             77             86   \n",
       "1                67                49             62             71   \n",
       "2                75                50             65             67   \n",
       "3                53                71             71             75   \n",
       "4                71                72             80             78   \n",
       "\n",
       "   power_strength  power_long_shots  mentality_aggression  \\\n",
       "0        0.471429                66                    56   \n",
       "1        0.585714                42                    66   \n",
       "2        0.514286                28                    69   \n",
       "3        0.885714                69                    52   \n",
       "4        0.642857                67                    53   \n",
       "\n",
       "   mentality_interceptions  mentality_positioning  mentality_vision  \\\n",
       "0                 0.858824               0.542553                48   \n",
       "1                 0.705882               0.489362                62   \n",
       "2                 0.658824               0.595745                60   \n",
       "3                 0.141176               0.787234                60   \n",
       "4                 0.670588               0.617021                52   \n",
       "\n",
       "   mentality_penalties  mentality_composure  defending_marking_awareness  \\\n",
       "0                   58                   66                           65   \n",
       "1                   46                   59                           65   \n",
       "2                   31                   65                           60   \n",
       "3                   75                   75                           16   \n",
       "4                   52                   58                           64   \n",
       "\n",
       "   defending_standing_tackle  defending_sliding_tackle  goalkeeping_diving  \\\n",
       "0                         66                  0.686047                  14   \n",
       "1                         61                  0.604651                  10   \n",
       "2                         58                  0.616279                  10   \n",
       "3                         22                  0.151163                  16   \n",
       "4                         61                  0.604651                  12   \n",
       "\n",
       "   goalkeeping_handling  goalkeeping_kicking  goalkeeping_positioning  \\\n",
       "0                    11                   12                       12   \n",
       "1                    13                    7                        6   \n",
       "2                    10                    8                       10   \n",
       "3                    15                   13                        8   \n",
       "4                     5                   11                       12   \n",
       "\n",
       "   goalkeeping_reflexes  goalkeeping_speed position  test_train  yearinclub  \\\n",
       "0              0.113636                NaN       LB           1         2.0   \n",
       "1              0.102273                NaN      LDM           1         2.0   \n",
       "2              0.102273                NaN      LWB           1         2.0   \n",
       "3              0.079545                NaN       LS           1         5.0   \n",
       "4              0.147727                NaN       LB           1         4.0   \n",
       "\n",
       "   age  years_until_contract_expires  body_type_encoded  work_rate_encoded  \\\n",
       "0   33                             3                  1                  6   \n",
       "1   27                            -1                  0                  8   \n",
       "2   22                             3                  1                  2   \n",
       "3   31                             0                  0                  2   \n",
       "4   33                            -1                  1                  6   \n",
       "\n",
       "   preferred_foot_encoded  real_face_encoded      id  position_CAM  \\\n",
       "0                     1.0                  0  216302           0.0   \n",
       "1                     1.0                  0  237867           0.0   \n",
       "2                     0.0                  0  253472           0.0   \n",
       "3                     1.0                  0  223994           0.0   \n",
       "4                     0.0                  0  251635           0.0   \n",
       "\n",
       "   position_CB  position_CDM  position_CM  position_GK  position_LB  \\\n",
       "0          0.0           0.0          0.0          0.0          1.0   \n",
       "1          0.0           0.0          0.0          0.0          0.0   \n",
       "2          0.0           0.0          0.0          0.0          0.0   \n",
       "3          0.0           0.0          0.0          0.0          0.0   \n",
       "4          0.0           0.0          0.0          0.0          1.0   \n",
       "\n",
       "   position_LCB  position_LCM  position_LDM  position_LF  position_LM  \\\n",
