Machine Learning Project.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "bb97d86c",
   "metadata": {},
   "source": [
    "<h1 align='center'>MACHINE LEARNING PROJECT</h1>\n",
    "<h2 align='center'>EUROPEAN SOCCER REGRESSION ANALYSIS</h2>"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "abb77255",
   "metadata": {},
   "source": [
    "## Packages"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "2f3a2414",
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "\n",
    "import sqlite3\n",
    "from sklearn.tree import DecisionTreeRegressor\n",
    "from sklearn.linear_model import LinearRegression\n",
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn.metrics import mean_squared_error\n",
    "from math import sqrt\n",
    "from sklearn.metrics import mean_absolute_error\n",
    "\n",
    "pd.set_option('display.max_columns',80)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b184ff49",
   "metadata": {},
   "source": [
    "### Fetching Data from Database"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "8f37fcf2",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "\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>id</th>\n",
       "      <th>player_fifa_api_id</th>\n",
       "      <th>player_api_id</th>\n",
       "      <th>date</th>\n",
       "      <th>overall_rating</th>\n",
       "      <th>potential</th>\n",
       "      <th>preferred_foot</th>\n",
       "      <th>attacking_work_rate</th>\n",
       "      <th>defensive_work_rate</th>\n",
       "      <th>crossing</th>\n",
       "      <th>finishing</th>\n",
       "      <th>heading_accuracy</th>\n",
       "      <th>short_passing</th>\n",
       "      <th>volleys</th>\n",
       "      <th>dribbling</th>\n",
       "      <th>curve</th>\n",
       "      <th>free_kick_accuracy</th>\n",
       "      <th>long_passing</th>\n",
       "      <th>ball_control</th>\n",
       "      <th>acceleration</th>\n",
       "      <th>sprint_speed</th>\n",
       "      <th>agility</th>\n",
       "      <th>reactions</th>\n",
       "      <th>balance</th>\n",
       "      <th>shot_power</th>\n",
       "      <th>jumping</th>\n",
       "      <th>stamina</th>\n",
       "      <th>strength</th>\n",
       "      <th>long_shots</th>\n",
       "      <th>aggression</th>\n",
       "      <th>interceptions</th>\n",
       "      <th>positioning</th>\n",
       "      <th>vision</th>\n",
       "      <th>penalties</th>\n",
       "      <th>marking</th>\n",
       "      <th>standing_tackle</th>\n",
       "      <th>sliding_tackle</th>\n",
       "      <th>gk_diving</th>\n",
       "      <th>gk_handling</th>\n",
       "      <th>gk_kicking</th>\n",
       "      <th>gk_positioning</th>\n",
       "      <th>gk_reflexes</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>218353</td>\n",
       "      <td>505942</td>\n",
       "      <td>2016-02-18 00:00:00</td>\n",
       "      <td>67.0</td>\n",
       "      <td>71.0</td>\n",
       "      <td>right</td>\n",
       "      <td>medium</td>\n",
       "      <td>medium</td>\n",
       "      <td>49.0</td>\n",
       "      <td>44.0</td>\n",
       "      <td>71.0</td>\n",
       "      <td>61.0</td>\n",
       "      <td>44.0</td>\n",
       "      <td>51.0</td>\n",
       "      <td>45.0</td>\n",
       "      <td>39.0</td>\n",
       "      <td>64.0</td>\n",
       "      <td>49.0</td>\n",
       "      <td>60.0</td>\n",
       "      <td>64.0</td>\n",
       "      <td>59.0</td>\n",
       "      <td>47.0</td>\n",
       "      <td>65.0</td>\n",
       "      <td>55.0</td>\n",
       "      <td>58.0</td>\n",
       "      <td>54.0</td>\n",
       "      <td>76.0</td>\n",
       "      <td>35.0</td>\n",
       "      <td>71.0</td>\n",
       "      <td>70.0</td>\n",
       "      <td>45.0</td>\n",
       "      <td>54.0</td>\n",
       "      <td>48.0</td>\n",
       "      <td>65.0</td>\n",
       "      <td>69.0</td>\n",
       "      <td>69.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>11.0</td>\n",
       "      <td>10.0</td>\n",
       "      <td>8.0</td>\n",
       "      <td>8.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>218353</td>\n",
       "      <td>505942</td>\n",
       "      <td>2015-11-19 00:00:00</td>\n",
       "      <td>67.0</td>\n",
       "      <td>71.0</td>\n",
       "      <td>right</td>\n",
       "      <td>medium</td>\n",
       "      <td>medium</td>\n",
       "      <td>49.0</td>\n",
       "      <td>44.0</td>\n",
       "      <td>71.0</td>\n",
       "      <td>61.0</td>\n",
       "      <td>44.0</td>\n",
       "      <td>51.0</td>\n",
       "      <td>45.0</td>\n",
       "      <td>39.0</td>\n",
       "      <td>64.0</td>\n",
       "      <td>49.0</td>\n",
       "      <td>60.0</td>\n",
       "      <td>64.0</td>\n",
       "      <td>59.0</td>\n",
       "      <td>47.0</td>\n",
       "      <td>65.0</td>\n",
       "      <td>55.0</td>\n",
       "      <td>58.0</td>\n",
       "      <td>54.0</td>\n",
       "      <td>76.0</td>\n",
       "      <td>35.0</td>\n",
       "      <td>71.0</td>\n",
       "      <td>70.0</td>\n",
       "      <td>45.0</td>\n",
       "      <td>54.0</td>\n",
       "      <td>48.0</td>\n",
       "      <td>65.0</td>\n",
       "      <td>69.0</td>\n",
       "      <td>69.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>11.0</td>\n",
       "      <td>10.0</td>\n",
       "      <td>8.0</td>\n",
       "      <td>8.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   id  player_fifa_api_id  player_api_id                 date  overall_rating  \\\n",
       "0   1              218353         505942  2016-02-18 00:00:00            67.0   \n",
       "1   2              218353         505942  2015-11-19 00:00:00            67.0   \n",
