Final.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# FIFA PLAYER PRICE PREDICTION"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Authors :\n",
    "\n",
    "- Daniel FU : fu.2121690@studenti.uniroma1.it / Matricola : 2121690\n",
    "- Marvin BERGER : berger.2117502@studenti.uniroma1.it / Matricola : 2117502\n",
    "- Lukas WEIGMANN : weigmann.2117702@studenti.uniroma1.it / Matricola : 2117702\n",
    "- Srinjan GHOSH : ghosh.2053796@studenti.uniroma1.it / Matricola : 2053796\n",
    "- Agnese LORELLI : lorelli.1966415@studenti.uniroma1.it / Matricola : 1966415"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Abstract"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "In our final project for the \"Foundations of Data Science\" course, we predict FIFA player market values using a dataset spanning FIFA 15 to FIFA 23 with 110 features. Four models, including regression, single regression trees, Random Forests, and XGBoost, are explored. While regression models perform as expected, the Random Forest surprisingly outperforms, emphasizing its predictive abilities. The importance of the 'overall' feature in the XGBoost model suggests a unique influence of video game data. The project concludes with promising results, acknowledging potential gaming context bias, and proposes future research to explore distinctions in real-life FIFA data. Despite this, the established methodology provides a robust foundation for predicting continuous values in the soccer market."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Introduction"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "This is the final project of the course **\"Foundations of Data Science\"**. Our goal is to predict the market value of a player in the game FIFA. We chose this topic because of our interest in soccer and the fact that the data for the FIFA games is very closely related to market data from reality. This way we can develop an approach for predicting players' market values while still having a lot of other data on top for experimenting. We found a Dataset on **Kaggle** : ​​​[FIFA](https://www.kaggle.com/datasets/stefanoleone992/fifa-23-complete-player-dataset?select=male_players.csv). This dataset contains players' characteristics from FIFA 15 to FIFA 23, it has 110 features and over 1 million instances.\n",
    "We want to optimize several models on the prediction of the player price, compare the models considering the resulting Root Mean Squared Error and R^2 values and be able to make conclusion about the importance of the features for such a prediction."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Team assignments\n",
    "\n",
    "We decided to split the workload :\n",
    "\n",
    "- Daniel FU : XGBoost + Feature Importance + Report\n",
    "- Marvin BERGER : Feature Engineering + Linear Regression\n",
    "- Lukas WEIGMANN : Feature Engineering + Linear Regression + Plot + Report\n",
    "- Srinjan GHOSH : Feature Engineering + Linear and Polynomial Regression\n",
    "- Agnese LORELLI : Decision Tree + Random Forest"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Import Packages"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "\n",
    "from sklearn.preprocessing import LabelEncoder\n",
    "from sklearn.preprocessing import PolynomialFeatures\n",
    "\n",
    "from sklearn.model_selection import train_test_split\n",
    "\n",
    "from sklearn.linear_model import LinearRegression\n",
    "from sklearn.linear_model import SGDRegressor\n",
    "from sklearn.tree import DecisionTreeRegressor\n",
    "from sklearn.ensemble import RandomForestRegressor\n",
    "\n",
    "from sklearn.metrics import mean_squared_error\n",
    "from sklearn.metrics import r2_score"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 1. Dataset Loading and Pre-processing"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/var/folders/nh/3_7mpjj50cj494n6nfn7c77h0000gn/T/ipykernel_46916/3223005786.py:2: DtypeWarning: Columns (108) have mixed types. Specify dtype option on import or set low_memory=False.\n",
      "  dataset_df = pd.read_csv(DATASET_PATH)\n"
     ]
    },
    {
     "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>player_id</th>\n",
       "      <th>player_url</th>\n",
       "      <th>fifa_version</th>\n",
       "      <th>fifa_update</th>\n",
       "      <th>fifa_update_date</th>\n",
       "      <th>short_name</th>\n",
       "      <th>long_name</th>\n",
       "      <th>player_positions</th>\n",
       "      <th>overall</th>\n",
       "      <th>potential</th>\n",
       "      <th>...</th>\n",
       "      <th>cdm</th>\n",
       "      <th>rdm</th>\n",
       "      <th>rwb</th>\n",
       "      <th>lb</th>\n",
       "      <th>lcb</th>\n",
       "      <th>cb</th>\n",
       "      <th>rcb</th>\n",
       "      <th>rb</th>\n",
       "      <th>gk</th>\n",
       "      <th>player_face_url</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>158023</td>\n",
       "      <td>/player/158023/lionel-messi/230009</td>\n",
       "      <td>23</td>\n",
       "      <td>9</td>\n",
       "      <td>2023-01-13</td>\n",
       "      <td>L. Messi</td>\n",
       "      <td>Lionel Andrés Messi Cuccittini</td>\n",
       "      <td>RW</td>\n",
       "      <td>91</td>\n",
       "      <td>91</td>\n",
       "      <td>...</td>\n",
       "      <td>63+3</td>\n",
       "      <td>63+3</td>\n",
       "      <td>64+3</td>\n",
       "      <td>59+3</td>\n",
       "      <td>50+3</td>\n",
       "      <td>50+3</td>\n",
       "      <td>50+3</td>\n",
       "      <td>59+3</td>\n",
       "      <td>19+3</td>\n",
       "      <td>https://cdn.sofifa.net/players/158/023/23_120.png</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>165153</td>\n",
       "      <td>/player/165153/karim-benzema/230009</td>\n",
       "      <td>23</td>\n",
       "      <td>9</td>\n",
       "      <td>2023-01-13</td>\n",
       "      <td>K. Benzema</td>\n",
       "      <td>Karim Benzema</td>\n",
       "      <td>CF, ST</td>\n",
       "      <td>91</td>\n",
       "      <td>91</td>\n",
       "      <td>...</td>\n",
       "      <td>64+3</td>\n",
       "      <td>64+3</td>\n",
       "      <td>64+3</td>\n",
       "      <td>60+3</td>\n",
       "      <td>55+3</td>\n",
       "      <td>55+3</td>\n",
       "      <td>55+3</td>\n",
       "      <td>60+3</td>\n",
       "      <td>18+3</td>\n",
       "      <td>https://cdn.sofifa.net/players/165/153/23_120.png</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>188545</td>\n",
       "      <td>/player/188545/robert-lewandowski/230009</td>\n",
       "      <td>23</td>\n",
       "      <td>9</td>\n",
       "      <td>2023-01-13</td>\n",
       "      <td>R. Lewandowski</td>\n",
       "      <td>Robert Lewandowski</td>\n",
       "      <td>ST</td>\n",
       "      <td>91</td>\n",
       "      <td>91</td>\n",
       "      <td>...</td>\n",
       "      <td>66+3</td>\n",
       "      <td>66+3</td>\n",
       "      <td>64+3</td>\n",
       "      <td>61+3</td>\n",
       "      <td>60+3</td>\n",
       "      <td>60+3</td>\n",
       "      <td>60+3</td>\n",
       "      <td>61+3</td>\n",
       "      <td>19+3</td>\n",
       "      <td>https://cdn.sofifa.net/players/188/545/23_120.png</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>192985</td>\n",
       "      <td>/player/192985/kevin-de-bruyne/230009</td>\n",
       "      <td>23</td>\n",
       "      <td>9</td>\n",
       "      <td>2023-01-13</td>\n",
       "      <td>K. De Bruyne</td>\n",
       "      <td>Kevin De Bruyne</td>\n",
       "      <td>CM, CAM</td>\n",
       "      <td>91</td>\n",
       "      <td>91</td>\n",
       "      <td>...</td>\n",
       "      <td>79+3</td>\n",
       "      <td>79+3</td>\n",
       "      <td>78+3</td>\n",
       "      <td>74+3</td>\n",
       "      <td>68+3</td>\n",
       "      <td>68+3</td>\n",
       "      <td>68+3</td>\n",
       "      <td>74+3</td>\n",
       "      <td>21+3</td>\n",
       "      <td>https://cdn.sofifa.net/players/192/985/23_120.png</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>231747</td>\n",
       "      <td>/player/231747/kylian-mbappe/230009</td>\n",
       "      <td>23</td>\n",
       "      <td>9</td>\n",
       "      <td>2023-01-13</td>\n",
       "      <td>K. Mbappé</td>\n",
       "      <td>Kylian Mbappé Lottin</td>\n",
       "      <td>ST, LW</td>\n",
       "      <td>91</td>\n",
       "      <td>95</td>\n",
       "      <td>...</td>\n",
       "      <td>63+3</td>\n",
       "      <td>63+3</td>\n",
       "      <td>67+3</td>\n",
       "      <td>63+3</td>\n",
       "      <td>54+3</td>\n",
       "      <td>54+3</td>\n",
       "      <td>54+3</td>\n",
       "      <td>63+3</td>\n",
       "      <td>18+3</td>\n",
       "      <td>https://cdn.sofifa.net/players/231/747/23_120.png</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 110 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "   player_id                                player_url  fifa_version  \\\n",
       "0     158023        /player/158023/lionel-messi/230009            23   \n",