       "0           0.0           0.0           0.0          0.0          0.0   \n",
       "1           0.0           0.0           1.0          0.0          0.0   \n",
       "2           0.0           0.0           0.0          0.0          0.0   \n",
       "3           0.0           0.0           0.0          0.0          0.0   \n",
       "4           0.0           0.0           0.0          0.0          0.0   \n",
       "\n",
       "   position_LS  position_LW  position_LWB  position_RB  position_RCB  \\\n",
       "0          0.0          0.0           0.0          0.0           0.0   \n",
       "1          0.0          0.0           0.0          0.0           0.0   \n",
       "2          0.0          0.0           1.0          0.0           0.0   \n",
       "3          1.0          0.0           0.0          0.0           0.0   \n",
       "4          0.0          0.0           0.0          0.0           0.0   \n",
       "\n",
       "   position_RCM  position_RDM  position_RF  position_RM  position_RS  \\\n",
       "0           0.0           0.0          0.0          0.0          0.0   \n",
       "1           0.0           0.0          0.0          0.0          0.0   \n",
       "2           0.0           0.0          0.0          0.0          0.0   \n",
       "3           0.0           0.0          0.0          0.0          0.0   \n",
       "4           0.0           0.0          0.0          0.0          0.0   \n",
       "\n",
       "   position_RW  position_RWB  position_ST  \n",
       "0          0.0           0.0          0.0  \n",
       "1          0.0           0.0          0.0  \n",
       "2          0.0           0.0          0.0  \n",
       "3          0.0           0.0          0.0  \n",
       "4          0.0           0.0          0.0  "
      ]
     },
     "execution_count": 153,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train_data.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 164,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1400x800 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Let's calculate the correlations\n",
    "import matplotlib.pyplot as plt\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "import seaborn as sns\n",
    "import warnings\n",
    "warnings.filterwarnings(\"ignore\")\n",
    "\n",
    "df2 = train_data[['goalkeeping_handling', 'goalkeeping_positioning', 'skill_moves', 'goalkeeping_reflexes', 'skill_ball_control', 'attacking_heading_accuracy', 'goalkeeping_kicking', 'skill_dribbling', 'goalkeeping_diving', 'mentality_positioning', 'attacking_volleys', 'attacking_finishing', 'attacking_crossing', 'power_stamina', 'defending_standing_tackle', 'position_GK' ]]\n",
    "df2_corr = df2.corr()\n",
    "\n",
    "# Plot correlations\n",
    "# Remove upper triangle\n",
    "fig, ax = plt.subplots(figsize=(14,8))\n",
    "mask = np.zeros_like(df2_corr)\n",
    "mask[np.triu_indices_from(mask)] = True\n",
    "ax = sns.heatmap(df2_corr, mask = mask, annot = True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 154,
   "metadata": {},
   "outputs": [],
   "source": [
    "#first we try to classify for GK\n",
    "from sklearn.model_selection import train_test_split\n",
    "columns_for_prediction = ['overall', 'potential', 'value_eur', 'wage_eur', 'height_cm', 'weight_kg', 'league_level', 'club_jersey_number', 'weak_foot', 'skill_moves', 'international_reputation', 'release_clause_eur', 'pace', 'shooting', 'passing', 'dribbling', 'defending', 'physic', 'attacking_crossing', 'attacking_finishing', 'attacking_heading_accuracy', 'attacking_short_passing', 'attacking_volleys', 'skill_dribbling', 'skill_curve', 'skill_fk_accuracy', 'skill_long_passing', 'skill_ball_control', 'movement_acceleration', 'movement_sprint_speed', 'movement_agility', 'movement_reactions', 'movement_balance', 'power_shot_power', 'power_jumping', 'power_stamina', 'power_strength', 'power_long_shots', 'mentality_aggression', 'mentality_interceptions', 'mentality_positioning', 'mentality_vision', 'mentality_penalties', 'mentality_composure', 'defending_marking_awareness', 'defending_standing_tackle', 'defending_sliding_tackle', 'goalkeeping_diving', 'goalkeeping_handling', 'goalkeeping_kicking', 'goalkeeping_positioning', 'goalkeeping_reflexes', 'yearinclub', 'age', 'years_until_contract_expires', 'body_type_encoded', 'work_rate_encoded', 'preferred_foot_encoded', 'real_face_encoded']\n",
    "X = train_data[columns_for_prediction] # Features\n",
    "y = train_data['position_GK']  # Target\n",
    "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 159,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Feature importances:\n",
      "                         Feature  Importance\n",
      "48          goalkeeping_handling    0.116719\n",
      "50       goalkeeping_positioning    0.105512\n",
      "9                    skill_moves    0.089471\n",
      "51          goalkeeping_reflexes    0.077889\n",
      "27            skill_ball_control    0.072198\n",
      "20    attacking_heading_accuracy    0.069336\n",
      "49           goalkeeping_kicking    0.067364\n",