       "\n",
       "   potential preferred_foot attacking_work_rate defensive_work_rate  crossing  \\\n",
       "0       71.0          right              medium              medium      49.0   \n",
       "1       71.0          right              medium              medium      49.0   \n",
       "\n",
       "   finishing  heading_accuracy  short_passing  volleys  dribbling  curve  \\\n",
       "0       44.0              71.0           61.0     44.0       51.0   45.0   \n",
       "1       44.0              71.0           61.0     44.0       51.0   45.0   \n",
       "\n",
       "   free_kick_accuracy  long_passing  ball_control  acceleration  sprint_speed  \\\n",
       "0                39.0          64.0          49.0          60.0          64.0   \n",
       "1                39.0          64.0          49.0          60.0          64.0   \n",
       "\n",
       "   agility  reactions  balance  shot_power  jumping  stamina  strength  \\\n",
       "0     59.0       47.0     65.0        55.0     58.0     54.0      76.0   \n",
       "1     59.0       47.0     65.0        55.0     58.0     54.0      76.0   \n",
       "\n",
       "   long_shots  aggression  interceptions  positioning  vision  penalties  \\\n",
       "0        35.0        71.0           70.0         45.0    54.0       48.0   \n",
       "1        35.0        71.0           70.0         45.0    54.0       48.0   \n",
       "\n",
       "   marking  standing_tackle  sliding_tackle  gk_diving  gk_handling  \\\n",
       "0     65.0             69.0            69.0        6.0         11.0   \n",
       "1     65.0             69.0            69.0        6.0         11.0   \n",
       "\n",
       "   gk_kicking  gk_positioning  gk_reflexes  \n",
       "0        10.0             8.0          8.0  \n",
       "1        10.0             8.0          8.0  "
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "db = sqlite3.connect('Data/database.sqlite')\n",
    "df = pd.read_sql_query(\"SELECT * FROM Player_Attributes\", db)\n",
    "df.head(2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "6feea7de",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(183978, 42)"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "04800e64",
   "metadata": {},
   "outputs": [],
   "source": [
    "df.to_csv('European Soccer Data.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "1d5c4503",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "preferred_foot\n",
      "Unique Elements : ['right' 'left' None] \n",
      "No.of Unique Elements : 3\n",
      "\n",
      "attacking_work_rate\n",
      "Unique Elements : ['medium' 'high' None 'low' 'None' 'le' 'norm' 'stoc' 'y'] \n",
      "No.of Unique Elements : 9\n",
      "\n",
      "defensive_work_rate\n",
      "Unique Elements : ['medium' 'high' 'low' '_0' None '5' 'ean' 'o' '1' 'ormal' '7' '2' '8' '4'\n",
      " 'tocky' '0' '3' '6' '9' 'es'] \n",
      "No.of Unique Elements : 20\n"
     ]
    }
   ],
   "source": [
    "# Checking for Categoriccal Features\n",
    "\n",
    "for i in df.columns:\n",
    "    if (i == 'date') :\n",
    "        continue\n",
    "    elif df[i].dtypes == object:\n",
    "        print(f'\\n{i}\\nUnique Elements : {df[i].unique()} \\nNo.of Unique Elements : {len(df[i].unique())}')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "f2551589",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Count</th>\n",
       "      <th>%</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>attacking_work_rate</th>\n",
       "      <td>3230</td>\n",
       "      <td>1.755645</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>agility</th>\n",
       "      <td>2713</td>\n",
       "      <td>1.474633</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>jumping</th>\n",
       "      <td>2713</td>\n",
       "      <td>1.474633</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>curve</th>\n",
       "      <td>2713</td>\n",
       "      <td>1.474633</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>volleys</th>\n",
       "      <td>2713</td>\n",
       "      <td>1.474633</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>balance</th>\n",
       "      <td>2713</td>\n",
       "      <td>1.474633</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>vision</th>\n",
       "      <td>2713</td>\n",
       "      <td>1.474633</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>sliding_tackle</th>\n",
       "      <td>2713</td>\n",
       "      <td>1.474633</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>standing_tackle</th>\n",
       "      <td>836</td>\n",
       "      <td>0.454402</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>marking</th>\n",
       "      <td>836</td>\n",
       "      <td>0.454402</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                     Count         %\n",
       "attacking_work_rate   3230  1.755645\n",
       "agility               2713  1.474633\n",
       "jumping               2713  1.474633\n",
       "curve                 2713  1.474633\n",
       "volleys               2713  1.474633\n",
       "balance               2713  1.474633\n",
       "vision                2713  1.474633\n",
       "sliding_tackle        2713  1.474633\n",
       "standing_tackle        836  0.454402\n",
       "marking                836  0.454402"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Checking for NULL Values\n",
    "\n",
    "df_null = pd.DataFrame()\n",
    "df_null['Count'] = df.isnull().sum()\n",
    "df_null['%'] = (df_null['Count']/df.shape[0]) * 100\n",
    "df_null.sort_values(by='%', inplace=True, ascending=False)\n",
    "df_null.head(10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "71b21143",