       "1     165153       /player/165153/karim-benzema/230009            23   \n",
       "2     188545  /player/188545/robert-lewandowski/230009            23   \n",
       "3     192985     /player/192985/kevin-de-bruyne/230009            23   \n",
       "4     231747       /player/231747/kylian-mbappe/230009            23   \n",
       "\n",
       "   fifa_update fifa_update_date      short_name  \\\n",
       "0            9       2023-01-13        L. Messi   \n",
       "1            9       2023-01-13      K. Benzema   \n",
       "2            9       2023-01-13  R. Lewandowski   \n",
       "3            9       2023-01-13    K. De Bruyne   \n",
       "4            9       2023-01-13       K. Mbappé   \n",
       "\n",
       "                        long_name player_positions  overall  potential  ...  \\\n",
       "0  Lionel Andrés Messi Cuccittini               RW       91         91  ...   \n",
       "1                   Karim Benzema           CF, ST       91         91  ...   \n",
       "2              Robert Lewandowski               ST       91         91  ...   \n",
       "3                 Kevin De Bruyne          CM, CAM       91         91  ...   \n",
       "4            Kylian Mbappé Lottin           ST, LW       91         95  ...   \n",
       "\n",
       "    cdm   rdm   rwb    lb   lcb    cb   rcb    rb    gk  \\\n",
       "0  63+3  63+3  64+3  59+3  50+3  50+3  50+3  59+3  19+3   \n",
       "1  64+3  64+3  64+3  60+3  55+3  55+3  55+3  60+3  18+3   \n",
       "2  66+3  66+3  64+3  61+3  60+3  60+3  60+3  61+3  19+3   \n",
       "3  79+3  79+3  78+3  74+3  68+3  68+3  68+3  74+3  21+3   \n",
       "4  63+3  63+3  67+3  63+3  54+3  54+3  54+3  63+3  18+3   \n",
       "\n",
       "                                     player_face_url  \n",
       "0  https://cdn.sofifa.net/players/158/023/23_120.png  \n",
       "1  https://cdn.sofifa.net/players/165/153/23_120.png  \n",
       "2  https://cdn.sofifa.net/players/188/545/23_120.png  \n",
       "3  https://cdn.sofifa.net/players/192/985/23_120.png  \n",
       "4  https://cdn.sofifa.net/players/231/747/23_120.png  \n",
       "\n",
       "[5 rows x 110 columns]"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "DATASET_PATH = \"./male_players.csv\"\n",
    "dataset_df = pd.read_csv(DATASET_PATH)\n",
    "dataset_df.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Lets have a look at the columns we have to work with"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['player_id',\n",
       " 'player_url',\n",
       " 'fifa_version',\n",
       " 'fifa_update',\n",
       " 'fifa_update_date',\n",
       " 'short_name',\n",
       " 'long_name',\n",
       " 'player_positions',\n",
       " 'overall',\n",
       " 'potential',\n",
       " 'value_eur',\n",
       " 'wage_eur',\n",
       " 'age',\n",
       " 'dob',\n",
       " 'height_cm',\n",
       " 'weight_kg',\n",
       " 'league_id',\n",
       " 'league_name',\n",
       " 'league_level',\n",
       " 'club_team_id',\n",
       " 'club_name',\n",
       " 'club_position',\n",
       " 'club_jersey_number',\n",
       " 'club_loaned_from',\n",
       " 'club_joined_date',\n",
       " 'club_contract_valid_until_year',\n",
       " 'nationality_id',\n",
       " 'nationality_name',\n",
       " 'nation_team_id',\n",
       " 'nation_position',\n",
       " 'nation_jersey_number',\n",
       " 'preferred_foot',\n",
       " 'weak_foot',\n",
       " 'skill_moves',\n",
       " 'international_reputation',\n",
       " 'work_rate',\n",
       " 'body_type',\n",
       " 'real_face',\n",
       " 'release_clause_eur',\n",
       " 'player_tags',\n",
       " 'player_traits',\n",
       " 'pace',\n",
       " 'shooting',\n",
       " 'passing',\n",
       " 'dribbling',\n",
       " 'defending',\n",
       " 'physic',\n",
       " 'attacking_crossing',\n",
       " 'attacking_finishing',\n",
       " 'attacking_heading_accuracy',\n",
       " 'attacking_short_passing',\n",
       " 'attacking_volleys',\n",
       " 'skill_dribbling',\n",
       " 'skill_curve',\n",
       " 'skill_fk_accuracy',\n",
       " 'skill_long_passing',\n",
       " 'skill_ball_control',\n",
       " 'movement_acceleration',\n",
       " 'movement_sprint_speed',\n",
       " 'movement_agility',\n",
       " 'movement_reactions',\n",
       " 'movement_balance',\n",
       " 'power_shot_power',\n",
       " 'power_jumping',\n",
       " 'power_stamina',\n",
       " 'power_strength',\n",
       " 'power_long_shots',\n",
       " 'mentality_aggression',\n",
       " 'mentality_interceptions',\n",
       " 'mentality_positioning',\n",
       " 'mentality_vision',\n",
       " 'mentality_penalties',\n",
       " 'mentality_composure',\n",
       " 'defending_marking_awareness',\n",
       " 'defending_standing_tackle',\n",
       " 'defending_sliding_tackle',\n",
       " 'goalkeeping_diving',\n",
       " 'goalkeeping_handling',\n",
       " 'goalkeeping_kicking',\n",
       " 'goalkeeping_positioning',\n",
       " 'goalkeeping_reflexes',\n",
       " 'goalkeeping_speed',\n",
       " 'ls',\n",
       " 'st',\n",
       " 'rs',\n",
       " 'lw',\n",
       " 'lf',\n",
       " 'cf',\n",
       " 'rf',\n",
       " 'rw',\n",
       " 'lam',\n",
       " 'cam',\n",
       " 'ram',\n",
       " 'lm',\n",
       " 'lcm',\n",
       " 'cm',\n",
       " 'rcm',\n",
       " 'rm',\n",
       " 'lwb',\n",
       " 'ldm',\n",
       " 'cdm',\n",
       " 'rdm',\n",
       " 'rwb',\n",
       " 'lb',\n",
       " 'lcb',\n",
       " 'cb',\n",
       " 'rcb',\n",
       " 'rb',\n",
       " 'gk',\n",
       " 'player_face_url']"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dataset_df.columns.to_list()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We have to deal with **Feature Engineering** because the dataset has too many features, we need to reduce it:\n",
    "Taking the mean of the related attributes in order to form a summary of each attribute."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "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>player_id</th>\n",
       "      <th>player_url</th>\n",
       "      <th>fifa_version</th>\n",
       "      <th>fifa_update</th>\n",
       "      <th>fifa_update_date</th>\n",
       "      <th>short_name</th>\n",
       "      <th>long_name</th>\n",
       "      <th>player_positions</th>\n",
       "      <th>overall</th>\n",
       "      <th>potential</th>\n",
       "      <th>...</th>\n",
       "      <th>rb</th>\n",
       "      <th>gk</th>\n",
       "      <th>player_face_url</th>\n",
       "      <th>attacking_mean</th>\n",
       "      <th>skill_mean</th>\n",
       "      <th>movement_mean</th>\n",
       "      <th>power_mean</th>\n",
       "      <th>mentality_mean</th>\n",
       "      <th>defending_mean</th>\n",
       "      <th>goalkeeping_mean</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>158023</td>\n",
       "      <td>/player/158023/lionel-messi/230009</td>\n",
       "      <td>23</td>\n",
       "      <td>9</td>\n",
       "      <td>2023-01-13</td>\n",
       "      <td>L. Messi</td>\n",
       "      <td>Lionel Andrés Messi Cuccittini</td>\n",
       "      <td>RW</td>\n",
       "      <td>91</td>\n",
       "      <td>91</td>\n",
       "      <td>...</td>\n",
       "      <td>59+3</td>\n",
       "      <td>19+3</td>\n",
       "      <td>https://cdn.sofifa.net/players/158/023/23_120.png</td>\n",
       "      <td>84.6</td>\n",
       "      <td>92.8</td>\n",
       "      <td>88.2</td>\n",
       "      <td>76.6</td>\n",
       "      <td>73.666667</td>\n",
       "      <td>26.333333</td>\n",
       "      <td>10.8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>165153</td>\n",
       "      <td>/player/165153/karim-benzema/230009</td>\n",
       "      <td>23</td>\n",
       "      <td>9</td>\n",
       "      <td>2023-01-13</td>\n",
       "      <td>K. Benzema</td>\n",
       "      <td>Karim Benzema</td>\n",
       "      <td>CF, ST</td>\n",
       "      <td>91</td>\n",
       "      <td>91</td>\n",
       "      <td>...</td>\n",
       "      <td>60+3</td>\n",
       "      <td>18+3</td>\n",
       "      <td>https://cdn.sofifa.net/players/165/153/23_120.png</td>\n",
       "      <td>86.8</td>\n",
       "      <td>81.8</td>\n",
       "      <td>80.2</td>\n",
       "      <td>82.0</td>\n",
       "      <td>76.166667</td>\n",
       "      <td>28.333333</td>\n",
       "      <td>8.2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>188545</td>\n",
       "      <td>/player/188545/robert-lewandowski/230009</td>\n",
       "      <td>23</td>\n",
       "      <td>9</td>\n",
       "      <td>2023-01-13</td>\n",
       "      <td>R. Lewandowski</td>\n",
       "      <td>Robert Lewandowski</td>\n",
       "      <td>ST</td>\n",
       "      <td>91</td>\n",
       "      <td>91</td>\n",
       "      <td>...</td>\n",
       "      <td>61+3</td>\n",
       "      <td>19+3</td>\n",
       "      <td>https://cdn.sofifa.net/players/188/545/23_120.png</td>\n",
       "      <td>85.8</td>\n",
       "      <td>81.6</td>\n",
