02_National_Team_Value_Analysis.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# FIFA World Rankings Analysis with Python and R\n",
    "## Part 2 - National Team Value Analysis\n",
    "Author: [Kenneth Leung](https://github.com/kennethleungty)  \n",
    "Data Source: https://www.transfermarkt.co.uk/wettbewerbe/fifa/wettbewerbe\n",
    "___"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### 1. Initial setup"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Import dependencies\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "import re\n",
    "from statsmodels.api import OLS\n",
    "from scipy.optimize import curve_fit\n",
    "from sklearn.linear_model import LinearRegression\n",
    "import statsmodels.api as sm"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "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>rank</th>\n",
       "      <th>nation</th>\n",
       "      <th>squad_size</th>\n",
       "      <th>average_age</th>\n",
       "      <th>total_value_pounds</th>\n",
       "      <th>confederation</th>\n",
       "      <th>points</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>Belgium</td>\n",
       "      <td>26</td>\n",
       "      <td>29.2</td>\n",
       "      <td>£601.56m</td>\n",
       "      <td>UEFA</td>\n",
       "      <td>1783</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>France</td>\n",
       "      <td>26</td>\n",
       "      <td>28.0</td>\n",
       "      <td>£925.20m</td>\n",
       "      <td>UEFA</td>\n",
       "      <td>1757</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "      <td>Brazil</td>\n",
       "      <td>25</td>\n",
       "      <td>27.5</td>\n",
       "      <td>£826.20m</td>\n",
       "      <td>CONMEBOL</td>\n",
       "      <td>1743</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4</td>\n",
       "      <td>England</td>\n",
       "      <td>26</td>\n",
       "      <td>25.4</td>\n",
       "      <td>£1.16bn</td>\n",
       "      <td>UEFA</td>\n",
       "      <td>1687</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5</td>\n",
       "      <td>Portugal</td>\n",
       "      <td>26</td>\n",
       "      <td>27.9</td>\n",
       "      <td>£747.45m</td>\n",
       "      <td>UEFA</td>\n",
       "      <td>1666</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>145</th>\n",
       "      <td>147</td>\n",
       "      <td>Nicaragua</td>\n",
       "      <td>24</td>\n",
       "      <td>27.4</td>\n",
       "      <td>£1.87m</td>\n",
       "      <td>CONCACAF</td>\n",
       "      <td>1061</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>146</th>\n",
       "      <td>148</td>\n",
       "      <td>Kuwait</td>\n",
       "      <td>23</td>\n",
       "      <td>26.8</td>\n",
       "      <td>£6.55m</td>\n",
       "      <td>AFC</td>\n",
       "      <td>1056</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>147</th>\n",
       "      <td>149</td>\n",
       "      <td>Afghanistan</td>\n",
       "      <td>23</td>\n",
       "      <td>26.8</td>\n",
       "      <td>£2.01m</td>\n",
       "      <td>AFC</td>\n",
       "      <td>1052</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>148</th>\n",
       "      <td>150</td>\n",
       "      <td>Botswana</td>\n",
       "      <td>21</td>\n",
       "      <td>27.1</td>\n",
       "      <td>£1.13m</td>\n",
       "      <td>CAF</td>\n",
       "      <td>1052</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>149</th>\n",
       "      <td>151</td>\n",
       "      <td>Liberia</td>\n",
       "      <td>27</td>\n",
       "      <td>24.9</td>\n",
       "      <td>£6.21m</td>\n",
       "      <td>CAF</td>\n",
       "      <td>1047</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>150 rows × 7 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "    rank       nation  squad_size  average_age total_value_pounds  \\\n",
       "0      1      Belgium          26         29.2           £601.56m   \n",
       "1      2       France          26         28.0           £925.20m   \n",
       "2      3       Brazil          25         27.5           £826.20m   \n",
       "3      4      England          26         25.4            £1.16bn   \n",
       "4      5     Portugal          26         27.9           £747.45m   \n",
       "..   ...          ...         ...          ...                ...   \n",
       "145  147    Nicaragua          24         27.4             £1.87m   \n",
       "146  148       Kuwait          23         26.8             £6.55m   \n",
       "147  149  Afghanistan          23         26.8             £2.01m   \n",
       "148  150     Botswana          21         27.1             £1.13m   \n",
       "149  151      Liberia          27         24.9             £6.21m   \n",
       "\n",
       "    confederation  points  \n",
       "0            UEFA    1783  \n",
       "1            UEFA    1757  \n",
       "2        CONMEBOL    1743  \n",
       "3            UEFA    1687  \n",
       "4            UEFA    1666  \n",
       "..            ...     ...  \n",
       "145      CONCACAF    1061  \n",
       "146           AFC    1056  \n",
       "147           AFC    1052  \n",