   "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>mean</th>\n",
       "      <th>50%</th>\n",
       "      <th>difference</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>id</th>\n",
       "      <td>91989.500000</td>\n",
       "      <td>91989.5</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>player_fifa_api_id</th>\n",
       "      <td>165671.524291</td>\n",
       "      <td>183488.0</td>\n",
       "      <td>17816.475709</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>player_api_id</th>\n",
       "      <td>135900.617324</td>\n",
       "      <td>77741.0</td>\n",
       "      <td>58159.617324</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>overall_rating</th>\n",
       "      <td>68.600015</td>\n",
       "      <td>69.0</td>\n",
       "      <td>0.399985</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>potential</th>\n",
       "      <td>73.460353</td>\n",
       "      <td>74.0</td>\n",
       "      <td>0.539647</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>crossing</th>\n",
       "      <td>55.086883</td>\n",
       "      <td>59.0</td>\n",
       "      <td>3.913117</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>finishing</th>\n",
       "      <td>49.921078</td>\n",
       "      <td>53.0</td>\n",
       "      <td>3.078922</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>heading_accuracy</th>\n",
       "      <td>57.266023</td>\n",
       "      <td>60.0</td>\n",
       "      <td>2.733977</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>short_passing</th>\n",
       "      <td>62.429672</td>\n",
       "      <td>65.0</td>\n",
       "      <td>2.570328</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>volleys</th>\n",
       "      <td>49.468436</td>\n",
       "      <td>52.0</td>\n",
       "      <td>2.531564</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                             mean       50%    difference\n",
       "id                   91989.500000   91989.5      0.000000\n",
       "player_fifa_api_id  165671.524291  183488.0  17816.475709\n",
       "player_api_id       135900.617324   77741.0  58159.617324\n",
       "overall_rating          68.600015      69.0      0.399985\n",
       "potential               73.460353      74.0      0.539647\n",
       "crossing                55.086883      59.0      3.913117\n",
       "finishing               49.921078      53.0      3.078922\n",
       "heading_accuracy        57.266023      60.0      2.733977\n",
       "short_passing           62.429672      65.0      2.570328\n",
       "volleys                 49.468436      52.0      2.531564"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_mean_median = df.describe().T[['mean', '50%']]\n",
    "df_mean_median['difference'] = abs(df_mean_median['mean'] - df_mean_median['50%'])\n",
    "df_mean_median.head(10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "98090fd1",
   "metadata": {},
   "outputs": [],
   "source": [
    "df_shape_before_dropping = df.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "e860de3e",
   "metadata": {},
   "outputs": [],
   "source": [
    "df.dropna(inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "f51e71f6",
   "metadata": {},
   "outputs": [],
   "source": [
    "df_shape_after_dropping = df.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "cfd3c92b",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Drop(%) : 1.97%\n"
     ]
    }
   ],
   "source": [
    "drop_percentage = ((df_shape_before_dropping[0] - df_shape_after_dropping[0])/ df_shape_before_dropping[0])*100\n",
    "print(f'Drop(%) : {round(drop_percentage,2)}%')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "28620694",
   "metadata": {},
   "outputs": [
    {
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       "<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>id</th>\n",
       "      <th>player_fifa_api_id</th>\n",
       "      <th>player_api_id</th>\n",
       "      <th>date</th>\n",
       "      <th>overall_rating</th>\n",
       "      <th>potential</th>\n",
       "      <th>preferred_foot</th>\n",
       "      <th>attacking_work_rate</th>\n",
       "      <th>defensive_work_rate</th>\n",
       "      <th>crossing</th>\n",
       "      <th>finishing</th>\n",
       "      <th>heading_accuracy</th>\n",
       "      <th>short_passing</th>\n",
       "      <th>volleys</th>\n",
       "      <th>dribbling</th>\n",
       "      <th>curve</th>\n",
       "      <th>free_kick_accuracy</th>\n",
       "      <th>long_passing</th>\n",
       "      <th>ball_control</th>\n",
       "      <th>acceleration</th>\n",
       "      <th>sprint_speed</th>\n",
       "      <th>agility</th>\n",
       "      <th>reactions</th>\n",
       "      <th>balance</th>\n",
       "      <th>shot_power</th>\n",
       "      <th>jumping</th>\n",
       "      <th>stamina</th>\n",
       "      <th>strength</th>\n",
       "      <th>long_shots</th>\n",
       "      <th>aggression</th>\n",
       "      <th>interceptions</th>\n",
       "      <th>positioning</th>\n",
       "      <th>vision</th>\n",
       "      <th>penalties</th>\n",
       "      <th>marking</th>\n",
       "      <th>standing_tackle</th>\n",
       "      <th>sliding_tackle</th>\n",
       "      <th>gk_diving</th>\n",
       "      <th>gk_handling</th>\n",
       "      <th>gk_kicking</th>\n",
       "      <th>gk_positioning</th>\n",
       "      <th>gk_reflexes</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>218353</td>\n",
       "      <td>505942</td>\n",
       "      <td>2016-02-18 00:00:00</td>\n",
       "      <td>67.0</td>\n",
       "      <td>71.0</td>\n",
       "      <td>right</td>\n",
       "      <td>medium</td>\n",