       "      <td>80.6</td>\n",
       "      <td>84.6</td>\n",
       "      <td>80.500000</td>\n",
       "      <td>32.000000</td>\n",
       "      <td>10.2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>192985</td>\n",
       "      <td>/player/192985/kevin-de-bruyne/230009</td>\n",
       "      <td>23</td>\n",
       "      <td>9</td>\n",
       "      <td>2023-01-13</td>\n",
       "      <td>K. De Bruyne</td>\n",
       "      <td>Kevin De Bruyne</td>\n",
       "      <td>CM, CAM</td>\n",
       "      <td>91</td>\n",
       "      <td>91</td>\n",
       "      <td>...</td>\n",
       "      <td>74+3</td>\n",
       "      <td>21+3</td>\n",
       "      <td>https://cdn.sofifa.net/players/192/985/23_120.png</td>\n",
       "      <td>82.0</td>\n",
       "      <td>88.4</td>\n",
       "      <td>78.8</td>\n",
       "      <td>81.8</td>\n",
       "      <td>82.166667</td>\n",
       "      <td>61.000000</td>\n",
       "      <td>11.2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>231747</td>\n",
       "      <td>/player/231747/kylian-mbappe/230009</td>\n",
       "      <td>23</td>\n",
       "      <td>9</td>\n",
       "      <td>2023-01-13</td>\n",
       "      <td>K. Mbappé</td>\n",
       "      <td>Kylian Mbappé Lottin</td>\n",
       "      <td>ST, LW</td>\n",
       "      <td>91</td>\n",
       "      <td>95</td>\n",
       "      <td>...</td>\n",
       "      <td>63+3</td>\n",
       "      <td>18+3</td>\n",
       "      <td>https://cdn.sofifa.net/players/231/747/23_120.png</td>\n",
       "      <td>82.2</td>\n",
       "      <td>80.8</td>\n",
       "      <td>92.2</td>\n",
       "      <td>82.0</td>\n",
       "      <td>74.166667</td>\n",
       "      <td>30.666667</td>\n",
       "      <td>8.4</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 82 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "   player_id                                player_url  fifa_version  \\\n",
       "0     158023        /player/158023/lionel-messi/230009            23   \n",
       "1     165153       /player/165153/karim-benzema/230009            23   \n",
       "2     188545  /player/188545/robert-lewandowski/230009            23   \n",
       "3     192985     /player/192985/kevin-de-bruyne/230009            23   \n",
       "4     231747       /player/231747/kylian-mbappe/230009            23   \n",
       "\n",
       "   fifa_update fifa_update_date      short_name  \\\n",
       "0            9       2023-01-13        L. Messi   \n",
       "1            9       2023-01-13      K. Benzema   \n",
       "2            9       2023-01-13  R. Lewandowski   \n",
       "3            9       2023-01-13    K. De Bruyne   \n",
       "4            9       2023-01-13       K. Mbappé   \n",
       "\n",
       "                        long_name player_positions  overall  potential  ...  \\\n",
       "0  Lionel Andrés Messi Cuccittini               RW       91         91  ...   \n",
       "1                   Karim Benzema           CF, ST       91         91  ...   \n",
       "2              Robert Lewandowski               ST       91         91  ...   \n",
       "3                 Kevin De Bruyne          CM, CAM       91         91  ...   \n",
       "4            Kylian Mbappé Lottin           ST, LW       91         95  ...   \n",
       "\n",
       "     rb    gk                                    player_face_url  \\\n",
       "0  59+3  19+3  https://cdn.sofifa.net/players/158/023/23_120.png   \n",
       "1  60+3  18+3  https://cdn.sofifa.net/players/165/153/23_120.png   \n",
       "2  61+3  19+3  https://cdn.sofifa.net/players/188/545/23_120.png   \n",
       "3  74+3  21+3  https://cdn.sofifa.net/players/192/985/23_120.png   \n",
       "4  63+3  18+3  https://cdn.sofifa.net/players/231/747/23_120.png   \n",
       "\n",
       "  attacking_mean  skill_mean  movement_mean  power_mean mentality_mean  \\\n",
       "0           84.6        92.8           88.2        76.6      73.666667   \n",
       "1           86.8        81.8           80.2        82.0      76.166667   \n",
       "2           85.8        81.6           80.6        84.6      80.500000   \n",
       "3           82.0        88.4           78.8        81.8      82.166667   \n",
       "4           82.2        80.8           92.2        82.0      74.166667   \n",
       "\n",
       "   defending_mean  goalkeeping_mean  \n",
       "0       26.333333              10.8  \n",
       "1       28.333333               8.2  \n",
       "2       32.000000              10.2  \n",
       "3       61.000000              11.2  \n",
       "4       30.666667               8.4  \n",
       "\n",
       "[5 rows x 82 columns]"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dataset_df['attacking_mean'] = np.mean(dataset_df[['attacking_crossing',\n",
    "                                       'attacking_finishing',\n",
    "                                       'attacking_heading_accuracy',\n",
    "                                       'attacking_short_passing',\n",
    "                                       'attacking_volleys']], axis=1)\n",
    "\n",
    "dataset_df['skill_mean'] = np.mean(dataset_df[['skill_dribbling',\n",
    "                                   'skill_curve',\n",
    "                                   'skill_fk_accuracy',\n",
    "                                   'skill_long_passing',\n",
    "                                   'skill_ball_control']], axis=1)\n",
    "\n",
    "dataset_df['movement_mean'] = np.mean(dataset_df[['movement_acceleration',\n",
    "                                      'movement_sprint_speed',\n",
    "                                      'movement_agility',\n",
    "                                      'movement_reactions',\n",
    "                                      'movement_balance']], axis=1)\n",
    "\n",
    "dataset_df['power_mean'] = np.mean(dataset_df[['power_shot_power',\n",
    "                                   'power_jumping',\n",
    "                                   'power_stamina',\n",
    "                                   'power_strength',\n",
    "                                   'power_long_shots']], axis=1)\n",
    "\n",
    "dataset_df['mentality_mean'] = np.mean(dataset_df[['mentality_aggression',\n",
    "                                       'mentality_interceptions',\n",
    "                                       'mentality_positioning',\n",
    "                                       'mentality_vision',\n",
    "                                       'mentality_penalties',\n",
    "                                       'mentality_composure']], axis=1)\n",
    "\n",
    "dataset_df['defending_mean'] = np.mean(dataset_df[['defending_marking_awareness',\n",
    "                                       'defending_standing_tackle',\n",
    "                                       'defending_sliding_tackle']], axis=1)\n",
    "\n",
    "dataset_df['goalkeeping_mean'] = np.mean(dataset_df[['goalkeeping_diving',\n",
    "                                         'goalkeeping_handling',\n",
    "                                         'goalkeeping_kicking',\n",
    "                                         'goalkeeping_positioning',\n",
    "                                         'goalkeeping_reflexes',\n",
    "                                         'goalkeeping_speed']], axis=1)\n",
    "\n",
    "columns_to_remove = ['attacking_crossing', 'attacking_finishing', 'attacking_heading_accuracy',\n",
    "                      'attacking_short_passing', 'attacking_volleys', 'skill_dribbling', 'skill_curve',\n",
    "                      'skill_fk_accuracy', 'skill_long_passing', 'skill_ball_control',\n",
    "                      'movement_acceleration', 'movement_sprint_speed', 'movement_agility',\n",
    "                      'movement_reactions', 'movement_balance', 'power_shot_power', 'power_jumping',\n",
    "                      'power_stamina', 'power_strength', 'power_long_shots', 'mentality_aggression',\n",
    "                      'mentality_interceptions', 'mentality_positioning', 'mentality_vision',\n",
    "                      'mentality_penalties', 'mentality_composure', 'defending_marking_awareness',\n",
    "                      'defending_standing_tackle', 'defending_sliding_tackle', 'goalkeeping_diving',\n",
    "                      'goalkeeping_handling', 'goalkeeping_kicking', 'goalkeeping_positioning',\n",
    "                      'goalkeeping_reflexes', 'goalkeeping_speed']\n",
    "\n",
    "# Remove the original columns\n",
    "dataset_df.drop(columns=columns_to_remove, inplace=True)\n",
    "\n",
    "# Display the modified DataFrame\n",
    "dataset_df.head()\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We use label encoding to encode categorical variables which might decide the market value of a player"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "dataset_df['preferred_foot'] = LabelEncoder().fit_transform(dataset_df['preferred_foot'])\n",
    "dataset_df['work_rate'] = LabelEncoder().fit_transform(dataset_df['work_rate'])\n",
