world-happiness-report-analysis.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Analysis of The World Happiness Report\n",
    "\n",
    "The World Happiness Report is an annual publication that ranks countries based on their citizens' happiness levels. This notebook does an exploratory data analysis on the data using Data Science principles.\n",
    "\n",
    "---\n",
    "By Wanga Mulaudzi\n",
    "<br>\n",
    "21 July 2024"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Import Statements"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "import geopandas as gpd # For loading a map of the world\n",
    "import io # For handling bytes\n",
    "import ipywidgets as widgets # For interactive plotting\n",
    "from IPython.display import display # For interactive plotting\n",
    "import matplotlib.pyplot as plt # For plotting\n",
    "import pandas as pd # For handling the data in tabular form\n",
    "import requests # For downloading data\n",
    "import seaborn as sns # For plotting\n",
    "from sklearn.cluster import KMeans # For unsupervised clustering\n",
    "from sklearn.decomposition import PCA # For calculating principal components of features\n",
    "from sklearn.ensemble import RandomForestRegressor # # For supervised learning\n",
    "from sklearn.metrics import mean_squared_error, r2_score # For calculating metrics of trained model\n",
    "from sklearn.model_selection import train_test_split # For train test splitting the data\n",
    "from sklearn.preprocessing import StandardScaler # For notmalizing the features"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Read in the Data"
   ]
  },
  {
   "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>Country</th>\n",
       "      <th>Region</th>\n",
       "      <th>Happiness Rank</th>\n",
       "      <th>Happiness Score</th>\n",
       "      <th>Standard Error</th>\n",
       "      <th>Economy (GDP per Capita)</th>\n",
       "      <th>Family</th>\n",
       "      <th>Health (Life Expectancy)</th>\n",
       "      <th>Freedom</th>\n",
       "      <th>Trust (Government Corruption)</th>\n",
       "      <th>Generosity</th>\n",
       "      <th>Dystopia Residual</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Switzerland</td>\n",
       "      <td>Western Europe</td>\n",
       "      <td>1</td>\n",
       "      <td>7.587</td>\n",
       "      <td>0.03411</td>\n",
       "      <td>1.39651</td>\n",
       "      <td>1.34951</td>\n",
       "      <td>0.94143</td>\n",
       "      <td>0.66557</td>\n",
       "      <td>0.41978</td>\n",
       "      <td>0.29678</td>\n",
       "      <td>2.51738</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Iceland</td>\n",
       "      <td>Western Europe</td>\n",
       "      <td>2</td>\n",
       "      <td>7.561</td>\n",
       "      <td>0.04884</td>\n",
       "      <td>1.30232</td>\n",
       "      <td>1.40223</td>\n",
       "      <td>0.94784</td>\n",
       "      <td>0.62877</td>\n",
       "      <td>0.14145</td>\n",
       "      <td>0.43630</td>\n",
       "      <td>2.70201</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Denmark</td>\n",
       "      <td>Western Europe</td>\n",
       "      <td>3</td>\n",
       "      <td>7.527</td>\n",
       "      <td>0.03328</td>\n",
       "      <td>1.32548</td>\n",
       "      <td>1.36058</td>\n",
       "      <td>0.87464</td>\n",
       "      <td>0.64938</td>\n",
       "      <td>0.48357</td>\n",
       "      <td>0.34139</td>\n",
       "      <td>2.49204</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       Country          Region  Happiness Rank  Happiness Score  \\\n",
       "0  Switzerland  Western Europe               1            7.587   \n",
       "1      Iceland  Western Europe               2            7.561   \n",
       "2      Denmark  Western Europe               3            7.527   \n",
       "\n",
       "   Standard Error  Economy (GDP per Capita)   Family  \\\n",
       "0         0.03411                   1.39651  1.34951   \n",
       "1         0.04884                   1.30232  1.40223   \n",
       "2         0.03328                   1.32548  1.36058   \n",
       "\n",
       "   Health (Life Expectancy)  Freedom  Trust (Government Corruption)  \\\n",
       "0                   0.94143  0.66557                        0.41978   \n",