       "148           CAF    1052  \n",
       "149           CAF    1047  \n",
       "\n",
       "[150 rows x 7 columns]"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Import datasets\n",
    "df_national_team_value = pd.read_csv('./data/fifa_rankings_national_teams_value_20210807.csv', \n",
    "                                     encoding = \"ISO-8859-1\", \n",
    "                                     engine='python')\n",
    "df_population = pd.read_excel('./data/country_population_2020.xlsx')\n",
    "\n",
    "# Review dataset of top 150 ranked teams\n",
    "df_national_team_value"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "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>nation</th>\n",
       "      <th>squad_size</th>\n",
       "      <th>average_age</th>\n",
       "      <th>team_value_mil_pounds</th>\n",
       "      <th>avg_value_mil_pounds</th>\n",
       "      <th>confederation</th>\n",
       "      <th>rank</th>\n",
       "      <th>points</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Belgium</td>\n",
       "      <td>26</td>\n",
       "      <td>29.2</td>\n",
       "      <td>601.56</td>\n",
       "      <td>23.136923</td>\n",
       "      <td>UEFA</td>\n",
       "      <td>1</td>\n",
       "      <td>1783</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>France</td>\n",
       "      <td>26</td>\n",
       "      <td>28.0</td>\n",
       "      <td>925.20</td>\n",
       "      <td>35.584615</td>\n",
       "      <td>UEFA</td>\n",
       "      <td>2</td>\n",
       "      <td>1757</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Brazil</td>\n",
       "      <td>25</td>\n",
       "      <td>27.5</td>\n",
       "      <td>826.20</td>\n",
       "      <td>33.048000</td>\n",
       "      <td>CONMEBOL</td>\n",
       "      <td>3</td>\n",
       "      <td>1743</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>England</td>\n",
       "      <td>26</td>\n",
       "      <td>25.4</td>\n",
       "      <td>1160.00</td>\n",
       "      <td>44.615385</td>\n",
       "      <td>UEFA</td>\n",
       "      <td>4</td>\n",
       "      <td>1687</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Portugal</td>\n",
       "      <td>26</td>\n",
       "      <td>27.9</td>\n",
       "      <td>747.45</td>\n",
       "      <td>28.748077</td>\n",
       "      <td>UEFA</td>\n",
       "      <td>5</td>\n",
       "      <td>1666</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>Spain</td>\n",
       "      <td>24</td>\n",
       "      <td>26.7</td>\n",
       "      <td>839.70</td>\n",
       "      <td>34.987500</td>\n",
       "      <td>UEFA</td>\n",
       "      <td>6</td>\n",
       "      <td>1648</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>Italy</td>\n",
       "      <td>26</td>\n",
       "      <td>27.9</td>\n",
       "      <td>695.70</td>\n",
       "      <td>26.757692</td>\n",
       "      <td>UEFA</td>\n",
       "      <td>7</td>\n",
       "      <td>1642</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>Argentina</td>\n",
       "      <td>28</td>\n",
       "      <td>27.7</td>\n",
       "      <td>598.95</td>\n",
       "      <td>21.391071</td>\n",
       "      <td>CONMEBOL</td>\n",
       "      <td>8</td>\n",
       "      <td>1642</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>Uruguay</td>\n",
       "      <td>26</td>\n",
       "      <td>27.5</td>\n",
       "      <td>340.56</td>\n",
       "      <td>13.098462</td>\n",
       "      <td>CONMEBOL</td>\n",
       "      <td>9</td>\n",
       "      <td>1639</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>Denmark</td>\n",
       "      <td>26</td>\n",
       "      <td>27.9</td>\n",
       "      <td>297.63</td>\n",
       "      <td>11.447308</td>\n",
       "      <td>UEFA</td>\n",
       "      <td>10</td>\n",
       "      <td>1632</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      nation  squad_size  average_age  team_value_mil_pounds  \\\n",
       "0    Belgium          26         29.2                 601.56   \n",
       "1     France          26         28.0                 925.20   \n",
       "2     Brazil          25         27.5                 826.20   \n",
       "3    England          26         25.4                1160.00   \n",
       "4   Portugal          26         27.9                 747.45   \n",
       "5      Spain          24         26.7                 839.70   \n",
       "6      Italy          26         27.9                 695.70   \n",
       "7  Argentina          28         27.7                 598.95   \n",
       "8    Uruguay          26         27.5                 340.56   \n",
       "9    Denmark          26         27.9                 297.63   \n",
       "\n",
       "   avg_value_mil_pounds confederation rank  points  \n",
       "0             23.136923          UEFA    1    1783  \n",
       "1             35.584615          UEFA    2    1757  \n",
       "2             33.048000      CONMEBOL    3    1743  \n",
       "3             44.615385          UEFA    4    1687  \n",
       "4             28.748077          UEFA    5    1666  \n",