      "23               skill_dribbling    0.067309\n",
      "47            goalkeeping_diving    0.062871\n",
      "40         mentality_positioning    0.053679\n",
      "22             attacking_volleys    0.034638\n",
      "19           attacking_finishing    0.033212\n",
      "18            attacking_crossing    0.027338\n",
      "35                 power_stamina    0.023459\n",
      "45     defending_standing_tackle    0.019134\n",
      "37              power_long_shots    0.016374\n",
      "42           mentality_penalties    0.014852\n",
      "38          mentality_aggression    0.011583\n",
      "21       attacking_short_passing    0.008977\n",
      "24                   skill_curve    0.008040\n",
      "44   defending_marking_awareness    0.005947\n",
      "26            skill_long_passing    0.005580\n",
      "39       mentality_interceptions    0.002718\n",
      "30              movement_agility    0.002593\n",
      "46      defending_sliding_tackle    0.001890\n",
      "13                      shooting    0.000475\n",
      "15                     dribbling    0.000268\n",
      "25             skill_fk_accuracy    0.000261\n",
      "43           mentality_composure    0.000085\n",
      "17                        physic    0.000062\n",
      "7             club_jersey_number    0.000046\n",
      "31            movement_reactions    0.000042\n",
      "33              power_shot_power    0.000029\n",
      "14                       passing    0.000023\n",
      "28         movement_acceleration    0.000015\n",
      "8                      weak_foot    0.000007\n",
      "52                    yearinclub    0.000005\n",
      "53                           age    0.000000\n",
      "54  years_until_contract_expires    0.000000\n",
      "55             body_type_encoded    0.000000\n",
      "56             work_rate_encoded    0.000000\n",
      "57        preferred_foot_encoded    0.000000\n",
      "0                        overall    0.000000\n",
      "29         movement_sprint_speed    0.000000\n",
      "41              mentality_vision    0.000000\n",
      "36                power_strength    0.000000\n",
      "34                 power_jumping    0.000000\n",
      "32              movement_balance    0.000000\n",
      "1                      potential    0.000000\n",
      "16                     defending    0.000000\n",
      "12                          pace    0.000000\n",
      "11            release_clause_eur    0.000000\n",
      "10      international_reputation    0.000000\n",
      "6                   league_level    0.000000\n",
      "5                      weight_kg    0.000000\n",
      "4                      height_cm    0.000000\n",
      "3                       wage_eur    0.000000\n",
      "2                      value_eur    0.000000\n",
      "58             real_face_encoded    0.000000\n",
      "\n",
      "Top 15 selected features:\n",
      "['goalkeeping_handling', 'goalkeeping_positioning', 'skill_moves', 'goalkeeping_reflexes', 'skill_ball_control', 'attacking_heading_accuracy', 'goalkeeping_kicking', 'skill_dribbling', 'goalkeeping_diving', 'mentality_positioning', 'attacking_volleys', 'attacking_finishing', 'attacking_crossing', 'power_stamina', 'defending_standing_tackle']\n"
     ]
    }
   ],
   "source": [
    "#Since there is too many columns we first choose which are the most important features\n",
    "from sklearn.ensemble import RandomForestClassifier\n",
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn.metrics import accuracy_score\n",
    "\n",
    "\n",
    "# Train a RandomForestClassifier to get feature importances\n",
    "clf = RandomForestClassifier(n_estimators=100, random_state=42)\n",
    "clf.fit(X_train, y_train)\n",
    "\n",
    "# Get feature importances\n",
    "feature_importances = clf.feature_importances_\n",
    "\n",
    "# Create a DataFrame with feature names and their importances\n",
    "feature_importance_df = pd.DataFrame({'Feature': X.columns, 'Importance': feature_importances})\n",
    "\n",
    "# Sort the DataFrame by importance in descending order\n",
    "feature_importance_df = feature_importance_df.sort_values(by='Importance', ascending=False)\n",
    "\n",
    "# Print the sorted feature importances\n",
    "print(\"Feature importances:\")\n",
    "print(feature_importance_df)\n",
    "\n",
    "# Select the top k features based on importance\n",
    "k = 15  # You can choose your desired number of top features\n",
    "selected_featuresgk = feature_importance_df.head(k)['Feature'].tolist()\n",
    "\n",
    "# Print the selected features\n",
    "print(f\"\\nTop {k} selected features:\")\n",
    "print(selected_featuresgk)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 160,
   "metadata": {},
   "outputs": [],
   "source": [
    "target = ['position_GK']\n",
    "X = train_data[selected_featuresgk] # Features selected from the model above\n",
    "y = train_data[target]  # Target\n",
    "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 176,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Accuracy: 1.0\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "         0.0       1.00      1.00      1.00      1257\n",