       "1                   0.94784  0.62877                        0.14145   \n",
       "2                   0.87464  0.64938                        0.48357   \n",
       "\n",
       "   Generosity  Dystopia Residual  \n",
       "0     0.29678            2.51738  \n",
       "1     0.43630            2.70201  \n",
       "2     0.34139            2.49204  "
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "happiness_2015 = pd.read_csv(\"data/2015.csv\")\n",
    "happiness_2015.head(3)"
   ]
  },
  {
   "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>Region</th>\n",
       "      <th>Happiness Rank</th>\n",
       "      <th>Happiness Score</th>\n",
       "      <th>Lower Confidence Interval</th>\n",
       "      <th>Upper Confidence Interval</th>\n",
       "      <th>Economy (GDP per Capita)</th>\n",
       "      <th>Family</th>\n",
       "      <th>Health (Life Expectancy)</th>\n",
       "      <th>Freedom</th>\n",
       "      <th>Trust (Government Corruption)</th>\n",
       "      <th>Generosity</th>\n",
       "      <th>Dystopia Residual</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Denmark</td>\n",
       "      <td>Western Europe</td>\n",
       "      <td>1</td>\n",
       "      <td>7.526</td>\n",
       "      <td>7.460</td>\n",
       "      <td>7.592</td>\n",
       "      <td>1.44178</td>\n",
       "      <td>1.16374</td>\n",
       "      <td>0.79504</td>\n",
       "      <td>0.57941</td>\n",
       "      <td>0.44453</td>\n",
       "      <td>0.36171</td>\n",
       "      <td>2.73939</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Switzerland</td>\n",
       "      <td>Western Europe</td>\n",
       "      <td>2</td>\n",
       "      <td>7.509</td>\n",
       "      <td>7.428</td>\n",
       "      <td>7.590</td>\n",
       "      <td>1.52733</td>\n",
       "      <td>1.14524</td>\n",
       "      <td>0.86303</td>\n",
       "      <td>0.58557</td>\n",
       "      <td>0.41203</td>\n",
       "      <td>0.28083</td>\n",
       "      <td>2.69463</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Iceland</td>\n",
       "      <td>Western Europe</td>\n",
       "      <td>3</td>\n",
       "      <td>7.501</td>\n",
       "      <td>7.333</td>\n",
       "      <td>7.669</td>\n",
       "      <td>1.42666</td>\n",
       "      <td>1.18326</td>\n",
       "      <td>0.86733</td>\n",
       "      <td>0.56624</td>\n",
       "      <td>0.14975</td>\n",
       "      <td>0.47678</td>\n",
       "      <td>2.83137</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       Country          Region  Happiness Rank  Happiness Score  \\\n",
       "0      Denmark  Western Europe               1            7.526   \n",
       "1  Switzerland  Western Europe               2            7.509   \n",
       "2      Iceland  Western Europe               3            7.501   \n",
       "\n",
       "   Lower Confidence Interval  Upper Confidence Interval  \\\n",
       "0                      7.460                      7.592   \n",
       "1                      7.428                      7.590   \n",
       "2                      7.333                      7.669   \n",
       "\n",
       "   Economy (GDP per Capita)   Family  Health (Life Expectancy)  Freedom  \\\n",
       "0                   1.44178  1.16374                   0.79504  0.57941   \n",
       "1                   1.52733  1.14524                   0.86303  0.58557   \n",
       "2                   1.42666  1.18326                   0.86733  0.56624   \n",
       "\n",
       "   Trust (Government Corruption)  Generosity  Dystopia Residual  \n",
       "0                        0.44453     0.36171            2.73939  \n",
       "1                        0.41203     0.28083            2.69463  \n",
       "2                        0.14975     0.47678            2.83137  "
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "happiness_2016 = pd.read_csv(\"data/2016.csv\")\n",
    "happiness_2016.head(3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Country</th>\n",
       "      <th>Happiness.Rank</th>\n",
       "      <th>Happiness.Score</th>\n",
       "      <th>Whisker.high</th>\n",
       "      <th>Whisker.low</th>\n",
       "      <th>Economy..GDP.per.Capita.</th>\n",
       "      <th>Family</th>\n",
