Predict-And-Show.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "60b6bca1",
   "metadata": {},
   "source": [
    "# Predicting The Ratings"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "3fb0edf6",
   "metadata": {},
   "source": [
    "## Table of Contents\n",
    "\n",
    "* [Load Data](#Load-Data)\n",
    "* [Functions](#Functions)\n",
    "* [Model Function](#Model-Function)\n",
    "* [PFI Function](#PFI)\n",
    "* [Training and testing the models](#Training-and-testing-the-model)\n",
    "* [Predict Fifa23 Data](#Predict-Fifa23-Data)\n",
    "* [Results](#Results)\n",
    "* [Feature Importance](#Feature-Importance)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "33bf239b",
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "from sklearn import preprocessing\n",
    "from sklearn.preprocessing import LabelEncoder\n",
    "from sklearn.impute import SimpleImputer\n",
    "from sklearn.ensemble import RandomForestClassifier\n",
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn.metrics import accuracy_score\n",
    "from sklearn.impute import SimpleImputer\n",
    "import xgboost as xgb\n",
    "from sklearn.neighbors import KNeighborsClassifier\n",
    "from xgboost import XGBRegressor\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "028b28a8",
   "metadata": {},
   "source": [
    "Data Path "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "b81034ad",
   "metadata": {},
   "outputs": [],
   "source": [
    "folder = f\"Dataset\\After-PreProcess\\predict-by-rating\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "00992e08",
   "metadata": {},
   "outputs": [],
   "source": [
    "g_folder = f\"Dataset\\After-PreProcess\\predict-with-real-life-stats\""
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f77fe200",
   "metadata": {},
   "source": [
    "## Load Data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "6a1b2289",
   "metadata": {},
   "outputs": [],
   "source": [
    "## Ratings With Stats\n",
    "g_fielders = pd.read_csv(g_folder+\"/stats_Full_df.csv\")\n",
    "g_keepers = pd.read_csv(g_folder+\"/stats_gk_df.csv\")\n",
    "g_defenders = pd.read_csv(g_folder+\"/stats_defenders_df.csv\")\n",
    "stats_forward_df = pd.read_csv(g_folder+\"/stats_forward_df.csv\")\n",
    "stats_midfielders_df = pd.read_csv(g_folder+\"/stats_midfielders_df.csv\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "a1cc9431",
   "metadata": {},
   "outputs": [],
   "source": [
    "## Fifa 23 Ratings With STATS \n",
    "g_stats_midfielders_df_f23 = pd.read_csv(g_folder+\"/f23-stats_midfielders_df.csv\")\n",
    "g_stats_forward_df_f23 = pd.read_csv(g_folder+\"/f23-stats_forward_df.csv\")\n",
    "g_defenders_f23 = pd.read_csv(g_folder+\"/f23-stats_defenders_df.csv\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "0760edaa",
   "metadata": {},
   "outputs": [],
   "source": [
    "defenders_df23 = pd.read_csv(folder+\"/f23-defenders_df.csv\")\n",
    "forward_df23 = pd.read_csv(folder+\"/f23-forward_df.csv\")\n",
    "gk_df23 = pd.read_csv(folder+\"/f23-gk_df.csv\")\n",
    "midfielders_df23 = pd.read_csv(folder+\"/f23-midfielders_df.csv\")\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "17c0dd3d",
   "metadata": {},
   "outputs": [],
   "source": [
    "gk_df =pd.read_csv(folder+\"/defenders_df.csv\")\n",
    "defenders_df=pd.read_csv(folder+\"/defenders_df.csv\")\n",
    "midfielders_df=pd.read_csv(folder+\"/defenders_df.csv\")\n",
    "forward_df= pd.read_csv(folder+\"/defenders_df.csv\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d0595cde",
   "metadata": {},
   "source": [
    "## Functions"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "bd08503d",
   "metadata": {},
   "outputs": [],
   "source": [
    "def drop_not_needed_fields(df , position_group):\n",
    "    if position_group == \"defenders\":\n",
    "        df = df.drop(['Unnamed: 0', 'Next_Year_Club','GKDiving', 'GKHandling',\n",
    "       'GKKicking', 'GKPositioning', 'GKReflexes', 'Best Position',  'Name' , 'Club', \"Work Rate\", \"Positioning\", \"Position\",\n",
    "                     \"Height\", \"Weight\" , \"Next_Year_Club\"],axis = 1)\n",
    "    elif position_group == \"midfielders\":\n",
    "        df = df.drop(['Unnamed: 0', 'Next_Year_Club','GKDiving', 'GKHandling',\n",
    "       'GKKicking', 'GKPositioning', 'GKReflexes', 'Best Position',  'Name' , 'Club', \"Work Rate\", \"Positioning\", \"Position\",\n",
    "                     \"Height\", \"Weight\" , \"Next_Year_Club\"],axis = 1)\n",
    "    elif position_group == \"forwards\":\n",
    "        df = df.drop(['Unnamed: 0', 'Next_Year_Club','GKDiving', 'GKHandling',\n",
    "       'GKKicking', 'GKPositioning', 'GKReflexes', 'Best Position',  'Name' , 'Club', \"Work Rate\", \"Positioning\", \"Position\",\n",
    "                     \"Height\", \"Weight\" , \"Next_Year_Club\"],axis = 1)   \n",
    "    elif  position_group == \"goalkeepers\":\n",
    "        df = df.drop(['Unnamed: 0', 'Next_Year_Club', 'Best Position',  'Name' , 'Club', \"Work Rate\", \"Positioning\", \"Position\",\n",
    "                     \"Height\", \"Weight\" , \"Next_Year_Club\",\n",
    "                     'Crossing', 'Finishing','HeadingAccuracy', 'ShortPassing', 'Volleys', 'Dribbling', 'Curve',\n",
    "                      'FKAccuracy', 'LongPassing', 'BallControl', 'Agility', 'Reactions', 'Balance', 'ShotPower',\n",
    "                       'Jumping', 'Stamina', 'Strength', 'LongShots', 'Aggression',\n",
    "                       'Interceptions', 'Positioning', 'Vision', 'Penalties', 'Composure',\n",
    "                       'StandingTackle', 'DefensiveAwareness'],axis = 1)\n",
    "    return df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "db346462",
   "metadata": {},
   "outputs": [],
   "source": [
    "def split_df(df, test_size=0.25, random_state=42):\n",
    "    \"\"\"Splits a Pandas dataframe into train and test sets.\n",
    "\n",
    "    Args:\n",
    "    df: The Pandas dataframe to split.\n",
    "    test_size: The size of the test set, as a fraction of the total dataset.\n",
    "    random_state: The random state to use for shuffling the data.\n",
    "\n",
    "    Returns:\n",
    "    A tuple of train and test dataframes.\n",
    "    \"\"\"\n",
    "\n",
    "    # Shuffle the data.\n",
    "    df = df.sample(frac=1, random_state=random_state)\n",
    "\n",
    "    # Split the data into train and test sets.\n",
    "    train_size = int(len(df) * (1 - test_size))\n",
    "    train_df = df[:train_size]\n",
    "    test_df = df[train_size:]\n",
    "\n",
    "    return train_df, test_df\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "b4733945",
   "metadata": {},
   "outputs": [],
   "source": [
    "def get_features(position_group):\n",
    "    if position_group == \"defenders\":\n",
    "        return ['Age', 'Overall', 'Potential' , 'Value', 'Wage',\n",
    "       'International Reputation', 'Weak Foot', 'Skill Moves', 'Crossing', 'Finishing',\n",
    "       'HeadingAccuracy', 'ShortPassing', 'Volleys', 'Dribbling', 'Curve',\n",
    "       'FKAccuracy', 'LongPassing', 'BallControl', 'Acceleration',\n",
    "       'SprintSpeed', 'Agility', 'Reactions', 'Balance', 'ShotPower',\n",
    "       'Jumping', 'Stamina', 'Strength', 'LongShots', 'Aggression',\n",
    "       'Interceptions', 'Positioning', 'Vision', 'Penalties', 'Composure',\n",
    "       'StandingTackle', 'SlidingTackle']\n",
    "    elif position_group == \"midfielders\":\n",
    "        return ['Age', 'Overall', 'Potential' , 'Value', 'Wage',\n",
    "       'International Reputation', 'Weak Foot', 'Skill Moves', 'Crossing', 'Finishing',\n",
    "       'HeadingAccuracy', 'ShortPassing', 'Volleys', 'Dribbling', 'Curve',\n",
    "       'FKAccuracy', 'LongPassing', 'BallControl', 'Acceleration',\n",
    "       'SprintSpeed', 'Agility', 'Reactions', 'Balance', 'ShotPower',\n",
    "       'Jumping', 'Stamina', 'Strength', 'LongShots', 'Aggression',\n",
    "       'Interceptions', 'Positioning', 'Vision', 'Penalties', 'Composure',\n",
    "       'StandingTackle', 'SlidingTackle']\n",
    "    elif position_group == \"forwards\":\n",
    "        return ['Age', 'Overall', 'Potential' , 'Value', 'Wage',\n",
    "       'International Reputation', 'Weak Foot', 'Skill Moves', 'Crossing', 'Finishing',\n",
    "       'HeadingAccuracy', 'ShortPassing', 'Volleys', 'Dribbling', 'Curve',\n",
    "       'FKAccuracy', 'LongPassing', 'BallControl', 'Acceleration',\n",
    "       'SprintSpeed', 'Agility', 'Reactions', 'Balance', 'ShotPower',\n",
    "       'Jumping', 'Stamina', 'Strength', 'LongShots', 'Aggression',\n",
    "       'Interceptions', 'Positioning', 'Vision', 'Penalties', 'Composure',\n",
    "       'StandingTackle', 'SlidingTackle']\n",
    "    elif  position_group == \"goalkeepers\":\n",
    "        return ['GKDiving', 'GKHandling','GKKicking', 'GKPositioning', 'GKReflexes','Age', 'Overall', 'Potential' , 'Value', 'Wage',\n",
    "       'International Reputation', 'Acceleration','SprintSpeed']\n",
    "        "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "43c889d1",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Direction functions\n",
    "def analyze_movement_direction(df):\n",
    "    same_count = 0\n",
    "    higher_count = 0\n",
    "    lower_count = 0\n",
    "\n",
    "    for _, row in df.iterrows():\n",
    "        next_overall = row['Next_Overall']\n",
    "        prediction = row['predictions']\n",
    "\n",
    "        if prediction > next_overall:\n",
    "            higher_count += 1\n",
    "        elif prediction < next_overall:\n",
    "            lower_count += 1\n",
    "        else:\n",
    "            same_count += 1\n",
    "\n",
    "    result = {\n",
    "        'Predicted Right': same_count,\n",
    "        'Predicted Higher': higher_count,\n",
    "        'Predicted Lower': lower_count\n",
    "    }\n",
    "\n",
    "    return result\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "e5bd875a",
   "metadata": {},
   "outputs": [],
   "source": [
    "def analyze_movement(df):\n",
    "    total_count = len(df)\n",
    "    move_up_count = 0\n",
    "    move_down_count = 0\n",
    "    pred_move_up_count = 0\n",
    "    pred_move_down_count = 0\n",
    "    pred_same = 0\n",
    "    same = 0\n",
    "    for _, row in df.iterrows():\n",
    "        overall = row['Overall']\n",
    "        next_overall = row['Next_Overall']\n",
    "        prediction = row['predictions']\n",
    "\n",
    "        if next_overall > overall:\n",
    "            move_up_count += 1\n",
    "            if prediction > overall:\n",
    "                pred_move_up_count += 1\n",
    "        elif next_overall < overall:\n",
    "            move_down_count += 1\n",
    "            if prediction < overall:\n",
    "                pred_move_down_count += 1\n",
    "        else:\n",
    "            same += 1\n",
    "            if prediction == overall:\n",
    "                pred_same += 1\n",
    "\n",
    "    result = {\n",
    "        'Total Rows': total_count,\n",
    "        'Moved Up': move_up_count,\n",
    "        'Predicted Move Up': pred_move_up_count,\n",
    "        'Moved Down': move_down_count,\n",
    "        'Predicted Move Down': pred_move_down_count,\n",
    "        'Stayed the Same': same,\n",
    "        'Predicted to Stay the Same': pred_same\n",
    "    }\n",
    "\n",
    "    return result\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "a875499b",
   "metadata": {},
   "outputs": [],
   "source": [
    "def generate_prediction_report(df):\n",
    "    diff_counts = {'Exactly': 0,'Off by 1': 0, 'Off by 2': 0,'Off by 3': 0, 'Off by 4 or more': 0}\n",
    "\n",
    "    for _, row in df.iterrows():\n",
    "        next_overall = row['Next_Overall']\n",
    "        prediction = row['predictions']\n",
    "        diff = abs(next_overall - prediction)\n",
    "\n",
    "        if diff == 1:\n",
    "            diff_counts['Off by 1'] += 1\n",
    "        elif diff == 2:\n",
    "            diff_counts['Off by 2'] += 1\n",
    "        elif diff == 3:\n",
    "            diff_counts['Off by 3'] += 1\n",
    "        elif diff >= 4:\n",
    "            diff_counts['Off by 4 or more'] += 1\n",
    "        else:\n",
    "            diff_counts['Exactly'] += 1\n",
    "\n",
    "    return diff_counts\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "15bc37b8",
   "metadata": {},
   "outputs": [],
   "source": [
    "def get_features_with_goals(position_group):\n",
    "    if position_group == \"defenders\":\n",
    "        return ['Age', 'Overall', 'Potential' , 'Value', 'Wage',\n",
    "       'International Reputation', 'Weak Foot', 'Skill Moves', 'Crossing', 'Finishing',\n",
    "       'HeadingAccuracy', 'ShortPassing', 'Volleys', 'Dribbling', 'Curve',\n",
    "       'FKAccuracy', 'LongPassing', 'BallControl', 'Acceleration',\n",
    "       'SprintSpeed', 'Agility', 'Reactions', 'Balance', 'ShotPower',\n",
    "       'Jumping', 'Stamina', 'Strength', 'LongShots', 'Aggression',\n",
    "       'Interceptions', 'Positioning', 'Vision', 'Penalties', 'Composure',\n",
    "       'StandingTackle', 'SlidingTackle', 'MP', 'Starts', 'Min', '90s',\n",
    "       'Gls', 'Ast', 'G+A', 'G-PK', 'PK', 'PKatt', 'CrdY', 'CrdR', 'xG',\n",
    "       'npxG', 'xAG', 'npxG+xAG', 'PrgC', 'PrgP', 'PrgR', 'Gls.1', 'Ast.1',\n",
    "       'G+A.1', 'G-PK.1', 'G+A-PK', 'xG.1', 'xAG.1', 'xG+xAG', 'npxG.1',\n",
    "       'npxG+xAG.1']\n",
    "    elif position_group == \"midfielders\":\n",
    "        return ['Age', 'Overall', 'Potential' , 'Value', 'Wage',\n",
    "       'International Reputation', 'Weak Foot', 'Skill Moves', 'Crossing', 'Finishing',\n",
    "       'HeadingAccuracy', 'ShortPassing', 'Volleys', 'Dribbling', 'Curve',\n",
    "       'FKAccuracy', 'LongPassing', 'BallControl', 'Acceleration',\n",
    "       'SprintSpeed', 'Agility', 'Reactions', 'Balance', 'ShotPower',\n",
    "       'Jumping', 'Stamina', 'Strength', 'LongShots', 'Aggression',\n",
    "       'Interceptions', 'Positioning', 'Vision', 'Penalties', 'Composure',\n",
    "       'StandingTackle', 'SlidingTackle', 'MP', 'Starts', 'Min', '90s',\n",
    "       'Gls', 'Ast', 'G+A', 'G-PK', 'PK', 'PKatt', 'CrdY', 'CrdR', 'xG',\n",
    "       'npxG', 'xAG', 'npxG+xAG', 'PrgC', 'PrgP', 'PrgR', 'Gls.1', 'Ast.1',\n",
    "       'G+A.1', 'G-PK.1', 'G+A-PK', 'xG.1', 'xAG.1', 'xG+xAG', 'npxG.1',\n",
    "       'npxG+xAG.1']\n",
    "    elif position_group == \"forwards\":\n",
    "        return ['Age', 'Overall', 'Potential' , 'Value', 'Wage',\n",
    "       'International Reputation', 'Weak Foot', 'Skill Moves', 'Crossing', 'Finishing',\n",
    "       'HeadingAccuracy', 'ShortPassing', 'Volleys', 'Dribbling', 'Curve',\n",
    "       'FKAccuracy', 'LongPassing', 'BallControl', 'Acceleration',\n",
    "       'SprintSpeed', 'Agility', 'Reactions', 'Balance', 'ShotPower',\n",
    "       'Jumping', 'Stamina', 'Strength', 'LongShots', 'Aggression',\n",
    "       'Interceptions', 'Positioning', 'Vision', 'Penalties', 'Composure',\n",
    "       'StandingTackle', 'SlidingTackle', 'MP', 'Starts', 'Min', '90s',\n",
    "       'Gls', 'Ast', 'G+A', 'G-PK', 'PK', 'PKatt', 'CrdY', 'CrdR', 'xG',\n",
    "       'npxG', 'xAG', 'npxG+xAG', 'PrgC', 'PrgP', 'PrgR', 'Gls.1', 'Ast.1',\n",
    "       'G+A.1', 'G-PK.1', 'G+A-PK', 'xG.1', 'xAG.1', 'xG+xAG', 'npxG.1',\n",
    "       'npxG+xAG.1']\n",
    "    elif  position_group == \"goalkeepers\":\n",
    "        return\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "598e9fac",
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "\n",
    "def calculate_change(df, field1, field2, new_field):\n",
    "    df[new_field] = pd.NA\n",
    "    df.loc[df[field2] > df[field1], new_field] = \"Improvement\"\n",
    "    df.loc[df[field2] == df[field1], new_field] = \"Staying the same\"\n",
    "    df.loc[df[field2] < df[field1], new_field] = \"Decline\"\n",
    "    return df\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "eff9b2e5",
   "metadata": {},
   "outputs": [],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "\n",
    "def bar(dictionary):\n",
    "    plt.figure(figsize=(10, 5))\n",
    "    plt.bar(range(len(dictionary)), list(dictionary.values()), align='center')\n",
    "    plt.xticks(range(len(dictionary)), list(dictionary.keys()))\n",
    "    plt.show()\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "d1346ae4",
   "metadata": {},
   "outputs": [],
   "source": [
    "def create_pie_chart(data_dict):\n",
    "    keys = list(data_dict.keys())\n",
    "    values = list(data_dict.values())\n",
    "\n",
    "    plt.figure(figsize=(6, 6))\n",
    "    plt.pie(values, labels=keys, autopct='%1.1f%%', startangle=90)\n",
    "    plt.axis('equal')\n",
    "    plt.show()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "831f96b5",
   "metadata": {},
   "outputs": [],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "\n",
    "def plot_bar_chart(dictionary , title):\n",
    "    \"\"\"\n",
    "    Plots a bar chart with vertical labels from a dictionary.\n",
    "\n",
    "    Args:\n",
    "    dictionary: A dictionary where the keys are the labels and the values are the values for the bars.\n",
    "\n",
    "    Returns:\n",
    "    None.\n",
    "    \"\"\"\n",
    "    # Get the labels and values from the dictionary.\n",
    "    labels = list(dictionary.keys())\n",
    "    values = list(dictionary.values())\n",
    "    plt.figure(figsize=(12, 6))\n",
    "    # Plot the bar chart.\n",
    "    plt.bar(labels, values)\n",
    "\n",
    "    # Set the labels to be vertical.\n",
    "    plt.xticks(rotation=90)\n",
    "    plt.title(title)\n",
    "\n",
    "    # Show the plot.\n",
    "    plt.show()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "1cadadf3",
   "metadata": {},
   "outputs": [],
   "source": [
    "def calculate_delta(df, field1, field2, new_field):\n",