       "5             34.987500          UEFA    6    1648  \n",
       "6             26.757692          UEFA    7    1642  \n",
       "7             21.391071      CONMEBOL    8    1642  \n",
       "8             13.098462      CONMEBOL    9    1639  \n",
       "9             11.447308          UEFA   10    1632  "
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Convert all values to be based on million dolloar pounds\n",
    "def convert_to_million(x):\n",
    "    if 'bn' in x:\n",
    "        billion_value = float(re.sub(\"[^0-9.]\", \"\", x))\n",
    "        million_value = billion_value * 1000 \n",
    "    elif 'Th.' in x:\n",
    "        thousand_value = float(re.sub(\"[^0-9.]\", \"\", x))\n",
    "        million_value = thousand_value / 1000\n",
    "    else:\n",
    "        million_value = float(re.sub(\"[^0-9.]\", \"\", x))\n",
    "    return million_value\n",
    "\n",
    "# Process total team value column\n",
    "df_national_team_value['total_value_pounds'] = df_national_team_value['total_value_pounds'].apply(lambda x : x.replace('£', ''))\n",
    "df_national_team_value['total_value_pounds'] = df_national_team_value['total_value_pounds'].apply(lambda x : convert_to_million(x))\n",
    "df_national_team_value.rename(columns={\"total_value_pounds\": \"team_value_mil_pounds\"}, inplace=True)\n",
    "\n",
    "# Create column to calculate average value per player in national team\n",
    "# Using average value (instead of total value) because different teams have different squad sizes\n",
    "df_national_team_value['avg_value_mil_pounds'] = df_national_team_value['team_value_mil_pounds'] / \\\n",
    "                                                 df_national_team_value['squad_size']\n",
    "\n",
    "# Rearrange columns\n",
    "df_national_team_value = df_national_team_value[['nation','squad_size','average_age',\n",
    "                                                 'team_value_mil_pounds', 'avg_value_mil_pounds',\n",
    "                                                 'confederation', 'rank','points']]\n",
    "df_national_team_value.head(10)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### 2. Include population data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "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>Country</th>\n",
       "      <th>Population</th>\n",
       "      <th>Yearly Change</th>\n",
       "      <th>Net Change</th>\n",
       "      <th>Density (P/KmÂ²)</th>\n",
       "      <th>Land Area (KmÂ²)</th>\n",
       "      <th>Migrants (net)</th>\n",
       "      <th>Fert. Rate</th>\n",
       "      <th>Med. Age</th>\n",
       "      <th>Urban Pop %</th>\n",
       "      <th>World Share</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>China</td>\n",
       "      <td>1440297825</td>\n",
       "      <td>0.0039</td>\n",
       "      <td>5540090.0</td>\n",
       "      <td>153.0</td>\n",
       "      <td>9388211.0</td>\n",
       "      <td>-348399.0</td>\n",
       "      <td>1.7</td>\n",
       "      <td>38</td>\n",
       "      <td>0.61</td>\n",
       "      <td>0.1847</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>India</td>\n",
       "      <td>1382345085</td>\n",
       "      <td>0.0099</td>\n",
       "      <td>13586631.0</td>\n",
       "      <td>464.0</td>\n",
       "      <td>2973190.0</td>\n",
       "      <td>-532687.0</td>\n",
       "      <td>2.2</td>\n",
       "      <td>28</td>\n",
       "      <td>0.35</td>\n",
       "      <td>0.1770</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>United States</td>\n",
       "      <td>331341050</td>\n",
       "      <td>0.0059</td>\n",
       "      <td>1937734.0</td>\n",
       "      <td>36.0</td>\n",
       "      <td>9147420.0</td>\n",
       "      <td>954806.0</td>\n",
       "      <td>1.8</td>\n",
       "      <td>38</td>\n",
       "      <td>0.83</td>\n",
       "      <td>0.0425</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Indonesia</td>\n",
       "      <td>274021604</td>\n",
       "      <td>0.0107</td>\n",
       "      <td>2898047.0</td>\n",
       "      <td>151.0</td>\n",
       "      <td>1811570.0</td>\n",
       "      <td>-98955.0</td>\n",
       "      <td>2.3</td>\n",
       "      <td>30</td>\n",
       "      <td>0.56</td>\n",
       "      <td>0.0351</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Pakistan</td>\n",
       "      <td>221612785</td>\n",
       "      <td>0.0200</td>\n",
       "      <td>4327022.0</td>\n",
       "      <td>287.0</td>\n",
       "      <td>770880.0</td>\n",
       "      <td>-233379.0</td>\n",
       "      <td>3.6</td>\n",
       "      <td>23</td>\n",
       "      <td>0.35</td>\n",
       "      <td>0.0283</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "         Country  Population  Yearly Change  Net Change  Density (P/KmÂ²)  \\\n",
       "0          China  1440297825         0.0039   5540090.0             153.0   \n",
       "1          India  1382345085         0.0099  13586631.0             464.0   \n",
       "2  United States   331341050         0.0059   1937734.0              36.0   \n",
       "3      Indonesia   274021604         0.0107   2898047.0             151.0   \n",