    "dataset_df['body_type'] = LabelEncoder().fit_transform(dataset_df['body_type'])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let's have a look at the values of the changed attrbiutes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "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>preferred_foot</th>\n",
       "      <th>work_rate</th>\n",
       "      <th>body_type</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0</td>\n",
       "      <td>4</td>\n",
       "      <td>9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>8</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10003585</th>\n",
       "      <td>1</td>\n",
       "      <td>8</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10003586</th>\n",
       "      <td>1</td>\n",
       "      <td>8</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10003587</th>\n",
       "      <td>1</td>\n",
       "      <td>8</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10003588</th>\n",
       "      <td>1</td>\n",
       "      <td>8</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10003589</th>\n",
       "      <td>0</td>\n",
       "      <td>8</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>10003590 rows × 3 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "          preferred_foot  work_rate  body_type\n",
       "0                      0          4          9\n",
       "1                      1          8          4\n",
       "2                      1          2          9\n",
       "3                      1          2          9\n",
       "4                      1          1          9\n",
       "...                  ...        ...        ...\n",
       "10003585               1          8          4\n",
       "10003586               1          8          1\n",
       "10003587               1          8          5\n",
       "10003588               1          8          1\n",
       "10003589               0          8          5\n",
       "\n",
       "[10003590 rows x 3 columns]"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dataset_df[['preferred_foot', 'work_rate', 'body_type']]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let's now visualize how the attributes might be correlated with our dependent variable **value_eur**."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/srinjanghosh/miniconda3/envs/fds/lib/python3.9/site-packages/seaborn/_oldcore.py:1498: FutureWarning: is_categorical_dtype is deprecated and will be removed in a future version. Use isinstance(dtype, CategoricalDtype) instead\n",
      "  if pd.api.types.is_categorical_dtype(vector):\n",
      "/Users/srinjanghosh/miniconda3/envs/fds/lib/python3.9/site-packages/seaborn/_oldcore.py:1498: FutureWarning: is_categorical_dtype is deprecated and will be removed in a future version. Use isinstance(dtype, CategoricalDtype) instead\n",
      "  if pd.api.types.is_categorical_dtype(vector):\n",
      "/Users/srinjanghosh/miniconda3/envs/fds/lib/python3.9/site-packages/seaborn/_oldcore.py:1498: FutureWarning: is_categorical_dtype is deprecated and will be removed in a future version. Use isinstance(dtype, CategoricalDtype) instead\n",
      "  if pd.api.types.is_categorical_dtype(vector):\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "Text(0.5, 1.0, 'Correlation of Column value_eur with Other Columns')"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 2000x1000 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "selected_column = 'value_eur'\n",
    "\n",
    "correlations= dataset_df.corrwith(dataset_df[selected_column], numeric_only=True)\n",
    "\n",
    "\n",
    "plt.figure(figsize=(20, 10))\n",
    "sns.barplot(x=correlations.index, y=correlations.values, palette='viridis')\n",
    "plt.xticks(rotation=45, ha=\"right\")\n",
    "\n",
    "# Set plot labels and title\n",
    "plt.xlabel('Columns')\n",
    "plt.ylabel('Correlation Coefficient')\n",
    "plt.title(f'Correlation of Column {selected_column} with Other Columns')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Notice that some features disappeared, it has removed features whose values are String (e.g. player_url, player_face_url ...). We have now **39 features**"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let's remove the uncessary columns above a particular threshold"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "def keepOnlyDataOverAThreshold(data, selected_column, threshold):\n",
    "    correlations = dataset_df.corrwith(dataset_df[selected_column], numeric_only=True)\n",
    "    columns_to_keep = correlations[correlations.abs()>threshold].index.to_list()\n",
    "    columns_to_delete = list(set(dataset_df.columns.to_list()) - set(columns_to_keep))\n",
    "    return data.drop(columns=columns_to_delete)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "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>overall</th>\n",
       "      <th>potential</th>\n",
       "      <th>value_eur</th>\n",
       "      <th>wage_eur</th>\n",
       "      <th>league_level</th>\n",
       "      <th>club_team_id</th>\n",
       "      <th>club_contract_valid_until_year</th>\n",
       "      <th>nation_team_id</th>\n",
       "      <th>weak_foot</th>\n",
       "      <th>skill_moves</th>\n",
       "      <th>...</th>\n",
       "      <th>passing</th>\n",
       "      <th>dribbling</th>\n",
       "      <th>defending</th>\n",
       "      <th>physic</th>\n",
       "      <th>attacking_mean</th>\n",
       "      <th>skill_mean</th>\n",
       "      <th>movement_mean</th>\n",
       "      <th>power_mean</th>\n",
       "      <th>mentality_mean</th>\n",
       "      <th>defending_mean</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>91</td>\n",
       "      <td>91</td>\n",
       "      <td>54000000.0</td>\n",
       "      <td>195000.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>73.0</td>\n",
       "      <td>2023.0</td>\n",
       "      <td>1369.0</td>\n",
       "      <td>4</td>\n",
       "      <td>4</td>\n",
       "      <td>...</td>\n",
       "      <td>90.0</td>\n",
       "      <td>94.0</td>\n",
       "      <td>34.0</td>\n",
       "      <td>64.0</td>\n",
       "      <td>84.6</td>\n",
       "      <td>92.8</td>\n",
       "      <td>88.2</td>\n",
       "      <td>76.6</td>\n",
       "      <td>73.666667</td>\n",
       "      <td>26.333333</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>91</td>\n",
       "      <td>91</td>\n",
       "      <td>64000000.0</td>\n",
       "      <td>450000.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>243.0</td>\n",
       "      <td>2023.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>4</td>\n",
       "      <td>4</td>\n",
       "      <td>...</td>\n",
       "      <td>83.0</td>\n",
       "      <td>87.0</td>\n",
       "      <td>39.0</td>\n",
       "      <td>78.0</td>\n",
       "      <td>86.8</td>\n",
       "      <td>81.8</td>\n",
       "      <td>80.2</td>\n",
       "      <td>82.0</td>\n",
       "      <td>76.166667</td>\n",
       "      <td>28.333333</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>91</td>\n",
       "      <td>91</td>\n",
       "      <td>84000000.0</td>\n",
       "      <td>420000.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>241.0</td>\n",
       "      <td>2025.0</td>\n",
       "      <td>1353.0</td>\n",
       "      <td>4</td>\n",
       "      <td>4</td>\n",
       "      <td>...</td>\n",
       "      <td>79.0</td>\n",
       "      <td>86.0</td>\n",
       "      <td>44.0</td>\n",
       "      <td>83.0</td>\n",
       "      <td>85.8</td>\n",
       "      <td>81.6</td>\n",
       "      <td>80.6</td>\n",
       "      <td>84.6</td>\n",
       "      <td>80.500000</td>\n",
       "      <td>32.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>91</td>\n",
       "      <td>91</td>\n",
       "      <td>107500000.0</td>\n",
       "      <td>350000.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>10.0</td>\n",
       "      <td>2025.0</td>\n",
       "      <td>1325.0</td>\n",
       "      <td>5</td>\n",
       "      <td>4</td>\n",
       "      <td>...</td>\n",
       "      <td>93.0</td>\n",
       "      <td>87.0</td>\n",
       "      <td>63.0</td>\n",
       "      <td>77.0</td>\n",
       "      <td>82.0</td>\n",
       "      <td>88.4</td>\n",
       "      <td>78.8</td>\n",
       "      <td>81.8</td>\n",
       "      <td>82.166667</td>\n",
       "      <td>61.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>91</td>\n",
       "      <td>95</td>\n",
       "      <td>190500000.0</td>\n",
       "      <td>230000.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>73.0</td>\n",
       "      <td>2024.0</td>\n",
       "      <td>1335.0</td>\n",
       "      <td>4</td>\n",
       "      <td>5</td>\n",
       "      <td>...</td>\n",
       "      <td>80.0</td>\n",
       "      <td>92.0</td>\n",
       "      <td>36.0</td>\n",
       "      <td>76.0</td>\n",
       "      <td>82.2</td>\n",
       "      <td>80.8</td>\n",
       "      <td>92.2</td>\n",
       "      <td>82.0</td>\n",
       "      <td>74.166667</td>\n",