    "    df[new_field] = defenderswithpredictions[field1]-defenderswithpredictions[field2]\n",
    "    return df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "80f0911d",
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "\n",
    "def calculate_average_delta(df):\n",
    "    predictions = df['predictions']\n",
    "    next_overall = df['Next_Overall']\n",
    "\n",
    "    # Calculate the deltas\n",
    "    deltas = np.abs(predictions - next_overall)\n",
    "\n",
    "    # Calculate the average delta and standard deviation\n",
    "    average_delta = np.mean(deltas)\n",
    "    std_dev = np.std(deltas)\n",
    "\n",
    "    return average_delta, std_dev"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "6c197e4d",
   "metadata": {},
   "outputs": [],
   "source": [
    "def accuracy_of_trend(df):\n",
    "    df = calculate_change(df.copy() ,\"Overall\", \"Next_Overall\" , \"real_trend\")\n",
    "    df = calculate_change(df.copy() ,\"Overall\", \"predictions\" , \"predicted_trend\")\n",
    "    print(\"accuracy of trend: \", accuracy_score(df[\"real_trend\"], df[\"predicted_trend\"]))\n",
    "    return df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 75,
   "id": "6e726965",
   "metadata": {},
   "outputs": [],
   "source": [
    "def predict_new_data(model , new_data , features):\n",
    "    \n",
    "    imputer = SimpleImputer(strategy='mean')\n",
    "    X_test = new_data[features].copy()\n",
    "    X_test_imputed = imputer.fit_transform(X_test)\n",
    "    y_pred = model.predict(X_test_imputed)\n",
    "    # Add the predictions to the dataframe in a column\n",
    "    new_data['Next_Overall'] = np.round(y_pred).astype(int)\n",
    "    return new_data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "362d8204",
   "metadata": {},
   "outputs": [],
   "source": [
    "column_mapping = {\n",
    "    'Heading Accuracy':'HeadingAccuracy',\n",
    "    'Sprint Speed':'SprintSpeed',\n",
    "    'Known As': 'Name',\n",
    "    'Positions Played': 'Position',\n",
    "    'Image Link': 'ID',\n",
    "    'Club Name': 'Club',\n",
    "    'Club Position': 'Position',\n",
    "    'Contract Until': 'Contract',\n",
    "    'Club Jersey Number': 'Jersey Number',\n",
    "    'Preferred Foot': 'Foot',\n",
    "    'Weak Foot Rating': 'Weak Foot',\n",
    "    'Shot Power':'ShotPower',\n",
    "    'Long Shots':'LongShots',\n",
    "    'Standing Tackle':'StandingTackle',\n",
    "    'Sliding Tackle':'SlidingTackle',\n",
    "    'Short Passing':'ShortPassing', \n",
    "    'Freekick Accuracy':'FKAccuracy', \n",
    "}\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "7523bd44",
   "metadata": {},
   "outputs": [],
   "source": [
    "def rename_columns(df_f23, df_f22 , column_mapping):\n",
    "    renamed_columns = []\n",
    "    for column in df_f23.columns:\n",
    "        if column in column_mapping:\n",
    "            renamed_columns.append(column_mapping[column])\n",
    "        else:\n",
    "            renamed_columns.append(column)\n",
    "    \n",
    "    df_f23.columns = renamed_columns\n",
    "    \n",
    "    return df_f23\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "8fdb477a",
   "metadata": {},
   "source": [
    "## Model Function"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "id": "55e1c4a2",
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.ensemble import RandomForestRegressor\n",
    "from sklearn.model_selection import GridSearchCV\n",
    "from sklearn.impute import KNNImputer\n",
    "\n",
    "from sklearn.inspection import permutation_importance\n",
    "\n",
    "\n",
    "def run_model(df, features, model_type = \"Random Forest\"):   \n",
    "    train_df, test_df = split_df(df)\n",
    "    \n",
    "    y_train = train_df['Next_Overall']\n",
    "    y_test = test_df['Next_Overall']\n",
    "    \n",
    "    X_train = train_df[features].copy()\n",
    "    X_test = test_df[features].copy()\n",
    "\n",
    "    # Create a SimpleImputer to handle missing values\n",
    "    imputer = KNNImputer()\n",
    "\n",
    "    # Impute missing values in training data\n",
    "    X_train_imputed = imputer.fit_transform(X_train)\n",
    "\n",
    "    # Impute missing values in testing data\n",
    "    X_test_imputed = imputer.transform(X_test)\n",
    "\n",
    "\n",
    "\n",
    "    # Create the Random Forest Regression model\n",
    "    if model_type.lower() == \"xgboost\":\n",
    "        model = XGBRegressor()\n",
    "        param_grid = {\n",
    "            'n_estimators': [100, 200, 300],\n",
    "            'max_depth': [None, 5, 10]\n",
    "        }\n",
    "    else:   \n",
    "        model = RandomForestRegressor(random_state=42)\n",
    "            # Define the parameter grid for GridSearchCV\n",
    "        param_grid = {\n",
    "            'n_estimators': [100, 200, 300],\n",
    "            'max_depth': [None, 5, 10],\n",
    "            'min_samples_split': [2, 5, 10]\n",
    "        }\n",
    "\n",
    "    # Perform grid search to find the best model\n",
    "    grid_search = GridSearchCV(estimator=model, param_grid=param_grid, scoring='neg_mean_squared_error', cv=5)\n",
    "    grid_search.fit(X_train_imputed, y_train)\n",
    "\n",
    "    # Get the best model from grid search\n",
    "    best_model = grid_search.best_estimator_\n",
    "    best_model.fit(X_train_imputed, y_train)\n",
    "    # Make predictions on the test set\n",
    "    y_pred = np.round(best_model.predict(X_test_imputed))\n",
    "\n",
    "    # Evaluate the model\n",
    "    accuracy = accuracy_score(y_test, y_pred)\n",
    "\n",
    "    # Compute permutation feature importance\n",
    "    result = permutation_importance(best_model, X_train_imputed, y_train)\n",
    "    feature_importance = {feature: importance for feature, importance in zip(features, result.importances_mean)}\n",
    "\n",
    "    # Train the model with adjusted weights\n",
    "\n",
    "    # Add the predictions to the dataframe in a column\n",
    "    test_df['predictions'] = np.round(best_model.predict(X_test_imputed)).astype(int)\n",
    "    test_df['Next_Overall'] = test_df['Next_Overall'].astype(int)\n",
    "    print(f'Best Model Accuracy: {accuracy}')\n",
    "    \n",
    "    # Return the best model, test dataframe, feature importance, and best weight set\n",
    "    return best_model, test_df, feature_importance\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a13e199c",
   "metadata": {},
   "source": [
    "## PFI"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "id": "91314cc5",
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "from sklearn.metrics import r2_score\n",
    "\n",
    "def permutation_feature_importance(model, X, y, num_iterations=100):\n",
    "    \"\"\"\n",
    "    Perform Permutation Feature Importance on a trained random forest model.\n",
    "\n",
    "    Parameters:\n",
    "        - model: Trained random forest model\n",
    "        - X: Input features\n",
    "        - y: Target variable\n",
    "        - num_iterations: Number of iterations for permutation\n",
    "\n",
    "    Returns:\n",
    "        - feature_importances: Dictionary of feature importances\n",
    "    \"\"\"\n",
    "    # Calculate the baseline score\n",
    "    baseline_score = r2_score(y, model.predict(X))\n",
    "\n",
    "    # Initialize an empty dictionary to store feature importances\n",
    "    feature_importances = {}\n",
    "\n",
    "    # Iterate over each feature\n",
    "    for feature in X.columns:\n",
    "        # Make a copy of the original feature values\n",
    "        X_permuted = X.copy()\n",
    "\n",
    "        # Permute the values of the current feature\n",
    "        X_permuted[feature] = np.random.permutation(X_permuted[feature])\n",
    "\n",
    "        # Calculate the score after permuting the feature\n",
    "        permuted_score = r2_score(y, model.predict(X_permuted))\n",
    "\n",
    "        # Calculate the feature importance as the difference between the baseline score and permuted score\n",
    "        feature_importance = baseline_score - permuted_score\n",
    "\n",
    "        # Store the feature importance in the dictionary\n",
    "        feature_importances[feature] = feature_importance\n",
    "\n",
    "    return feature_importances\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "186f7ba3",
   "metadata": {},
   "source": [
    "## Model Report"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "id": "4f7f3177",
   "metadata": {},
   "outputs": [],
   "source": [
    "def report_results(results_df):\n",
    "    accuracy_of_trend(results_df.copy())\n",
    "    average_delta ,std_dev = calculate_average_delta(results_df.copy())\n",
    "    print(\"average delta of prediction from Next Overall: \",average_delta)\n",
    "    print(\"standard deviation: \",std_dev)\n",
    "    create_pie_chart(generate_prediction_report(results_df.copy()))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ced5048c",
   "metadata": {},
   "source": [
    "## Training and testing the model"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "7dc33412",
   "metadata": {},
   "source": [
    "Random Forest"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "id": "89f38d01",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Best Model Accuracy: 0.2317773788150808\n"
     ]
    }
   ],
   "source": [
    "model_def , defenderswithpredictions , def_pfi  = run_model(defenders_df.copy(), get_features(\"defenders\"))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "id": "db7e8557",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "accuracy of trend:  0.44542190305206464\n",
      "average delta of prediction from Next Overall:  1.4798922800718133\n",
      "standard deviation:  1.4088874059651704\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x432 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "report_results(defenderswithpredictions)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "id": "766873c7",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Best Model Accuracy: 0.2317773788150808\n"
     ]
    }
   ],
   "source": [
    "model_mid, midsswithpredictions, mid_pfi  = run_model(midfielders_df.copy(), get_features(\"midfielders\"))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "id": "2012f74c",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "accuracy of trend:  0.44542190305206464\n",
      "average delta of prediction from Next Overall:  1.4798922800718133\n",
      "standard deviation:  1.4088874059651704\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x432 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "report_results(midsswithpredictions)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "id": "d7b31595",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Best Model Accuracy: 0.2317773788150808\n"
     ]
    }
   ],
   "source": [
    "model_fw, fwwithpredictions, fwd_pfi = run_model(forward_df.copy() , get_features(\"forwards\"))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "id": "1ec06836",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "accuracy of trend:  0.44542190305206464\n",
      "average delta of prediction from Next Overall:  1.4798922800718133\n",
      "standard deviation:  1.4088874059651704\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x432 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "report_results(fwwithpredictions)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "66f751d4",
   "metadata": {},
   "source": [
    "XGBoost"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "id": "95f8ab06",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Best Model Accuracy: 0.23087971274685817\n"
     ]
    }
   ],
   "source": [
    "xgmodel_def , xgdefenderswithpredictions , xgdef_pfi  = run_model(defenders_df.copy(), get_features(\"defenders\"), model_type =\"xgboost\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "id": "d643fc0c",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "accuracy of trend:  0.4488330341113106\n",
      "average delta of prediction from Next Overall:  1.5136445242369838\n",
      "standard deviation:  1.4430831132826647\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x432 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "report_results(xgdefenderswithpredictions)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "id": "b6f7db6e",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Best Model Accuracy: 0.23087971274685817\n"
     ]
    }
   ],
   "source": [
    "xgmodel_mid, xgmidsswithpredictions, xgmid_pfi  = run_model(midfielders_df.copy(), get_features(\"midfielders\"), model_type =\"xgboost\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "id": "4116d406",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "accuracy of trend:  0.4488330341113106\n",
      "average delta of prediction from Next Overall:  1.5136445242369838\n",
      "standard deviation:  1.4430831132826647\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x432 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "report_results(xgmidsswithpredictions)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "id": "36dd789e",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Best Model Accuracy: 0.23087971274685817\n"
     ]
    }
   ],
   "source": [
    "xgmodel_fw, xgfwwithpredictions, xgfwd_pfi = run_model(forward_df.copy() , get_features(\"forwards\"), model_type =\"xgboost\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "id": "8d4f0d85",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "accuracy of trend:  0.4488330341113106\n",
      "average delta of prediction from Next Overall:  1.5136445242369838\n",
      "standard deviation:  1.4430831132826647\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x432 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "report_results(xgfwwithpredictions)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b2e62cf1",
   "metadata": {},
   "source": [
    "## With Stats"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "408d0145",
   "metadata": {},
   "source": [
    "Random Forest"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "id": "a780939d",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Best Model Accuracy: 0.26596858638743454\n"
     ]
    }
   ],
   "source": [
    "model_defenders_g , defenderswithpredictions_g , def_pfi_g  = run_model(g_defenders.copy(), get_features_with_goals(\"defenders\"))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "id": "e4930f87",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "accuracy of trend:  0.5099476439790576\n",
      "average delta of prediction from Next Overall:  1.3350785340314135\n",
      "standard deviation:  1.3195171531255725\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x432 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "report_results(defenderswithpredictions_g)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "id": "8e623a50",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Best Model Accuracy: 0.30422794117647056\n"
     ]
    }
   ],
   "source": [
    "model_mids_g , midswithpredictions_g, mid_pfi_g  = run_model(stats_midfielders_df.copy(), get_features_with_goals(\"defenders\"))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "id": "dc29c652",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "accuracy of trend:  0.5119485294117647\n",
      "average delta of prediction from Next Overall:  1.228860294117647\n",
      "standard deviation:  1.2134410336917762\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x432 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "report_results(midswithpredictions_g)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "id": "a467d879",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Best Model Accuracy: 0.2953271028037383\n"
     ]
    }
   ],
   "source": [
    "model_forwards_g , forwardswithpredictions_g, fw_pfi_g  = run_model(stats_forward_df.copy(), get_features_with_goals(\"defenders\"))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "id": "c26a5563",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "accuracy of trend:  0.5401869158878505\n",
      "average delta of prediction from Next Overall:  1.2149532710280373\n",
      "standard deviation:  1.2606780358068899\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x432 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "report_results(forwardswithpredictions_g)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "73a11f82",
   "metadata": {},
   "source": [
    "XGBoost"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "id": "75dac07a",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Best Model Accuracy: 0.27225130890052357\n"
     ]
    }
   ],
   "source": [
    "xgmodel_defenders_g , xgdefenderswithpredictions_g , xgdef_pfi_g  = run_model(g_defenders.copy(), get_features_with_goals(\"defenders\"),model_type =\"xgboost\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "id": "1c07ce93",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "accuracy of trend:  0.5319371727748691\n",
      "average delta of prediction from Next Overall:  1.4041884816753927\n",
      "standard deviation:  1.3567682753353074\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x432 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "report_results(xgdefenderswithpredictions_g)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "id": "089de5a9",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Best Model Accuracy: 0.25735294117647056\n"
     ]
    }
   ],
   "source": [
    "xgmodel_mids_g , xgmidswithpredictions_g, xgmid_pfi_g  = run_model(stats_midfielders_df.copy(), get_features_with_goals(\"defenders\"),model_type =\"xgboost\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "id": "2238fcad",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "accuracy of trend:  0.5202205882352942\n",