       "4       Pakistan   221612785         0.0200   4327022.0             287.0   \n",
       "\n",
       "   Land Area (KmÂ²)  Migrants (net) Fert. Rate Med. Age Urban Pop %  \\\n",
       "0         9388211.0       -348399.0        1.7       38        0.61   \n",
       "1         2973190.0       -532687.0        2.2       28        0.35   \n",
       "2         9147420.0        954806.0        1.8       38        0.83   \n",
       "3         1811570.0        -98955.0        2.3       30        0.56   \n",
       "4          770880.0       -233379.0        3.6       23        0.35   \n",
       "\n",
       "   World Share  \n",
       "0       0.1847  \n",
       "1       0.1770  \n",
       "2       0.0425  \n",
       "3       0.0351  \n",
       "4       0.0283  "
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Merge population data with country\n",
    "df_population.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "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>nation</th>\n",
       "      <th>squad_size</th>\n",
       "      <th>average_age</th>\n",
       "      <th>team_value_mil_pounds</th>\n",
       "      <th>avg_value_mil_pounds</th>\n",
       "      <th>confederation</th>\n",
       "      <th>rank</th>\n",
       "      <th>points</th>\n",
       "      <th>country</th>\n",
       "      <th>population</th>\n",
       "      <th>urban_pop_percent</th>\n",
       "      <th>log_population</th>\n",
       "      <th>log_team_value</th>\n",
       "      <th>log_avg_value</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Belgium</td>\n",
       "      <td>26</td>\n",
       "      <td>29.2</td>\n",
       "      <td>601.56</td>\n",
       "      <td>23.136923</td>\n",
       "      <td>UEFA</td>\n",
       "      <td>1</td>\n",
       "      <td>1783</td>\n",
       "      <td>Belgium</td>\n",
       "      <td>11598451</td>\n",
       "      <td>0.98</td>\n",
       "      <td>16.266382</td>\n",
       "      <td>6.399526</td>\n",
       "      <td>3.141430</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>France</td>\n",
       "      <td>26</td>\n",
       "      <td>28.0</td>\n",
       "      <td>925.20</td>\n",
       "      <td>35.584615</td>\n",
       "      <td>UEFA</td>\n",
       "      <td>2</td>\n",
       "      <td>1757</td>\n",
       "      <td>France</td>\n",
       "      <td>65298930</td>\n",
       "      <td>0.82</td>\n",
       "      <td>17.994486</td>\n",
       "      <td>6.830010</td>\n",
       "      <td>3.571913</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Brazil</td>\n",
       "      <td>25</td>\n",
       "      <td>27.5</td>\n",
       "      <td>826.20</td>\n",
       "      <td>33.048000</td>\n",
       "      <td>CONMEBOL</td>\n",
       "      <td>3</td>\n",
       "      <td>1743</td>\n",
       "      <td>Brazil</td>\n",
       "      <td>212821986</td>\n",
       "      <td>0.88</td>\n",
       "      <td>19.175967</td>\n",
       "      <td>6.716837</td>\n",
       "      <td>3.497961</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>England</td>\n",
       "      <td>26</td>\n",
       "      <td>25.4</td>\n",
       "      <td>1160.00</td>\n",
       "      <td>44.615385</td>\n",
       "      <td>UEFA</td>\n",
       "      <td>4</td>\n",
       "      <td>1687</td>\n",
       "      <td>England</td>\n",
       "      <td>56286961</td>\n",
       "      <td>0.83</td>\n",
       "      <td>17.845973</td>\n",
       "      <td>7.056175</td>\n",
       "      <td>3.798079</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Portugal</td>\n",
       "      <td>26</td>\n",
       "      <td>27.9</td>\n",
       "      <td>747.45</td>\n",
       "      <td>28.748077</td>\n",
       "      <td>UEFA</td>\n",
       "      <td>5</td>\n",
       "      <td>1666</td>\n",
       "      <td>Portugal</td>\n",
       "      <td>10191409</td>\n",
       "      <td>0.66</td>\n",
       "      <td>16.137056</td>\n",
       "      <td>6.616667</td>\n",
       "      <td>3.358571</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>Spain</td>\n",
       "      <td>24</td>\n",
       "      <td>26.7</td>\n",
       "      <td>839.70</td>\n",
       "      <td>34.987500</td>\n",
       "      <td>UEFA</td>\n",
       "      <td>6</td>\n",
       "      <td>1648</td>\n",
       "      <td>Spain</td>\n",
       "      <td>46757980</td>\n",
       "      <td>0.8</td>\n",
       "      <td>17.660495</td>\n",
       "      <td>6.733045</td>\n",
       "      <td>3.554991</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>Italy</td>\n",
       "      <td>26</td>\n",
       "      <td>27.9</td>\n",
       "      <td>695.70</td>\n",
       "      <td>26.757692</td>\n",
       "      <td>UEFA</td>\n",
       "      <td>7</td>\n",
       "      <td>1642</td>\n",
       "      <td>Italy</td>\n",
       "      <td>60446035</td>\n",
       "      <td>0.69</td>\n",
       "      <td>17.917262</td>\n",
       "      <td>6.544919</td>\n",
       "      <td>3.286822</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>Argentina</td>\n",
       "      <td>28</td>\n",