       "      <td>30.666667</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 25 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "   overall  potential    value_eur  wage_eur  league_level  club_team_id  \\\n",
       "0       91         91   54000000.0  195000.0           1.0          73.0   \n",
       "1       91         91   64000000.0  450000.0           1.0         243.0   \n",
       "2       91         91   84000000.0  420000.0           1.0         241.0   \n",
       "3       91         91  107500000.0  350000.0           1.0          10.0   \n",
       "4       91         95  190500000.0  230000.0           1.0          73.0   \n",
       "\n",
       "   club_contract_valid_until_year  nation_team_id  weak_foot  skill_moves  \\\n",
       "0                          2023.0          1369.0          4            4   \n",
       "1                          2023.0             NaN          4            4   \n",
       "2                          2025.0          1353.0          4            4   \n",
       "3                          2025.0          1325.0          5            4   \n",
       "4                          2024.0          1335.0          4            5   \n",
       "\n",
       "   ...  passing  dribbling  defending  physic  attacking_mean  skill_mean  \\\n",
       "0  ...     90.0       94.0       34.0    64.0            84.6        92.8   \n",
       "1  ...     83.0       87.0       39.0    78.0            86.8        81.8   \n",
       "2  ...     79.0       86.0       44.0    83.0            85.8        81.6   \n",
       "3  ...     93.0       87.0       63.0    77.0            82.0        88.4   \n",
       "4  ...     80.0       92.0       36.0    76.0            82.2        80.8   \n",
       "\n",
       "   movement_mean  power_mean  mentality_mean  defending_mean  \n",
       "0           88.2        76.6       73.666667       26.333333  \n",
       "1           80.2        82.0       76.166667       28.333333  \n",
       "2           80.6        84.6       80.500000       32.000000  \n",
       "3           78.8        81.8       82.166667       61.000000  \n",
       "4           92.2        82.0       74.166667       30.666667  \n",
       "\n",
       "[5 rows x 25 columns]"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dataset_df = keepOnlyDataOverAThreshold(dataset_df, 'value_eur', 0.1)\n",
    "dataset_df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(10003590, 25)"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dataset_df.shape"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let's remove all the rows which have N/A values --> **250k instances**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "dataset_df = dataset_df.dropna()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(242956, 25)"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dataset_df.shape"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We drop this feature because it has too much correlation with the target (almost 1) and thus we want to avoid a scenario where we predict a player price basically already using the market value. It has been a debate inside the team if we should keep it or not, but thanks to our results, we know that it is better to remove it."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "dataset_df = dataset_df.drop(columns=\"release_clause_eur\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let's scale the dataset"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "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>overall</th>\n",
       "      <th>potential</th>\n",
       "      <th>value_eur</th>\n",
       "      <th>wage_eur</th>\n",
       "      <th>league_level</th>\n",
       "      <th>club_team_id</th>\n",
       "      <th>club_contract_valid_until_year</th>\n",
       "      <th>nation_team_id</th>\n",
       "      <th>weak_foot</th>\n",
       "      <th>skill_moves</th>\n",
       "      <th>...</th>\n",
       "      <th>passing</th>\n",
       "      <th>dribbling</th>\n",
       "      <th>defending</th>\n",
       "      <th>physic</th>\n",
       "      <th>attacking_mean</th>\n",
       "      <th>skill_mean</th>\n",
       "      <th>movement_mean</th>\n",
       "      <th>power_mean</th>\n",
       "      <th>mentality_mean</th>\n",
       "      <th>defending_mean</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2.519552</td>\n",
       "      <td>1.996863</td>\n",
       "      <td>1.997707</td>\n",
       "      <td>2.450759</td>\n",
       "      <td>-0.306742</td>\n",
       "      <td>-0.509122</td>\n",
       "      <td>0.654471</td>\n",
       "      <td>-0.525043</td>\n",
       "      <td>1.033704</td>\n",
       "      <td>1.451021</td>\n",
       "      <td>...</td>\n",
       "      <td>2.362914</td>\n",
       "      <td>2.381919</td>\n",
       "      <td>-1.457136</td>\n",
       "      <td>-0.991061</td>\n",
       "      <td>2.326984</td>\n",
       "      <td>2.579284</td>\n",
       "      <td>1.875310</td>\n",
       "      <td>1.008086</td>\n",
       "      <td>1.174331</td>\n",
       "      <td>-1.637561</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2.519552</td>\n",
       "      <td>1.996863</td>\n",
       "      <td>3.523617</td>\n",
       "      <td>6.168126</td>\n",
       "      <td>-0.306742</td>\n",
       "      <td>-0.505230</td>\n",
       "      <td>1.702788</td>\n",
       "      <td>-0.525400</td>\n",
       "      <td>1.033704</td>\n",
       "      <td>1.451021</td>\n",
       "      <td>...</td>\n",
       "      <td>1.218671</td>\n",
       "      <td>1.543173</td>\n",
       "      <td>-0.891854</td>\n",
       "      <td>1.381656</td>\n",
       "      <td>2.457943</td>\n",
       "      <td>1.519709</td>\n",
       "      <td>1.014020</td>\n",
       "      <td>2.198524</td>\n",
       "      <td>2.095098</td>\n",
       "      <td>-1.349836</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2.519552</td>\n",
       "      <td>1.996863</td>\n",
       "      <td>4.718914</td>\n",
       "      <td>5.011612</td>\n",
       "      <td>-0.306742</td>\n",
       "      <td>-0.510581</td>\n",
       "      <td>1.702788</td>\n",
       "      <td>-0.526024</td>\n",
       "      <td>2.419850</td>\n",
       "      <td>1.451021</td>\n",
       "      <td>...</td>\n",
       "      <td>2.674980</td>\n",
       "      <td>1.648016</td>\n",
       "      <td>0.182182</td>\n",
       "      <td>0.632377</td>\n",
       "      <td>2.043240</td>\n",
       "      <td>2.163022</td>\n",
       "      <td>0.810031</td>\n",
       "      <td>1.781871</td>\n",
       "      <td>2.319675</td>\n",
       "      <td>0.122639</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2.519552</td>\n",
       "      <td>2.640849</td>\n",
       "      <td>8.940598</td>\n",
       "      <td>3.029016</td>\n",
       "      <td>-0.306742</td>\n",
       "      <td>-0.509122</td>\n",
       "      <td>1.178630</td>\n",
       "      <td>-0.525801</td>\n",
       "      <td>1.033704</td>\n",
       "      <td>2.769523</td>\n",
       "      <td>...</td>\n",
       "      <td>1.322693</td>\n",
       "      <td>2.172232</td>\n",
       "      <td>-1.344080</td>\n",
       "      <td>0.507497</td>\n",
       "      <td>2.065066</td>\n",
       "      <td>1.444025</td>\n",
       "      <td>2.328620</td>\n",
       "      <td>1.811632</td>\n",
       "      <td>1.241704</td>\n",
       "      <td>-1.417536</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>2.188912</td>\n",
       "      <td>1.674870</td>\n",
       "      <td>4.617186</td>\n",
       "      <td>3.194232</td>\n",
       "      <td>-0.306742</td>\n",
       "      <td>-0.510396</td>\n",
       "      <td>1.178630</td>\n",
       "      <td>-0.526180</td>\n",
       "      <td>2.419850</td>\n",
       "      <td>0.132520</td>\n",
       "      <td>...</td>\n",
       "      <td>1.634759</td>\n",
       "      <td>1.228643</td>\n",
       "      <td>-0.722269</td>\n",
       "      <td>1.256776</td>\n",
       "      <td>2.457943</td>\n",
       "      <td>1.406183</td>\n",
       "      <td>0.266059</td>\n",
       "      <td>2.198524</td>\n",
       "      <td>2.229845</td>\n",
       "      <td>-0.875936</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 24 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     overall  potential  value_eur  wage_eur  league_level  club_team_id  \\\n",
       "0   2.519552   1.996863   1.997707  2.450759     -0.306742     -0.509122   \n",
       "2   2.519552   1.996863   3.523617  6.168126     -0.306742     -0.505230   \n",
       "3   2.519552   1.996863   4.718914  5.011612     -0.306742     -0.510581   \n",
       "4   2.519552   2.640849   8.940598  3.029016     -0.306742     -0.509122   \n",
       "11  2.188912   1.674870   4.617186  3.194232     -0.306742     -0.510396   \n",
       "\n",
       "    club_contract_valid_until_year  nation_team_id  weak_foot  skill_moves  \\\n",
       "0                         0.654471       -0.525043   1.033704     1.451021   \n",
       "2                         1.702788       -0.525400   1.033704     1.451021   \n",