      "         1.0       1.00      1.00      1.00       122\n",
      "\n",
      "    accuracy                           1.00      1379\n",
      "   macro avg       1.00      1.00      1.00      1379\n",
      "weighted avg       1.00      1.00      1.00      1379\n",
      "\n"
     ]
    }
   ],
   "source": [
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn.tree import DecisionTreeClassifier\n",
    "from sklearn.ensemble import RandomForestClassifier\n",
    "from sklearn.metrics import accuracy_score, classification_report\n",
    "\n",
    "\n",
    "# Prepare data for modeling\n",
    "X = train_data[selected_featuresgk]\n",
    "y = train_data['position_GK']\n",
    "\n",
    "# Train-test split\n",
    "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)\n",
    "\n",
    "# Train a decision tree model\n",
    "rf_gk = RandomForestClassifier(random_state=42)\n",
    "rf_gk.fit(X_train, y_train)\n",
    "\n",
    "# Model evaluation\n",
    "y_pred = rf_gk.predict(X_test)\n",
    "accuracy = accuracy_score(y_test, y_pred)\n",
    "print(f\"Accuracy: {accuracy}\")\n",
    "print(classification_report(y_test, y_pred))\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 177,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Predict probabilities\n",
    "proba_predictions = rf_gk.predict_proba(X)\n",
    "\n",
    "# Add probability column to the DataFrame\n",
    "train_data['gk_probability'] = proba_predictions[:, 1]\n",
    "\n",
    "# Identify candidates for other positions\n",
    "train_other_position_candidates = train_data[train_data['gk_probability'] < 0.5]\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 174,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(6264, 101)"
      ]
     },
     "execution_count": 174,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train_other_position_candidates.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 170,
   "metadata": {},
   "outputs": [],
   "source": [
    "#first we try to classify for the rest\n",
    "from sklearn.model_selection import train_test_split\n",
    "columns_for_prediction = ['overall', 'potential', 'value_eur', 'wage_eur', 'height_cm', 'weight_kg', 'league_level', 'club_jersey_number', 'weak_foot', 'skill_moves', 'international_reputation', 'release_clause_eur', 'pace', 'shooting', 'passing', 'dribbling', 'defending', 'physic', 'attacking_crossing', 'attacking_finishing', 'attacking_heading_accuracy', 'attacking_short_passing', 'attacking_volleys', 'skill_dribbling', 'skill_curve', 'skill_fk_accuracy', 'skill_long_passing', 'skill_ball_control', 'movement_acceleration', 'movement_sprint_speed', 'movement_agility', 'movement_reactions', 'movement_balance', 'power_shot_power', 'power_jumping', 'power_stamina', 'power_strength', 'power_long_shots', 'mentality_aggression', 'mentality_interceptions', 'mentality_positioning', 'mentality_vision', 'mentality_penalties', 'mentality_composure', 'defending_marking_awareness', 'defending_standing_tackle', 'defending_sliding_tackle', 'goalkeeping_diving', 'goalkeeping_handling', 'goalkeeping_kicking', 'goalkeeping_positioning', 'goalkeeping_reflexes', 'yearinclub', 'age', 'years_until_contract_expires', 'body_type_encoded', 'work_rate_encoded', 'preferred_foot_encoded', 'real_face_encoded']\n",
    "X = train_other_position_candidates[columns_for_prediction] # Features\n",
    "y = train_other_position_candidates['position']  # Target\n",
    "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 171,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Feature importances:\n",
      "                         Feature  Importance\n",
      "18            attacking_crossing    0.031220\n",
      "57        preferred_foot_encoded    0.030428\n",
      "20    attacking_heading_accuracy    0.028602\n",
      "46      defending_sliding_tackle    0.027944\n",
      "16                     defending    0.027326\n",
      "19           attacking_finishing    0.025980\n",
      "40         mentality_positioning    0.024909\n",
      "44   defending_marking_awareness    0.024063\n",
      "39       mentality_interceptions    0.023816\n",
      "36                power_strength    0.023396\n",
      "13                      shooting    0.022240\n",
      "29         movement_sprint_speed    0.021727\n",
      "41              mentality_vision    0.021452\n",
      "45     defending_standing_tackle    0.020920\n",
      "12                          pace    0.020734\n",
      "26            skill_long_passing    0.020667\n",
      "28         movement_acceleration    0.020476\n",
      "23               skill_dribbling    0.020467\n",
      "30              movement_agility    0.020264\n",
      "4                      height_cm    0.019729\n",
      "32              movement_balance    0.018966\n",
      "22             attacking_volleys    0.018943\n",
      "34                 power_jumping    0.018685\n",
      "5                      weight_kg    0.018501\n",
      "25             skill_fk_accuracy    0.018466\n",