       "      <td>27.7</td>\n",
       "      <td>598.95</td>\n",
       "      <td>21.391071</td>\n",
       "      <td>CONMEBOL</td>\n",
       "      <td>8</td>\n",
       "      <td>1642</td>\n",
       "      <td>Argentina</td>\n",
       "      <td>45267449</td>\n",
       "      <td>0.93</td>\n",
       "      <td>17.628099</td>\n",
       "      <td>6.395178</td>\n",
       "      <td>3.062974</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>Uruguay</td>\n",
       "      <td>26</td>\n",
       "      <td>27.5</td>\n",
       "      <td>340.56</td>\n",
       "      <td>13.098462</td>\n",
       "      <td>CONMEBOL</td>\n",
       "      <td>9</td>\n",
       "      <td>1639</td>\n",
       "      <td>Uruguay</td>\n",
       "      <td>3475842</td>\n",
       "      <td>0.96</td>\n",
       "      <td>15.061347</td>\n",
       "      <td>5.830591</td>\n",
       "      <td>2.572495</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>Denmark</td>\n",
       "      <td>26</td>\n",
       "      <td>27.9</td>\n",
       "      <td>297.63</td>\n",
       "      <td>11.447308</td>\n",
       "      <td>UEFA</td>\n",
       "      <td>10</td>\n",
       "      <td>1632</td>\n",
       "      <td>Denmark</td>\n",
       "      <td>5795780</td>\n",
       "      <td>0.88</td>\n",
       "      <td>15.572641</td>\n",
       "      <td>5.695851</td>\n",
       "      <td>2.437755</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      nation  squad_size  average_age  team_value_mil_pounds  \\\n",
       "0    Belgium          26         29.2                 601.56   \n",
       "1     France          26         28.0                 925.20   \n",
       "2     Brazil          25         27.5                 826.20   \n",
       "3    England          26         25.4                1160.00   \n",
       "4   Portugal          26         27.9                 747.45   \n",
       "5      Spain          24         26.7                 839.70   \n",
       "6      Italy          26         27.9                 695.70   \n",
       "7  Argentina          28         27.7                 598.95   \n",
       "8    Uruguay          26         27.5                 340.56   \n",
       "9    Denmark          26         27.9                 297.63   \n",
       "\n",
       "   avg_value_mil_pounds confederation rank  points    country  population  \\\n",
       "0             23.136923          UEFA    1    1783    Belgium    11598451   \n",
       "1             35.584615          UEFA    2    1757     France    65298930   \n",
       "2             33.048000      CONMEBOL    3    1743     Brazil   212821986   \n",
       "3             44.615385          UEFA    4    1687    England    56286961   \n",
       "4             28.748077          UEFA    5    1666   Portugal    10191409   \n",
       "5             34.987500          UEFA    6    1648      Spain    46757980   \n",
       "6             26.757692          UEFA    7    1642      Italy    60446035   \n",
       "7             21.391071      CONMEBOL    8    1642  Argentina    45267449   \n",
       "8             13.098462      CONMEBOL    9    1639    Uruguay     3475842   \n",
       "9             11.447308          UEFA   10    1632    Denmark     5795780   \n",
       "\n",
       "  urban_pop_percent  log_population  log_team_value  log_avg_value  \n",
       "0              0.98       16.266382        6.399526       3.141430  \n",
       "1              0.82       17.994486        6.830010       3.571913  \n",
       "2              0.88       19.175967        6.716837       3.497961  \n",
       "3              0.83       17.845973        7.056175       3.798079  \n",
       "4              0.66       16.137056        6.616667       3.358571  \n",
       "5               0.8       17.660495        6.733045       3.554991  \n",
       "6              0.69       17.917262        6.544919       3.286822  \n",
       "7              0.93       17.628099        6.395178       3.062974  \n",
       "8              0.96       15.061347        5.830591       2.572495  \n",
       "9              0.88       15.572641        5.695851       2.437755  "
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Create subset of population df\n",
    "df_population_sm = df_population[['Country', 'Population', 'Urban Pop %']]\n",
    "\n",
    "# Merge dataframes of national team value and population\n",
    "df_national_merged = df_national_team_value.merge(df_population_sm, left_on='nation',\n",
    "                                                 right_on='Country', how='left')\n",
    "\n",
    "# Standardize all column names\n",
    "df_national_merged.rename(columns={\"Country\": \"country\", \n",
    "                                   \"Population\": \"population\",\n",
    "                                   \"Urban Pop %\": \"urban_pop_percent\"}, \n",
    "                          inplace=True)\n",
    "\n",
    "# Add log columns\n",
    "df_national_merged['log_population'] = np.log(df_national_merged['population'])\n",
    "df_national_merged['log_team_value'] = np.log(df_national_merged['team_value_mil_pounds'])\n",