       "3                         1.702788       -0.526024   2.419850     1.451021   \n",
       "4                         1.178630       -0.525801   1.033704     2.769523   \n",
       "11                        1.178630       -0.526180   2.419850     0.132520   \n",
       "\n",
       "    ...   passing  dribbling  defending    physic  attacking_mean  skill_mean  \\\n",
       "0   ...  2.362914   2.381919  -1.457136 -0.991061        2.326984    2.579284   \n",
       "2   ...  1.218671   1.543173  -0.891854  1.381656        2.457943    1.519709   \n",
       "3   ...  2.674980   1.648016   0.182182  0.632377        2.043240    2.163022   \n",
       "4   ...  1.322693   2.172232  -1.344080  0.507497        2.065066    1.444025   \n",
       "11  ...  1.634759   1.228643  -0.722269  1.256776        2.457943    1.406183   \n",
       "\n",
       "    movement_mean  power_mean  mentality_mean  defending_mean  \n",
       "0        1.875310    1.008086        1.174331       -1.637561  \n",
       "2        1.014020    2.198524        2.095098       -1.349836  \n",
       "3        0.810031    1.781871        2.319675        0.122639  \n",
       "4        2.328620    1.811632        1.241704       -1.417536  \n",
       "11       0.266059    2.198524        2.229845       -0.875936  \n",
       "\n",
       "[5 rows x 24 columns]"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.preprocessing import StandardScaler\n",
    "\n",
    "columns = dataset_df.columns.to_list()\n",
    "\n",
    "scaler = StandardScaler()\n",
    "dataset_df[columns] = scaler.fit_transform(dataset_df[columns])\n",
    "\n",
    "dataset_df.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 2. Training Various Models\n",
    "\n",
    "We decided to use 4 main approaches for our task.\n",
    "First we wanted to use regression. We will use a linear regression and a stochastic gradient descent regressor on polynomial and non-polynomial features. We expect the regression to maybe not perform that well since we have a lot of features that are not correlated over 0.5 with our label and also literature implies that regression models perform worse than the following methods.$^2$ However we have a lot of features and training data which might help improving the results.\n",
    "Furthermore we decided to use regression trees. In the first variant we only want to train one regression tree for prediction and in the second variant a Random Forest of regression trees. Lastly we use a XGBoost model. Since it's suggested that on this kind of data XGBoost perform very well we expect a similar good performance for our models.$^1$ However, also Random Forests should perform very well.$^2$\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "First let's perform the train test split"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [],
   "source": [
    "y = dataset_df[\"value_eur\"]\n",
    "X = dataset_df.drop(\"value_eur\", axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Training data shape:           overall  potential  wage_eur  league_level  club_team_id  \\\n",
      "5410182  0.205073   0.225901  0.055122     -0.306742     -0.468933   \n",
      "3157350 -0.125567  -0.418085  0.055122     -0.306742     -0.467289   \n",
      "4906844 -0.125567   0.225901 -0.110094     -0.306742     -0.510511   \n",
      "3247683  1.362313   0.869887  1.459461     -0.306742     -0.509770   \n",
      "5894729  0.535713   0.869887  1.211636     -0.306742     -0.505230   \n",
      "...           ...        ...       ...           ...           ...   \n",
      "3397587 -0.290886  -0.740078 -0.539657      2.556185     -0.465598   \n",
      "2971185  1.196993   0.708891  0.220338     -0.306742     -0.500134   \n",
      "3716367 -0.952166  -1.062071 -0.523135      2.556185     -0.465598   \n",
      "4098663 -1.282806  -0.418085 -0.721395     -0.306742      2.063654   \n",
      "3453311 -0.125567   0.869887 -0.589222     -0.306742     -0.495224   \n",
      "\n",
      "         club_contract_valid_until_year  nation_team_id  weak_foot  \\\n",
      "5410182                       -0.393846        1.784823  -0.352442   \n",
      "3157350                        0.130313        1.927962  -0.352442   \n",
      "4906844                       -0.393846       -0.525155   1.033704   \n",
      "3247683                       -0.918005       -0.525801  -1.738587   \n",
      "5894729                       -0.393846       -0.525801  -0.352442   \n",
      "...                                 ...             ...        ...   \n",
      "3397587                       -0.918005       -0.525400  -0.352442   \n",
      "2971185                        0.130313       -0.525199  -0.352442   \n",
      "3716367                       -0.918005       -0.524018  -0.352442   \n",
      "4098663                       -0.918005       -0.524464  -1.738587   \n",
      "3453311                        0.130313        1.920163  -0.352442   \n",
      "\n",
      "         skill_moves  international_reputation  ...   passing  dribbling  \\\n",
      "5410182     0.132520                  0.441324  ...  0.594538   0.389898   \n",
      "3157350     0.132520                  0.441324  ... -0.757749  -0.239162   \n",
      "4906844    -1.185982                  0.441324  ...  0.282472  -0.553691   \n",
      "3247683    -1.185982                  1.587228  ...  0.906605   0.704427   \n",
      "5894729     0.132520                  0.441324  ...  0.802582   0.389898   \n",
      "...              ...                       ...  ...       ...        ...   \n",
      "3397587     0.132520                  0.441324  ...  0.386494   0.285054   \n",
      "2971185     1.451021                  1.587228  ...  0.906605   1.018957   \n",
      "3716367     0.132520                 -0.704580  ... -0.237638  -0.344005   \n",
      "4098663     0.132520                 -0.704580  ... -1.069815  -0.658535   \n",
      "3453311    -1.185982                 -0.704580  ... -1.381882  -1.287594   \n",
      "\n",
      "         defending    physic  attacking_mean  skill_mean  movement_mean  \\\n",
      "5410182   0.238711  0.382617        0.537210    0.800712      -0.277913   \n",
      "3157350  -1.231023  1.131896        0.253466    0.270924      -0.209917   \n",
      "4906844   0.860521  0.382617        0.297119    0.195240      -1.093872   \n",
      "3247683   1.312747  1.131896        0.886435    0.308766       0.923358   \n",
      "5894729   0.860521  0.382617        0.057027    0.346608       0.651372   \n",
      "...            ...       ...             ...         ...            ...   \n",
      "3397587  -1.231023 -1.490580        0.755476    0.762870       0.764700   \n",
      "2971185  -0.891854 -0.116902        1.715842    0.819633       1.354003   \n",
      "3716367   0.577880  1.007016        0.253466   -0.145337      -0.504568   \n",
      "4098663   0.125654  0.757257       -1.929185   -0.958939       0.492714   \n",
      "3453311   0.803993  0.507497       -1.754573   -1.526569      -0.595230   \n",
      "\n",
      "         power_mean  mentality_mean  defending_mean  \n",
      "5410182    1.275935        0.882381        0.241114  \n",
      "3157350    1.127130        0.343395       -1.468311  \n",
      "4906844    0.769999        0.904838        0.748863  \n",
      "3247683    1.275935        1.443824        1.307388  \n",
      "5894729    0.710477        0.680261        0.951963  \n",
      "...             ...             ...             ...  \n",
      "3397587   -0.539484       -0.397710       -1.552936  \n",
      "2971185    0.918803        0.680261       -0.859011  \n",
      "3716367    0.889042        0.523057        0.545763  \n",
      "4098663   -0.539484       -0.824407        0.241114  \n",
      "3453311   -1.968009       -1.408308        0.816563  \n",
      "\n",
      "[194364 rows x 23 columns]\n",
      "Test data shape:           overall  potential  wage_eur  league_level  club_team_id  \\\n",
      "4432694  2.023592   1.996863  4.185530     -0.306742     -0.505184   \n",
      "2015172 -0.125567   0.386898 -0.523135     -0.306742     -0.509237   \n",
      "3966621 -0.125567  -0.579082 -0.523135      2.556185     -0.469257   \n",
      "1585691  2.354232   2.640849  1.872501     -0.306742     -0.509122   \n",
      "2093092 -0.786846  -1.223068 -0.291832     -0.306742     -0.469211   \n",
      "...           ...        ...       ...           ...           ...   \n",
      "5732010  1.196993   0.708891  0.715987     -0.306742     -0.508612   \n",
      "2380009 -1.117486  -1.545061 -0.605743     -0.306742      2.092562   \n",
      "5570680  1.362313   0.869887  1.542069     -0.306742     -0.509122   \n",
      "5901    -1.117486   0.547894 -0.572700     -0.306742     -0.510650   \n",
      "1001940 -0.456206   0.064905 -0.192703     -0.306742     -0.503284   \n",
      "\n",
      "         club_contract_valid_until_year  nation_team_id  weak_foot  \\\n",
      "4432694                        0.130313       -0.525199  -0.352442   \n",
      "2015172                        1.178630        1.920230  -0.352442   \n",