      "35                 power_stamina    0.018427\n",
      "37              power_long_shots    0.018376\n",
      "42           mentality_penalties    0.018225\n",
      "7             club_jersey_number    0.018127\n",
      "24                   skill_curve    0.017697\n",
      "17                        physic    0.017605\n",
      "38          mentality_aggression    0.017539\n",
      "33              power_shot_power    0.016948\n",
      "14                       passing    0.016407\n",
      "15                     dribbling    0.015372\n",
      "11            release_clause_eur    0.014956\n",
      "53                           age    0.014910\n",
      "43           mentality_composure    0.014908\n",
      "21       attacking_short_passing    0.014720\n",
      "31            movement_reactions    0.014177\n",
      "2                      value_eur    0.013990\n",
      "27            skill_ball_control    0.013933\n",
      "3                       wage_eur    0.013672\n",
      "48          goalkeeping_handling    0.013509\n",
      "51          goalkeeping_reflexes    0.013365\n",
      "50       goalkeeping_positioning    0.013346\n",
      "49           goalkeeping_kicking    0.013204\n",
      "47            goalkeeping_diving    0.013055\n",
      "1                      potential    0.012818\n",
      "0                        overall    0.011165\n",
      "52                    yearinclub    0.010821\n",
      "56             work_rate_encoded    0.008567\n",
      "54  years_until_contract_expires    0.008150\n",
      "8                      weak_foot    0.005809\n",
      "55             body_type_encoded    0.004877\n",
      "9                    skill_moves    0.004176\n",
      "6                   league_level    0.004082\n",
      "58             real_face_encoded    0.001748\n",
      "10      international_reputation    0.001397\n",
      "\n",
      "Top 15 selected features:\n",
      "['goalkeeping_handling', 'goalkeeping_positioning', 'skill_moves', 'goalkeeping_reflexes', 'skill_ball_control', 'attacking_heading_accuracy', 'goalkeeping_kicking', 'skill_dribbling', 'goalkeeping_diving', 'mentality_positioning', 'attacking_volleys', 'attacking_finishing', 'attacking_crossing', 'power_stamina', 'defending_standing_tackle']\n"
     ]
    }
   ],
   "source": [
    "#Since there is too many columns we first choose which are the most important features\n",
    "from sklearn.ensemble import RandomForestClassifier\n",
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn.metrics import accuracy_score\n",
    "\n",
    "\n",
    "# Train a RandomForestClassifier to get feature importances\n",
    "clf = RandomForestClassifier(n_estimators=100, random_state=42)\n",
    "clf.fit(X_train, y_train)\n",
    "\n",
    "# Get feature importances\n",
    "feature_importances = clf.feature_importances_\n",
    "\n",
    "# Create a DataFrame with feature names and their importances\n",
    "feature_importance_df = pd.DataFrame({'Feature': X.columns, 'Importance': feature_importances})\n",
    "\n",
    "# Sort the DataFrame by importance in descending order\n",
    "feature_importance_df = feature_importance_df.sort_values(by='Importance', ascending=False)\n",
    "\n",
    "# Print the sorted feature importances\n",
    "print(\"Feature importances:\")\n",
    "print(feature_importance_df)\n",
    "\n",
    "# Select the top k features based on importance\n",
    "k = 15  # You can choose your desired number of top features\n",
    "selected_features = feature_importance_df.head(k)['Feature'].tolist()\n",
    "\n",
    "# Print the selected features\n",
    "print(f\"\\nTop {k} selected features:\")\n",
    "print(selected_features)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.ensemble import RandomForestClassifier, GradientBoostingClassifier\n",
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn.metrics import accuracy_score, f1_score\n",
    "from sklearn.metrics import roc_curve, roc_auc_score\n",
    "\n",
    "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=1)\n",
    "random_forest = RandomForestClassifier()\n",
    "random_forest.fit(X_train, y_train)\n",
    "rf_predictions = random_forest.predict(X_test)\n",
    "rf_accuracy = accuracy_score(y_test, rf_predictions)\n",
    "print(\"RF Accuracy:\", rf_accuracy)\n",
    "probabilities_rf = random_forest.predict_proba(X_test)[:, 1]\n",
    "fpr_rf, tpr_rf, thresholds_rf = roc_curve(y_test, probabilities_rf)\n",
    "roc_auc_rf = roc_auc_score(y_test, rf_predictions)\n",
    "print(\"ROC-AUC:\\n\", roc_auc_rf)\n",
    "rf_f1score = f1_score(y_test, rf_predictions)\n",
    "print(\"F1-score RF\", rf_f1score)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "from imblearn.under_sampling import RandomUnderSampler\n",
    "# Calculate and print the initial class distribution\n",
    "unique, counts = np.unique(y, return_counts=True)\n",
    "initial_class_distribution = dict(zip(unique, counts))\n",
    "print(\"Initial Class Distribution:\", initial_class_distribution)\n",
    "\n",
    "# Initialize the RandomUnderSampler\n",