      "average delta of prediction from Next Overall:  1.3134191176470589\n",
      "standard deviation:  1.2232896526744494\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x432 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "report_results(xgmidswithpredictions_g)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "id": "52b61e2a",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Best Model Accuracy: 0.2616822429906542\n"
     ]
    }
   ],
   "source": [
    "xgmodel_forwards_g , xgforwardswithpredictions_g, xgfw_pfi_g  = run_model(stats_forward_df.copy(), get_features_with_goals(\"defenders\"),model_type =\"xgboost\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "id": "b11ab616",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "accuracy of trend:  0.4934579439252336\n",
      "average delta of prediction from Next Overall:  1.388785046728972\n",
      "standard deviation:  1.3569707262597612\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x432 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "report_results(xgforwardswithpredictions_g)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "7c53af73",
   "metadata": {},
   "source": [
    "### Predict Fifa23 Data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "id": "7a34380e",
   "metadata": {},
   "outputs": [],
   "source": [
    "defenders_df23 = rename_columns(defenders_df23 , defenders_df , column_mapping)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "id": "636095df",
   "metadata": {},
   "outputs": [],
   "source": [
    "midfielders_df23 = rename_columns(midfielders_df23 , midfielders_df , column_mapping)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "id": "b8db81cf",
   "metadata": {},
   "outputs": [],
   "source": [
    "forward_df23 = rename_columns(forward_df23 , forward_df , column_mapping)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 76,
   "id": "cb39a486",
   "metadata": {},
   "outputs": [],
   "source": [
    "g_stats_midfielders_df_f23 = rename_columns(g_stats_midfielders_df_f23 , midfielders_df , column_mapping)\n",
    "g_stats_forward_df_f23 = rename_columns(g_stats_forward_df_f23 , forward_df , column_mapping)\n",
    "g_defenders_f23 = rename_columns(g_defenders_f23 , defenders_df , column_mapping)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ec4ac524",
   "metadata": {},
   "source": [
    "Predicted fifa 23 data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "id": "bfd98b53",
   "metadata": {},
   "outputs": [],
   "source": [
    "predicted_df_def = predict_new_data(model_def , defenders_df23.copy() , get_features(\"defenders\"))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "id": "1eee6baa",
   "metadata": {},
   "outputs": [],
   "source": [
    "predicted_df_mid = predict_new_data(model_mid , midfielders_df23.copy()  , get_features(\"midfielders\"))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "id": "5186feaf",
   "metadata": {},
   "outputs": [],
   "source": [
    "predicted_df_fw = predict_new_data(model_fw , forward_df23.copy() , get_features(\"forwards\"))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "5ac04dee",
   "metadata": {},
   "source": [
    "XGBoost"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "id": "981bd32f",
   "metadata": {},
   "outputs": [],
   "source": [
    "xgpredicted_df_def = predict_new_data(xgmodel_def , defenders_df23.copy() , get_features(\"defenders\"))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 70,
   "id": "6ae98576",
   "metadata": {},
   "outputs": [],
   "source": [
    "xgpredicted_df_mid = predict_new_data(xgmodel_mid , midfielders_df23.copy()  , get_features(\"midfielders\"))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 71,
   "id": "4de76d82",
   "metadata": {},
   "outputs": [],
   "source": [
    "xgpredicted_df_fw = predict_new_data(xgmodel_fw , forward_df23.copy() , get_features(\"forwards\"))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "bdf5d16c",
   "metadata": {},
   "source": [
    "With goals"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 72,
   "id": "431c7b98",
   "metadata": {},
   "outputs": [],
   "source": [
    "predicted_df_def_g = predict_new_data(model_defenders_g , g_defenders_f23.copy() , get_features_with_goals(\"defenders\"))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 80,
   "id": "ad638ab7",
   "metadata": {},
   "outputs": [],
   "source": [
    "predicted_df_mid_g = predict_new_data(model_mids_g , g_stats_midfielders_df_f23.copy() , get_features_with_goals(\"midfielders\"))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 81,
   "id": "e2a5bda5",
   "metadata": {},
   "outputs": [],
   "source": [
    "predicted_df_fw_g = predict_new_data(model_forwards_g , g_stats_forward_df_f23.copy() , get_features_with_goals(\"forwards\"))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "cf4a3071",
   "metadata": {},
   "source": [
    "XGBoost"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 82,
   "id": "ceebcdf3",
   "metadata": {},
   "outputs": [],
   "source": [
    "xgpredicted_df_def_g = predict_new_data(xgmodel_defenders_g , g_defenders_f23.copy() , get_features_with_goals(\"defenders\"))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 83,
   "id": "d600ade4",
   "metadata": {},
   "outputs": [],
   "source": [
    "xgpredicted_df_mid_g = predict_new_data(xgmodel_mids_g , g_stats_midfielders_df_f23.copy() , get_features_with_goals(\"midfielders\"))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 84,
   "id": "54384259",
   "metadata": {},
   "outputs": [],
   "source": [
    "xgpredicted_df_fw_g = predict_new_data(xgmodel_forwards_g , g_stats_forward_df_f23.copy() , get_features_with_goals(\"forwards\"))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "3b39ad54",
   "metadata": {},
   "source": [
    "## Results"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 85,
   "id": "00e5a0eb",
   "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>Name</th>\n",
       "      <th>ID</th>\n",
       "      <th>Overall</th>\n",
       "      <th>Next_Overall</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>V. van Dijk</td>\n",
       "      <td>https://cdn.sofifa.net/players/203/376/23_60.png</td>\n",
       "      <td>90</td>\n",
       "      <td>89</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Rúben Dias</td>\n",
       "      <td>https://cdn.sofifa.net/players/239/818/23_60.png</td>\n",
       "      <td>88</td>\n",
       "      <td>88</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>João Cancelo</td>\n",
       "      <td>https://cdn.sofifa.net/players/210/514/23_60.png</td>\n",
       "      <td>88</td>\n",
       "      <td>87</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Marquinhos</td>\n",
       "      <td>https://cdn.sofifa.net/players/207/865/23_60.png</td>\n",
       "      <td>88</td>\n",
       "      <td>88</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>A. Rüdiger</td>\n",
       "      <td>https://cdn.sofifa.net/players/205/452/23_60.png</td>\n",
       "      <td>87</td>\n",
       "      <td>86</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>A. Robertson</td>\n",
       "      <td>https://cdn.sofifa.net/players/216/267/23_60.png</td>\n",
       "      <td>87</td>\n",
       "      <td>87</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>K. Koulibaly</td>\n",
       "      <td>https://cdn.sofifa.net/players/201/024/23_60.png</td>\n",
       "      <td>87</td>\n",
       "      <td>86</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>D. Alaba</td>\n",
       "      <td>https://cdn.sofifa.net/players/197/445/23_60.png</td>\n",
       "      <td>86</td>\n",
       "      <td>86</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>Thiago Silva</td>\n",
       "      <td>https://cdn.sofifa.net/players/164/240/23_60.png</td>\n",
       "      <td>86</td>\n",
       "      <td>85</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>A. Laporte</td>\n",
       "      <td>https://cdn.sofifa.net/players/212/218/23_60.png</td>\n",
       "      <td>86</td>\n",
       "      <td>86</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "           Name                                                ID  Overall  \\\n",
       "0   V. van Dijk  https://cdn.sofifa.net/players/203/376/23_60.png       90   \n",
       "1    Rúben Dias  https://cdn.sofifa.net/players/239/818/23_60.png       88   \n",
       "2  João Cancelo  https://cdn.sofifa.net/players/210/514/23_60.png       88   \n",
       "3    Marquinhos  https://cdn.sofifa.net/players/207/865/23_60.png       88   \n",
       "4    A. Rüdiger  https://cdn.sofifa.net/players/205/452/23_60.png       87   \n",
       "5  A. Robertson  https://cdn.sofifa.net/players/216/267/23_60.png       87   \n",
       "6  K. Koulibaly  https://cdn.sofifa.net/players/201/024/23_60.png       87   \n",
       "7      D. Alaba  https://cdn.sofifa.net/players/197/445/23_60.png       86   \n",
       "8  Thiago Silva  https://cdn.sofifa.net/players/164/240/23_60.png       86   \n",
       "9    A. Laporte  https://cdn.sofifa.net/players/212/218/23_60.png       86   \n",
       "\n",
       "   Next_Overall  \n",
       "0            89  \n",
       "1            88  \n",
       "2            87  \n",
       "3            88  \n",
       "4            86  \n",
       "5            87  \n",
       "6            86  \n",
       "7            86  \n",
       "8            85  \n",
       "9            86  "
      ]
     },
     "execution_count": 85,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "predicted_df_def[[\"Name\",\"ID\",\"Overall\",\"Next_Overall\"]].head(10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 86,
   "id": "d4f55c64",
   "metadata": {},
   "outputs": [
    {
     "ename": "KeyError",
     "evalue": "\"['predictions'] not in index\"",
     "output_type": "error",
     "traceback": [
      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[1;31mKeyError\u001b[0m                                  Traceback (most recent call last)",
      "\u001b[1;32m~\\AppData\\Local\\Temp/ipykernel_16352/298923250.py\u001b[0m in \u001b[0;36m<module>\u001b[1;34m\u001b[0m\n\u001b[1;32m----> 1\u001b[1;33m \u001b[0mpredicted_df_def_g\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;34m\"Name\"\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;34m\"ID\"\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;34m\"Overall\"\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;34m\"predictions\"\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;34m\"Next_Overall\"\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mhead\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;36m20\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m",
      "\u001b[1;32m~\\AppData\\Roaming\\Python\\Python38\\site-packages\\pandas\\core\\frame.py\u001b[0m in \u001b[0;36m__getitem__\u001b[1;34m(self, key)\u001b[0m\n\u001b[0;32m   3811\u001b[0m             \u001b[1;32mif\u001b[0m \u001b[0mis_iterator\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mkey\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   3812\u001b[0m                 \u001b[0mkey\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mlist\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mkey\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m-> 3813\u001b[1;33m             \u001b[0mindexer\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mcolumns\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_get_indexer_strict\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mkey\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;34m\"columns\"\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;36m1\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m   3814\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   3815\u001b[0m         \u001b[1;31m# take() does not accept boolean indexers\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32m~\\AppData\\Roaming\\Python\\Python38\\site-packages\\pandas\\core\\indexes\\base.py\u001b[0m in \u001b[0;36m_get_indexer_strict\u001b[1;34m(self, key, axis_name)\u001b[0m\n\u001b[0;32m   6068\u001b[0m             \u001b[0mkeyarr\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mindexer\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mnew_indexer\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_reindex_non_unique\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mkeyarr\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   6069\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m-> 6070\u001b[1;33m         \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_raise_if_missing\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mkeyarr\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mindexer\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0maxis_name\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m   6071\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   6072\u001b[0m         \u001b[0mkeyarr\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mtake\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mindexer\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32m~\\AppData\\Roaming\\Python\\Python38\\site-packages\\pandas\\core\\indexes\\base.py\u001b[0m in \u001b[0;36m_raise_if_missing\u001b[1;34m(self, key, indexer, axis_name)\u001b[0m\n\u001b[0;32m   6131\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   6132\u001b[0m             \u001b[0mnot_found\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mlist\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mensure_index\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mkey\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m[\u001b[0m\u001b[0mmissing_mask\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mnonzero\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;36m0\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0munique\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m-> 6133\u001b[1;33m             \u001b[1;32mraise\u001b[0m \u001b[0mKeyError\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;34mf\"{not_found} not in index\"\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m   6134\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   6135\u001b[0m     \u001b[1;33m@\u001b[0m\u001b[0moverload\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;31mKeyError\u001b[0m: \"['predictions'] not in index\""
     ]
    }
   ],
   "source": [
    "predicted_df_def_g[[\"Name\",\"ID\",\"Overall\",\"predictions\",\"Next_Overall\"]].head(20)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 88,
   "id": "fd25d956",
   "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>Name</th>\n",
       "      <th>ID</th>\n",
       "      <th>Overall</th>\n",
       "      <th>Next_Overall</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>L. Messi</td>\n",
       "      <td>https://cdn.sofifa.net/players/158/023/23_60.png</td>\n",
       "      <td>91</td>\n",
       "      <td>87</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>K. De Bruyne</td>\n",
       "      <td>https://cdn.sofifa.net/players/192/985/23_60.png</td>\n",
       "      <td>91</td>\n",
       "      <td>88</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Casemiro</td>\n",
       "      <td>https://cdn.sofifa.net/players/200/145/23_60.png</td>\n",
       "      <td>89</td>\n",
       "      <td>88</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>S. Mané</td>\n",
       "      <td>https://cdn.sofifa.net/players/208/722/23_60.png</td>\n",
       "      <td>89</td>\n",
       "      <td>88</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>J. Kimmich</td>\n",
       "      <td>https://cdn.sofifa.net/players/212/622/23_60.png</td>\n",
       "      <td>89</td>\n",
       "      <td>88</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>N. Kanté</td>\n",
       "      <td>https://cdn.sofifa.net/players/215/914/23_60.png</td>\n",
       "      <td>89</td>\n",
       "      <td>88</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>Bernardo Silva</td>\n",
       "      <td>https://cdn.sofifa.net/players/218/667/23_60.png</td>\n",
       "      <td>88</td>\n",
       "      <td>87</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>T. Kroos</td>\n",
       "      <td>https://cdn.sofifa.net/players/182/521/23_60.png</td>\n",
       "      <td>88</td>\n",
       "      <td>87</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>L. Modrić</td>\n",
       "      <td>https://cdn.sofifa.net/players/177/003/23_60.png</td>\n",
       "      <td>88</td>\n",
       "      <td>86</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>Rodri</td>\n",
       "      <td>https://cdn.sofifa.net/players/231/866/23_60.png</td>\n",
       "      <td>87</td>\n",
       "      <td>86</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>Fabinho</td>\n",
       "      <td>https://cdn.sofifa.net/players/209/499/23_60.png</td>\n",
       "      <td>87</td>\n",
       "      <td>87</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>T. Müller</td>\n",
       "      <td>https://cdn.sofifa.net/players/189/596/23_60.png</td>\n",
       "      <td>87</td>\n",
       "      <td>86</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>M. Verratti</td>\n",
       "      <td>https://cdn.sofifa.net/players/199/556/23_60.png</td>\n",
       "      <td>87</td>\n",
       "      <td>86</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>T. Alexander-Arnold</td>\n",
       "      <td>https://cdn.sofifa.net/players/231/281/23_60.png</td>\n",
       "      <td>87</td>\n",
       "      <td>87</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>L. Goretzka</td>\n",
       "      <td>https://cdn.sofifa.net/players/209/658/23_60.png</td>\n",
       "      <td>87</td>\n",
       "      <td>86</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>F. de Jong</td>\n",
       "      <td>https://cdn.sofifa.net/players/228/702/23_60.png</td>\n",
       "      <td>87</td>\n",
       "      <td>86</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>Thiago</td>\n",