       "      <td>medium</td>\n",
       "      <td>49.0</td>\n",
       "      <td>44.0</td>\n",
       "      <td>71.0</td>\n",
       "      <td>61.0</td>\n",
       "      <td>44.0</td>\n",
       "      <td>51.0</td>\n",
       "      <td>45.0</td>\n",
       "      <td>39.0</td>\n",
       "      <td>64.0</td>\n",
       "      <td>49.0</td>\n",
       "      <td>60.0</td>\n",
       "      <td>64.0</td>\n",
       "      <td>59.0</td>\n",
       "      <td>47.0</td>\n",
       "      <td>65.0</td>\n",
       "      <td>55.0</td>\n",
       "      <td>58.0</td>\n",
       "      <td>54.0</td>\n",
       "      <td>76.0</td>\n",
       "      <td>35.0</td>\n",
       "      <td>71.0</td>\n",
       "      <td>70.0</td>\n",
       "      <td>45.0</td>\n",
       "      <td>54.0</td>\n",
       "      <td>48.0</td>\n",
       "      <td>65.0</td>\n",
       "      <td>69.0</td>\n",
       "      <td>69.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>11.0</td>\n",
       "      <td>10.0</td>\n",
       "      <td>8.0</td>\n",
       "      <td>8.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   id  player_fifa_api_id  player_api_id                 date  overall_rating  \\\n",
       "0   1              218353         505942  2016-02-18 00:00:00            67.0   \n",
       "\n",
       "   potential preferred_foot attacking_work_rate defensive_work_rate  crossing  \\\n",
       "0       71.0          right              medium              medium      49.0   \n",
       "\n",
       "   finishing  heading_accuracy  short_passing  volleys  dribbling  curve  \\\n",
       "0       44.0              71.0           61.0     44.0       51.0   45.0   \n",
       "\n",
       "   free_kick_accuracy  long_passing  ball_control  acceleration  sprint_speed  \\\n",
       "0                39.0          64.0          49.0          60.0          64.0   \n",
       "\n",
       "   agility  reactions  balance  shot_power  jumping  stamina  strength  \\\n",
       "0     59.0       47.0     65.0        55.0     58.0     54.0      76.0   \n",
       "\n",
       "   long_shots  aggression  interceptions  positioning  vision  penalties  \\\n",
       "0        35.0        71.0           70.0         45.0    54.0       48.0   \n",
       "\n",
       "   marking  standing_tackle  sliding_tackle  gk_diving  gk_handling  \\\n",
       "0     65.0             69.0            69.0        6.0         11.0   \n",
       "\n",
       "   gk_kicking  gk_positioning  gk_reflexes  \n",
       "0        10.0             8.0          8.0  "
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head(1)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "4cb37ab6",
   "metadata": {},
   "source": [
    "### Converting Categorical Features to Numerical"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "bf98a619",
   "metadata": {},
   "outputs": [],
   "source": [
    "foot_map = {'right': 1, 'left': 0}\n",
    "df['preferred_foot'] = df['preferred_foot'].map(foot_map)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "baab6413",
   "metadata": {},
   "outputs": [],
   "source": [
    "attacking_work_rate_map = {'high': 5, 'medium':4, 'low':3, 'None':2, 'norm':1, 'y':1,'le':1, 'stoc':1}\n",
    "df['attacking_work_rate'] = df['attacking_work_rate'].map(attacking_work_rate_map)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "693c1085",
   "metadata": {},
   "outputs": [],
   "source": [
    "defensive_work_rate_map = {'high':4, 'medium':3, 'low' : 2, '_0' : 1, 'o' : 1, '1' : 1, 'ormal' : 1, '2' : 1, '3' : 1, \n",
    "                           '5' : 1, '7' : 1 ,'6' : 1, '0' : 1, '9' : 1, '4' : 1, 'es' : 1, 'ean' : 1, 'tocky' : 1, '8' : 1}\n",
    "\n",
    "df['defensive_work_rate'] = df['defensive_work_rate'].map(defensive_work_rate_map)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "9c789ca7",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "overall_rating\tNo.of Unique Elements : 61\n",
      "potential\tNo.of Unique Elements : 56\n",
      "crossing\tNo.of Unique Elements : 95\n",
      "finishing\tNo.of Unique Elements : 97\n",
      "heading_accuracy\tNo.of Unique Elements : 96\n",
      "short_passing\tNo.of Unique Elements : 95\n",
      "volleys\tNo.of Unique Elements : 93\n",
      "dribbling\tNo.of Unique Elements : 97\n",
      "curve\tNo.of Unique Elements : 92\n",
      "free_kick_accuracy\tNo.of Unique Elements : 97\n",
      "long_passing\tNo.of Unique Elements : 95\n",
      "ball_control\tNo.of Unique Elements : 93\n",
      "acceleration\tNo.of Unique Elements : 86\n",
      "sprint_speed\tNo.of Unique Elements : 85\n",
      "agility\tNo.of Unique Elements : 81\n",
      "reactions\tNo.of Unique Elements : 78\n",
      "balance\tNo.of Unique Elements : 81\n",
      "shot_power\tNo.of Unique Elements : 96\n",
      "jumping\tNo.of Unique Elements : 79\n",
      "stamina\tNo.of Unique Elements : 84\n",
      "strength\tNo.of Unique Elements : 82\n",
      "long_shots\tNo.of Unique Elements : 96\n",
      "aggression\tNo.of Unique Elements : 91\n",
      "interceptions\tNo.of Unique Elements : 96\n",
      "positioning\tNo.of Unique Elements : 94\n",
      "vision\tNo.of Unique Elements : 97\n",
      "penalties\tNo.of Unique Elements : 94\n",
      "marking\tNo.of Unique Elements : 94\n",
      "standing_tackle\tNo.of Unique Elements : 95\n",
      "sliding_tackle\tNo.of Unique Elements : 94\n",
      "gk_diving\tNo.of Unique Elements : 93\n",
      "gk_handling\tNo.of Unique Elements : 89\n",
      "gk_kicking\tNo.of Unique Elements : 97\n",
      "gk_positioning\tNo.of Unique Elements : 94\n",
      "gk_reflexes\tNo.of Unique Elements : 92\n"
     ]
    }
   ],
   "source": [
    "for i in df.columns:\n",
    "    if (i == 'date') :\n",
    "        continue\n",