    "df_national_merged['log_avg_value'] = np.log(df_national_merged['avg_value_mil_pounds'])\n",
    "df_national_merged.head(10)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "___\n",
    "#### Evaluate skew"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.histplot(data=df_national_merged, x=\"avg_value_mil_pounds\", kde=True);"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.histplot(data=df_national_merged, x=\"log_avg_value\", kde=True);"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Analysis 1 - National Team Value and FIFA Points"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.scatterplot(data=df_national_merged, x=\"avg_value_mil_pounds\", y=\"points\");"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Displaying effect of applying log transformation on average national team value\n",
    "sns.scatterplot(data=df_national_merged, x=\"log_avg_value\", y=\"points\");"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Analysis 2 - Population and FIFA Points"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.scatterplot(data=df_national_merged, x=\"log_population\", y=\"points\");"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.scatterplot(data=df_national_merged, x=\"average_age\", y=\"points\");"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Analysis 3 - Regression model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "scrolled": false
   },
   "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>nation</th>\n",
       "      <th>squad_size</th>\n",
       "      <th>average_age</th>\n",
       "      <th>team_value_mil_pounds</th>\n",
       "      <th>avg_value_mil_pounds</th>\n",
       "      <th>confederation</th>\n",
       "      <th>rank</th>\n",
       "      <th>points</th>\n",
       "      <th>country</th>\n",
       "      <th>population</th>\n",
       "      <th>urban_pop_percent</th>\n",
       "      <th>log_population</th>\n",
       "      <th>log_team_value</th>\n",
       "      <th>log_avg_value</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Belgium</td>\n",
       "      <td>26</td>\n",
       "      <td>29.2</td>\n",
       "      <td>601.56</td>\n",
       "      <td>23.136923</td>\n",
       "      <td>UEFA</td>\n",
       "      <td>1</td>\n",
       "      <td>1783</td>\n",
       "      <td>Belgium</td>\n",
       "      <td>11598451</td>\n",
       "      <td>0.98</td>\n",
       "      <td>16.266382</td>\n",
       "      <td>6.399526</td>\n",
       "      <td>3.141430</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>France</td>\n",
       "      <td>26</td>\n",
       "      <td>28.0</td>\n",
       "      <td>925.20</td>\n",
       "      <td>35.584615</td>\n",
       "      <td>UEFA</td>\n",
       "      <td>2</td>\n",
       "      <td>1757</td>\n",
       "      <td>France</td>\n",
       "      <td>65298930</td>\n",
       "      <td>0.82</td>\n",
       "      <td>17.994486</td>\n",
       "      <td>6.830010</td>\n",
       "      <td>3.571913</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Brazil</td>\n",
       "      <td>25</td>\n",
       "      <td>27.5</td>\n",
       "      <td>826.20</td>\n",
       "      <td>33.048000</td>\n",
       "      <td>CONMEBOL</td>\n",
       "      <td>3</td>\n",
       "      <td>1743</td>\n",
       "      <td>Brazil</td>\n",
       "      <td>212821986</td>\n",
       "      <td>0.88</td>\n",
       "      <td>19.175967</td>\n",
       "      <td>6.716837</td>\n",
       "      <td>3.497961</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>England</td>\n",
       "      <td>26</td>\n",
       "      <td>25.4</td>\n",
       "      <td>1160.00</td>\n",
       "      <td>44.615385</td>\n",
       "      <td>UEFA</td>\n",
       "      <td>4</td>\n",
       "      <td>1687</td>\n",
       "      <td>England</td>\n",
       "      <td>56286961</td>\n",
       "      <td>0.83</td>\n",
       "      <td>17.845973</td>\n",
       "      <td>7.056175</td>\n",
       "      <td>3.798079</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Portugal</td>\n",
       "      <td>26</td>\n",
       "      <td>27.9</td>\n",
       "      <td>747.45</td>\n",
       "      <td>28.748077</td>\n",
       "      <td>UEFA</td>\n",
       "      <td>5</td>\n",
       "      <td>1666</td>\n",
       "      <td>Portugal</td>\n",
       "      <td>10191409</td>\n",
       "      <td>0.66</td>\n",
       "      <td>16.137056</td>\n",
       "      <td>6.616667</td>\n",
       "      <td>3.358571</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>Spain</td>\n",
       "      <td>24</td>\n",
       "      <td>26.7</td>\n",
       "      <td>839.70</td>\n",
       "      <td>34.987500</td>\n",
       "      <td>UEFA</td>\n",
       "      <td>6</td>\n",
       "      <td>1648</td>\n",
       "      <td>Spain</td>\n",
       "      <td>46757980</td>\n",
       "      <td>0.8</td>\n",
       "      <td>17.660495</td>\n",
       "      <td>6.733045</td>\n",
       "      <td>3.554991</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>Italy</td>\n",
       "      <td>26</td>\n",
       "      <td>27.9</td>\n",
       "      <td>695.70</td>\n",
       "      <td>26.757692</td>\n",