    "rus = RandomUnderSampler(sampling_strategy='auto', random_state=42)\n",
    "\n",
    "# Fit and transform the data using random under-sampling\n",
    "X_resampled, y_resampled = rus.fit_resample(X, y)\n",
    "\n",
    "# Calculate and print the class distribution after under-sampling\n",
    "unique, counts = np.unique(y_resampled, return_counts=True)\n",
    "under_sampled_class_distribution = dict(zip(unique, counts))\n",
    "print(\"Class Distribution after Random Under-sampling:\", under_sampled_class_distribution)\n",
    "\n",
    "X_train, X_test, y_train, y_test = train_test_split(X_resampled, y_resampled, test_size=0.2, random_state=1)\n",
    "random_forest1 = RandomForestClassifier()\n",
    "random_forest1.fit(X_train, y_train)\n",
    "rf_predictions = random_forest1.predict(X_test)\n",
    "rf_accuracy = accuracy_score(y_test, rf_predictions)\n",
    "print(\"RF Accuracy:\", rf_accuracy)\n",
    "probabilities_rf = random_forest1.predict_proba(X_test)[:, 1]\n",
    "fpr_rf, tpr_rf, thresholds_rf = roc_curve(y_test, probabilities_rf)\n",
    "roc_auc_rf = roc_auc_score(y_test, rf_predictions)\n",
    "print(\"ROC-AUC:\\n\", roc_auc_rf)\n",
    "rf_f1score = f1_score(y_test, rf_predictions)\n",
    "print(\"F1-score RF\", rf_f1score)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 190,
   "metadata": {},
   "outputs": [],
   "source": [
    "#now we classify for the rest of the positions \n",
    "target = ['position']\n",
    "X = train_other_position_candidates[selected_features] # Features selected from the model above\n",
    "y = train_other_position_candidates[target]  # Target\n",
    "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 191,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Random Forest Accuracy: 0.27693535514764567\n",
      "Gradient Boosting Accuracy: 0.26256983240223464\n"
     ]
    }
   ],
   "source": [
    "from sklearn.ensemble import RandomForestClassifier, GradientBoostingClassifier\n",
    "from sklearn.metrics import accuracy_score\n",
    "import lightgbm as lgb\n",
    "\n",
    "# Train a Random Forest Classifier\n",
    "rf_model = RandomForestClassifier(n_estimators=100, random_state=42)\n",
    "rf_model.fit(X_train, y_train)\n",
    "rf_predictions = rf_model.predict(X_test)\n",
    "\n",
    "# Evaluate the Random Forest model\n",
    "rf_accuracy = accuracy_score(y_test, rf_predictions)\n",
    "print(\"Random Forest Accuracy:\", rf_accuracy)\n",
    "\n",
    "# Train a Gradient Boosting Classifier\n",
    "gb_model = GradientBoostingClassifier(n_estimators=100, random_state=42)\n",
    "gb_model.fit(X_train, y_train)\n",
    "\n",
    "# Predict on the test set\n",
    "gb_predictions = gb_model.predict(X_test)\n",
    "# Evaluate the Gradient Boosting model\n",
    "gb_accuracy = accuracy_score(y_test, gb_predictions)\n",
    "print(\"Gradient Boosting Accuracy:\", gb_accuracy)\n",
    "\n",
    "#Lightgbm estimator\n",
    "#light_model = lgb.LGBMClassifier(n_estimators=100, random_state=42)\n",
    "#light_model.fit(X_train, y_train)\n",
    "#light_predictions = light_model.predict(X_test)\n",
    "# Evaluate the Loghtgbm model\n",
    "#light_accuracy = accuracy_score(y_test, light_predictions)\n",
    "#print(\"Light Accuracy:\", light_accuracy)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 216,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.model_selection import train_test_split\n",
    "columns_for_prediction = ['overall','gk_probability', 'potential', 'value_eur', 'wage_eur', 'height_cm', 'weight_kg', 'league_level', 'club_jersey_number', 'weak_foot', 'skill_moves', 'international_reputation', 'release_clause_eur', 'pace', 'shooting', 'passing', 'dribbling', 'defending', 'physic', 'attacking_crossing', 'attacking_finishing', 'attacking_heading_accuracy', 'attacking_short_passing', 'attacking_volleys', 'skill_dribbling', 'skill_curve', 'skill_fk_accuracy', 'skill_long_passing', 'skill_ball_control', 'movement_acceleration', 'movement_sprint_speed', 'movement_agility', 'movement_reactions', 'movement_balance', 'power_shot_power', 'power_jumping', 'power_stamina', 'power_strength', 'power_long_shots', 'mentality_aggression', 'mentality_interceptions', 'mentality_positioning', 'mentality_vision', 'mentality_penalties', 'mentality_composure', 'defending_marking_awareness', 'defending_standing_tackle', 'defending_sliding_tackle', 'goalkeeping_diving', 'goalkeeping_handling', 'goalkeeping_kicking', 'goalkeeping_positioning', 'goalkeeping_reflexes', 'yearinclub', 'age', 'years_until_contract_expires', 'body_type_encoded', 'work_rate_encoded', 'preferred_foot_encoded', 'real_face_encoded']\n",
    "X = train_data[columns_for_prediction] # Features\n",
    "y = train_data['position']  # Target\n",
    "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 217,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Feature importances:\n",
      "                         Feature  Importance\n",
      "41         mentality_positioning    0.032123\n",
      "19            attacking_crossing    0.031769\n",
      "21    attacking_heading_accuracy    0.031570\n",
      "58        preferred_foot_encoded    0.029663\n",