    "    elif df[i].dtypes == int or df[i].dtypes == float:\n",
    "        print(f'{i}\\tNo.of Unique Elements : {len(df[i].unique())}')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "f20d1591",
   "metadata": {},
   "outputs": [],
   "source": [
    "df.drop(['id','player_fifa_api_id', 'player_api_id', 'date'], axis=1, inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "81df00b8",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>overall_rating</th>\n",
       "      <th>potential</th>\n",
       "      <th>preferred_foot</th>\n",
       "      <th>attacking_work_rate</th>\n",
       "      <th>defensive_work_rate</th>\n",
       "      <th>crossing</th>\n",
       "      <th>finishing</th>\n",
       "      <th>heading_accuracy</th>\n",
       "      <th>short_passing</th>\n",
       "      <th>volleys</th>\n",
       "      <th>dribbling</th>\n",
       "      <th>curve</th>\n",
       "      <th>free_kick_accuracy</th>\n",
       "      <th>long_passing</th>\n",
       "      <th>ball_control</th>\n",
       "      <th>acceleration</th>\n",
       "      <th>sprint_speed</th>\n",
       "      <th>agility</th>\n",
       "      <th>reactions</th>\n",
       "      <th>balance</th>\n",
       "      <th>shot_power</th>\n",
       "      <th>jumping</th>\n",
       "      <th>stamina</th>\n",
       "      <th>strength</th>\n",
       "      <th>long_shots</th>\n",
       "      <th>aggression</th>\n",
       "      <th>interceptions</th>\n",
       "      <th>positioning</th>\n",
       "      <th>vision</th>\n",
       "      <th>penalties</th>\n",
       "      <th>marking</th>\n",
       "      <th>standing_tackle</th>\n",
       "      <th>sliding_tackle</th>\n",
       "      <th>gk_diving</th>\n",
       "      <th>gk_handling</th>\n",
       "      <th>gk_kicking</th>\n",
       "      <th>gk_positioning</th>\n",
       "      <th>gk_reflexes</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>67.0</td>\n",
       "      <td>71.0</td>\n",
       "      <td>1</td>\n",
       "      <td>4</td>\n",
       "      <td>3</td>\n",
       "      <td>49.0</td>\n",
       "      <td>44.0</td>\n",
       "      <td>71.0</td>\n",
       "      <td>61.0</td>\n",
       "      <td>44.0</td>\n",
       "      <td>51.0</td>\n",
       "      <td>45.0</td>\n",
       "      <td>39.0</td>\n",
       "      <td>64.0</td>\n",
       "      <td>49.0</td>\n",
       "      <td>60.0</td>\n",
       "      <td>64.0</td>\n",
       "      <td>59.0</td>\n",
       "      <td>47.0</td>\n",
       "      <td>65.0</td>\n",
       "      <td>55.0</td>\n",
       "      <td>58.0</td>\n",
       "      <td>54.0</td>\n",
       "      <td>76.0</td>\n",
       "      <td>35.0</td>\n",
       "      <td>71.0</td>\n",
       "      <td>70.0</td>\n",
       "      <td>45.0</td>\n",
       "      <td>54.0</td>\n",
       "      <td>48.0</td>\n",
       "      <td>65.0</td>\n",
       "      <td>69.0</td>\n",
       "      <td>69.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>11.0</td>\n",
       "      <td>10.0</td>\n",
       "      <td>8.0</td>\n",
       "      <td>8.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>67.0</td>\n",
       "      <td>71.0</td>\n",
       "      <td>1</td>\n",
       "      <td>4</td>\n",
       "      <td>3</td>\n",
       "      <td>49.0</td>\n",
       "      <td>44.0</td>\n",
       "      <td>71.0</td>\n",
       "      <td>61.0</td>\n",
       "      <td>44.0</td>\n",
       "      <td>51.0</td>\n",
       "      <td>45.0</td>\n",
       "      <td>39.0</td>\n",
       "      <td>64.0</td>\n",
       "      <td>49.0</td>\n",
       "      <td>60.0</td>\n",
       "      <td>64.0</td>\n",
       "      <td>59.0</td>\n",
       "      <td>47.0</td>\n",
       "      <td>65.0</td>\n",
       "      <td>55.0</td>\n",
       "      <td>58.0</td>\n",
       "      <td>54.0</td>\n",
       "      <td>76.0</td>\n",
       "      <td>35.0</td>\n",
       "      <td>71.0</td>\n",
       "      <td>70.0</td>\n",
       "      <td>45.0</td>\n",
       "      <td>54.0</td>\n",
       "      <td>48.0</td>\n",
       "      <td>65.0</td>\n",
       "      <td>69.0</td>\n",
       "      <td>69.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>11.0</td>\n",
       "      <td>10.0</td>\n",
       "      <td>8.0</td>\n",
       "      <td>8.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   overall_rating  potential  preferred_foot  attacking_work_rate  \\\n",
       "0            67.0       71.0               1                    4   \n",
       "1            67.0       71.0               1                    4   \n",
       "\n",
       "   defensive_work_rate  crossing  finishing  heading_accuracy  short_passing  \\\n",
       "0                    3      49.0       44.0              71.0           61.0   \n",
       "1                    3      49.0       44.0              71.0           61.0   \n",
       "\n",
       "   volleys  dribbling  curve  free_kick_accuracy  long_passing  ball_control  \\\n",
       "0     44.0       51.0   45.0                39.0          64.0          49.0   \n",
       "1     44.0       51.0   45.0                39.0          64.0          49.0   \n",
       "\n",
       "   acceleration  sprint_speed  agility  reactions  balance  shot_power  \\\n",
       "0          60.0          64.0     59.0       47.0     65.0        55.0   \n",
       "1          60.0          64.0     59.0       47.0     65.0        55.0   \n",
       "\n",
       "   jumping  stamina  strength  long_shots  aggression  interceptions  \\\n",
       "0     58.0     54.0      76.0        35.0        71.0           70.0   \n",
       "1     58.0     54.0      76.0        35.0        71.0           70.0   \n",
       "\n",