       "      <td>https://cdn.sofifa.net/players/189/509/23_60.png</td>\n",
       "      <td>86</td>\n",
       "      <td>86</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>P. Dybala</td>\n",
       "      <td>https://cdn.sofifa.net/players/211/110/23_60.png</td>\n",
       "      <td>86</td>\n",
       "      <td>86</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>Bruno Fernandes</td>\n",
       "      <td>https://cdn.sofifa.net/players/212/198/23_60.png</td>\n",
       "      <td>86</td>\n",
       "      <td>86</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>Parejo</td>\n",
       "      <td>https://cdn.sofifa.net/players/189/513/23_60.png</td>\n",
       "      <td>86</td>\n",
       "      <td>86</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                   Name                                                ID  \\\n",
       "0              L. Messi  https://cdn.sofifa.net/players/158/023/23_60.png   \n",
       "1          K. De Bruyne  https://cdn.sofifa.net/players/192/985/23_60.png   \n",
       "2              Casemiro  https://cdn.sofifa.net/players/200/145/23_60.png   \n",
       "3               S. Mané  https://cdn.sofifa.net/players/208/722/23_60.png   \n",
       "4            J. Kimmich  https://cdn.sofifa.net/players/212/622/23_60.png   \n",
       "5              N. Kanté  https://cdn.sofifa.net/players/215/914/23_60.png   \n",
       "6        Bernardo Silva  https://cdn.sofifa.net/players/218/667/23_60.png   \n",
       "7              T. Kroos  https://cdn.sofifa.net/players/182/521/23_60.png   \n",
       "8             L. Modrić  https://cdn.sofifa.net/players/177/003/23_60.png   \n",
       "9                 Rodri  https://cdn.sofifa.net/players/231/866/23_60.png   \n",
       "10              Fabinho  https://cdn.sofifa.net/players/209/499/23_60.png   \n",
       "11            T. Müller  https://cdn.sofifa.net/players/189/596/23_60.png   \n",
       "12          M. Verratti  https://cdn.sofifa.net/players/199/556/23_60.png   \n",
       "13  T. Alexander-Arnold  https://cdn.sofifa.net/players/231/281/23_60.png   \n",
       "14          L. Goretzka  https://cdn.sofifa.net/players/209/658/23_60.png   \n",
       "15           F. de Jong  https://cdn.sofifa.net/players/228/702/23_60.png   \n",
       "16               Thiago  https://cdn.sofifa.net/players/189/509/23_60.png   \n",
       "17            P. Dybala  https://cdn.sofifa.net/players/211/110/23_60.png   \n",
       "18      Bruno Fernandes  https://cdn.sofifa.net/players/212/198/23_60.png   \n",
       "19               Parejo  https://cdn.sofifa.net/players/189/513/23_60.png   \n",
       "\n",
       "    Overall  Next_Overall  \n",
       "0        91            87  \n",
       "1        91            88  \n",
       "2        89            88  \n",
       "3        89            88  \n",
       "4        89            88  \n",
       "5        89            88  \n",
       "6        88            87  \n",
       "7        88            87  \n",
       "8        88            86  \n",
       "9        87            86  \n",
       "10       87            87  \n",
       "11       87            86  \n",
       "12       87            86  \n",
       "13       87            87  \n",
       "14       87            86  \n",
       "15       87            86  \n",
       "16       86            86  \n",
       "17       86            86  \n",
       "18       86            86  \n",
       "19       86            86  "
      ]
     },
     "execution_count": 88,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "predicted_df_mid[[\"Name\",\"ID\",\"Overall\",\"Next_Overall\"]].head(20)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 90,
   "id": "5870d8ea",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Name</th>\n",
       "      <th>ID</th>\n",
       "      <th>Overall</th>\n",
       "      <th>Next_Overall</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>R. Nainggolan</td>\n",
       "      <td>178518</td>\n",
       "      <td>86</td>\n",
       "      <td>85</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>P. Pogba</td>\n",
       "      <td>195864</td>\n",
       "      <td>88</td>\n",
       "      <td>88</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>A. Vidal</td>\n",
       "      <td>181872</td>\n",
       "      <td>86</td>\n",
       "      <td>85</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>K. De Bruyne</td>\n",
       "      <td>192985</td>\n",
       "      <td>91</td>\n",
       "      <td>90</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>L. Modrić</td>\n",
       "      <td>177003</td>\n",
       "      <td>89</td>\n",
       "      <td>89</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>L. Goretzka</td>\n",
       "      <td>209658</td>\n",
       "      <td>83</td>\n",
       "      <td>82</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>Marlos</td>\n",
       "      <td>187754</td>\n",
       "      <td>82</td>\n",
       "      <td>82</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>Morales</td>\n",
       "      <td>216475</td>\n",
       "      <td>79</td>\n",
       "      <td>80</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>Mosquera</td>\n",
       "      <td>178625</td>\n",
       "      <td>75</td>\n",
       "      <td>75</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>Montoro</td>\n",
       "      <td>186682</td>\n",
       "      <td>71</td>\n",
       "      <td>71</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
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      ],
      "text/plain": [
       "             Name      ID  Overall  Next_Overall\n",
       "0   R. Nainggolan  178518       86            85\n",
       "1        P. Pogba  195864       88            88\n",
       "2        A. Vidal  181872       86            85\n",
       "3    K. De Bruyne  192985       91            90\n",
       "4       L. Modrić  177003       89            89\n",
       "5     L. Goretzka  209658       83            82\n",
       "6          Marlos  187754       82            82\n",
       "7         Morales  216475       79            80\n",
       "8        Mosquera  178625       75            75\n",
       "9         Montoro  186682       71            71"
      ]
     },
     "execution_count": 90,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "predicted_df_mid_g[[\"Name\",\"ID\",\"Overall\",\"Next_Overall\"]].head(10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 91,
   "id": "e7b68774",
   "metadata": {},
   "outputs": [
    {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Name</th>\n",
       "      <th>ID</th>\n",
       "      <th>Overall</th>\n",
       "      <th>Next_Overall</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>K. Benzema</td>\n",
       "      <td>https://cdn.sofifa.net/players/165/153/23_60.png</td>\n",
       "      <td>91</td>\n",
       "      <td>88</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>R. Lewandowski</td>\n",
       "      <td>https://cdn.sofifa.net/players/188/545/23_60.png</td>\n",
       "      <td>91</td>\n",
       "      <td>88</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>K. Mbappé</td>\n",
       "      <td>https://cdn.sofifa.net/players/231/747/23_60.png</td>\n",
       "      <td>91</td>\n",
       "      <td>88</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>M. Salah</td>\n",
       "      <td>https://cdn.sofifa.net/players/209/331/23_60.png</td>\n",
       "      <td>90</td>\n",
       "      <td>88</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Cristiano Ronaldo</td>\n",
       "      <td>https://cdn.sofifa.net/players/020/801/23_60.png</td>\n",
       "      <td>90</td>\n",
       "      <td>88</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>H. Kane</td>\n",
       "      <td>https://cdn.sofifa.net/players/202/126/23_60.png</td>\n",
       "      <td>89</td>\n",
       "      <td>88</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>Neymar Jr</td>\n",
       "      <td>https://cdn.sofifa.net/players/190/871/23_60.png</td>\n",
       "      <td>89</td>\n",
       "      <td>88</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>H. Son</td>\n",
       "      <td>https://cdn.sofifa.net/players/200/104/23_60.png</td>\n",
       "      <td>89</td>\n",
       "      <td>88</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>E. Haaland</td>\n",
       "      <td>https://cdn.sofifa.net/players/239/085/23_60.png</td>\n",
       "      <td>88</td>\n",
       "      <td>87</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>C. Immobile</td>\n",
       "      <td>https://cdn.sofifa.net/players/192/387/23_60.png</td>\n",
       "      <td>86</td>\n",
       "      <td>85</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>R. Lukaku</td>\n",
       "      <td>https://cdn.sofifa.net/players/192/505/23_60.png</td>\n",
       "      <td>86</td>\n",
       "      <td>86</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>R. Sterling</td>\n",
       "      <td>https://cdn.sofifa.net/players/202/652/23_60.png</td>\n",
       "      <td>86</td>\n",
       "      <td>86</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>R. Mahrez</td>\n",
       "      <td>https://cdn.sofifa.net/players/204/485/23_60.png</td>\n",
       "      <td>86</td>\n",
       "      <td>86</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>L. Martínez</td>\n",
       "      <td>https://cdn.sofifa.net/players/231/478/23_60.png</td>\n",
       "      <td>86</td>\n",
       "      <td>86</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>Vinícius Jr.</td>\n",
       "      <td>https://cdn.sofifa.net/players/238/794/23_60.png</td>\n",
       "      <td>86</td>\n",
       "      <td>86</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>P. Aubameyang</td>\n",
       "      <td>https://cdn.sofifa.net/players/188/567/23_60.png</td>\n",
       "      <td>85</td>\n",
       "      <td>84</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>Iago Aspas</td>\n",
       "      <td>https://cdn.sofifa.net/players/192/629/23_60.png</td>\n",
       "      <td>85</td>\n",
       "      <td>84</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>M. Depay</td>\n",
       "      <td>https://cdn.sofifa.net/players/202/556/23_60.png</td>\n",
       "      <td>85</td>\n",
       "      <td>84</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>Gerard Moreno</td>\n",
       "      <td>https://cdn.sofifa.net/players/208/093/23_60.png</td>\n",
       "      <td>85</td>\n",
       "      <td>84</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>J. Vardy</td>\n",
       "      <td>https://cdn.sofifa.net/players/208/830/23_60.png</td>\n",
       "      <td>85</td>\n",
       "      <td>84</td>\n",
       "    </tr>\n",
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      ],
      "text/plain": [
       "                 Name                                                ID  \\\n",
       "0          K. Benzema  https://cdn.sofifa.net/players/165/153/23_60.png   \n",
       "1      R. Lewandowski  https://cdn.sofifa.net/players/188/545/23_60.png   \n",
       "2           K. Mbappé  https://cdn.sofifa.net/players/231/747/23_60.png   \n",
       "3            M. Salah  https://cdn.sofifa.net/players/209/331/23_60.png   \n",
       "4   Cristiano Ronaldo  https://cdn.sofifa.net/players/020/801/23_60.png   \n",
       "5             H. Kane  https://cdn.sofifa.net/players/202/126/23_60.png   \n",
       "6           Neymar Jr  https://cdn.sofifa.net/players/190/871/23_60.png   \n",
       "7              H. Son  https://cdn.sofifa.net/players/200/104/23_60.png   \n",
       "8          E. Haaland  https://cdn.sofifa.net/players/239/085/23_60.png   \n",
       "9         C. Immobile  https://cdn.sofifa.net/players/192/387/23_60.png   \n",
       "10          R. Lukaku  https://cdn.sofifa.net/players/192/505/23_60.png   \n",
       "11        R. Sterling  https://cdn.sofifa.net/players/202/652/23_60.png   \n",
       "12          R. Mahrez  https://cdn.sofifa.net/players/204/485/23_60.png   \n",
       "13        L. Martínez  https://cdn.sofifa.net/players/231/478/23_60.png   \n",
       "14       Vinícius Jr.  https://cdn.sofifa.net/players/238/794/23_60.png   \n",
       "15      P. Aubameyang  https://cdn.sofifa.net/players/188/567/23_60.png   \n",
       "16         Iago Aspas  https://cdn.sofifa.net/players/192/629/23_60.png   \n",
       "17           M. Depay  https://cdn.sofifa.net/players/202/556/23_60.png   \n",
       "18      Gerard Moreno  https://cdn.sofifa.net/players/208/093/23_60.png   \n",
       "19           J. Vardy  https://cdn.sofifa.net/players/208/830/23_60.png   \n",
       "\n",
       "    Overall  Next_Overall  \n",
       "0        91            88  \n",
       "1        91            88  \n",
       "2        91            88  \n",
       "3        90            88  \n",
       "4        90            88  \n",
       "5        89            88  \n",
       "6        89            88  \n",
       "7        89            88  \n",
       "8        88            87  \n",
       "9        86            85  \n",
       "10       86            86  \n",
       "11       86            86  \n",
       "12       86            86  \n",
       "13       86            86  \n",
       "14       86            86  \n",
       "15       85            84  \n",
       "16       85            84  \n",
       "17       85            84  \n",
       "18       85            84  \n",
       "19       85            84  "
      ]
     },
     "execution_count": 91,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "predicted_df_fw[[\"Name\",\"ID\",\"Overall\",\"Next_Overall\"]].head(20)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 92,
   "id": "f1bf965a",
   "metadata": {},
   "outputs": [
    {
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       "      <th>0</th>\n",
       "      <td>L. Suárez</td>\n",
       "      <td>176580</td>\n",
       "      <td>92</td>\n",
       "      <td>91</td>\n",
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       "      <th>1</th>\n",
       "      <td>A. Florenzi</td>\n",
       "      <td>203551</td>\n",
       "      <td>82</td>\n",
       "      <td>81</td>\n",
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       "      <th>2</th>\n",
       "      <td>Cristiano Ronaldo</td>\n",
       "      <td>20801</td>\n",
       "      <td>94</td>\n",
       "      <td>93</td>\n",
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       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>A. Sánchez</td>\n",
       "      <td>184941</td>\n",
       "      <td>88</td>\n",
       "      <td>84</td>\n",
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       "      <td>184941</td>\n",
       "      <td>88</td>\n",
       "      <td>85</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>Roberto Firmino</td>\n",
       "      <td>201942</td>\n",
       "      <td>85</td>\n",
       "      <td>85</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>Crislan</td>\n",
       "      <td>224126</td>\n",
       "      <td>70</td>\n",
       "      <td>70</td>\n",
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       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>Crislan</td>\n",
       "      <td>224126</td>\n",
       "      <td>70</td>\n",
       "      <td>69</td>\n",
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       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>A. Griezmann</td>\n",
       "      <td>194765</td>\n",
       "      <td>88</td>\n",
       "      <td>86</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>R. Lewandowski</td>\n",
       "      <td>188545</td>\n",
       "      <td>91</td>\n",
       "      <td>90</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>Jonathas</td>\n",
       "      <td>189169</td>\n",
       "      <td>78</td>\n",
       "      <td>76</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>Jonathas</td>\n",
       "      <td>189169</td>\n",
       "      <td>78</td>\n",
       "      <td>76</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>M. Salah</td>\n",
       "      <td>209331</td>\n",
       "      <td>87</td>\n",
       "      <td>89</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>D. Alli</td>\n",
       "      <td>211117</td>\n",
       "      <td>84</td>\n",
       "      <td>85</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>Adaílton</td>\n",
       "      <td>190139</td>\n",
       "      <td>67</td>\n",
       "      <td>68</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>E. Cavani</td>\n",
       "      <td>179813</td>\n",
       "      <td>88</td>\n",
       "      <td>89</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>Mata</td>\n",
       "      <td>223578</td>\n",
       "      <td>75</td>\n",
       "      <td>74</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>Mata</td>\n",
       "      <td>223578</td>\n",
       "      <td>75</td>\n",
       "      <td>74</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>M. Mandžukić</td>\n",
       "      <td>181783</td>\n",
       "      <td>83</td>\n",
       "      <td>84</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>Rodrigo</td>\n",
       "      <td>198329</td>\n",
       "      <td>81</td>\n",
       "      <td>80</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                  Name      ID  Overall  Next_Overall\n",
       "0            L. Suárez  176580       92            91\n",
       "1          A. Florenzi  203551       82            81\n",
       "2    Cristiano Ronaldo   20801       94            93\n",
       "3           A. Sánchez  184941       88            84\n",
       "4           A. Sánchez  184941       88            85\n",
       "5      Roberto Firmino  201942       85            85\n",
       "6              Crislan  224126       70            70\n",
       "7              Crislan  224126       70            69\n",