      "3966621                       -0.393846        1.897257  -0.352442   \n",
      "1585691                        0.130313       -0.525801   1.033704   \n",
      "2093092                        0.654471       -0.525355  -1.738587   \n",
      "...                                 ...             ...        ...   \n",
      "5732010                       -0.393846       -0.525377   1.033704   \n",
      "2380009                        0.654471       -0.524464  -0.352442   \n",
      "5570680                       -1.442163       -0.525043  -1.738587   \n",
      "5901                           2.751106       -0.525266  -0.352442   \n",
      "1001940                        2.226947       -0.525333   1.033704   \n",
      "\n",
      "         skill_moves  international_reputation  ...   passing  dribbling  \\\n",
      "4432694     1.451021                  1.587228  ...  1.842803   2.277076   \n",
      "2015172    -1.185982                 -0.704580  ... -0.133616  -0.239162   \n",
      "3966621    -1.185982                 -0.704580  ...  1.114649  -0.344005   \n",
      "1585691     2.769523                  1.587228  ...  1.114649   2.067389   \n",
      "2093092    -1.185982                 -0.704580  ... -1.797970  -1.706967   \n",
      "...              ...                       ...  ...       ...        ...   \n",
      "5732010     0.132520                  1.587228  ...  1.530737   1.018957   \n",
      "2380009     0.132520                 -0.704580  ... -0.237638  -0.029475   \n",
      "5570680     2.769523                  2.733132  ...  1.426715   1.438330   \n",
      "5901       -1.185982                 -0.704580  ... -0.861771  -0.239162   \n",
      "1001940     0.132520                 -0.704580  ...  0.698560   0.180211   \n",
      "\n",
      "         defending    physic  attacking_mean  skill_mean  movement_mean  \\\n",
      "4432694   0.069126 -1.240820        1.017394    2.049496       0.900693   \n",
      "2015172   0.690936  1.506535       -0.794207   -0.485915      -0.708558   \n",
      "3966621   0.577880 -1.115940        0.493557    0.743949      -0.731224   \n",
      "1585691  -1.174495  0.507497        1.999587    1.254815       2.260623   \n",
      "2093092   0.634408  0.257737       -1.951012   -1.734699      -1.864499   \n",
      "...            ...       ...             ...         ...            ...   \n",
      "5732010   1.030106  0.007978        1.126526    1.462946      -0.277913   \n",
      "2380009  -0.043931 -0.491541       -0.073932   -0.145337      -0.663227   \n",
      "5570680  -0.778798 -0.741301        1.039220    1.330499       1.467331   \n",
      "5901      0.351767 -0.241782       -0.968819   -0.826492      -0.119255   \n",
      "1001940   0.125654  0.007978        0.035201    0.725028       0.175397   \n",
      "\n",
      "         power_mean  mentality_mean  defending_mean  \n",
      "4432694   -0.003786        1.376451       -0.012761  \n",
      "2015172    0.918803        0.388311        0.731938  \n",
      "3966621   -0.241874        0.590430        0.630388  \n",
      "1585691    1.633066        1.017127       -1.282136  \n",
      "2093092   -1.343029       -1.093900        0.647313  \n",
      "...             ...             ...             ...  \n",
      "5732010    0.621194        2.252302        0.935038  \n",
      "2380009   -0.301396       -0.846865        0.071864  \n",
      "5570680    0.472389        0.882381       -0.452811  \n",
      "5901      -0.896615       -1.273562        0.461138  \n",
      "1001940    0.918803        0.882381        0.207264  \n",
      "\n",
      "[48592 rows x 23 columns]\n"
     ]
    }
   ],
   "source": [
    "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)\n",
    "print(f\"Training data shape: {X_train}\")\n",
    "print(f\"Test data shape: {X_test}\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### 2.1. Linear Regression"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [],
   "source": [
    "def linearReg(X_train, X_test, y_train, y_test):\n",
    "  model = LinearRegression()\n",
    "  model.fit(X_train, y_train)\n",
    "  y_pred = model.predict(X_test)\n",
    "  mse = mean_squared_error(y_test, y_pred)\n",
    "  rmse = np.sqrt(mse)\n",
    "  r2 = r2_score(y_test, y_pred)\n",
    "\n",
    "  print(f'Mean Squared Error: {mse}')\n",
    "  print(f'Root Mean Squared Error: {rmse}')\n",
    "  print(f'R-squared: {r2}')\n",
    "\n",
    "  return rmse,r2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Mean Squared Error: 0.23386675158166678\n",
      "Root Mean Squared Error: 0.4835977166836779\n",
      "R-squared: 0.7672698481890075\n"
     ]
    }
   ],
   "source": [
    "rmse_lin,r2_lin = linearReg(X_train, X_test, y_train, y_test)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### 2.2. Stochastic Gradient Descent Regressor"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [],
   "source": [
    "def sgdRegressor(X_train, X_test, y_train, y_test, iterations):\n",
    "  model = SGDRegressor(max_iter=iterations)\n",
    "  model.fit(X_train, y_train)\n",
    "  y_pred = model.predict(X_test)\n",
    "  mse = mean_squared_error(y_test, y_pred)\n",
    "  rmse = np.sqrt(mse)\n",
    "  r2 = r2_score(y_test, y_pred)\n",
    "\n",
    "  print(f'Mean Squared Error: {mse}')\n",
    "  print(f'Root Mean Squared Error: {rmse}')\n",
    "  print(f'R-squared: {r2}')\n",
    "\n",
    "  return rmse, r2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Mean Squared Error: 0.23494259281524638\n",
      "Root Mean Squared Error: 0.48470877113504596\n",
      "R-squared: 0.7661992355776714\n"
     ]
    }
   ],
   "source": [
    "rmse_stoch, r2_stoch = sgdRegressor(X_train, X_test, y_train, y_test, 10000)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### 2.3. Polynomial Regression"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [],
   "source": [
    "small_X = X\n",
    "poly_features = PolynomialFeatures(degree=3, include_bias=False)\\\n",
    "                                            .fit_transform(small_X)\n",
    "\n",
    "X_train, X_test, y_train, y_test = train_test_split(poly_features, y, test_size=0.2, random_state=42)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Linear Regression with polynomial features"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Mean Squared Error: 0.029998657800751505\n",
      "Root Mean Squared Error: 0.1732012061180623\n",
      "R-squared: 0.9701471366199871\n"
     ]
    }
   ],
   "source": [
    "rmse_lin_polynom, r2_lin_polynom = linearReg(X_train, X_test, y_train, y_test)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Stochastic Regression with polynomial features"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Mean Squared Error: 1.5246418277286745e+24\n",
      "Root Mean Squared Error: 1234763875293.0354\n",
      "R-squared: -1.5172320204771656e+24\n"
     ]
    }
   ],
   "source": [
    "rmse_stoch_polynom, r2_stoch_polynom = sgdRegressor(X_train, X_test, y_train, y_test, 10000)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 2.4. Regression Tree"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let's first reinitialize the train and test datasets"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [],
   "source": [
    "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "RMSE= 0.017444533151279817, R2= 0.9996971672277494\n"
     ]
    }
   ],
   "source": [
    "# create a regressor object \n",
    "regressor = DecisionTreeRegressor(random_state = 0)  \n",
    "  \n",
    "# fit the regressor with X and Y data \n",
    "regressor.fit(X_train, y_train) \n",
    "\n",
    "#test the regressor\n",
    "y_reg = regressor.predict(X_test)\n",
    "\n",
    "# evaluate model\n",
    "rmse_DT = mean_squared_error(y_test, y_reg, squared=False)\n",
    "r2_DT = r2_score(y_test, y_reg)\n",
    "print(f'RMSE= {rmse_DT}, R2= {r2_DT}')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "RMSE= 0.01588137141823719, R2= 0.9997490078290878\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/srinjanghosh/miniconda3/envs/fds/lib/python3.9/site-packages/sklearn/ensemble/_forest.py:578: UserWarning: Some inputs do not have OOB scores. This probably means too few trees were used to compute any reliable OOB estimates.\n",
      "  warn(\n"
     ]
    }
   ],
   "source": [
    "label_encoder = LabelEncoder()\n",
    "x_categorical = dataset_df.select_dtypes(include=['object']).apply(label_encoder.fit_transform)\n",
    "x_numerical = dataset_df.select_dtypes(exclude=['object']).values\n",
    "x = pd.concat([pd.DataFrame(x_numerical), x_categorical], axis=1).values\n",
    "\n",
    "# Fitting Random Forest Regression to the dataset\n",
    "regressor_rf = RandomForestRegressor(n_estimators=10, random_state=0, oob_score=True)\n",
    "\n",
    "# Fit the regressor with x and y data\n",
    "regressor_rf.fit(X_train, y_train)\n",
    "\n",
    "#test the regressor\n",
    "y_reg_rf = regressor_rf.predict(X_test)\n",
    "\n",
    "# evaluate model\n",
    "rmse_RF = mean_squared_error(y_test, y_reg_rf, squared=False)\n",