       "   positioning  vision  penalties  marking  standing_tackle  sliding_tackle  \\\n",
       "0         45.0    54.0       48.0     65.0             69.0            69.0   \n",
       "1         45.0    54.0       48.0     65.0             69.0            69.0   \n",
       "\n",
       "   gk_diving  gk_handling  gk_kicking  gk_positioning  gk_reflexes  \n",
       "0        6.0         11.0        10.0             8.0          8.0  \n",
       "1        6.0         11.0        10.0             8.0          8.0  "
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head(2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "21437832",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "id": "fdd5b702",
   "metadata": {},
   "source": [
    "#### Defining the Dependable n the Independent Variables/Features"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "031eddd9",
   "metadata": {},
   "outputs": [],
   "source": [
    "X = df.drop(['overall_rating'], axis=1)\n",
    "Y = df['overall_rating']"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "1e67aff3",
   "metadata": {},
   "source": [
    "#### Train Test Split"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "dfc87834",
   "metadata": {},
   "outputs": [],
   "source": [
    "X_train, X_test, y_train, y_test = train_test_split(X, Y, test_size=0.3)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e5715576",
   "metadata": {},
   "source": [
    "#### Linear Regression: Fit a model to the training set"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "3520f063",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "LinearRegression()"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "linear_reg = LinearRegression()\n",
    "linear_reg.fit(X_train, y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "51292562",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([70.50367074, 66.13914837, 67.56769562, ..., 67.77473343,\n",
       "       79.51953828, 71.80809713])"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y_pred = linear_reg.predict(X_test)\n",
    "y_pred"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "9cdee63b",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "MAE: 2.136903091064806\n",
      "MSE: 7.815163138949423\n",
      "RMSE: 2.795561328060864\n"
     ]
    }
   ],
   "source": [
    "print(f'MAE: {mean_absolute_error(y_test, y_pred)}')\n",
    "print(f'MSE: {mean_squared_error(y_test, y_pred)}')\n",
    "print(f'RMSE: {sqrt(mean_squared_error(y_test, y_pred))}')\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "5c42704e",
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.metrics import mean_absolute_error"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "dbf7ea53",
   "metadata": {},
   "source": [
    "#### Decision Tree Regressor: Fit a new regression model to the training set"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "id": "237bb947",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "DecisionTreeRegressor(max_depth=20)"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "d_tree_reg = DecisionTreeRegressor(max_depth=20)\n",
    "d_tree_reg.fit(X_train, y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "id": "1da7c7fd",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([72.06666667, 70.        , 68.        , ..., 67.        ,\n",
       "       83.        , 74.        ])"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y_pred = d_tree_reg.predict(X_test)\n",
    "y_pred"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "id": "d71fe88a",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "MAE: 0.7225393188717042\n",
      "MSE: 2.0514476227513105\n",
      "RMSE: 1.4322875489060536\n"
     ]
    }
   ],
   "source": [
    "print(f'MAE: {mean_absolute_error(y_test, y_pred)}')\n",
    "\n",
    "print(f'MSE: {mean_squared_error(y_test, y_pred)}')\n",
    "print(f'RMSE: {sqrt(mean_squared_error(y_test, y_pred))}')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "id": "d9a53db4",
   "metadata": {},
   "outputs": [],
   "source": [
    "d1= pd.Series(['potential', 'preferred_foot',\n",
    "       'attacking_work_rate', 'defensive_work_rate', 'crossing',\n",
    "       'finishing', 'heading_accuracy', 'short_passing', 'volleys',\n",
    "       'dribbling', 'curve', 'free_kick_accuracy', 'long_passing',\n",
    "       'ball_control', 'acceleration', 'sprint_speed', 'agility',\n",
    "       'reactions', 'balance', 'shot_power', 'jumping', 'stamina',\n",
    "       'strength', 'long_shots', 'aggression', 'interceptions',\n",
    "       'positioning', 'vision', 'penalties', 'marking', 'standing_tackle',\n",
    "       'sliding_tackle', 'gk_diving', 'gk_handling', 'gk_kicking',\n",
    "       'gk_positioning', 'gk_reflexes'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "id": "5a62c90c",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x720 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "importance = d_tree_reg.feature_importances_\n",
    "\n",
    "importance = pd.Series(d_tree_reg.feature_importances_)\n",
    "d = pd.DataFrame({'Importance': (importance)*100, 'ColumnName': d1})\n",
    "d.sort_values(by='Importance', ascending=True, inplace=True)\n",
    "\n",
    "# summarize feature importance\n",
    "# for i,v in enumerate(importance):\n",
    "#     print('Feature: %0d, Score: %.5f' % (i,v))\n",
    "\n",
    "# plot feature importance\n",
    "plt.figure(figsize=[10,10])\n",
    "plt.barh(d['ColumnName'], d['Importance'])\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "id": "6ddb17da",
   "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>Importance</th>\n",
       "      <th>ColumnName</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>48.300620</td>\n",