       "8         A. Griezmann  194765       88            86\n",
       "9       R. Lewandowski  188545       91            90\n",
       "10            Jonathas  189169       78            76\n",
       "11            Jonathas  189169       78            76\n",
       "12            M. Salah  209331       87            89\n",
       "13             D. Alli  211117       84            85\n",
       "14            Adaílton  190139       67            68\n",
       "15           E. Cavani  179813       88            89\n",
       "16                Mata  223578       75            74\n",
       "17                Mata  223578       75            74\n",
       "18        M. Mandžukić  181783       83            84\n",
       "19             Rodrigo  198329       81            80"
      ]
     },
     "execution_count": 92,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "predicted_df_fw_g[[\"Name\",\"ID\",\"Overall\",\"Next_Overall\"]].head(20)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "5229db48",
   "metadata": {},
   "source": [
    "XGBoost"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 93,
   "id": "641dd395",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Name</th>\n",
       "      <th>ID</th>\n",
       "      <th>Overall</th>\n",
       "      <th>Next_Overall</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>V. van Dijk</td>\n",
       "      <td>https://cdn.sofifa.net/players/203/376/23_60.png</td>\n",
       "      <td>90</td>\n",
       "      <td>91</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Rúben Dias</td>\n",
       "      <td>https://cdn.sofifa.net/players/239/818/23_60.png</td>\n",
       "      <td>88</td>\n",
       "      <td>88</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>João Cancelo</td>\n",
       "      <td>https://cdn.sofifa.net/players/210/514/23_60.png</td>\n",
       "      <td>88</td>\n",
       "      <td>88</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Marquinhos</td>\n",
       "      <td>https://cdn.sofifa.net/players/207/865/23_60.png</td>\n",
       "      <td>88</td>\n",
       "      <td>89</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>A. Rüdiger</td>\n",
       "      <td>https://cdn.sofifa.net/players/205/452/23_60.png</td>\n",
       "      <td>87</td>\n",
       "      <td>87</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "           Name                                                ID  Overall  \\\n",
       "0   V. van Dijk  https://cdn.sofifa.net/players/203/376/23_60.png       90   \n",
       "1    Rúben Dias  https://cdn.sofifa.net/players/239/818/23_60.png       88   \n",
       "2  João Cancelo  https://cdn.sofifa.net/players/210/514/23_60.png       88   \n",
       "3    Marquinhos  https://cdn.sofifa.net/players/207/865/23_60.png       88   \n",
       "4    A. Rüdiger  https://cdn.sofifa.net/players/205/452/23_60.png       87   \n",
       "\n",
       "   Next_Overall  \n",
       "0            91  \n",
       "1            88  \n",
       "2            88  \n",
       "3            89  \n",
       "4            87  "
      ]
     },
     "execution_count": 93,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "xgpredicted_df_def[[\"Name\",\"ID\",\"Overall\",\"Next_Overall\"]].head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 95,
   "id": "0a07a693",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Name</th>\n",
       "      <th>ID</th>\n",
       "      <th>Overall</th>\n",
       "      <th>Next_Overall</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>D. Alaba</td>\n",
       "      <td>197445</td>\n",
       "      <td>85</td>\n",
       "      <td>84</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Jordi Alba</td>\n",
       "      <td>189332</td>\n",
       "      <td>87</td>\n",
       "      <td>86</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>A. Kolarov</td>\n",
       "      <td>185103</td>\n",
       "      <td>81</td>\n",
       "      <td>83</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Marcelo</td>\n",
       "      <td>176676</td>\n",
       "      <td>87</td>\n",
       "      <td>86</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Alex Sandro</td>\n",
       "      <td>191043</td>\n",
       "      <td>86</td>\n",
       "      <td>86</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>Fábinho</td>\n",
       "      <td>188522</td>\n",
       "      <td>66</td>\n",
       "      <td>64</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>Felipe</td>\n",
       "      <td>768</td>\n",
       "      <td>75</td>\n",
       "      <td>74</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>A. Valencia</td>\n",
       "      <td>167905</td>\n",
       "      <td>83</td>\n",
       "      <td>78</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>L. Baines</td>\n",
       "      <td>163631</td>\n",
       "      <td>80</td>\n",
       "      <td>76</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>R. Rodríguez</td>\n",
       "      <td>193352</td>\n",
       "      <td>80</td>\n",
       "      <td>80</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>K. Walker</td>\n",
       "      <td>188377</td>\n",
       "      <td>84</td>\n",
       "      <td>84</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>J. Kimmich</td>\n",
       "      <td>212622</td>\n",
       "      <td>84</td>\n",
       "      <td>85</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>K. Asamoah</td>\n",
       "      <td>186832</td>\n",
       "      <td>79</td>\n",
       "      <td>79</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>Marcos Alonso</td>\n",
       "      <td>192638</td>\n",
       "      <td>82</td>\n",
       "      <td>82</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>Cédric</td>\n",
       "      <td>201118</td>\n",
       "      <td>78</td>\n",
       "      <td>78</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>Cédric</td>\n",
       "      <td>201118</td>\n",
       "      <td>78</td>\n",
       "      <td>77</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>Capa</td>\n",
       "      <td>216189</td>\n",
       "      <td>78</td>\n",
       "      <td>80</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>Capa</td>\n",
       "      <td>216189</td>\n",
       "      <td>78</td>\n",
       "      <td>78</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>Carvajal</td>\n",
       "      <td>204963</td>\n",
       "      <td>84</td>\n",
       "      <td>85</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>Carvajal</td>\n",
       "      <td>204963</td>\n",
       "      <td>84</td>\n",
       "      <td>83</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "              Name      ID  Overall  Next_Overall\n",
       "0         D. Alaba  197445       85            84\n",
       "1       Jordi Alba  189332       87            86\n",
       "2       A. Kolarov  185103       81            83\n",
       "3          Marcelo  176676       87            86\n",
       "4      Alex Sandro  191043       86            86\n",
       "5          Fábinho  188522       66            64\n",
       "6           Felipe     768       75            74\n",
       "7      A. Valencia  167905       83            78\n",
       "8        L. Baines  163631       80            76\n",
       "9     R. Rodríguez  193352       80            80\n",
       "10       K. Walker  188377       84            84\n",
       "11      J. Kimmich  212622       84            85\n",
       "12      K. Asamoah  186832       79            79\n",
       "13   Marcos Alonso  192638       82            82\n",
       "14          Cédric  201118       78            78\n",
       "15          Cédric  201118       78            77\n",
       "16            Capa  216189       78            80\n",
       "17            Capa  216189       78            78\n",
       "18        Carvajal  204963       84            85\n",
       "19        Carvajal  204963       84            83"
      ]
     },
     "execution_count": 95,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "xgpredicted_df_def_g[[\"Name\",\"ID\",\"Overall\",\"Next_Overall\"]].head(20)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 96,
   "id": "6074279d",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <th>ID</th>\n",
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       "      <th>Next_Overall</th>\n",
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       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>L. Messi</td>\n",
       "      <td>https://cdn.sofifa.net/players/158/023/23_60.png</td>\n",
       "      <td>91</td>\n",
       "      <td>89</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>K. De Bruyne</td>\n",
       "      <td>https://cdn.sofifa.net/players/192/985/23_60.png</td>\n",
       "      <td>91</td>\n",
       "      <td>85</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Casemiro</td>\n",
       "      <td>https://cdn.sofifa.net/players/200/145/23_60.png</td>\n",
       "      <td>89</td>\n",
       "      <td>87</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>S. Mané</td>\n",
       "      <td>https://cdn.sofifa.net/players/208/722/23_60.png</td>\n",
       "      <td>89</td>\n",
       "      <td>91</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>J. Kimmich</td>\n",
       "      <td>https://cdn.sofifa.net/players/212/622/23_60.png</td>\n",
       "      <td>89</td>\n",
       "      <td>88</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>N. Kanté</td>\n",
       "      <td>https://cdn.sofifa.net/players/215/914/23_60.png</td>\n",
       "      <td>89</td>\n",
       "      <td>87</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>Bernardo Silva</td>\n",
       "      <td>https://cdn.sofifa.net/players/218/667/23_60.png</td>\n",
       "      <td>88</td>\n",
       "      <td>88</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>T. Kroos</td>\n",
       "      <td>https://cdn.sofifa.net/players/182/521/23_60.png</td>\n",
       "      <td>88</td>\n",
       "      <td>85</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>L. Modrić</td>\n",
       "      <td>https://cdn.sofifa.net/players/177/003/23_60.png</td>\n",
       "      <td>88</td>\n",
       "      <td>84</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>Rodri</td>\n",
       "      <td>https://cdn.sofifa.net/players/231/866/23_60.png</td>\n",
       "      <td>87</td>\n",
       "      <td>87</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>Fabinho</td>\n",
       "      <td>https://cdn.sofifa.net/players/209/499/23_60.png</td>\n",
       "      <td>87</td>\n",
       "      <td>87</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>T. Müller</td>\n",
       "      <td>https://cdn.sofifa.net/players/189/596/23_60.png</td>\n",
       "      <td>87</td>\n",
       "      <td>86</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>M. Verratti</td>\n",
       "      <td>https://cdn.sofifa.net/players/199/556/23_60.png</td>\n",
       "      <td>87</td>\n",
       "      <td>85</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>T. Alexander-Arnold</td>\n",
       "      <td>https://cdn.sofifa.net/players/231/281/23_60.png</td>\n",
       "      <td>87</td>\n",
       "      <td>87</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>L. Goretzka</td>\n",
       "      <td>https://cdn.sofifa.net/players/209/658/23_60.png</td>\n",
       "      <td>87</td>\n",
       "      <td>86</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>F. de Jong</td>\n",
       "      <td>https://cdn.sofifa.net/players/228/702/23_60.png</td>\n",
       "      <td>87</td>\n",
       "      <td>86</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>Thiago</td>\n",
       "      <td>https://cdn.sofifa.net/players/189/509/23_60.png</td>\n",
       "      <td>86</td>\n",
       "      <td>85</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>P. Dybala</td>\n",
       "      <td>https://cdn.sofifa.net/players/211/110/23_60.png</td>\n",
       "      <td>86</td>\n",
       "      <td>89</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>Bruno Fernandes</td>\n",
       "      <td>https://cdn.sofifa.net/players/212/198/23_60.png</td>\n",
       "      <td>86</td>\n",
       "      <td>83</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>Parejo</td>\n",
       "      <td>https://cdn.sofifa.net/players/189/513/23_60.png</td>\n",
       "      <td>86</td>\n",
       "      <td>81</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                   Name                                                ID  \\\n",
       "0              L. Messi  https://cdn.sofifa.net/players/158/023/23_60.png   \n",
       "1          K. De Bruyne  https://cdn.sofifa.net/players/192/985/23_60.png   \n",
       "2              Casemiro  https://cdn.sofifa.net/players/200/145/23_60.png   \n",
       "3               S. Mané  https://cdn.sofifa.net/players/208/722/23_60.png   \n",
       "4            J. Kimmich  https://cdn.sofifa.net/players/212/622/23_60.png   \n",
       "5              N. Kanté  https://cdn.sofifa.net/players/215/914/23_60.png   \n",
       "6        Bernardo Silva  https://cdn.sofifa.net/players/218/667/23_60.png   \n",
       "7              T. Kroos  https://cdn.sofifa.net/players/182/521/23_60.png   \n",
       "8             L. Modrić  https://cdn.sofifa.net/players/177/003/23_60.png   \n",
       "9                 Rodri  https://cdn.sofifa.net/players/231/866/23_60.png   \n",
       "10              Fabinho  https://cdn.sofifa.net/players/209/499/23_60.png   \n",
       "11            T. Müller  https://cdn.sofifa.net/players/189/596/23_60.png   \n",
       "12          M. Verratti  https://cdn.sofifa.net/players/199/556/23_60.png   \n",
       "13  T. Alexander-Arnold  https://cdn.sofifa.net/players/231/281/23_60.png   \n",
       "14          L. Goretzka  https://cdn.sofifa.net/players/209/658/23_60.png   \n",
       "15           F. de Jong  https://cdn.sofifa.net/players/228/702/23_60.png   \n",
       "16               Thiago  https://cdn.sofifa.net/players/189/509/23_60.png   \n",
       "17            P. Dybala  https://cdn.sofifa.net/players/211/110/23_60.png   \n",
       "18      Bruno Fernandes  https://cdn.sofifa.net/players/212/198/23_60.png   \n",
       "19               Parejo  https://cdn.sofifa.net/players/189/513/23_60.png   \n",
       "\n",
       "    Overall  Next_Overall  \n",
       "0        91            89  \n",
       "1        91            85  \n",
       "2        89            87  \n",
       "3        89            91  \n",
       "4        89            88  \n",
       "5        89            87  \n",
       "6        88            88  \n",
       "7        88            85  \n",
       "8        88            84  \n",
       "9        87            87  \n",
       "10       87            87  \n",
       "11       87            86  \n",
       "12       87            85  \n",
       "13       87            87  \n",
       "14       87            86  \n",
       "15       87            86  \n",
       "16       86            85  \n",
       "17       86            89  \n",
       "18       86            83  \n",
       "19       86            81  "
      ]
     },
     "execution_count": 96,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "xgpredicted_df_mid[[\"Name\",\"ID\",\"Overall\",\"Next_Overall\"]].head(20)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 97,
   "id": "947ff549",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "    }\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>Name</th>\n",
       "      <th>ID</th>\n",
       "      <th>Overall</th>\n",
       "      <th>Next_Overall</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>R. Nainggolan</td>\n",
       "      <td>178518</td>\n",
       "      <td>86</td>\n",
       "      <td>85</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>P. Pogba</td>\n",
       "      <td>195864</td>\n",
       "      <td>88</td>\n",
       "      <td>88</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>A. Vidal</td>\n",
       "      <td>181872</td>\n",
       "      <td>86</td>\n",
       "      <td>85</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>K. De Bruyne</td>\n",
       "      <td>192985</td>\n",
       "      <td>91</td>\n",
       "      <td>91</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>L. Modrić</td>\n",
       "      <td>177003</td>\n",
       "      <td>89</td>\n",
       "      <td>88</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>L. Goretzka</td>\n",
       "      <td>209658</td>\n",
       "      <td>83</td>\n",
       "      <td>84</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>Marlos</td>\n",
       "      <td>187754</td>\n",
       "      <td>82</td>\n",
       "      <td>82</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>Morales</td>\n",
       "      <td>216475</td>\n",
       "      <td>79</td>\n",
       "      <td>81</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>Mosquera</td>\n",
       "      <td>178625</td>\n",
       "      <td>75</td>\n",
       "      <td>75</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>Montoro</td>\n",
       "      <td>186682</td>\n",
       "      <td>71</td>\n",
       "      <td>71</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>Magno</td>\n",
       "      <td>192259</td>\n",
       "      <td>69</td>\n",
       "      <td>70</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>Ander Herrera</td>\n",
       "      <td>191740</td>\n",
       "      <td>83</td>\n",
       "      <td>81</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>G. Bonaventura</td>\n",
       "      <td>189125</td>\n",
       "      <td>82</td>\n",
       "      <td>82</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>C. Tolisso</td>\n",
       "      <td>219683</td>\n",
       "      <td>82</td>\n",
       "      <td>83</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>Fernandinho</td>\n",