      "47      defending_sliding_tackle    0.023538\n",
      "20           attacking_finishing    0.023381\n",
      "51       goalkeeping_positioning    0.023348\n",
      "40       mentality_interceptions    0.022737\n",
      "45   defending_marking_awareness    0.022531\n",
      "37                power_strength    0.022158\n",
      "17                     defending    0.021908\n",
      "46     defending_standing_tackle    0.021496\n",
      "48            goalkeeping_diving    0.020543\n",
      "23             attacking_volleys    0.020403\n",
      "30         movement_sprint_speed    0.020378\n",
      "5                      height_cm    0.020262\n",
      "49          goalkeeping_handling    0.019783\n",
      "14                      shooting    0.019701\n",
      "52          goalkeeping_reflexes    0.019394\n",
      "24               skill_dribbling    0.019274\n",
      "38              power_long_shots    0.019119\n",
      "27            skill_long_passing    0.019096\n",
      "42              mentality_vision    0.018486\n",
      "13                          pace    0.018441\n",
      "31              movement_agility    0.018295\n",
      "29         movement_acceleration    0.018244\n",
      "39          mentality_aggression    0.017747\n",
      "26             skill_fk_accuracy    0.017654\n",
      "28            skill_ball_control    0.017290\n",
      "33              movement_balance    0.016953\n",
      "25                   skill_curve    0.016934\n",
      "8             club_jersey_number    0.016778\n",
      "35                 power_jumping    0.016729\n",
      "36                 power_stamina    0.016631\n",
      "43           mentality_penalties    0.016422\n",
      "50           goalkeeping_kicking    0.016210\n",
      "18                        physic    0.015970\n",
      "6                      weight_kg    0.015927\n",
      "16                     dribbling    0.015146\n",
      "22       attacking_short_passing    0.014805\n",
      "15                       passing    0.014474\n",
      "34              power_shot_power    0.014409\n",
      "12            release_clause_eur    0.013665\n",
      "44           mentality_composure    0.013418\n",
      "3                      value_eur    0.013209\n",
      "54                           age    0.013081\n",
      "32            movement_reactions    0.012717\n",
      "4                       wage_eur    0.011813\n",
      "2                      potential    0.011473\n",
      "10                   skill_moves    0.010716\n",
      "0                        overall    0.010112\n",
      "53                    yearinclub    0.009891\n",
      "1                 gk_probability    0.009739\n",
      "57             work_rate_encoded    0.008390\n",
      "55  years_until_contract_expires    0.007764\n",
      "9                      weak_foot    0.005397\n",
      "56             body_type_encoded    0.004484\n",
      "7                   league_level    0.003754\n",
      "59             real_face_encoded    0.001430\n",
      "11      international_reputation    0.001228\n",
      "\n",
      "Top 30 selected features:\n",
      "['mentality_positioning', 'attacking_crossing', 'attacking_heading_accuracy', 'preferred_foot_encoded', 'defending_sliding_tackle', 'attacking_finishing', 'goalkeeping_positioning', 'mentality_interceptions', 'defending_marking_awareness', 'power_strength', 'defending', 'defending_standing_tackle', 'goalkeeping_diving', 'attacking_volleys', 'movement_sprint_speed', 'height_cm', 'goalkeeping_handling', 'shooting', 'goalkeeping_reflexes', 'skill_dribbling', 'power_long_shots', 'skill_long_passing', 'mentality_vision', 'pace', 'movement_agility', 'movement_acceleration', 'mentality_aggression', 'skill_fk_accuracy', 'skill_ball_control', 'movement_balance']\n"
     ]
    }
   ],
   "source": [
    "#Since there is too many columns we first choose which are the most important features\n",
    "from sklearn.ensemble import RandomForestClassifier\n",
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn.metrics import accuracy_score\n",
    "\n",
    "\n",
    "# Train a RandomForestClassifier to get feature importances\n",
    "clf = RandomForestClassifier(n_estimators=100, random_state=42)\n",
    "clf.fit(X_train, y_train)\n",
    "\n",
    "# Get feature importances\n",
    "feature_importances = clf.feature_importances_\n",
    "\n",
    "# Create a DataFrame with feature names and their importances\n",
    "feature_importance_df = pd.DataFrame({'Feature': X.columns, 'Importance': feature_importances})\n",
    "\n",
    "# Sort the DataFrame by importance in descending order\n",
    "feature_importance_df = feature_importance_df.sort_values(by='Importance', ascending=False)\n",
    "\n",
    "# Print the sorted feature importances\n",
    "print(\"Feature importances:\")\n",
    "print(feature_importance_df)\n",
    "\n",
    "# Select the top k features based on importance\n",
    "k = 30  # You can choose your desired number of top features\n",
    "selected_features2 = feature_importance_df.head(k)['Feature'].tolist()\n",