       "      <td>reactions</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>23.844913</td>\n",
       "      <td>potential</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>5.700008</td>\n",
       "      <td>ball_control</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4.268800</td>\n",
       "      <td>standing_tackle</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2.152917</td>\n",
       "      <td>gk_diving</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>1.729345</td>\n",
       "      <td>heading_accuracy</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>1.282525</td>\n",
       "      <td>gk_positioning</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>1.158987</td>\n",
       "      <td>positioning</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>1.130552</td>\n",
       "      <td>gk_handling</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>0.965466</td>\n",
       "      <td>marking</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>0.930668</td>\n",
       "      <td>crossing</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>0.901907</td>\n",
       "      <td>strength</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>0.872541</td>\n",
       "      <td>interceptions</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>0.785831</td>\n",
       "      <td>dribbling</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>0.785643</td>\n",
       "      <td>finishing</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>0.762640</td>\n",
       "      <td>shot_power</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>0.422353</td>\n",
       "      <td>gk_reflexes</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>0.395821</td>\n",
       "      <td>short_passing</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>0.379785</td>\n",
       "      <td>aggression</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>0.379280</td>\n",
       "      <td>stamina</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    Importance        ColumnName\n",
       "0    48.300620         reactions\n",
       "1    23.844913         potential\n",
       "2     5.700008      ball_control\n",
       "3     4.268800   standing_tackle\n",
       "4     2.152917         gk_diving\n",
       "5     1.729345  heading_accuracy\n",
       "6     1.282525    gk_positioning\n",
       "7     1.158987       positioning\n",
       "8     1.130552       gk_handling\n",
       "9     0.965466           marking\n",
       "10    0.930668          crossing\n",
       "11    0.901907          strength\n",
       "12    0.872541     interceptions\n",
       "13    0.785831         dribbling\n",
       "14    0.785643         finishing\n",
       "15    0.762640        shot_power\n",
       "16    0.422353       gk_reflexes\n",
       "17    0.395821     short_passing\n",
       "18    0.379785        aggression\n",
       "19    0.379280           stamina"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_top_20 = d.nlargest(20, 'Importance').reset_index()\n",
    "df_top_20.drop(['index'], axis=1, inplace=True)\n",
    "df_top_20"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "id": "e6f22fac",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(['reactions', 'potential', 'ball_control', 'standing_tackle',\n",
       "       'gk_diving', 'heading_accuracy', 'gk_positioning', 'positioning',\n",
       "       'gk_handling', 'marking', 'crossing', 'strength', 'interceptions',\n",
       "       'dribbling', 'finishing', 'shot_power', 'gk_reflexes',\n",
       "       'short_passing', 'aggression', 'stamina'], dtype=object)"
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_top_20['ColumnName'].values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "id": "fcd1dfaa",
   "metadata": {},
   "outputs": [],
   "source": [
    "X_20 = df[['aggression', 'stamina', 'reactions', 'potential', 'positioning', 'ball_control', 'strength', 'marking', \n",
    "           'crossing', 'finishing', 'dribbling','interceptions',  'standing_tackle','heading_accuracy', 'shot_power', \n",
    "           'short_passing','gk_reflexes', 'gk_handling', 'gk_diving', 'gk_positioning']]"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "9bbed364",
   "metadata": {},
   "source": [
    "#### Re Building the Models with Top 20 Features"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "76a1faa4",
   "metadata": {},
   "source": [
    "#### Train Test Split"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "id": "0ab1d302",
   "metadata": {},
   "outputs": [],
   "source": [
    "X_train, X_test, y_train, y_test = train_test_split(X_20, Y, test_size=0.3)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ac0deae7",
   "metadata": {},
   "source": [
    "#### Linear Regression: Fit a model to the training set"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "id": "cc2b0656",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "LinearRegression()"
      ]
     },
     "execution_count": 53,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "linear_reg = LinearRegression()\n",
    "linear_reg.fit(X_train, y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "id": "f62510d8",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([79.54478546, 65.26914322, 64.76666428, ..., 73.99359371,\n",
       "       68.70168534, 62.9803517 ])"
      ]
     },
     "execution_count": 54,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y_pred = linear_reg.predict(X_test)\n",
    "y_pred"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "id": "53d90a7e",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "MAE: 2.186518181601008\n",
      "MSE: 8.092515847908842\n",
      "RMSE: 2.8447347587971787\n"
     ]
    }
   ],
   "source": [