       "      <td>135507</td>\n",
       "      <td>85</td>\n",
       "      <td>87</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>B. Matuidi</td>\n",
       "      <td>170890</td>\n",
       "      <td>85</td>\n",
       "      <td>85</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>B. Matuidi</td>\n",
       "      <td>170890</td>\n",
       "      <td>85</td>\n",
       "      <td>85</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>Z. Junuzović</td>\n",
       "      <td>173474</td>\n",
       "      <td>79</td>\n",
       "      <td>77</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>João Moutinho</td>\n",
       "      <td>162347</td>\n",
       "      <td>82</td>\n",
       "      <td>83</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>M. Pjanić</td>\n",
       "      <td>180206</td>\n",
       "      <td>86</td>\n",
       "      <td>86</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>T. Kroos</td>\n",
       "      <td>182521</td>\n",
       "      <td>90</td>\n",
       "      <td>89</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>Thiago</td>\n",
       "      <td>189509</td>\n",
       "      <td>88</td>\n",
       "      <td>87</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>Tyronne</td>\n",
       "      <td>198964</td>\n",
       "      <td>69</td>\n",
       "      <td>67</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>K. Boateng</td>\n",
       "      <td>173909</td>\n",
       "      <td>81</td>\n",
       "      <td>80</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>M. Sanson</td>\n",
       "      <td>210896</td>\n",
       "      <td>81</td>\n",
       "      <td>80</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>K. Walker</td>\n",
       "      <td>183885</td>\n",
       "      <td>67</td>\n",
       "      <td>64</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26</th>\n",
       "      <td>T. Lemar</td>\n",
       "      <td>213565</td>\n",
       "      <td>83</td>\n",
       "      <td>83</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27</th>\n",
       "      <td>R. Guerreiro</td>\n",
       "      <td>209889</td>\n",
       "      <td>80</td>\n",
       "      <td>79</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28</th>\n",
       "      <td>J. Henderson</td>\n",
       "      <td>183711</td>\n",
       "      <td>81</td>\n",
       "      <td>82</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29</th>\n",
       "      <td>G. Wijnaldum</td>\n",
       "      <td>181291</td>\n",
       "      <td>82</td>\n",
       "      <td>82</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "               Name      ID  Overall  Next_Overall\n",
       "0     R. Nainggolan  178518       86            85\n",
       "1          P. Pogba  195864       88            88\n",
       "2          A. Vidal  181872       86            85\n",
       "3      K. De Bruyne  192985       91            91\n",
       "4         L. Modrić  177003       89            88\n",
       "5       L. Goretzka  209658       83            84\n",
       "6            Marlos  187754       82            82\n",
       "7           Morales  216475       79            81\n",
       "8          Mosquera  178625       75            75\n",
       "9           Montoro  186682       71            71\n",
       "10            Magno  192259       69            70\n",
       "11    Ander Herrera  191740       83            81\n",
       "12   G. Bonaventura  189125       82            82\n",
       "13       C. Tolisso  219683       82            83\n",
       "14      Fernandinho  135507       85            87\n",
       "15       B. Matuidi  170890       85            85\n",
       "16       B. Matuidi  170890       85            85\n",
       "17     Z. Junuzović  173474       79            77\n",
       "18    João Moutinho  162347       82            83\n",
       "19        M. Pjanić  180206       86            86\n",
       "20         T. Kroos  182521       90            89\n",
       "21           Thiago  189509       88            87\n",
       "22          Tyronne  198964       69            67\n",
       "23       K. Boateng  173909       81            80\n",
       "24        M. Sanson  210896       81            80\n",
       "25        K. Walker  183885       67            64\n",
       "26         T. Lemar  213565       83            83\n",
       "27     R. Guerreiro  209889       80            79\n",
       "28     J. Henderson  183711       81            82\n",
       "29     G. Wijnaldum  181291       82            82"
      ]
     },
     "execution_count": 97,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "xgpredicted_df_mid_g[[\"Name\",\"ID\",\"Overall\",\"Next_Overall\"]].head(30)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 98,
   "id": "feb0eaf2",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Name</th>\n",
       "      <th>ID</th>\n",
       "      <th>Overall</th>\n",
       "      <th>Next_Overall</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>K. Benzema</td>\n",
       "      <td>https://cdn.sofifa.net/players/165/153/23_60.png</td>\n",
       "      <td>91</td>\n",
       "      <td>92</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>R. Lewandowski</td>\n",
       "      <td>https://cdn.sofifa.net/players/188/545/23_60.png</td>\n",
       "      <td>91</td>\n",
       "      <td>90</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>K. Mbappé</td>\n",
       "      <td>https://cdn.sofifa.net/players/231/747/23_60.png</td>\n",
       "      <td>91</td>\n",
       "      <td>92</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>M. Salah</td>\n",
       "      <td>https://cdn.sofifa.net/players/209/331/23_60.png</td>\n",
       "      <td>90</td>\n",
       "      <td>88</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Cristiano Ronaldo</td>\n",
       "      <td>https://cdn.sofifa.net/players/020/801/23_60.png</td>\n",
       "      <td>90</td>\n",
       "      <td>91</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>H. Kane</td>\n",
       "      <td>https://cdn.sofifa.net/players/202/126/23_60.png</td>\n",
       "      <td>89</td>\n",
       "      <td>90</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>Neymar Jr</td>\n",
       "      <td>https://cdn.sofifa.net/players/190/871/23_60.png</td>\n",
       "      <td>89</td>\n",
       "      <td>90</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>H. Son</td>\n",
       "      <td>https://cdn.sofifa.net/players/200/104/23_60.png</td>\n",
       "      <td>89</td>\n",
       "      <td>91</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>E. Haaland</td>\n",
       "      <td>https://cdn.sofifa.net/players/239/085/23_60.png</td>\n",
       "      <td>88</td>\n",
       "      <td>90</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>C. Immobile</td>\n",
       "      <td>https://cdn.sofifa.net/players/192/387/23_60.png</td>\n",
       "      <td>86</td>\n",
       "      <td>91</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>R. Lukaku</td>\n",
       "      <td>https://cdn.sofifa.net/players/192/505/23_60.png</td>\n",
       "      <td>86</td>\n",
       "      <td>91</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>R. Sterling</td>\n",
       "      <td>https://cdn.sofifa.net/players/202/652/23_60.png</td>\n",
       "      <td>86</td>\n",
       "      <td>88</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>R. Mahrez</td>\n",
       "      <td>https://cdn.sofifa.net/players/204/485/23_60.png</td>\n",
       "      <td>86</td>\n",
       "      <td>89</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>L. Martínez</td>\n",
       "      <td>https://cdn.sofifa.net/players/231/478/23_60.png</td>\n",
       "      <td>86</td>\n",
       "      <td>91</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>Vinícius Jr.</td>\n",
       "      <td>https://cdn.sofifa.net/players/238/794/23_60.png</td>\n",
       "      <td>86</td>\n",
       "      <td>92</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>P. Aubameyang</td>\n",
       "      <td>https://cdn.sofifa.net/players/188/567/23_60.png</td>\n",
       "      <td>85</td>\n",
       "      <td>89</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>Iago Aspas</td>\n",
       "      <td>https://cdn.sofifa.net/players/192/629/23_60.png</td>\n",
       "      <td>85</td>\n",
       "      <td>88</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>M. Depay</td>\n",
       "      <td>https://cdn.sofifa.net/players/202/556/23_60.png</td>\n",
       "      <td>85</td>\n",
       "      <td>90</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>Gerard Moreno</td>\n",
       "      <td>https://cdn.sofifa.net/players/208/093/23_60.png</td>\n",
       "      <td>85</td>\n",
       "      <td>90</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>J. Vardy</td>\n",
       "      <td>https://cdn.sofifa.net/players/208/830/23_60.png</td>\n",
       "      <td>85</td>\n",
       "      <td>87</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                 Name                                                ID  \\\n",
       "0          K. Benzema  https://cdn.sofifa.net/players/165/153/23_60.png   \n",
       "1      R. Lewandowski  https://cdn.sofifa.net/players/188/545/23_60.png   \n",
       "2           K. Mbappé  https://cdn.sofifa.net/players/231/747/23_60.png   \n",
       "3            M. Salah  https://cdn.sofifa.net/players/209/331/23_60.png   \n",
       "4   Cristiano Ronaldo  https://cdn.sofifa.net/players/020/801/23_60.png   \n",
       "5             H. Kane  https://cdn.sofifa.net/players/202/126/23_60.png   \n",
       "6           Neymar Jr  https://cdn.sofifa.net/players/190/871/23_60.png   \n",
       "7              H. Son  https://cdn.sofifa.net/players/200/104/23_60.png   \n",
       "8          E. Haaland  https://cdn.sofifa.net/players/239/085/23_60.png   \n",
       "9         C. Immobile  https://cdn.sofifa.net/players/192/387/23_60.png   \n",
       "10          R. Lukaku  https://cdn.sofifa.net/players/192/505/23_60.png   \n",
       "11        R. Sterling  https://cdn.sofifa.net/players/202/652/23_60.png   \n",
       "12          R. Mahrez  https://cdn.sofifa.net/players/204/485/23_60.png   \n",
       "13        L. Martínez  https://cdn.sofifa.net/players/231/478/23_60.png   \n",
       "14       Vinícius Jr.  https://cdn.sofifa.net/players/238/794/23_60.png   \n",
       "15      P. Aubameyang  https://cdn.sofifa.net/players/188/567/23_60.png   \n",
       "16         Iago Aspas  https://cdn.sofifa.net/players/192/629/23_60.png   \n",
       "17           M. Depay  https://cdn.sofifa.net/players/202/556/23_60.png   \n",
       "18      Gerard Moreno  https://cdn.sofifa.net/players/208/093/23_60.png   \n",
       "19           J. Vardy  https://cdn.sofifa.net/players/208/830/23_60.png   \n",
       "\n",
       "    Overall  Next_Overall  \n",
       "0        91            92  \n",
       "1        91            90  \n",
       "2        91            92  \n",
       "3        90            88  \n",
       "4        90            91  \n",
       "5        89            90  \n",
       "6        89            90  \n",
       "7        89            91  \n",
       "8        88            90  \n",
       "9        86            91  \n",
       "10       86            91  \n",
       "11       86            88  \n",
       "12       86            89  \n",
       "13       86            91  \n",
       "14       86            92  \n",
       "15       85            89  \n",
       "16       85            88  \n",
       "17       85            90  \n",
       "18       85            90  \n",
       "19       85            87  "
      ]
     },
     "execution_count": 98,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "xgpredicted_df_fw[[\"Name\",\"ID\",\"Overall\",\"Next_Overall\"]].head(20)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 100,
   "id": "4e0051e2",
   "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>Name</th>\n",
       "      <th>ID</th>\n",
       "      <th>Overall</th>\n",
       "      <th>Next_Overall</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>L. Suárez</td>\n",
       "      <td>176580</td>\n",
       "      <td>92</td>\n",
       "      <td>91</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>A. Florenzi</td>\n",
       "      <td>203551</td>\n",
       "      <td>82</td>\n",
       "      <td>80</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Cristiano Ronaldo</td>\n",
       "      <td>20801</td>\n",
       "      <td>94</td>\n",
       "      <td>94</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>A. Sánchez</td>\n",
       "      <td>184941</td>\n",
       "      <td>88</td>\n",
       "      <td>83</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>A. Sánchez</td>\n",
       "      <td>184941</td>\n",
       "      <td>88</td>\n",
       "      <td>84</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>Roberto Firmino</td>\n",
       "      <td>201942</td>\n",
       "      <td>85</td>\n",
       "      <td>86</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>Crislan</td>\n",
       "      <td>224126</td>\n",
       "      <td>70</td>\n",
       "      <td>69</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>Crislan</td>\n",
       "      <td>224126</td>\n",
       "      <td>70</td>\n",
       "      <td>69</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>A. Griezmann</td>\n",
       "      <td>194765</td>\n",
       "      <td>88</td>\n",
       "      <td>87</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>R. Lewandowski</td>\n",
       "      <td>188545</td>\n",
       "      <td>91</td>\n",
       "      <td>90</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>Jonathas</td>\n",
       "      <td>189169</td>\n",
       "      <td>78</td>\n",
       "      <td>76</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>Jonathas</td>\n",
       "      <td>189169</td>\n",
       "      <td>78</td>\n",
       "      <td>76</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>M. Salah</td>\n",
       "      <td>209331</td>\n",
       "      <td>87</td>\n",
       "      <td>89</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>D. Alli</td>\n",
       "      <td>211117</td>\n",
       "      <td>84</td>\n",
       "      <td>85</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>Adaílton</td>\n",
       "      <td>190139</td>\n",
       "      <td>67</td>\n",
       "      <td>67</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>E. Cavani</td>\n",
       "      <td>179813</td>\n",
       "      <td>88</td>\n",
       "      <td>89</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>Mata</td>\n",
       "      <td>223578</td>\n",
       "      <td>75</td>\n",
       "      <td>74</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>Mata</td>\n",
       "      <td>223578</td>\n",
       "      <td>75</td>\n",
       "      <td>75</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>M. Mandžukić</td>\n",
       "      <td>181783</td>\n",
       "      <td>83</td>\n",
       "      <td>85</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>Rodrigo</td>\n",
       "      <td>198329</td>\n",
       "      <td>81</td>\n",
       "      <td>80</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                  Name      ID  Overall  Next_Overall\n",
       "0            L. Suárez  176580       92            91\n",
       "1          A. Florenzi  203551       82            80\n",
       "2    Cristiano Ronaldo   20801       94            94\n",
       "3           A. Sánchez  184941       88            83\n",
       "4           A. Sánchez  184941       88            84\n",
       "5      Roberto Firmino  201942       85            86\n",
       "6              Crislan  224126       70            69\n",
       "7              Crislan  224126       70            69\n",
       "8         A. Griezmann  194765       88            87\n",
       "9       R. Lewandowski  188545       91            90\n",
       "10            Jonathas  189169       78            76\n",
       "11            Jonathas  189169       78            76\n",
       "12            M. Salah  209331       87            89\n",
       "13             D. Alli  211117       84            85\n",
       "14            Adaílton  190139       67            67\n",
       "15           E. Cavani  179813       88            89\n",
       "16                Mata  223578       75            74\n",
       "17                Mata  223578       75            75\n",
       "18        M. Mandžukić  181783       83            85\n",
       "19             Rodrigo  198329       81            80"
      ]
     },
     "execution_count": 100,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "xgpredicted_df_fw_g[[\"Name\",\"ID\",\"Overall\",\"Next_Overall\"]].head(20)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "cb9bc2f3",
   "metadata": {},
   "source": [
    "## Feature Importance"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 101,
   "id": "812d3a75",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plot_bar_chart(def_pfi , \"def pfi\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 102,
   "id": "7195641f",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plot_bar_chart(fwd_pfi, \"fwd pfi\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 103,
   "id": "de7b3b70",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plot_bar_chart(mid_pfi, \"mid pfi\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 104,