    "\n",
    "# Print the selected features\n",
    "print(f\"\\nTop {k} selected features:\")\n",
    "print(selected_features2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 218,
   "metadata": {},
   "outputs": [],
   "source": [
    "#now we classify for the rest of the positions \n",
    "target = ['position']\n",
    "X = train_other_position_candidates[selected_features2] # Features selected from the model above\n",
    "y = train_other_position_candidates[target]  # Target\n",
    "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 219,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Random Forest Accuracy: 0.3758978451715882\n",
      "Gradient Boosting Accuracy: 0.36233040702314445\n"
     ]
    }
   ],
   "source": [
    "from sklearn.ensemble import RandomForestClassifier, GradientBoostingClassifier\n",
    "from sklearn.metrics import accuracy_score\n",
    "import lightgbm as lgb\n",
    "\n",
    "# Train a Random Forest Classifier\n",
    "rf_model = RandomForestClassifier(n_estimators=100, random_state=42)\n",
    "rf_model.fit(X_train, y_train)\n",
    "rf_predictions = rf_model.predict(X_test)\n",
    "\n",
    "# Evaluate the Random Forest model\n",
    "rf_accuracy = accuracy_score(y_test, rf_predictions)\n",
    "print(\"Random Forest Accuracy:\", rf_accuracy)\n",
    "\n",
    "# Train a Gradient Boosting Classifier\n",
    "gb_model = GradientBoostingClassifier(n_estimators=100, random_state=42)\n",
    "gb_model.fit(X_train, y_train)\n",
    "\n",
    "# Predict on the test set\n",
    "gb_predictions = gb_model.predict(X_test)\n",
    "# Evaluate the Gradient Boosting model\n",
    "gb_accuracy = accuracy_score(y_test, gb_predictions)\n",
    "print(\"Gradient Boosting Accuracy:\", gb_accuracy)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 186,
   "metadata": {},
   "outputs": [],
   "source": [
    "#we start the test predict with GK\n",
    "features_testgk = test_data[selected_featuresgk]\n",
    "proba_predictions1 = rf_gk.predict_proba(features_testgk)\n",
    "\n",
    "# Add probability column to the DataFrame\n",
    "test_data['gk_probability'] = proba_predictions1[:, 1]\n",
    "test_data['position'] = np.where(test_data['gk_probability'] > 0.5, 'GK', test_data['position'])\n",
    "test_other_position_candidates = test_data[test_data['gk_probability'] < 0.5]\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 192,
   "metadata": {},
   "outputs": [],
   "source": [
    "features_test = test_other_position_candidates[selected_features]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 193,
   "metadata": {},
   "outputs": [],
   "source": [
    "kaggle_predictions_rf = rf_model.predict(features_test)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 195,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Produce .csv for kaggle testing\n",
    "test_predictions_submit = pd.DataFrame({\"id\": test_other_position_candidates[\"id\"], \"position\":kaggle_predictions_rf  })\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 196,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Merge the predictions with the original test_data based on the 'id' column\n",
    "test_data = pd.merge(test_data, test_predictions_submit, on='id', how='left')\n",
    "\n",
    "# Fill missing values in the 'position' column with the predictions\n",
    "test_data['position_x'].fillna(test_data['position_y'], inplace=True)\n",
    "\n",
    "# Drop the extra columns if needed\n",
    "test_data.drop(['position_y', 'gk_probability'], axis=1, inplace=True)\n",
    "\n",
    "# Rename the 'position_x' column back to 'position'\n",
    "test_data.rename(columns={'position_x': 'position'}, inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 207,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0\n"
     ]
    }
   ],
   "source": [
    "print(test_data['position'].isnull().sum())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 208,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0\n"
     ]
    }
   ],
   "source": [
    "print(test_data['id'].isnull().sum())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 209,
   "metadata": {},
   "outputs": [],
   "source": [
    "result_data = test_data[['id', 'position']].copy()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 211,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "id          0\n",
      "position    0\n",
      "dtype: int64\n"
     ]
    }
   ],
   "source": [
    "print(result_data.isnull().sum())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 204,
   "metadata": {},
   "outputs": [],
   "source": [
    "test_predictions_submit1 = pd.DataFrame(test_data['id'], test_data['position'])\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 210,
   "metadata": {},
   "outputs": [],
   "source": [
    "result_data.to_csv(\"result_data.csv\", index = False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 205,
   "metadata": {},
   "outputs": [],
   "source": [
    "test_predictions_submit1.to_csv(\"test_predictions_submit.csv\", index = False)"
   ]
  }
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