    "print(f'MAE: {mean_absolute_error(y_test, y_pred)}')\n",
    "print(f'MSE: {mean_squared_error(y_test, y_pred)}')\n",
    "print(f'RMSE: {sqrt(mean_squared_error(y_test, y_pred))}')\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "e58b83bd",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "id": "b0c13e8c",
   "metadata": {},
   "source": [
    "#### Decision Tree Regressor: Fit a new regression model to the training set"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "id": "adbbfa84",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "DecisionTreeRegressor(max_depth=20)"
      ]
     },
     "execution_count": 56,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "d_tree_reg = DecisionTreeRegressor(max_depth=20)\n",
    "d_tree_reg.fit(X_train, y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "id": "3ce811cb",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([75.83333333, 62.46666667, 58.75      , ..., 77.        ,\n",
       "       71.29411765, 63.        ])"
      ]
     },
     "execution_count": 57,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y_pred = d_tree_reg.predict(X_test)\n",
    "y_pred"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "id": "0a6c7899",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "MAE: 0.7292036660587082\n",
      "MSE: 2.021604161854793\n",
      "RMSE: 1.42183127052924\n"
     ]
    }
   ],
   "source": [
    "print(f'MAE: {mean_absolute_error(y_test, y_pred)}')\n",
    "\n",
    "print(f'MSE: {mean_squared_error(y_test, y_pred)}')\n",
    "print(f'RMSE: {sqrt(mean_squared_error(y_test, y_pred))}')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "845b9b98",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "id": "6fee8c62",
   "metadata": {},
   "source": [
    "#### Testing the Decision Tree Regressor with 20 Features"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "id": "d1c7b145",
   "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>aggression</th>\n",
       "      <th>stamina</th>\n",
       "      <th>reactions</th>\n",
       "      <th>potential</th>\n",
       "      <th>positioning</th>\n",
       "      <th>ball_control</th>\n",
       "      <th>strength</th>\n",
       "      <th>marking</th>\n",
       "      <th>crossing</th>\n",
       "      <th>finishing</th>\n",
       "      <th>dribbling</th>\n",
       "      <th>interceptions</th>\n",
       "      <th>standing_tackle</th>\n",
       "      <th>heading_accuracy</th>\n",
       "      <th>shot_power</th>\n",
       "      <th>short_passing</th>\n",
       "      <th>gk_reflexes</th>\n",
       "      <th>gk_handling</th>\n",
       "      <th>gk_diving</th>\n",
       "      <th>gk_positioning</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>71.0</td>\n",
       "      <td>54.0</td>\n",
       "      <td>47.0</td>\n",
       "      <td>71.0</td>\n",
       "      <td>45.0</td>\n",
       "      <td>49.0</td>\n",
       "      <td>76.0</td>\n",
       "      <td>65.0</td>\n",
       "      <td>49.0</td>\n",
       "      <td>44.0</td>\n",
       "      <td>51.0</td>\n",
       "      <td>70.0</td>\n",
       "      <td>69.0</td>\n",
       "      <td>71.0</td>\n",
       "      <td>55.0</td>\n",
       "      <td>61.0</td>\n",
       "      <td>8.0</td>\n",
       "      <td>11.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>8.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   aggression  stamina  reactions  potential  positioning  ball_control  \\\n",
       "0        71.0     54.0       47.0       71.0         45.0          49.0   \n",
       "\n",
       "   strength  marking  crossing  finishing  dribbling  interceptions  \\\n",
       "0      76.0     65.0      49.0       44.0       51.0           70.0   \n",
       "\n",
       "   standing_tackle  heading_accuracy  shot_power  short_passing  gk_reflexes  \\\n",
       "0             69.0              71.0        55.0           61.0          8.0   \n",
       "\n",
       "   gk_handling  gk_diving  gk_positioning  \n",
       "0         11.0        6.0             8.0  "
      ]
     },
     "execution_count": 59,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X_20.head(1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "id": "25d41522",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([66.])"
      ]
     },
     "execution_count": 66,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data =[[80,65, 50,70,5,80,8,80,65,80,60,80,80,0,80,80,80,80,5,80]]\n",
    "d_tree_reg.predict(data)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "2778f65a",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "id": "5953d1f8",
   "metadata": {},
   "source": [
    "### Pickle File For Deployment"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "id": "d1bd5e1e",
   "metadata": {},
   "outputs": [],
   "source": [
    "import pickle\n",
    "\n",
    "# Open a file \n",
    "file = open('ml_project.pkl', 'wb')\n",
    "\n",
    "# dump information to that file\n",
    "pickle.dump(d_tree_reg,file)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 70,
   "id": "b16b48ae",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "66.0"
      ]
     },
     "execution_count": 70,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "d = np.array([80,65, 50,70,5,80,8,80,65,80,60,80,80,0,80,80,80,80,5,80]).reshape(1,-1)\n",
    "# Actual Value :- 24\n",
    "\n",
    "model = pickle.load(open('ml_project.pkl', 'rb'))\n",
    "prediction = model.predict(d)\n",
    "output = prediction[0]\n",
    "output\n",
    "\n",
    "# Deployed model with 20 attributes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "c69a46e1",
   "metadata": {},
   "outputs": [],
   "source": []
  }
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