       "      <td>UEFA</td>\n",
       "      <td>7</td>\n",
       "      <td>1642</td>\n",
       "      <td>Italy</td>\n",
       "      <td>60446035</td>\n",
       "      <td>0.69</td>\n",
       "      <td>17.917262</td>\n",
       "      <td>6.544919</td>\n",
       "      <td>3.286822</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>Argentina</td>\n",
       "      <td>28</td>\n",
       "      <td>27.7</td>\n",
       "      <td>598.95</td>\n",
       "      <td>21.391071</td>\n",
       "      <td>CONMEBOL</td>\n",
       "      <td>8</td>\n",
       "      <td>1642</td>\n",
       "      <td>Argentina</td>\n",
       "      <td>45267449</td>\n",
       "      <td>0.93</td>\n",
       "      <td>17.628099</td>\n",
       "      <td>6.395178</td>\n",
       "      <td>3.062974</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>Uruguay</td>\n",
       "      <td>26</td>\n",
       "      <td>27.5</td>\n",
       "      <td>340.56</td>\n",
       "      <td>13.098462</td>\n",
       "      <td>CONMEBOL</td>\n",
       "      <td>9</td>\n",
       "      <td>1639</td>\n",
       "      <td>Uruguay</td>\n",
       "      <td>3475842</td>\n",
       "      <td>0.96</td>\n",
       "      <td>15.061347</td>\n",
       "      <td>5.830591</td>\n",
       "      <td>2.572495</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>Denmark</td>\n",
       "      <td>26</td>\n",
       "      <td>27.9</td>\n",
       "      <td>297.63</td>\n",
       "      <td>11.447308</td>\n",
       "      <td>UEFA</td>\n",
       "      <td>10</td>\n",
       "      <td>1632</td>\n",
       "      <td>Denmark</td>\n",
       "      <td>5795780</td>\n",
       "      <td>0.88</td>\n",
       "      <td>15.572641</td>\n",
       "      <td>5.695851</td>\n",
       "      <td>2.437755</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>Mexico</td>\n",
       "      <td>23</td>\n",
       "      <td>28.1</td>\n",
       "      <td>117.54</td>\n",
       "      <td>5.110435</td>\n",
       "      <td>CONCACAF</td>\n",
       "      <td>11</td>\n",
       "      <td>1630</td>\n",
       "      <td>Mexico</td>\n",
       "      <td>129166028</td>\n",
       "      <td>0.84</td>\n",
       "      <td>18.676609</td>\n",
       "      <td>4.766779</td>\n",
       "      <td>1.631284</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>Germany</td>\n",
       "      <td>25</td>\n",
       "      <td>27.6</td>\n",
       "      <td>806.85</td>\n",
       "      <td>32.274000</td>\n",
       "      <td>UEFA</td>\n",
       "      <td>12</td>\n",
       "      <td>1609</td>\n",
       "      <td>Germany</td>\n",
       "      <td>83830972</td>\n",
       "      <td>0.76</td>\n",
       "      <td>18.244313</td>\n",
       "      <td>6.693138</td>\n",
       "      <td>3.474262</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>Switzerland</td>\n",
       "      <td>25</td>\n",
       "      <td>26.9</td>\n",
       "      <td>258.75</td>\n",
       "      <td>10.350000</td>\n",
       "      <td>UEFA</td>\n",
       "      <td>13</td>\n",
       "      <td>1606</td>\n",
       "      <td>Switzerland</td>\n",
       "      <td>8665615</td>\n",
       "      <td>0.74</td>\n",
       "      <td>15.974873</td>\n",
       "      <td>5.555862</td>\n",
       "      <td>2.336987</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>Croatia</td>\n",
       "      <td>26</td>\n",
       "      <td>28.0</td>\n",
       "      <td>339.12</td>\n",
       "      <td>13.043077</td>\n",
       "      <td>UEFA</td>\n",
       "      <td>14</td>\n",
       "      <td>1606</td>\n",
       "      <td>Croatia</td>\n",
       "      <td>4100719</td>\n",
       "      <td>0.58</td>\n",
       "      <td>15.226673</td>\n",
       "      <td>5.826354</td>\n",
       "      <td>2.568257</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>Colombia</td>\n",
       "      <td>28</td>\n",
       "      <td>28.2</td>\n",
       "      <td>264.42</td>\n",
       "      <td>9.443571</td>\n",
       "      <td>CONMEBOL</td>\n",
       "      <td>15</td>\n",
       "      <td>1601</td>\n",
       "      <td>Colombia</td>\n",
       "      <td>50976248</td>\n",
       "      <td>0.8</td>\n",
       "      <td>17.746870</td>\n",
       "      <td>5.577539</td>\n",
       "      <td>2.245334</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "         nation  squad_size  average_age  team_value_mil_pounds  \\\n",
       "0       Belgium          26         29.2                 601.56   \n",
       "1        France          26         28.0                 925.20   \n",
       "2        Brazil          25         27.5                 826.20   \n",
       "3       England          26         25.4                1160.00   \n",
       "4      Portugal          26         27.9                 747.45   \n",
       "5         Spain          24         26.7                 839.70   \n",
       "6         Italy          26         27.9                 695.70   \n",
       "7     Argentina          28         27.7                 598.95   \n",
       "8       Uruguay          26         27.5                 340.56   \n",
       "9       Denmark          26         27.9                 297.63   \n",