   "id": "ef17fe49",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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aS8GwiIiIiDSWgmERERERaSwFwyIiIiLSWAqGRURERKSxFAyLiIiISGMpGBYRERGRxlIwLCIiIiKNpWBYRERERBqrVjBsZtua2YNm9rCZfb5NmfeZ2X1mdq+ZndnfaoqIiIiI9N9c3QqY2Vjge8A2wBPA7WZ2kbvfl5VZFTgc2NTdnzOzJWZVhUVERERE+qVOy/AGwMPuPs3dXwLOBnYqlfkI8D13fw7A3Z/ubzVFRERERPqvTjC8NPB4tvxEWpdbDVjNzG40s1vMbNuqDZnZ/mY2ycwmPfPMM0OrsYiIiIhIn/RrAN1cwKrAFsDuwMlmtmi5kLuf5O4T3X3ihAkT+rRrEREREZGhqRMMPwksmy0vk9blngAucvf/uvsjwO+J4FhEREREZMSqEwzfDqxqZiua2TzAbsBFpTK/JFqFMbPxRLeJaf2rpoiIiIhI/3UNht39ZeBA4HLgfuAcd7/XzI42sx1TscuBv5rZfcDVwGfc/a+zqtIiIiIiIv3QNbUagLtfClxaWndE9r8Dn0o3EREREZFRQTPQiYiIiEhjKRgWERERkcZSMCwiIiIijaVgWEREREQaS8GwiIiIiDSWgmERERERaSwFwyIiIiLSWAqGRURERKSxFAyLiIiISGMpGBYRERGRxlIwLCIiIiKNpWBYRERERBpLwbCIiIiINJaCYRERERFpLAXDIiIiItJYCoZFREREpLEUDIuIiIhIYykYFhEREZHGUjAsIiIiIo2lYFhEREREGkvBsIiIiIg0loJhEREREWksBcMiIiIi0lgKhkVERESksRQMi4iIiEhjKRgWERERkcZSMCwiIiIijaVgWEREREQaS8GwiIiIiDSWgmERERERaSwFwyIiIiLSWAqGRURERKSxFAyLiIiISGMpGBYRERGRxlIwLCIiIiKNpWBYRERERBpLwbCIiIiINFatYNjMtjWzB83sYTP7fIdyu5iZm9nE/lVRRERERGTW6BoMm9lY4HvAdsDqwO5mtnpFuYWBQ4Bb+11JEREREZFZoU7L8AbAw+4+zd1fAs4Gdqoo91XgOOA/fayfiIiIiMgsUycYXhp4PFt+Iq2bwczWBZZ191/3sW4iIiIiIrPUTA+gM7MxwP8Bh9Uou7+ZTTKzSc8888zM7lpEREREZKbUCYafBJbNlpdJ6woLA2sC15jZo8BGwEVVg+jc/SR3n+juEydMmDD0WouIiIiI9EGdYPh2YFUzW9HM5gF2Ay4q7nT3v7v7eHdfwd1XAG4BdnT3SbOkxiIiIiIifdI1GHb3l4EDgcuB+4Fz3P1eMzvazHac1RUUEREREZlV5qpTyN0vBS4trTuiTdktZr5aIiIiIiKznmagExEREZHGUjAsIiIiIo2lYFhEREREGkvBsIiIiIg0loJhEREREWksBcMiIiIi0lgKhkVERESksRQMi4iIiEhjKRgWERERkcZSMCwiIiIijaVgWEREREQaS8GwiIiIiDSWgmERERERaSwFwyIiIiLSWAqGRURERKSxFAyLiIiISGMpGBYRERGRxlIwLCIiIiKNpWBYRERERBpLwbCIiIiINJaCYRERERFpLAXDIiIiItJYCoZFREREpLEUDIuIiIhIYykYFhEREZHGUjAsIiIiIo2lYFhEREREGkvBsIiIiIg0loJhEREREWksBcMiIiIi0lgKhkVERESksRQMi4iIiEhjKRgWERERkcZSMCwiIiIijaVgWEREREQaS8GwiIiIiDSWgmERERERaSwFwyIiIiLSWLWCYTPb1sweNLOHzezzFfd/yszuM7OpZnaVmS3f/6qKiIiIiPRX12DYzMYC3wO2A1YHdjez1UvFpgAT3X0t4Dzg+H5XVERERESk3+q0DG8APOzu09z9JeBsYKe8gLtf7e7/Sou3AMv0t5oiIiIiIv1XJxheGng8W34irWtnX+A3VXeY2f5mNsnMJj3zzDP1aykiIiIiMgv0dQCdme0JTAS+UXW/u5/k7hPdfeKECRP6uWsRERERkZ7NVaPMk8Cy2fIyaV0LM3sb8EVgc3d/sT/VExERERGZdeq0DN8OrGpmK5rZPMBuwEV5ATNbB/gRsKO7P93/aoqIiIiI9F/XYNjdXwYOBC4H7gfOcfd7zexoM9sxFfsGsBBwrpndaWYXtdmciIiIiMiIUaebBO5+KXBpad0R2f9v63O9RERERERmOc1AJyIiIiKNpWBYRERERBpLwbCIiIiINJaCYRERERFpLAXDIiIiItJYCoZFREREpLEUDIuIiIhIYykYFhEREZHGUjAsIiIiIo2lYFhEREREGkvBsIiIiIg0loJhEREREWksBcMiIiIi0lgKhkVERESksRQMi4iIiEhjKRgWERERkcZSMCwiIiIijaVgWEREREQaS8GwiIiIiDSWgmERERERaSwFwyIiIiLSWAqGRURERKSxFAyLiIiISGMpGBYRERGRxlIwLCIiIiKNpWBYRERERBpLwbCIiIiINJaCYRERERFpLAXDIiIiItJYCoZFREREpLEUDIuIiIhIYykYFhEREZHGUjAsIiIiIo2lYFhEREREGkvBsIiIiIg01lzDXYHRbIXP/7rj/Y8e+67ZVBMRERERGQq1DIuIiIhIY9UKhs1sWzN70MweNrPPV9w/r5n9It1/q5mt0PeaioiIiIj0WdduEmY2FvgesA3wBHC7mV3k7vdlxfYFnnP3VcxsN+A44P2zosIiIiLSbJ26KaqLovSqTp/hDYCH3X0agJmdDewE5MHwTsBR6f/zgBPNzNzd+1jXvtGHSERk5vTze7TOtjRGY/TTb6+MVHWC4aWBx7PlJ4AN25Vx95fN7O/Aa4Bn+1HJ0a6fX/Sz+0djJNV9OF8r1X321300vlZFuSbUvY7ZHcCO1Ne9n0bSMTO7f3P6ua2R/vma0+s+0li3xlszey+wrbvvl5Y/CGzo7gdmZe5JZZ5Iy39IZZ4tbWt/YP+0+HrgwX49kZkwnnpBe51ys3tbqvvwbGtO318/t6W6D8+2VPfh2ZbqPnL3189tqe7939bssLy7T6i8x9073oCNgcuz5cOBw0tlLgc2Tv/PRTxp67btkXADJvWr3OzelurejLrrtVLdR/r+VHfVfaTvT3Uf2XUf7ludbBK3A6ua2YpmNg+wG3BRqcxFwF7p//cCv/P0CoiIiIiIjFRd+wx79AE+kGj9HQuc4u73mtnRRLR/EfAT4HQzexj4GxEwi4iIiIiMaLVmoHP3S4FLS+uOyP7/D7Brf6s225zUx3Kze1uq+/Bsa07fXz+3pboPz7ZU9+HZluo+cvfXz22p7v3f1rDqOoBORERERGROpemYRURERKSxFAyLiIiISGMpGJ4Dmdmg/ttV67L7xpjZuFlbKxEREZGRR8HwbGRmC3S5f3kze1v6f34zW7iizCZm9gEz+1Bxq9jU4d3WmdmZZjbOzBYE7gHuM7PP1H82vTOz1/RpO5umemNme5rZ/5nZ8v3Ydod9HmRmi83KfcwKZvamWbDNzcxsn/T/BDNbMbtvrJl9ssvj7zazqaXb9Wb2raEeI2a2dPpsvLW4VZRZ0MzGpP9XM7MdzWzuoeyvn9Jr9jozW664DXedYMZ30Os73D/BzCaa2aKzqT613r865czs9IrHDVpX2uYHzezX2bq92pSd28zOypaX7bDd7UvL81aUmTf9HXKDRXodTh7q4/stfV6L432ubP070kRf5fLvNbNt+rTvEfValNU9rvqwn4X6ta05Qa1sEnMSM1sS+DrwOnffzsxWJyYM+Um6/26galShAe7ua/VSLpXdBPgxsBCwnJmtDXzU3T+RlfkIMTvf4sDKwDLAD4GtszKnp/vuBF5Jqx34Wbp/O+CdwNJm9t2sTuOAl0v1XN3d/2FmewC/AT4PTAa+UXq9plc8z78Dk4DD3H1aKvepitfi78Bkd78zLd9iZncCpwK/qcpFXXM7PwDWTq/jYcRr+zNg84rtVb1PRf2/RnwG2h4PmSWB283sDuAUYiKaqvp3er3GAS9UPD8A3H3H0rY2AVYg+5y6e/FeV+0n31bxw/n99GN6GnCGu/+93WPM7HjiNfk3cBmwFvBJd/95VuZIYCIxg+SpwNzAz4FN035fMbPdgW+12w9xvL0CnJmWdwMWAP6c6rlDqV5rAqsD82XP72fZ/ccB7wfuo/VzcV1pv9cBb0knNVcQOdTfD+yRbWteYBcGv+5Hl+p0SHr+04njbx3g8+5+RVYm/wwW/k6kpPxVKnMQcCTwF+DVrO5r5Q+yuLJzmbtPN7MvAesCX3P3O7IyHb/b6pZJ5XYAvgnMA6xoZm8Gji6OUTPbL23nD+n+/VOazUFqHlc7A8cBSxDfocX3aB4Adn3/eii3RqmOY4H1SuvmAd4FfAB4B3A+8Z1cOMTM5nX3k7LHLAhcCDyelbvSzLZ190dL2/8w8EXgkmz1zcR7S8W6KWb2RXc/mzbMbC3ifXsd8Evge8CJwIbA/2blNgNWyr5PziN+eyCOq9+l9eOAJd39obS8KzB/Knc58flfwd1vSPd/ividAzjT3R9O6w8H5s4+RzcDzxPH10+BY9L6I4B3Vzy1a4CLiddy1fQcVwbuBj7t7k8O9bVox8yOKH/uK8qc5O77dynTdTulcnWPq3bb2cbdr+xWjvi+XM7MlqHGe9hln3e7e8eGlzqv1bAa7lk/ZveN+CF+H3BXWp4LuDu7f/lOt17LpbK3AssCU7J195TK3El8MeRl7i6VuZ8OM/sBaxOTnzyW/ha3nYHFSmXvJb7IzgU2T+vuqtjmV4GPAgsTwdz+xI/W+4FrsnJnAr8nvmT+l5hq+1zih+izqYwB2wBnAQ8TP6arlfZXZzt3pL9HAPvm6yrqfzzxRfumdPsfIlD7HPHl2vF4KG3LiB/Fs7P6r9zD6zWFCNgrb6XtnA7cBHwfOCHdvtvm/flEtr+PE0FLXmbV9Bo8nF7fbdo8vzvT3/cQucMXKR8TxHFqtB6nU0tlvkX86LyF+BFfF1g3u3/Qe5W9p+Vj/kjgaiJYPJUImM8rlXkQmLfGZ7/Yx0HZsXRnqcxlwC+AzxInWocRJ33lbRXHyzuAC4jg6o5SmZOIwOygdLsmPYeLgG+nMg8Dr6lR96np72ZpO+8Cbi2V6Xos1ymT1k9O73/l9xFxNWlC+n8l4OYOda9zXD0MvHFm379u5YgrZNOJxoF/pNt04K/AManM29P79CRxorcD8GjFfhYHbgMOTssTiO+pY0vl3kl8p62arTucCOSWScuvJYLx+4kTq+JzswXwQCqzPBEQXQms0uY1uhXYmzhZPYT43BwPzFcqdxXRIDLjvU37fytx0pUfw3uX3qcTiBPAHxLf5duXPouHAV8mTr5nvCfAgtnylPR3LHBDtr7tTGUMfAauBz6SnuNngAtm5rXosL8/Zu9z1e01wBN1t9Pj/roeV3X2B3yqze0w4G+pTN33cOc2t12AZ/rxWg3nbdgrMNufMNye/k7J1t05i/d5a8U+yz8GLWWIH6lykHEusFSN/c1NBNZrptvcFWUOJr7sLyWCm+WB6yvKVQXId5bvI370F8qWFwKuJVoR7qvYxpZp/8+nchvX3U5aPpz4gXkt0d2nXQDbMfDq9XggTji+DTxAtFBPAY4fwuvV7f3peOLTZX9V68amL60n07YfAHYulbkn/f0xsG2b4/S20mu4YMVxenXF7Xd5/YANsuX1GQjOppS2dXd6f4v7lwSuLJX5TX7MdHitphDTy98CrFFsv+o1qLGt4of5O8B72tT9FmBstjwX0SI2NjuWrwbmqlP39PcY4ANt9tf1WK57vAO3VJSbmv1fDvwrT0bTfffWOK5u7Mf718P7fEyH/bxKfMesmK2b1qbsOOBG4Fjic3VIm3JbE4HkmsT3x01kDRREo8XVRGD+u+xz8ysGf063IwK7S4gTq4uAi9q83+3qfXtp+YLs/xuz/6eQfQ+VjocbKo6D/P7rs//L5fbO/p+c/f/7qs8D8Zv2UJvn2K4hpOtrwcAJUfk2HXg5lXkFmAY8kt2K5ZfqbqfHch2Pq/x9L90uBl7Iyv2HaDA5suL2fJv3pt17+F/iyt2pFbfpdV+rkXprXDcJ4AWLfokOYGYbEZcuScvtLj9XXbYrHn8C8EYiwBlLHIx5ucfTJW9PfdcOIQ7w3LVm9gVg/tQ36hPEgZ0bT/TtvQ14sVjppcvrwCZEt4FHU72XNbO93P267DHfBfLLuI+Z2ZYVz/tfZvY+4Ly0/F7iAwatr9MSeZ2ID86S7v5vM3sRZvQZ3hP4IPFlfhDxAX4zEeivWGc7RCvrB4hW4T9b9LFs6d6RGWtmG7j7bakO6xPvEUTrUMfjoZAui38IeJb4Uf+Mu//Xom/iQ0RLYq3Xy8y2IC4NPkqb94doeXst8FSb51V4IXV1OTttf3eyrhjpUuE+REvilcAO7n6Hmb2OCMwuyLZ1iZk9QFzO/riZTcjqXjjHzH4ELJq69nwYaOl/5+5Vx1FuP+CU1GfNiB+D/dKlwGNKZf/t7q+a2cvpku3TxFUWzOyE9Jz/BdxpZlfR+rk4uLStQ4iTqAs9ZtFciQg4cjeZ2Zvc/e4uz2GymV1BHLOHW/Tvf7VUZjHiZK44nhYEFvfoSlLUcxpwjUVf1Lzu/1fa1pPpdd8GOC515yiP+ahzLNc63oF7zewDxOdnVeLk+abs/mVK3UBalkuv/cU1jqtJZvYL4nJ2/jrkx+ehdH//6pa7xMwWdPcXzGxPohX2O+7+WPp/N+C3ZjaN+GyNLT2+6NoB0Xr6f0Rr6+PF+rzu7n6VRT/7a4jXcSuPyaqK+38K/NTMPuvux5f2k/fJfz3waaJ19HsMPubmM7N1iM8VwIv5sg90q1k0f5C775wtLpn9P5enKCf5YPb/ogw+BrfO/h+f/b+Qmc3t7v9N+zstPZ95icCvcAFwspkd6O4vpDILESedxetZfo7zt3mOdV6L54H13f0vpeeBmRVdEqYBW7v7HzuUqbOdWuVqHldvIX5L/1neDLBBtnwH8Et3n1yxv/3Sv/OV7mr3Hk4Fvunu91Rs623p3zqv1cg03NH47L4RX3Q3Ej8ANxJnomvNxPYmAasQZ9BjicDjmFKZ8cAZRAD4NHHp7TWlMmOISz/nEoHURyi1DFLj8noqNxl4fba8GtnZd1q3JHHJ8jdpeXVSl4NSuZWIoPxZ4Jn0/ypES+1mWbkvEx+84qxzEtGNYUHSpZb0Wn+ZdGmwtJ/P9bCdgyh1++jw/qxPtC4+QgSfU4kviwWJy8Xr1TkegK9Q6v6S3ffGXl6vmu/P1cBzRL+8ltafUrkViNajYn+/JPp/FfdfS/yAzV/x2A9WrFuc1JpJ9ON9bUWZbYiTj29S0eWih2NrEWCRLu/f94kf3Y8RJx1TgFPTfXt1uH2oYlu7dltH9KN7ibhUODUdO1MrHjeG+C5ZNC2/pnzcAPum4+5UokVlGnEisCDwjVSmqsXmyIr9LUBcklw1LS8FvL1Uput3W50y2f7+h7g8Oyn9P192f6fXfq/StuYtHVcLEie4eZmq1qZT6nzGh3JL760RV3qmAAcA11aU24Ro7PgTcQVi/y51HlR3otWvaP17iThZLZb/Udpf1ZWsyelv0Uq4XYfndQ3VV2aupvXqzMXAuyoevz3w62z5Lqq/A5ZOr+GtlLq6pfvfQLqKlJa/Toy1WCBbt2B6rY7J1s2VnuezxPfkHcT32rGkK2gdnl/5OXYtR/Rl36DNa3lc+nsAsHabMgfV3U4P++t6XKVjccs227ku+//1wPg25ZZMf+u+h28BlmuzrYl1X6uRemvkDHQWo1dfT3wZPujpbLVN2SVoHbjzx9L9k9x9oplN9YHBdVPcfZ1ZVPcliQAP4kB9uqLMjLq0W2dmvyE+XF9097XTazLFu3SC71K39YkfD4hLbZNK95vXOOBqbOdrRMtNx8FspccsAuAVA8jqHA+pBe1ed5+elscRQfCt3Z5PxbbqvD+bVz3W3a/tcV+Huvu3S+sOcffvtCnfdtBeun9F4ClPrVpmNj/xpfpoVqbjsWU1B6lV1G0FYJy7T+32fNqsu8Pd1+20ztpkJfFoMSzXZ2mie1H+HK4rlVmKgZaa2939T22e20Lp8eWWnrzMZkQwfGpqXV3I3R9J940lWm9PoPuxXPv7rx/qvO5dHr8I0dL7buLKkRONCr8i+lE+n8pdTOdBpTOuoBX7N7MjgCfd/Sed6pSuAL0N2M3dP1yn3r0wszcQ/c6PJ/rBFsYRV6HWSN97X/OsRXkm9rcK8GuilbpoSV2P+N7d3t1/n8rtSVxROYw4aYA4ofomcWXxmfT3f0rb+QJxaf83aTtjU5n9iDEtRlzhOYX4nmgZ4J2+V1ZJiw+7+79n9jlLNTPblhrv4ZyuccFwdgki93eiT9nTWbkdiQFcryO+eJcH7nf38ijk64gvyR8Tg3ueIvpDrZ2VOZWKL+n8S9W6ZD1w979aXH7/BnH2b8SZ2mfc/bz8QWZ2CnH5rBitvQfRKpPv73Z3Xz8P3M3sTnd/c2lbE4hW6hVo/dEf9IOQvvCWLJX7Y3b/lURL3PNpeTHgbHd/Ry/bSWWMGOiyD5Hd4BzgJ+7+h1K5joGXmU0lLoP+ovzY0namEIPAPC2PIQZ7lH/ku75e6Xh4hQ7vTypX58RnNaLv8pLuvqZFt4gd3f1r6f6qQKTyZM3aZCvx7JK3mU0CNnH3l9LyPMQJy/pZmY7HlpldRsoQku0Hd68c4d0t6Oz2HG0gy8r7iMFxhXHEIKINSo9dm/hsQfSZu6uiTpUZLHxwRpBudV+TGCy5eFr1LNGqfW9pO0eSsni4+2oW3VzOdfdNszK3lZ9Ldl/V994Mni7p1w0ozeyiLtvb0cxeS7Qg/pzo1lRcrh4H/NDd32CpW4ANdHkpb+dgM7uc6Ef7U3f/c9r/a4lW6K3d/e1pXdUJZLFNy08kzexaYrDkh4n3+mmij+la5Q2Y2WLu/lzF+g2JS9lFVoMPu3u5+xtmNh9xZWMVojX1lIrgbyci2N+RuApUmE58R95kZrvQ+b25oO77nPY5LzHYtzgBvJcYYLuRu1+TlduWCIzWSPu/lzgJKQLdNYluYmtk2zneqy+nDwpyzWxJL3UbqPM73cNvea1yQ2Fmry2OyX6pe1z1YT/7e8pY0ct72GZb27v7JV3K9P216qcm9hnelxhccXVa3oL4UV7RzI529yLX5FeBjYDfuvs6Fv1p96zY3geJ7hEHAp8kznZ3KZXJD5L5iFHV5RaiOummvkj0Nyq+DCYAv2Wgf2rh48TliiKIuZ643Jyr23fwV+nxvyULXMqsNUXUK8QPn9OaImpCEQgDuPtzFi3vvW4Hd3cz+zPx+rxM9M88z8yudPfPZkV/xUDglfdFLuxABDXnmNmrRLB0Tjn4Jn5MZ/wQefRjrfr81Hm9PkaX96fixOcEMxt04kP01/0M8KNUr6kWOaT/QAQgK5YCl4WBv7Wp10QiOOx0hjxXEQin/b2UAuJct2NrGXfftsM+ZmgXdALXWaRwq/Mc/0ScVO5IHAeF6cRnNt/fIcTJTBE0/NwiJdAJpaq9mwhMq46pct3vpTVtWt56fBLwKXe/Oj1mC+I93YRW7yGyDNwB4O5/ssF5yG80sxOJY3hGv3GPvpE70J4z8Hy/2aFcbmMizdNZxGVWqyjzDmI0/zJEw0JR5h9EcAUDYydarv6UrODux7VUOH5Uj7NIT1ZYlDi2vgdxckCMxHcie0yuGHewj8e4g7cSl+2rXMXgdGcQfXY/TbyfOxID495RUe6nxNiH64mTsjWI1tb8+fwK+JWZbezuN7epR5GTeAni+PhdWt6SaOG9gIH3uVOZYp8vmtn+xMnY8URXrm8Q3wMbZ+UuM7NBV9/MbB53fykFTB8q3bds+r5qGcuRWnjvtshN/QGLfulvJBqdcnV+p+v+ltct16LqJLvCT4jxGG21a3zoUK7ucdVuO3VTmM34zPbyHraxPq1xTpWur9Ww8hHQV2N23og+mEtmy0umdYuTjSQnpXgh+kyNKf7vUx3GADeV1nVNN8XgEdGdsijMQ6QSa5etoG5f2TtrPqeuKaKIL6DlsuXly8+75nYOSdu6HNiVgb5kY4A/lMrWyg6Qyq5KDDx8peK+C4jgde50O4QYmNDT60WcOD1Qoy53AUtkyxOqjj/aZAdIr+0WxCC5zbPburTJXkCNbCXEILwds+WdgKt6ObaIAPBNNd+TtmnTen2O1MuyMpXWFFCDsmWk9V0zWHSqe/4+11w301k8UplB9SEG9Q36fqhYl6dfGgtsSwR6U4i+kGtUPGYMsEed97rDa3QF0WpV/t7+HNFYUay7EVi29Dl4DbBc+RhN969DBH+Ppteqsk8jpawd2fpaGTVoTUk3V7ty6f75iBPl7xNdCE6h1Hc6vR5LZctLEV3FeiqTHUcnps/QPUR3lDEV5a6hdSzCBuXjlPiO+gQR9P+BGGyV3z8/0chzEXEi9Tzx+a3aX9ff6Tpleik33DcGrtTXztTSZjuDPrs9PLbjezgn35rYMryst16SeTqt+5uZ5X3nnrfox3cdcIaZPU3rKP1z3P191mbyDa+43JZZlThzz3XLegBwWbpkWMxC834iNVoLq5GtwN0np8uK3foOXmJm73T3QfspeZzqluXcF4Eb0iXKoptH+Qy2znYWJ9INtfTj9Git3b5Utmt2AIt+ou9Pt1cYyAyR+xjRr+pLxPt9VUXdocvr5ZFJ4EEzW84rRtxmxnjrJby/Uj1j5LNmtjIDrbDvJfr0Pkb0zdu44jHt1MlW8jHi83Ai8R4+TqlFocaxtRmwt5k9kvZjlCaqyUwjgthBLbBDeI5ds6yk9XmLfnF1oqxOBou2dc/LmNmXidY5iKtP0yrK9SOLB8AFZraTp8v0qbvBrylNOEGM6P+Qp8ukqRX+UFLrj7u/QnQzuCxdbt+dyIrxFXc/MavTqxYzEp5RVZk63S2Iz+Xn0/aLTAd/IYKq92XF53H3fMT6De7+V+CvNjBj5WqprrsTXVJ+QQQhLa+dDczsacBi2TI+0Id+0dIl+JZlH+iS8N9s3ctmVYfTDKcTaQ/fARxNdKEqXyJf1t3zLDN/IQL+XssUdfs3EajOBzzi7uUMFRBZXi6zyBiyNJHebZ90dWJnopV9NaLRYEV3XyZ/sJmdSXzfX0H0a/8d0U3imop9FfXv9jtd97e8a7l0fBSZa1YjBo/9Jv/eMrPT3T3PplG5LrtvwfTa7Obu70rr9vLIHFIuOzfx3bQ79Y+rSp5ljrAaE2r08B6Oo8MELF6dIWNpBuKYP3mpe9BI0sQ+w98nvhTOTat2AZ4gLjVfUnwpFh8OUssGMfL9jPTlipkt5e5PWY0BNzaQrq245P9n4HB3Pz8rsz7RCtCSboq4xPoudz8nlduFNNsX0Z/xwornOJnIRfpgWl4NOMvd18vK1O0rO51oPXiR+OIsApdyirmfEMFPxxRRZjae6H4Ckcv02SFup+1golK5+4g+apWBl5ndSgQs5xDdI6oCkdrqvF4W/czXIRKr55ez8wE+3yC6huQnPlPdveVyr0XaqJOIQO+59Dz3BH7u7pvZ4FSBle9f2tbmVc/JKwbtWYcBX2Z2A5HF4nqiP/H00v1dPzNZ2fOJEf9tg86K5wjVsyTW+Vx8iuiLWnyu3g2c5oMHIe7V5jn8NCtTp+6LEZlKNkurrgeO8uo+qtsQ/eSN+PG5snT/EW3qdHRW5iPEpfr3El26LiJm8Loif0w6rs4jfiDfQpzwbO/ZANQUBL+L+AFfIW3rFC/NBmZmRXaAcveNv5nZM3ToblEce+mEb+dU51eIVvcz3f0f2X4edvdVqGBmf3D3lS26Ql1PZDcpZkeb5u4rlcofmS1+jOiXX3x2ivEGp1bta6DqMQbAzF7JnrcRAcS/qPgsFpfKLQ2oTUHS9e6+UVbmRKJBJf9ueNjdD+qlTCp3F9G166vEyfAPiXywu5afUGpkuZJ4L9fx6F7yb+J77EvEyYe3eT3vJH5Lf0b0gX6iqlxWvuvvdA+/5XW2NZk4zhcjrjDcnl6HGbMW2uDBtmOJVv/Vs3VVsxZe4O4XF9sg+stXzi7n7vvWOa6s/gyCZxFxyyVp+UHi92IB4A3uvkcP7+FJxBXt09Lyw8QVsvmJHMkfs9JMg2b2R7KZBt29nDpzxGhiMGzEl2rx4/MccbZzQFZmLHH5rWtLi5kdVxGgDFrXQ/0WgeqsBz1so062grw1tFNf2br7PLJqvbt/pVRuMeJLOs/QkQ8o6rodqzGYKCvbNvCyGAT3GS/1R6xiMQhmX6K/X173nkeW1w0665z4ZGUXJFqTp7crU7NuHQftWY1MEBYZJ96SbhsRgeD1RMqwf5jZ4lRw90F9mWsGnV8lftzOJAKM3YjBJ3cAH3f3LVK5rp+LtG5dsuDU3adU1SH98K2WFgddWalT934ys8OyxfmIPqb3l49RMzuA6OKwAjEtfJ4/OC+3GpGq74/ExCL/zu77GdHV5FIiuGk70MbiCkCZu/tK6bt2GyKgXos4CT7LswGEZnZwei7XEYH8FOIH9j3AJzy1LprZGcSsmC0t5mb2UWALd9/dzN5NHB+bEi3bZwM/dvcVO9S/duaLmWVpEGQ6Yf4E0XByW0Vg8h5itjiIVFpVjSJ1ykz0wdl6PuilfrQWVy/eR1wNW4voa38Y8V2+G9EAcBbxO3JlVZBrkTFjd+I351mi0WNNr2hRTOV3YeBzeCNwvpcCljpl6pSzgewiBxFpKI+3NOg3BXhfYOAkBuJ75iXgJHc/3Mzenp7b24kuN78ATnD3FUr1WJw47n7u7t+1aMi5lOjG8/mq16HNa3MV0a3nvrR8N9E/f0HgC57GZFQE8FN8YGDx9e7+FjM7lBrvoQ0eRJ5v6waPxpc7gLf4QI7o4uRuLJG6cDNGKh8BfTVm943BfcUOrChzFV1yoKZyVX19i9mp1u10q3jcu4hL9EcUt7T+hvS3yFdZ3AblqUzlTiGyW2yRbifTIWcnFX1libPGts9hiK/7fsTo2OfS6/5vSn0aa27nTug6JfC49HfxqltWru30n6XtnUu0nvyBaDm8gkjS3/PrRZZ3stO6mvWqzOnb7nmXn39pW+8juh38NB0PjwDvLZWpO13xUsQX7PeIwW+XEa0w0DorUXGrnCmr5mtQd9a/tp+LusdLtq0t0mt1LRGkPQK8tYc6fzv9vZiKmaSycj199kv7mJc0ZTqDp2K9i7gk/yliAF/xmLuJftPF7c8M5FzOZ6B7NdWh53p1qe/eRLquA0t1ynNfF89pOVq/A5YgBoldzcB07tcQ/WHLeY0XJFrvLiZabX9AKW9zVnZKD8/hkqEex+nx+xGtk29Nn5GniROW4v6uYw7qlBlCvb5Nlquc6K9/Zba8EhEw3k1MqPI5KnLXZuXXIwZr/pHB42f69hx7KDeFETZrYafjivozCN5XKpf/9t1fuq/je1jxeqyZ/V/046410+BIvDWmz7DV7CuW+Scx6vVKWi/tHZy293HizH0liy4HhYWJAx3iy7gdB7bK6vdD4ot+S+IH+73EpQs8nU25e3n0eDt1skmUW4fLfWU/RbQCVD2HGXU3s2+7+6HWJiWTt/Y3PYRodbzF49LUG4hk7L1u5yV3dzMrzlCrRoGfSbQmTWagi0pe/+Ks97dm9mkqLuGWtreKu+9q0d/ypxZ94K7P7q/1eiXbMHh0+3bA57Iz7LrdG04j5fRNy79Pz6Xo21zVQTF//rk62Uq6ZoKwyGTxLPEe/IRowZjRD9E7tMJl2+ilT37dWRI7fS7Kx8uMqlD9ev0vETy1dLkA1qtZ96LlrWP2hiF89nMLEJkcIL6Xche0WV/uc9+uXmN6qYhF6qbVab2qUlziLXe3+C4D3VQKcxHfUfOS+jy6+x8tuhEU23sa2MTMtmIgRdSvPV0yLtX/BeI9PzNdrdqV+ExeUS5L66xr3SzdQ9kZzGxZd3/c3X+cVl1HOuYsGwfhNcYc1CnTK3c/NNVlXCz6Y8T3WGE+d/868PX0Xu9OtHhWdlvx6Nc62cw+w0Aaw9r1r/sce3gtDmWEzVpYUj6uFi09z52zxXwGwelmtpqnvNHF71r67W25iujRnazTe/iqZenRfGA8wdIMZMupO9PgyDPc0fjsujFw5rZKtq5taxRdZlYi+hCvQPwALp/dKlvdatRvaunvQmTzgqd1p1c87vTs/7w1ZdDI7tLjbiUuIx9O9D0a6uu6Xvq7edWtVLbIfHAnaVQ7MZFFr9v5NJFKbBqRButmhji7Da2tk21bKRkYzX8dcXl4fKfjp82+Pk6cdb9Aa+vbI6TZ9YZQ/8psEkPcVtdsJdTIBEGc9JxLtLKcRuSCXrlUZmfix+B/gXdXbGOp9Hf5qlup7ErUmCWxnzeqM0xM7bXupccvRpvZMOny2S/ev+yYupdoVRx01avm89sIWDhbHgdsOMRtHUkEFn8hTtz+DJyX7vsZ8T30NbKWporjaSrRkv8AkQ4NYuT7dUOpU5f67gQckC3fSnzXTKN0paTisT8Z4j4fIMvWkK3fh8EZcq4jApmraDM7ZZ0yPdZvYjq+HiWuiNxFlrWAyETxWQb6RJ9ANHqUt3MNrVkp1qf6yk7fnmO/Xgtm46yFFdssZxS5mHozCG5LNJDsRWSXehNx9eX3lGYyJNJqlre3Zfb/nkRf6rcSJ9ELE7/Pt5NmM6XmTIMj8TbsFZhtTzQGwpxNDNY4mZh/+5Euj5mfbNrcDuXGEnkSlytuFWXWJC5Df6i4le4vgq1b0rbmJQY85GXKlyDmIrsMkt9fLltRn67PK5XblfSjSLQ2XkAMnhjKe3AhcUZ7VPqC+hVwaYfynYKDblMC99RFpUbdO16+rPN60cMJFDWCn7TuGiJ9VJFyayPipK/nbi7p9byc+LLcm/giP65UptZ0xansQsTU2Y/R2gXn+0QL3D7pdhnwvS6v/zi6dPOo8R5uSgwA+j0Dwc20ijILpv/3JAL2qs9zT12ROtTpmuy5PUIEXv9XUa7jZz+ty4+ppalOL3claQrp7DNWlXJrCgxMB0+cGPWU4il77N3p8Xel5SVJl9ip2d2CaOl9b3Fcz8obNVO0UR00P0KNoLlin+9Mx+Wq2brD02u3TFpeJR2fm5duXyJNd16nzBBfk6lEX9BieTNau83UTdH2DiLw/wQx49kUqrsMluu/OYMbRbqW6VaONt2UqA6+i+/YI7LXu1OavDFEH+KZnlY8vb4fJAW66X1+kAgyD0q309IxtFrpsWsSJ52T0+1nVJx4pvftc7Se0NxcKrMt8dv9V6Lx4VqyoJqIhaqm0z6ONik9R8pt2Csw259wzb5iRPLyB0kBM/Dm8ocjrT8wvfH3El9cg4IDOreMHErkbPwSESjuzMBMdl9NZQ4nfhxepvXH4q+0zuueB8NTurwOixA/9JPS7X+p6CPNQGvXZsQP97uAWyvKlQONR+jc8r45kVB8ntL6a6gRHNR4n6/ucMvnr/9Q1a20rTHA+2rut+7r1fEEihrBT1q/LhU5fYmBHe1eh7b9tInBcf+Xbu+puH/5qlupzP+m9+1eImDci+zqA/FjWA607m9Tn48Sn4dHadNyT7QQfoFotT6Fitys2X63I/qWvqa4ld8/6rX+zEt0jbkg3T5JKYcv8Vl+KL03lf1pSZ9T4mTrK/kx1MtnP5VdmYErLlsQ3UEWLZW5s+K5DPquaFOu8qSnxmeiONGfTHy2jT73ae3njcH9MU/M/r8l+78qaF6cNnmNa+x3ayLP+ppEH92bgMWy+y+h4qoM0dp3cd0yQ3xNqo6R/PdmHuJk+s70HHbrsK0tiEw7TwGvLd03H/GbeCLx2a86oetapodtVQXKb023zUtlr02fx4eA1xLfW+0aAhZrs35DolX9n8SJwxs7vE7zEINEz02f+1OBHbL7502f8aJ//IfTc96ixvu5LDF4PF9X94TGqupaWp6fgZbo+dO6JbvVazhvw16BYX3y0Sqyf9UXF/HFvQitl58HJeim3iQRnVpGvkl86T2XPmxfJwLxQduky2UGIgh9DxHQ/IH4MZ5xK5U9n0jptFK6HUnWCT8rN6XYN5GWasa6UrmOgQY9DGRIfwcFBwxuPerHoJ0TstvJ6TU8r6Jc3YF2XV8vOpxA0UPwk21vLqLlrHIiiVn02VmC9oH8ezt98RE/2Mtny8vT5oea+OEZ36UuNxEtD+9Lx/4uwC4V5QadlFSU6an1p8u2HqbDj10qczcx2PAKor/2jOO9VK7rJUYiEJmLaDX6PRGcXFoq03Xim7T+AmpMMlPzdfg+caL/sfR+TgFOnR3H6VDftw73/SH7v1bQ3OO+35K+Gy4i+uHSbn/l46humSHW69tE17QtiIDx+8QJc3G16S4iL/Lc6Xj+FZHhp7ydL6djfmMiQH2A7HI/Md7h5+m+X5INUu6lTA/b2onW1v3bGGjd37VU9rXECfBmafmtlLqwZGXbTcAyibiqOS9xFbHqqszbicD3yVT/HYBH22wvb81dgIrW3Kxst0lRap3QUGMCluy+RYkB3VcReYaH/fPd9hgf7gqM1FvxZUaHjAVp3dV0af5noF9n25aRdCBuQvSHPZ/ob1TVErhYOviKs9e3Zved2uFW7nN0Z8W2q9ZdwkD/3EXTh7iqj1edQONXVFxyLpWpFRz08D7Ox0AL3vlES8F8HcovClxWsf7Y9N4sS+csA11fL+qdQNXqX9Xt+VE6IUq3rWmd3a52xgKiNf8h4qrKI8Rl7nsr6rUjcaL3TbLWjHTftUSKomvS7YW0rurS5GVk/c/avAaDjtvS/cUP9rHEl/3GtM/0UbT+/J6B1p98BrFzsuN0avlW2taNneqVyuyaHvv9tLwSkfapXPeu3V0YCOQ/S+pDz+ATsW2JEfynEz+0jwHvqKjXEkS3sqeJK1pn5sfMTHweV6BN16eRciMmCPlIxfqPEmnfZnyOO2yjMkjqUL747E0nuiG9UP4MAg91ePzDdcsM8TW5Ot1+l25Xl/5OrHjMByvWfZvOWSm6ztZXp0wP2xpxsxbSW2aKjq25RL/evYjub48QLchPtNlW3ROacleXO8i+i+hhpsGRdGtMNokhuNdi3vSxZrYq0UpSlY9zGjEz0qBJIszse0T/0Nss5mI/mQiIi0skufmJQHmRdPsT8YM7g5ntR7TQLEN8aDdK29kq7XOfHp7fv81sMx+YmWZTItVZ2fuIH9BvuvvzZrYUkay8qNO66d+rLSaKuKD0OtyRbWsx4nVtO9kE8WG8nAjQbk+jeosZbxbv9IS8Ik8t0T9qOnHGDNFF5nQiCKnyAtWZFt6f/h6QrfOKsh1fr+Rxusyy55G7smNO5qTb89uXCP6uTstbEMfgimZ2tLuf7r1lLPgqcdz91iN/5JZE39oZzOwY4oStmHXsYDPb2N2/kJaPqLGfwuHELIK30n6mt26zJJYzfEzM/ndaM328n3gN9/WYVGA54sevcEj6WyfrwiQz+wXRKpXX/YLs/3MZmAwAjxHdu3Soe65c9/9azBT3IaI1CeKHbeAB7pelz2wxicOhXpr4JpV7mvhBm2lmdpW7b522+2h53Qj0SeCX6fu/+P5ajzixfXdW7lYz+4hX5zW+rZcd1vzsTWqzv/2Iz3TdMkOxHYPzi7tn+cXLvJSrOK07tLRczkpRZ7a+ujP61Sk3EmctXJcamSmy59hpBsGnGTyhxnvabGtfH8g5/RSwk5kNyqTi7peb2ccoTcCSXoNeZxocMRo36UZdZrYAkWrq7WnV5cTMLv8plTuy6vHu/hUzO4Q4qF9HfIDOIrpDjHP3qenxJxGXuKcT/SxvIVqln6uo090MpCZ7c5GazFvTqtR9fm8m8skuQnx4/0bkBLyrVG5l4kzyRYsZiNYCfubuz6f7r6Y9d/cZP9bWwwxnber8CB3ShXl1ovf7PJshqLyulMptDJH+6RzvIQF6adsdX69Upusse+1OfPLXs+bzu5zoA/2XtLwkA9N+Xufua2aP6zrdqJlNcveJFjNXreMxfeld7r52VmYq8ObiS9ki4foUHzy5xThaJ+4YdDKTTpxuIE4M8/Rs+aQb04kWkpfSrV0auq7SD+B/PFIyVU7Lmsp1nWzHqmeSco8ZpD7rkdj/BKrTrx1c8dhudV+d6Ipws7ufZTH5yfu8NKlMnZMs68MkM2kbCxAnYlsw8LkdR1x9eUPtJzcMSina7vVSijYzW4KBE51BQbO3mUyizb7mI967VYgrBad4aera9Nm9kDjGi8B2IqlvaTp561qmbp1K+76MaOG7g4Hpyt1LM4P2sL3DvWI2MqsxW1+dMj1sa0TOWphtcxPiu3oXouX2Qm+dve4uOswgaDUn1OiFtZmAxd1/bT3ONDiSKBhuw8zW9dZWzW7lF3D3f7W5b3nigNyN+ECeRUwl+lD6khlPXOK4iXS5wyveGDO73d3XTwfchingutfd1yiX7aHe4wA8m9a0dP+dxJfpCkTOwV8RadveOdR9dqnP8USapX8Tl8jXAj7p7j8f4vZ+TvTluyUtb0i07h5N9N3Or468THx5PeWlKarzs/xcdsZflLuTLq9XpxOorEytE592z8/dP5SWW4Jli+aRe919dctmEEr3lWcrmou49J8//rdE69gxxHH7NNGdZZOszFRiEEeR03JxYqKEtdLy/sTr/x8iwC1+MKpOZlrqOBRmtkN6Ho+l5SOIH5fHgIM9tVam+7pOy5rKDZqVzCpms+tQp2lEC+7KVfd7xSx11iFXb1am26x4dU+yziUuhX6AeK/2IAY5HkJNqTHgUKIxIJ+ieTpwsrufWHdbI1m3oLnmNn5BtPJdT7TCPtbutba4GlOcxFbur06ZHut3T37iPLOqPj/DwUbJrIUWs6W+jejH++Fsfd0ZBFdKz2F34kT4SCKw/n3Fvm5294071OXbwOGeZqRM8c2P3X2btNzTTIMjho+Avhoj8Ua0ZtxPnHFV5r9M5TYm0k39MS2vTer/16b8OsQAkjzVlBFfXPsT6VEmEZcZvlJ67IXUSE0GlaM9i1Hmn+p0q3hc136Iad0hDPSH/jHRgvD2Upm8T+p/iBaGcp/UO9Pf9xATNixCdR/lvE/q9hX3F3067ycCrkfT7dX0fvU06pr6A+1qvV5p/ULAQm3ua5uTuVSu6vndnz3/76fnule6XZTWLQhcnbbRS8aCBYlLdnOl7R3M4IwMuxOB5mnE1YdHgPdn93cdFJeV/TrxuViKNn210zG3J/DltLwssEF2/1RSv2Oie8PviRa8/SgNYMnev4OAz6b/81nsOuWK/nlpW8sQn9mn0+18BtJkHUqc+D4KHE+XdIV0yEiTldmCLrPipbrPx8Dn7A10HjhbDOycmx4HhREnc0sx8Dkojr/vlt/Dpt+o2Q92GOvXNb94j9sbEc+PUTBrIW0yU/T4PFfP/l+T6Otb2Ye8bt2J3/uFu5RZjzYzDY6027BXYCTfiAE0BxMtRHcDX6oocyvx4zslW3dPqcxcRB++M9KP2NnAThXbWoY4m/oOMdrz+Q5125wICAdlD2DwYLmFSIMBiGDpjvRhOCL9yM64tXl+uxMt1ytWPb+0rsiU8Q4iAFij0xceEcC8Gzi2tL6Y1vHHwLb5trMyxxKjUz+cblcSraZ5meW73GZq1DXtB9p1fb3Sl9EUInB5jLicuUapTN0Tn27P04hW0G+l2xG0yelLn5KiEy3Gr0vH544MTp/UdVBcVvaRils5tdoPiGmf70/Li+XvL4OnZP5ctlwe0DKFDtOy0luu6CuJPMpzpdveZIOFsvfvc2m/D6T3Z9WKbbXNSJOVmUyWP5xoIZ5cKtPxJIs0GJj+TDJzR/G6EIN9/5SOxa9ScSLZ5FvFcTgigsWsPrXzi3fYxiMMpN38DzVScM7G57cVA/l6t6pRvm0mqnR/2wa0irJTejk2hvj8Ok6KQpZOjmgsGDRAPyvbcQKWNvu3qm2NpNuwV2A03IjWwtOJy6Xl+25Nf6dk64ofrG2IH98/Ey0iHyAl9M/KHkwEx38kAuDTidantSmNvqT+RAxHMzA6fTHizLeYtWltIpi8k2h5fRsVLcnZtlYnWnJ2T8srkgUTWbmiBek7pPy03T7kVWVS3R4ggoO5iXQwt5b3ReuI2bGdvpiJAQkHpy+6ddO6mRp1ner24FBer/R+5DP7bEGHs2YqcjITfTHnzpZfT/Td2rni8eUR0JWzkhGt8Ytky4uSZoejRlo74oTvGWLwxRPApm32s046/n6UXqvvAt/t9XOZba9ozR30GcyOl4WIYPIxspHvDJ64YnPis/q5tLxSu7pVHVel+++ss670ukwhu2qU3dc1V2/VZ4CBz+XX098L6XCSlb2WxSQzmzMwyczHenxf8vfge8BRdV6HJt5IV8myz9TLzGTayD7Xb/mq20xsb8pwP6c+vz47MYtmLezHa0X3rBOnZre/EnHLqVTna59KhwlY0rprqDHT4Ei6DXsFRuoNeGP6wbg7vbGfoCK1EHAekRLtDiJA+jTRcRxiJOV+dLjMQeRq3IU0hWuXOpVbD8ZSkX4t3Xc80Zn+dipyrqYyxbSR9wM7dtjvPETrUNs8tumDcwVxCXwBIqVLuVVq5+z2XiLwHZQTkbgUPjb9vwCDWxankrXEpfLtkp8fkd7Dr6TbXcTI2rOoTp+0H/CLivUXMzAz0SXpS+7Yqn3WeB+run3kgUPXnMxEILNq+n8VYgDkCUSL+TFEq+CRxInFDaSZ4Lps886KdVN6eF5TGZj5bkMqJqtI992Wjvt9qJjqvKJ8t9kbb02vWRHITaA1MP4wkc7uDrLWfCL4bNey0y2dW5EvteW4KpW5iui+MTbd9izvj/pXjbrm6qXDrHhUp3CqOsmq/X7XOB7uYaCl+QFa00AOurqkW3Nu/TzORsKNPs9amH3X7UU0YlR+9/VQv14mRek2e+2g9678GGrONDiSbkqt1t6pxGj/TxCXFv/TptzHiNbQpYlBIlekx+ClQSlV3P1T3cqY2eHEDFvzm1kx0M2Iy1b5yNJ8cNWtxA/2bYCb2c6epXQyswlEMPAmohXv6Tb73oLo9/lo2ueyZraXD07xtS8xS980d/+Xmb2GCHZyO2T/v5y2uVPFbl8HvC2NsC78zAZS1X0duMPMrkl1eivQLvvDHsDaxftnZkWr+FuBC81sDypGXVds55uluj/m7k+UC1mk4TuGwQOd8sFh09KI3GKQw57El2FR9hUze9DMlnP3P7Z5Xou5+0Pp/72I/KcHpQFUk4lL79cT/amLEdCfbLOtwpiKdZXfEWkkff78/gi87O4PpOVbzaxduqi56xz3aT9HEoHd6sSAxO2I4D4fOPZdosVzCTP7H+JE60tZ3U5JWTWWIILWwp8pHaNmtjFxxWQhYDkzW5uYdvsTpartSfVx9bWszIeJE5RvERkjbir2Z2bbEN1p3kl8Rs8G9nf3F6iQ7f+HadDtjIw0mY8Tg0OLTBTXE0E0RIrIxRjI6AADqRsXIk6mACaYWdv3xnvLHnAWcK2ZPUsMiL0ewMxWoUtqQZnj3TjcFeizrinaks/SmrJwXqJf7UJEzHFeWr9iqcwKpIHGQ6zf7cRVoPVJWSfMbBdPWSdK2uaqS641sx8Rn28nunZeYynNqrvf4R3Sr41UyiZRkkbQf534ISsCkWWJA/WLPnh09qbufmO3dX2q2zHufniH+0/t8HD3SOn0YaKVbT7ig3eOR07RdtucTMyk9mBaXo0IvNZLy29w9wdsIN9weae1M3Kk7VUGP+7+XhtIVbcU0er2KBGA3N7ug2aR+u09PpAKblFiwNBWabnWqGuLNFVPZcHP/MQAi0dL5W4gWmS/RQT/+xCXo47IyixGtCZuRnyZXE8MlnwuK3MdcbJSmZM5z1xgZjcC33D3X6blu1Ideh0BfQqRPul7adUBRAv83lmZHYlBJq8jTqCWJ/rqrmFmTxAtvoVP5ctFIGVmXyfeu4tpTS1XlVrtbtLUyO6+tkXqqJ/7wMjlMURGhL8Rk4kY0RJzf8W2qo7RvxMnNi+nMrcSwfRFnrJYVI2k73ZcdWNmvyMmsjjfK9IotnnM0sTrnaejK6dEm4e4qvUq0Y3npbT+ReJkPf+hc0qZPMzsKQZSQg3iWcaTmnXeiDSJThHop++QhXr9bhAZqaxGirb0/+3uvn5234nufmD6/xZ336ji8TOdmaJu1om0/u3ufkWHbV2d/i2CxyJIL75LtrIO6ddm5nnMSgqGS8zsW8Ql/k+6+/S0bhzRMvhvL6W7aZNiaZakjTGzt1atr2il7bSNV4nLl48VDy9ta8dS+UHpokqB2Enuvr9V5xt2b80z/FPgkCyAWAz4X29NFdMx+Elllqc1Vd2ZRID+UFamyN+6HHE2fGVa3obof9mSoqwbM5sEbJIFF/MQM4ytXyo32d3XM7O73f1NpXXzEaNvnyk9ZgmiX+B/snWbV9XDU05mi5RqfyYGJX2OGKz3rxSUXesp729qldiJaIXcimhRvbDqyy6V/TLRjxziNfta3lqZAu2tKE264e77WpuUcQNVn5Fr85E291elVrvN3TdIJ2VbEn0o7/csR63VTL9mZrcQ/XynMpDB5V5iUNzH3f0KM7vV3TfMt2mlPMpp3S+pOK6Iqyzruvtm1t8cwscRLTD30ZrndceszLuIrlF/SM9vRaJV+zc9vEYjIuWVzFnSb+iSxXe0me1KfHdDZHQZ2Wm3urAaKdrScq2gubS+1md3dkm/Yx0nYLEu6ddGInWTGGx7YDXPzhLc/R9m9nGiD8whMONy6iYMvqw4jvazxcysfCaz+YhZvibTOgtVcbC2S5i/ZY/7nGxmPyamboXodjDjDNPd909/62x3Lc8mn3D358ys/CH/t8dEDi+nL9CniZZ5ssc9BhwHHJcefwrREpq/7kUdJxOX0AvX1KhnlbmKQDjV4aUUEJe9mForHzKzA4nWuIXSfd8lWmkvKD1mU2Jyl49n2782fYGs6u6/tZgEJn9+HyGOxeWI9D5FjuvVybp0pED2TODMdPKxKxE8DwqGU9luk438193/amZjzGyMu1+dvvhmtBq2u1qS7adt63SFSdZ99sarzGwXomW209n9n4jE+femOq1ODDb9LPGeXAE8bpHo3s1sbuI1HtTKTBxT7Y6rIlhtaYmZSe8mMkW82KHM/xIDM4tuMSsTXb1+08N+ul0iFRmKbxLdhIoGi2OI43J+4nf0Y8NUr375JLNu1sJBs8D1k5ltBqzkKWe5mZ1HjMOBaAwpXy39JQMTsBQNOOVGtUNLy4/ROtPgiKNgeDCv+kH16MeZr5+HCHLmIlqSC/8gLrPOiorlfW4xs2WJud7LTicC93eQJcxP27i2x91+jPb9EFukIGIFWi/j5n07x5jZYsVlYYvJGMrHYNfgx6Iry3ZEy/DWRCByVF7GKyYtmEnPmNmO7n5RqsNORF+oskOIQX8HEymktiT69EKkn9m//AB3v9DM8r6mmNlHiEtMixMTMyxNtPoV09r+GzjWzA7xbNZAd7/JzFpaq7P7niP6mJ9Udb9FP/LPMvgkKj/Zet7MFiIG8J1hZk+TdeNITiBaYCvXWc0JTNK6On1lP0p0yXjZzP7DwOW68gx0qxWBcNr2fRbdfKbZwFStVWMADihtB3f/qUVXmeU8dSHKFMfevzymW54htYgNxTRigG6nYHh6EQhnj5me/v9Ozf1sDTM+m1Xb/2/FepFu1ic+p4Xp7n4QzOhaNqp5dDXcxFonYPl1RSDZNWhOvy3LuHvRXe0n6bsZIvf5efTXV4gB1oXXE2kgFyTGKpWfwzLuvm2dDVubmQZHInWTKEmXPy8o/zCb2Z7E1KblbgTLu/tjKUDA3f85G+tqpNnESuunpEvYU919rdTCdb1X9Efqsv2xaftdp001s9OJoO1OWi/jHpyV+RDx4SoChF2B//HUbyl94JcnUps9b2Yr0Dp1ddWgo195xaAjMzvH3d+Xul1UndzUmiks297KxIj/16VVTxAje4tWuCXSc1uFGJh0jJdm9TOz+939jW2233KfxUx2GxBp5YrL9TO6XmTlqrrpDOmympldQUzX+WkiKNwLeMZbpxhekBgMNYY4yVoEOCO1FhdXSw4l+kwXxhH9a4uuGydk981HBGB3uPugk0gzu8rdt+62rubz+wXRt/jstOr9xGCSDxKD8jYips7eo3oLLdvagWjtmsfdV7SY3vzoUreFvnWhMrPzie5DV9Hazzr/fP2A+PycQxzzuxLjHn6byl5grdOPF/5OtGL/yAf6xD9KXJF5jji5WJTolvMXIgvLZERqKn93mdma7n5P+r+vs9uNBtZh1kKLMSC7eRqQl34LtiaC01OH8t3XpS7lfswXeOpGaGY3uvumpfInASe4+910MZq6XalleLADgAssBprlWQbmpzrLwMJmNoV0WcFi5PRexQe9n6y1D+IYIntD1SCUovXmeYspXP9MjKTvidfLalCYSMxy0/bsyt1/ZtH3tmhp3Nnd7wOwmCb260R/xxXNbP+iFTZzOHHJ/zDvPuio6Nu9fZdytXhMz7xRh5OenxHHywlpn98lzq5zT5vZBu7ecjksteQ+Uyr7YuqKUZSZiyyIMbPdibzVK5pZ/jotzEBmgF69xt1/klqbryVGDd+eF8hOPF41s18Df83e81pXS4oWoey5LMpAgFqsm49oYR9vrVkQxhGttnnZugHz3kSml0PT8o1E4P9fonvBK2a2vJnN41mXmDaOIk5WrknP6U6LKU8xs+2IE7alzey72WPGEZlIhqJI69fJfESwWvQ3f4b43tqBOHYuIFqLJxAjwSFOCKYTqfhOZuCS7JXExBiXp+f0dqKf4KnElaENh/g8pJleNbPXehronAXCSxODPRslBb/tpsium5miXxYt1S0fT7NkRfnNgL0txn68yMCVuJ4amEYaBcMl7v4ksGHpzO1Sd7+qzUNOIqYxvhrAIhXZSUQLWb/lfRBfJgaNVWWtOCkFEF8mfkAXIvLtDsViwL1mVpnVIHMPMWPfU+02ZDGy/F53PzEtjzOzDd39ViJAWcPdn0lBxRmUfvy95kj9VPap1LJ9mtfrz9yRRQaE47118N9h7l6k8FrK3b+Y/r/czKpOUj4DnGNmp9F6ovUhWtPtQASiRTq9bYgg7uLs/puI13o80Ve0MJ0YIDYUxUnUUxaDsf7EwEneRkRe6L8R3T9OT/seY2YfcvfLsgD6tB6vlrxAayohiEuqhxIt8Xkr5HQieXxPAXOqx78ZmHK1rKjjNODGdIKRH+/llGL/dfe/20D3Chj4Uf8T8VndsaLu3dLbVerSLaMoU05lWGUTbx30eXHRMmRm92brN3L3j2TbvsLMvunuHzWzeYfyHKTRvkEca4cROWchuk19M90nAxbLFzxlm0gm0H8PmNm7vJTpwcy2J2YcLNuu08ZSkFxkl1jKzKZBa9aakUjBcBtdztxyCxaBcHrcNbPo7K34QZwHeANxsLX7Ufxx+vdaYgatGdpcJs0fW6TuWoU4K/xyqchbqA54xwP3paA5v4ybB80/oLUv6T+zdS95yrLg0Ydzpn9wU0vfq2a2iLvPbF7T7dz9C9m2nzOzd5Llsy0FZC15Xd39b+5+m5ltSAS2e6dy9wIb+uD0dp8nBkHeTQSGlxITKhT7f4zICLLxTD6v3NfMbBHgMKKFexwDwduJRDeQRYjPxXbufouZvYFoZbws207HqyWlY3AsMejvnFJdbkrr3uvuJ5jZXkTL5KPE1QGoETDnLAbxHcXg9GT5Z+QP6TaG1tbtsnst+v2NtcgtfXCqMx59uO8yszM8pWybWXm3DOJqwJsZ3C1jRaLv3wq0Pr/8M7hQfqXHzJZjYIBn3hr+lJl9jtYuJX9JJ5iNa8mTmePuP0/fA18jGpmc+O47wt17GeDZBEMZZDczPgn82szeS2s/5k2ouLKafnva8myAtI2wTBidqM/wTDKzC4kDKJ9AYT13r+pSMbP7eicxhe2g1EmlcksSXQ5e5+7bWYya3zhdAq9M2VXwgdRdlxCpUVr6BZnZm4ipXcuD+TqmAktl7nT3N5ceV/RrfprWS+W75cs+hHRUafu/IvL1XklrS19P2zOzqcD6nkbzp1a6Se6+Rlp+lAgSqkbjz7IzYouJVo4jusEY7QePzex+Zrx3Nrh/c8sXnpndROTkzq+WfN3dN0nLmzMQDBcTmDxZ2t8dwNvc/W8WKQXPJgK9NwNv9Mg7vT7Rd7sqYD7KS3mLzewB4ot/MgP92kmXIHt9PRYAvkhkAQG4nBh5/R9r3199yJcTLVLLbUWkb6rMf2yR9u4nxAnUjIC19Bl8J4PTr32C6O7xEXf/dio3nsjQsll66I3EQJu/E63T+UA9kVrMzLJuVcW6Ot2SGsNi/MkviUalQYPsfBakoUuNTx8lGgogTlTOJK4QXTMT21Uw3BRWYwKFPu7rAVpnFFuZGLH6hlK53zAwScjaFv1Np3hp8FWXfbV0qi/dN2ggV81tXkD86P4grfoE0Vfz3SmQacuHmB2i3XZ73V5qJduBeF0hJtO42N2P62EblYP5yIKkDmWAwQP/zOxhYAevmGSiVxaTIfyAyAe6ppmtRUzT/TXLBkJYaVBExXJVXt67iKsUxeWzlqdFfPH/gThmr8q3YTHz4DPuflRavtPd31wnYC7V4VZ379jXtc2Vk0EDzLpsYymPbjrLV93frWWlzTZvcfeNrDX/cUsO8DrPL5Wbl7i6BDExR9fnJNIPFrOG7u1psiIz2wA4ufx9IZ0H2c2i/d1DNOodT4w1OA6Y6O5Dvvpo2aQiI52C4SGy6K/4MQayB5ziszjtUDlANTMjJpAoT/xQ9AHMfziLAKJWsGVmD7n7qm3qMSNxuJnd4DHBwHSqW8HGZY9bghhYtlUqexUxCcczWZldvSIdVXldLyylpfHSZBdD2M62ZBNSeBpcVCrTdpYzKvqx5jz62VYGUHmZ0v4GjfYdKjO7lujX/KNy66OZvUK0rBvxRVnkNTZgPnefO9tOz1dL0uX3NYnMFGumL+Y3u/vL6SRwf0+Ty2R16howl/ZxLNEt4wJau/LckZX5DoMHmP2DOF7HufsHU7krgV29tQ/52e7+jmxbCzKQN3s1IgD9zVC+J8zsJ8Tn5fNE6/fBxLTWH8vKfABYlUgHV/n8UrluKRCLE6NPV5Sr3W9fpMzM3kGk+fsu8X24HbBf+RiV2S99Xx1HtEIvTIzbOc7dG9EtSn2Gh+6nxICj64kP9BsZGKU+q0wys0tpTZ10e7pUjrsXkzm8YGavSWWKwU9Fn9m62RUmWXW/pf1o7Z+5R9p3p/6VpDJPkw0US10Ntmcg1RpExohy4Fu1rqN0onAkcCDR/9PM7GUiJczRHR/chrtfBlyWvjR2NrNfu/u7SsW+T+ssZ28iBhfOmOWsyz5mBLvWOunG/FR/XidZpAz7Ja0BUHlijzoW8OjXnK97OW2vl4lkPkxcLSnqcH1a15a7v0L0sy3Srp1FDMZ7lkjldj3M6MteHMtjzWwuj365WxN5mQtVr1XRajox3zWtk9bUHWA23gdPIFPO2HId8JYUKF8B3E4E111Tt1U4iOiW8SJx+fJyov9l7k1ENoitGOgm0fL8rE0KRCIbSu5cojvFj7NyIjPF3S83s48R3daeBdbxlGFCht1/ie/a+YnMNI/0GgjbKJ5pUMHw0K3uA9Pt/oRZ07G9rE7qJIgBUBcBK1vkLJxASm3VwyXaQ4ELzWwPWjMfzENrirkLGZhM4Xx336XTRlML4DuIfMHbEPldz7X+p6P6JDGz2/ru/kja90rAD8zsk+7+rY6PHlzveYB3EenM3gGcTwQLZZ1mOXvIzAblRKa6Fb086cYyZJNuZMYRrbRvz9blx0Ivnk1db4qTqPfSITtIO6mL0JD6eLv7j9Lf/zGzq4ClgCt84BLWGAYSxNcJmPNt18kqUneA2aulcssz+IqLeUyRvS/wfXc/3iJnaG0VV6A29vaD8nYlZpLq1P+yawrE5GV3/0GXMiI9MbMvA+8D3gqsBVxjZod5KZOBDIvbgV8RE6SMJyY62sXde5koaNTONKhgeOhmXOpMl3Jn+Q69S+okMzuUOBDvIALm1xOB1oPFpdm63RrSGdwmZrYlcfkaqmfUyZ9420FiFoOmPsDAhBmbEj/cxeX2fqej+iCwjbvPmCXOI0vFnkQrXa1g2CK/6u5EsHk10YK2fof3otMsZ3eXL913cABp0o20nYcqWh7rptOq6wAiLeAbzOxJ4BGG0IrZr0vs7n5LxbrfZ//XCZjzei1CXC14a1p1LZGRIQ+cDwNuMLOWAWbpakDez/wLqdy1qdxbaG2ZTru0jYnXcN+0rtep2nu5AnUPkTO0nJmkXKZjCsTkYjP7BHGym19xGGoOaxGA1wAbeKQ5vNliVskfE9OGy/Da192L9K1PATuZWa9TQY/amQbVZ3iIsj6U0NqPcpaM5k/7bDvAKd3/TeLs6w1EK9KNRHB806z6EbMOA6uyMk8QM2H9APilu083s0c8S8GSyo0FTnf3D/ShXm1nNep0X0XZV4lAZO+shXmat8kOYV1mOfPWPt9L0Drt8R+z+2519w1tYDbBuYhZ2oo+3Z9NLY35RCxk2xpSy2za9oJEQPkvYiakM3p8/F1EK3Y5a8OwzlpmMYvbPQwEtR8E1vbWJPNdB5iZ2RjiSsvviFnrAG7JT7xSuc2J4PpGdz8uXZk4tJf3xrLBqukYuK3qM5buv4ZobbudNukNzexqYoBhpxSIWOQKLfN2x71IL9LldHf36V0Ly6hho3imQbUMD1GPfSj75WTSAKdUh6lmdiap76C7fxpmXNKfSATG+xCTcDzv2bTNZravu/8k37iZHevun++xTmub2T9IJwTpf2g9KTiPmHf9/cArFunOqgK4V8xsWetPqp1Oj+9l2+sS/Zx/a5E8/Gw6t+7tTYdZzgDMbEdi4ofXEa14ywP3MzByGLpPulFkj8gnYhmS9MN0ADGg5VfE9L0HEIHcVGIgRS9G6iX2lUvdeL5S7rZgqf99/hgz+ztwt6dc0B4D4j7r7ucAl7TbmQ9MQrKQmS3k7tPovftIL1egjqyxvaPq7LR8oirSD2Y2kcjIs3As2vPAh4f7RFn6ZtTONKiW4VHEOmSJKJVbhJiMYdP0d1Hix3yfrMylxMj9M9Ly94D53b3jQKeZqLsBWxBdDt5JDCjbl5jd759ZuZ8Rl4K7zQDWbX95y33LXZSyH/SwzU1S/XcB7gIudPeThrCdu4hBTb9Nrb5bAnu6+75ZmTHE6/P2VOfLgR/nfT1TS/pxxUnQUKWTk+eAm4k+yUXO4kPc/c4etrN4+vdgIsgfUZfYzexm4DPufkNa3hT4pmepgyymmN6YaPUtjtnJRHeJo9399FTuWGIA0C9oPU7/lm3rTUS3msXTtp4BPpR3o6lR556uQFnkGC+uPtzmgydz6ba/rdz9dxUnBcCQB2aKAGCRr/0Ady/6929G9Kcf1VP5SkjdEA8hGlLKMw1+t/j+HIkUDI8iFvmDDwTOdfd1LQY47evu26X7TyJaF6cTfU1vIS7fDsp5bJGd4CLgFGBb4Hl3P2Q2PY+50z53A97h7uOz+ypbt9z9K7OjbmVmNreXUmGlQPVtwOfcfevSfV1nOTOzSe4+MQXF66SWxpbcvKmrwn88siwUge+8PtDHuih3s89EHsi0jfxS/Fiiv9hy5e4BNbaTT8MJpdb/4b7EbmZrE8HpImnVc8TMeFOzMpcTAetf0vKS6TG7A9cVl/nqdCOwLpOP9JuZvY+Y2vYaBvoxf8bdz7OaYwXM7CvufqSZncpgPqtOlqUZrGIShnbd62R0skhB+gVaZxo81kf4TIMKhkeR1OfwJKL7w3OkAU6eMkRYDEYYT/SLvIlo6bun1Jq4eLbJhYmUXDcCR8Dsb70zs/k9BlOU1y+U6vPPwY+afVIL+rvL3TZSYHWRuy9fWt91ljMz+y3RbeQY4v16mhiUt0lW5hZiQol/puWFiEFiLYGUmf2A6N5wLq0tlLVb8Mo/RkP9cbJIoP+4uz+VljvOCDdcUrcQ3P0fZnaop1nX0n33lboTGZHwfvWqH/Iu+6mcfKS8rl/SydU2RWuwRX7t386q/Yn0ysy+TVzdOIsIlN4P/Af4OQzOiS2jk9nom2lQwfAoZB0GOKUf7zWIgHkTIhPE34CbU4tP3npXng2spWVrFtS7asKPYnavr7n7X81sTWKyhiJof5YeLy33k5l9jbhsvkPRKpta+E4H9nH335bK15nlbEEiFdgYItPAIkSXlTxgrur+UrVuplvwer0U32E7Pc0INxKY2R/dfbls+fvAcgzktd6FmPL5M8AlntKzWUzH/CmiBX1/M1sVeL27X5Jta7ZN1Z72Vx68Mga4y93fVDoJHqQ4UTGzT3Up11N3JZGcxQBOGPgdyH+H3DWpyxzBRuFMgxpANwpYDwOc0tnYPWlgwt/TbXsiTdeRPrwDY35DtJaemZZ3AxYA/gycRuRLPgn4VOnS8slEYD/bufuXzOxLwOUWuZDfDnwbeI8PpKHJXW1m36DDLGfuXgSer6Y+qn8tn0UTE6esWzzOzNYjAuhy/WY6tZr3bzDo2Kz19/3ASe5+PnC+9ZhfdzYqj0g7ANiZmF4d4kRtyfSe5XmKTyVa/4vj8kkigM4H1OWTjxRTtc/KbgaXpW4e+ex5l6b/JzMQdCxHXFkyYjzBH4k+0RBXiyDSMq5PdKWC+GzOjlzqMmfbjjjBXIGB+MN9iBMhyYh1DPF9lM802M80oH2nYHh0OJ2BAU4fIWaiMiIgu7MoZGYHM9Ai/F9SWjWiX/Ddqcz6xKXsP6flDxFfTo8x6y9lv610+f3u4pJ86ngPsGARCAO4+zWpJXXYuPvXzOxfREBhwFbu/nCb4m1nObOYCfBYoqX+q8T7Oh4YY2Yf8pjhrnAoMRnJn9I+X0sENy1S15nvECm+nDhGDvWUBm4263VGuJGg3K/ZLbKGbERMYvEIMcFK2cru/n4z2z097l/pqgw2eKKMw3wWTtVuMcnIku7+mTTwrQjkbyadKBcnwWZ2MjHw89K0vB3RZYdU7itp/XXAup5SX5nZUSgXrMy8XwLPE1dMijEJujw9h/FRONPgSP2BklYr+cAApx/TfoDTCkTr1CeLfpsVfkQM/iJdyj6WgUvZJ5FmqptFxprZBu5+W9r/+gykKStm1ZpmMUtRfml52iysU0dmdjEDLWoTgIeB/0txz6D8rN55lrMTiYEFixDZCrZz91vM7A1Ea96MYNjdb0/rX59WPdgmoDoT+B4DswLuRnRP6NhVYxbpaUa42aVi0NiMu0hThVrk8N493YosEdbh/XzJYhBqMVvfygxcCZjdU7V/m5iyvOgrfkGq05vSfTtkZTdy948UC+7+GzM7vmKbS9KagvCltE5kZizj7tsOdyVk1rJRONOgguHRIc81+oqZPVERCOPuHfv7JcN5KXs/4JQ0GMyAfwD7pZbfY1KZ/NIyzPpLy918s83/lazzLGdzufsVqdzRnmZYc/cHrJQ/1swOIPoRF3kaFzOz3d39+6VdLuCt6Wp+bmafqf/0+sd7nBFuNtZr4e6leIA41rYvWv3NrNPMh0cRJy/LmtkZRBrD4jLg7J6qfUl3v7u80t3vNrMVSqv/lLr9/Dwt70HM/lj2M+C21O8ZovX4tL7UVprsJjN7U9XxKnOUUTfToAbQjQL9GuCUtnUP8GaPBP4PAPu7+3XFfT4bZohJASPeOg3uiGRmy3k2M1yN8m1nObMOs/VVLFcNlpvig9MSHUd0oTmbgdHZixEptoY9t+9oYWbvJlrVNyWC3LOJvM5t+9ib2WuI7hRGNgNdt/d2FtT9IXdftc19D7v7Ktny4rSerF0HfKXqODGzdYn0bBBp5aaUy4j0wszuI7oPPUJcSSl+w5RneA5ko2imQQXDDWNmXyQmvXiWGEizbuonuQrwU3ffdBbue14GD54gHzyRLld/uqLMsIwyLgWw53vrDGZV5dtmgchOavITGtJyy0QgFpk31ipaVy3y/0519zVK2y76Buejswvumj63J+kqxU5Ed4mtiBbSC4sW/azcVT44x/RV7r51P09ea9b5LOB37n5yaf1+RKq1QX3NO2xrnEfKucrsEzq5kplhZstXrfeUHlTmDFaaaZDoJz6iZxpUN4mGGeZL2b8i+o5OJsu0UHIu8EPiksorbcrMTnlwWSew/LeZbeats5z9G3rO2nAZ8Asz+1Fa/iiRjYO03WIgZDEwakTm9B1tPLJGnAmcaWaLEYPoPgcU3VvmIzKgjE/3F8fHOGLU9HBM1X4ocKGZ7UF8tiAGcM7DQF9yoNbJ5plE9pki+8SMh6ZlnVzJkCnobYxTgE9460yDpxL9h0cktQzLbFOnG4aZTXb39WZXnbrp1LWhTfmus5zV3O8YIhtD0fo4FXitux9Q1IVRltN3TmBmhxDB5+uIdGpFMPwPIo/micNUNSym9S4+X/e6++8qytxFnGyWJ4UZsS02IjK6tOnSN6JnGlQwLLONxXTRJ1QNnsguyx5MzMh2Ia15eoelpbNL14a2l7ytwyxnPex7HeADxKjcacD5RbBl2UxmZvY94Bl3PyotD+qqIf1lZge5+wnDXY9edTvZTP2E23LNECYiXdgonGlQwbDMNp0GT1jrzHgwOP/rqL08a6VZzrqUrUrx9WkfPO3zsA+EbDoz24TB3Q1+NmwVqsEiX3Dbk02LGcIGzUyZldMMYSLSkY3CmQYVDI8CXfKk9n1AzqzSafCExXSNj3vKjzwn9YE1s8fdfdmaZV8lUnztm6X4mlY+GRjOgZACZnY6sDJwJwPdDdzdDx62StWQDbjMzRhoOSd/DkVk9khjK0bVTIMKhmWWqzNCfU7uA9tjy/C7qZniy2JGu2Ig5Atp3WrAQiPxMtScxMzuJ/IJz1FfoHPy51BEZo+UV/h5YqbBvLHg/4atUl0oGB6FzGwJYL5i2XvIgzsczOwSd9++oisEpFap0d4Htkvr/fzu3lPmlropvmR4mNm5wMHefqbHEcvM1gRWp/U75GfpvlH9ORSR4Tcau+optdooYmY7Av9LjGR/GlgeuB9Yo9Pjhpu7b5/+tp3AgJiqeS53f5nIoLB/dt+IP0693ixnvWyvY4ovGXbjgfvM7DZa+97u2P4hw8/MjgS2IILhS4npom8gTrZglH8ORWREGHUzDerLbXT5KjHj1W/dfZ2USmnPYa5TVzVHqJ8FXGtmzxJ5eYv8hKsQuYkby92fA05KNxkZjhruCgzRe4G1gSnuvo+ZLcnA1Mygz6GIzLzNgL3T1eBRMdOgukmMImY2yd0nplyh67j7q/llzZEqG1k6HzEZwF3Eh2MtYJK7b5zKqQ+syCxkZre5+wZmNhnYEpgO3O/ub8jK6HMoIkM2GmcaVMvw6PK8mS0EXAecYWZPMzDt64jl7lsCmNkFRNaDu9PymmQtbO5+S8Vjfz+bqinS1RyQ2WWSmS0KnExMvPFP4Oa8gD6HIjIzRnLQ245ahkeRNKjqP8QP7x7ELGdnuPtfh7ViNZnZve6+Rrd1IjLrmdkKwDjvcXZEEZE5jYJhmW3M7CyiJbvoo7gHcel19+GrlUhzmNlV7r51t3UiIk2ibhKjiJntDBwHLEG0Do+WS7OFfYCPA4ek5euAHwxfdUSaISXBXwAYn7KTFOkNxwFLD1vFRERGALUMjyJm9jCwg7vfP9x1EZHRw8wOAQ4l0jI+md01HTjZ3U8cjnqJiIwEY4a7AtKTv4zmQNjMVjWz88zsPjObVtyGu14iDXATsAnw6TT18leAe4BriXzWIiKNpZbhUcTMvgO8FvglrYn+LxiuOvXCzG4AjgS+BexAdJsY4+5HDGvFROZwmmZZRKQ9BcOjiJmdWrHa3f3Ds70yQ2Bmk919PTO7293flK8b7rqJzMk0zbKISHsaQDeKuPs+w12HmfSimY0BHjKzA4m+iwsNc51EmkDTLIuItKE+w6OImS1jZhea2dPpdr6ZLTPc9erBIcSI9oOB9YippPca1hqJNEMxzfKv0DTLIiIt1E1iFDGzK4nBLqenVXsCe7j7NsNXq96Z2QLu/q/hrodIk2iaZRGRagqGR5Gqvn2jqb+fmW0M/IT48V3OzNYGPurunxjmqomIiEhDqZvE6PJXM9vTzMam257AqJiKOfk28A5Snd39LuCtw1khERERaTYFw6PLh4H3AX8GngLeS6QnGzXc/fHSqleGpSIiIiIiaBTxqOLujwE7Dnc9ZsLjZrYJ4GY2NzGgbtROIiIiIiKjn/oMjwJm9ll3P97MTgAGvWHufvAwVKtnZjYe+A7wNsCAK4BD3H00dfUQERGROYhahkeHovV00rDWYia5+7PAHsNdDxEREZGCguFRwN0vTv/+y93Pze8zs12HoUo9adeiXRgtLdsiIiIy51E3iVHEzO5w93W7rRtpzCyfWOMrwJH5/e7+09lbIxEREZGgYHgUMLPtgHcSmSR+kd01Dljd3TcYlooNgZlNcfd1hrseIiIiIqBuEqPFn4j+wjsCk7P104FPDkuNhk5nXyIiIjJiqGV4FDGzud39v8Ndj5kxGrp1iIiISHOoZXh0WcHMjgFWB+YrVrr7SsNXpe7MbDoDLcILmNk/irsAd/dxw1MzERERaToFw6PLqcTgs28BWxKzz434WQTdfeHhroOIiIhIFXWTGEXMbLK7r2dmd7v7m/J1w103ERERkdFILcOjy4tmNgZ4yMwOBJ4EFhrmOomIiIiMWmoZHkXMbH1iNrpFga8CiwDHu/stw1kvERERkdFKwbCIiIiINJa6SYwiZrYa8BlgebL3zt23GrZKiYiIiIxiahkeRczsLuCHxMQbrxTr3X1y2weJiIiISFsKhkcRZY4QERER6S8Fw6OImR0FPA1cCLxYrHf3vw1XnURERERGMwXDo4iZPVKx2kf6DHQiIiIiI5WC4VEi5Rfe1d1/Mdx1EREREZlTKBgeRcxskrtPHO56iIiIiMwpFAyPImZ2LPAs8AvghWK9+gyLiIiIDI2C4VFEfYZFRERE+kvBsIiIiIg01pjhroDUZ2YLmNmXzOyktLyqmW0/3PUSERERGa0UDI8upwIvAZuk5SeBrw1fdURERERGNwXDo8vK7n488F8Ad/8XYMNbJREREZHRS8Hw6PKSmc0POICZrUw2E52IiIiI9Gau4a6A9OQo4DJgWTM7A9gU2GdYayQiIiIyiimbxChjZq8BNiK6R9zi7s8Oc5VERERERi0Fw6OImV3l7lt3WyciIiIi9aibxChgZvMBCwDjzWwxBgbNjQOWHraKiYiIiIxyCoZHh48ChwKvAyYzEAz/AzhxmOokIiIiMuqpm8QoYmYHufsJw10PERERkTmFguFRxsw2AVYga9V3958NW4VERERERjF1kxhFzOx0YGXgTuCVtNoBBcMiIiIiQ6CW4VHEzO4HVne9aSIiIiJ9oRnoRpd7gNcOdyVERERE5hTqJjG6jAfuM7PbyKZhdvcdh69KIiIiIqOXguHR5ajhroCIiIjInER9hkVERESksdQyPAqY2XQia8SguwB393GzuUoiIiIicwS1DIuIiIhIYymbhIiIiIg0loJhEREREWksBcMiIiIi0lgKhkVERESksRQMi4iIiEhj/T+X2h4FB6X41AAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 864x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plot_bar_chart(def_pfi_g, \"def_pfi_with_stats\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 105,
   "id": "32dadbc4",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plot_bar_chart(mid_pfi_g, \"mid_pfi_with_stats\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 106,
   "id": "e68ddc17",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plot_bar_chart(fw_pfi_g, \"fw_pfi_with_stats\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "937dc273",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "a6ff964e",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
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