    "r2_RF = r2_score(y_test, y_reg_rf)\n",
    "print(f'RMSE= {rmse_RF}, R2= {r2_RF}')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 2.5. XGBoost"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Split data into training and test set\n",
    "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/srinjanghosh/miniconda3/envs/fds/lib/python3.9/site-packages/joblib/externals/loky/process_executor.py:700: UserWarning: A worker stopped while some jobs were given to the executor. This can be caused by a too short worker timeout or by a memory leak.\n",
      "  warnings.warn(\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Best Hyperparameters: {'learning_rate': 0.1, 'max_depth': 5, 'n_estimators': 150}\n",
      "RMSE: 0.11235471302513583\n",
      "R² Score: 0.9874377694644857\n"
     ]
    }
   ],
   "source": [
    "from sklearn.model_selection import GridSearchCV\n",
    "import xgboost as xgb\n",
    "\n",
    "# Hyperparameter grid\n",
    "param_grid = {\n",
    "    'learning_rate': [0.001, 0.01, 0.1],\n",
    "    'n_estimators': [50, 100, 150],\n",
    "    'max_depth': [3, 4, 5]\n",
    "}\n",
    "\n",
    "# Create a XGBRegressor instance\n",
    "xgb_model = xgb.XGBRegressor(objective='reg:squarederror',random_state=42)\n",
    "\n",
    "# Create GridSearchCV object\n",
    "grid_search = GridSearchCV(xgb_model, param_grid, cv=5, scoring='neg_mean_squared_error', n_jobs=-1)\n",
    "\n",
    "# Fit the grid search to the data\n",
    "grid_search.fit(X_train, y_train)\n",
    "\n",
    "# Print the best hyperparameters\n",
    "print(\"Best Hyperparameters:\", grid_search.best_params_)\n",
    "\n",
    "# Get the best model\n",
    "best_xgb_model = grid_search.best_estimator_\n",
    "\n",
    "# Make predictions on the test set using the best model\n",
    "y_pred_best = best_xgb_model.predict(X_test)\n",
    "\n",
    "# Metrics\n",
    "rmse_xgb = mean_squared_error(y_test, y_pred_best, squared=False)\n",
    "r2_xgb = r2_score(y_test, y_pred_best)\n",
    "\n",
    "print(f\"RMSE: {rmse_xgb}\")\n",
    "print(f\"R² Score: {r2_xgb}\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 2.5.1 Feature Importance"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "                           Feature  Importance\n",
      "0                          overall    0.607334\n",
      "1                        potential    0.109431\n",
      "5   club_contract_valid_until_year    0.043645\n",
      "7                        weak_foot    0.027669\n",
      "12                        shooting    0.024442\n",
      "20                      power_mean    0.019368\n",
      "4                     club_team_id    0.017697\n",
      "19                   movement_mean    0.016859\n",
      "14                       dribbling    0.016113\n",
      "17                  attacking_mean    0.015707\n",
      "22                  defending_mean    0.014595\n",
      "21                  mentality_mean    0.012990\n",
      "11                            pace    0.012748\n",
      "15                       defending    0.012283\n",
      "13                         passing    0.008248\n",
      "18                      skill_mean    0.007261\n",
      "8                      skill_moves    0.007036\n",
      "9         international_reputation    0.006506\n",
      "2                         wage_eur    0.005437\n",
      "6                   nation_team_id    0.005097\n",
      "10                       work_rate    0.004960\n",
      "16                          physic    0.004575\n",
      "3                     league_level    0.000000\n"
     ]
    },
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 800x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#Use Matplotlib and Importance\n",
    "\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "# Get Importances from features\n",
    "feature_importances = best_xgb_model.feature_importances_\n",
    "\n",
    "# X axis\n",
    "feature_names = X.columns \n",
    "\n",
    "# Create a DataFrame\n",
    "feature_importance_df = pd.DataFrame({'Feature': feature_names, 'Importance': feature_importances})\n",
    "\n",
    "# Sort by value (descending)\n",
    "feature_importance_df = feature_importance_df.sort_values(by='Importance', ascending=False)\n",
    "\n",
    "print(feature_importance_df)\n",
    "\n",
    "# Plot graph\n",
    "plt.figure(figsize=(8, 6))\n",
    "plt.barh(feature_importance_df['Feature'], feature_importance_df['Importance'])\n",
    "plt.xlabel('Importance')\n",
    "plt.title('Feature Importances')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 3. Results and Metrics\n",
    "\n",
    "### Feature importance\n",
    "We consider the feature importances of the gradient boosting model. By far the most important feature is the overall value which is a FIFA game specific value. The most important features after this one are potential, the remaining time the contract at the club is valid, the weak foot and shooting ability.\n",
    "\n",
    "### Model errors\n",
    "In the following plots we can see the Random Mean Squared Error and the R^2 values of all models compared. The linear regression and stochastic gradient descent regressor without polynomial features performed the worst with RMSE of almost 0.48 and R^2 of around 0.77. The linear regression with polynomial features resulted in a RMSE of 0.17 and R^2 of 0.97. Of the stochastic gradient descent regressor with polynomial features we could not obtain any meaningful results. We could not find the issue about this model and for this reason we left it out from our final comparison. XGBoost had a RMSE of around 0.11 and R^2 of almost 0.99. The Regression Tree and Random Forest performed the best with an extremely low RMSE of 0.017 and 0.016 and R^2 even higher than the one of the XGBoost model.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Function to plot performance measures\n",
    "# Params: values as list, title, categories as list\n",
    "def plotMeasures(values, title, categories):\n",
    "    values = np.array(values)\n",
    "    bar_width = 0.2\n",
    "\n",
    "    for i in range(len(categories)):\n",
    "        plt.bar(i * bar_width, values[i], width=bar_width, label=categories[i])\n",
    "\n",
    "\n",
    "    plt.xticks([])\n",
    "    plt.xlabel('Model')\n",
    "    plt.title(title)\n",
    "\n",
    "    if title=='R^2':\n",
    "        plt.legend(loc='lower center', fontsize='small')\n",
    "    else:\n",
    "        plt.legend(loc='upper right', fontsize='small')\n",
    "    plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plotMeasures([rmse_lin, rmse_stoch, rmse_lin_polynom, rmse_DT, rmse_RF,rmse_xgb], 'RMSE', ['Linear Regression', 'Stochastic Regression', 'Linear Regression (polynomial)', 'Regression Tree', 'Random Forest','XGBoost'])\n",
    "plotMeasures([r2_lin, r2_stoch, r2_lin_polynom, r2_DT, r2_RF,r2_xgb], 'R^2', ['Linear Regression', 'Stochastic Regression', 'Linear Regression (polynomial)', 'Regression Tree', 'Random Forest','XGBoost'])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Conclusion\n",
    "We obtained really good results for predicting the market value of the FIFA players that align with literature. As in previous research, regression models had a fairly high RMSE and lower R^2 value.$^2$ However, the Random Forest performed extraordinarily well and even better than in literature Future research could examine why this is the case.\n",
    "However we could also observe that the most important feature for the XGBoost was the value 'overall' with an importance of over 0.6. This is a very specific ingame value that was again derived by developers from the player's statistics (such as also market price) just for the game. Considering this fact the very good results might be attributed to the fact that it's data for a video game. Hypothetically using the same approach only on real life FIFA data would maybe not lead to such low error rates. Still the general approach that we developed within this project can be used and adapted for similar predictions of continuous values in the soccer market."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## References\n",
    "\n",
    "(1) Jishnu, V. B., Narayanan, P. H., Aanand, S., & Joy, P. T. (2022, November). Football Player Transfer Value Prediction Using Advanced Statistics and FIFA 22 Data. In 2022 IEEE 19th India Council International Conference (INDICON) (pp. 1-6). IEEE. https://www.scopus.com/inward/record.uri?eid=2-s2.0-85149243700&doi=10.1109%2fINDICON56171.2022.10040117&partnerID=40&md5=992a915b77af19a5c388c015600412d8\n",
    "\n",
    "(2) Al-Asadi, M. A., & Tasdemır, S. (2022). Predict the value of football players using FIFA video game data and machine learning techniques. IEEE Access, 10, 22631-22645. https://www.scopus.com/inward/record.uri?eid=2-s2.0-85125710686&doi=10.1109%2fACCESS.2022.3154767&partnerID=40&md5=6d86f32a2d88137955c95d43c67e0029"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "fds",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.9.18"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}