       "10       Mexico          23         28.1                 117.54   \n",
       "11      Germany          25         27.6                 806.85   \n",
       "12  Switzerland          25         26.9                 258.75   \n",
       "13      Croatia          26         28.0                 339.12   \n",
       "14     Colombia          28         28.2                 264.42   \n",
       "\n",
       "    avg_value_mil_pounds confederation rank  points      country  population  \\\n",
       "0              23.136923          UEFA    1    1783      Belgium    11598451   \n",
       "1              35.584615          UEFA    2    1757       France    65298930   \n",
       "2              33.048000      CONMEBOL    3    1743       Brazil   212821986   \n",
       "3              44.615385          UEFA    4    1687      England    56286961   \n",
       "4              28.748077          UEFA    5    1666     Portugal    10191409   \n",
       "5              34.987500          UEFA    6    1648        Spain    46757980   \n",
       "6              26.757692          UEFA    7    1642        Italy    60446035   \n",
       "7              21.391071      CONMEBOL    8    1642    Argentina    45267449   \n",
       "8              13.098462      CONMEBOL    9    1639      Uruguay     3475842   \n",
       "9              11.447308          UEFA   10    1632      Denmark     5795780   \n",
       "10              5.110435      CONCACAF   11    1630       Mexico   129166028   \n",
       "11             32.274000          UEFA   12    1609      Germany    83830972   \n",
       "12             10.350000          UEFA   13    1606  Switzerland     8665615   \n",
       "13             13.043077          UEFA   14    1606      Croatia     4100719   \n",
       "14              9.443571      CONMEBOL   15    1601     Colombia    50976248   \n",
       "\n",
       "   urban_pop_percent  log_population  log_team_value  log_avg_value  \n",
       "0               0.98       16.266382        6.399526       3.141430  \n",
       "1               0.82       17.994486        6.830010       3.571913  \n",
       "2               0.88       19.175967        6.716837       3.497961  \n",
       "3               0.83       17.845973        7.056175       3.798079  \n",
       "4               0.66       16.137056        6.616667       3.358571  \n",
       "5                0.8       17.660495        6.733045       3.554991  \n",
       "6               0.69       17.917262        6.544919       3.286822  \n",
       "7               0.93       17.628099        6.395178       3.062974  \n",
       "8               0.96       15.061347        5.830591       2.572495  \n",
       "9               0.88       15.572641        5.695851       2.437755  \n",
       "10              0.84       18.676609        4.766779       1.631284  \n",
       "11              0.76       18.244313        6.693138       3.474262  \n",
       "12              0.74       15.974873        5.555862       2.336987  \n",
       "13              0.58       15.226673        5.826354       2.568257  \n",
       "14               0.8       17.746870        5.577539       2.245334  "
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_national_merged.head(15)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Convert urban pop percent column dtype from object to numeric\n",
    "df_national_merged['urban_pop_percent'] = pd.to_numeric(df_national_merged['urban_pop_percent'], errors='coerce')\n",
    "\n",
    "# Drop rows with any NaN\n",
    "df_national_merged = df_national_merged.dropna()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "# Setup dataset for linear regression\n",
    "X_cols = [\n",
    "#           'average_age', \n",
    "#           'log_population', \n",
    "          'log_avg_value', # Selecting only 1 variable for simple monovariate linear regression\n",
    "#           'avg_value_mil_pounds',\n",
    "#           'urban_pop_percent'\n",
    "         ]\n",
    "\n",
    "# Separate the independent and target variables\n",
    "df_X = df_national_merged[X_cols]\n",
    "df_y = df_national_merged['points']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Coefficients: [84.0145744]\n",
      "Intercept: 1340.6809836886548\n",
      "R^2 score: 0.746271867541619\n",
      "Adjusted R^2 score: 0.7445098666217691\n"
     ]
    }
   ],
   "source": [
    "# Linear regression with sklearn\n",
    "lin_reg = LinearRegression()\n",
    "lin_reg.fit(df_X, df_y)\n",
    "\n",
    "# Get metrics\n",
    "print(f'Coefficients: {lin_reg.coef_}')\n",
    "print(f'Intercept: {lin_reg.intercept_}')\n",
    "print(f'R^2 score: {lin_reg.score(df_X, df_y)}')\n",
    "\n",
    "adjusted_r2 = 1 - (1-lin_reg.score(df_X, df_y))*(len(df_y)-1)/(len(df_y)-df_X.shape[1]-1)\n",
    "print(f'Adjusted R^2 score: {adjusted_r2}')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Visualize the linear regression\n",
    "sns.regplot(x=\"log_avg_value\", y=\"points\", data=df_national_merged);"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.8.3"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}