       "      <th>Health..Life.Expectancy.</th>\n",
       "      <th>Freedom</th>\n",
       "      <th>Generosity</th>\n",
       "      <th>Trust..Government.Corruption.</th>\n",
       "      <th>Dystopia.Residual</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Norway</td>\n",
       "      <td>1</td>\n",
       "      <td>7.537</td>\n",
       "      <td>7.594445</td>\n",
       "      <td>7.479556</td>\n",
       "      <td>1.616463</td>\n",
       "      <td>1.533524</td>\n",
       "      <td>0.796667</td>\n",
       "      <td>0.635423</td>\n",
       "      <td>0.362012</td>\n",
       "      <td>0.315964</td>\n",
       "      <td>2.277027</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Denmark</td>\n",
       "      <td>2</td>\n",
       "      <td>7.522</td>\n",
       "      <td>7.581728</td>\n",
       "      <td>7.462272</td>\n",
       "      <td>1.482383</td>\n",
       "      <td>1.551122</td>\n",
       "      <td>0.792566</td>\n",
       "      <td>0.626007</td>\n",
       "      <td>0.355280</td>\n",
       "      <td>0.400770</td>\n",
       "      <td>2.313707</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Iceland</td>\n",
       "      <td>3</td>\n",
       "      <td>7.504</td>\n",
       "      <td>7.622030</td>\n",
       "      <td>7.385970</td>\n",
       "      <td>1.480633</td>\n",
       "      <td>1.610574</td>\n",
       "      <td>0.833552</td>\n",
       "      <td>0.627163</td>\n",
       "      <td>0.475540</td>\n",
       "      <td>0.153527</td>\n",
       "      <td>2.322715</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Country  Happiness.Rank  Happiness.Score  Whisker.high  Whisker.low  \\\n",
       "0   Norway               1            7.537      7.594445     7.479556   \n",
       "1  Denmark               2            7.522      7.581728     7.462272   \n",
       "2  Iceland               3            7.504      7.622030     7.385970   \n",
       "\n",
       "   Economy..GDP.per.Capita.    Family  Health..Life.Expectancy.   Freedom  \\\n",
       "0                  1.616463  1.533524                  0.796667  0.635423   \n",
       "1                  1.482383  1.551122                  0.792566  0.626007   \n",
       "2                  1.480633  1.610574                  0.833552  0.627163   \n",
       "\n",
       "   Generosity  Trust..Government.Corruption.  Dystopia.Residual  \n",
       "0    0.362012                       0.315964           2.277027  \n",
       "1    0.355280                       0.400770           2.313707  \n",
       "2    0.475540                       0.153527           2.322715  "
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "happiness_2017 = pd.read_csv(\"data/2017.csv\")\n",
    "happiness_2017.head(3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Overall rank</th>\n",
       "      <th>Country or region</th>\n",
       "      <th>Score</th>\n",
       "      <th>GDP per capita</th>\n",
       "      <th>Social support</th>\n",
       "      <th>Healthy life expectancy</th>\n",
       "      <th>Freedom to make life choices</th>\n",
       "      <th>Generosity</th>\n",
       "      <th>Perceptions of corruption</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>Finland</td>\n",
       "      <td>7.632</td>\n",
       "      <td>1.305</td>\n",
       "      <td>1.592</td>\n",
       "      <td>0.874</td>\n",
       "      <td>0.681</td>\n",
       "      <td>0.202</td>\n",
       "      <td>0.393</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>Norway</td>\n",
       "      <td>7.594</td>\n",
       "      <td>1.456</td>\n",
       "      <td>1.582</td>\n",
       "      <td>0.861</td>\n",
       "      <td>0.686</td>\n",
       "      <td>0.286</td>\n",
       "      <td>0.340</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "      <td>Denmark</td>\n",
       "      <td>7.555</td>\n",
       "      <td>1.351</td>\n",
       "      <td>1.590</td>\n",
       "      <td>0.868</td>\n",
       "      <td>0.683</td>\n",
       "      <td>0.284</td>\n",
       "      <td>0.408</td>\n",
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       "</table>\n",
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      ],
      "text/plain": [
       "   Overall rank Country or region  Score  GDP per capita  Social support  \\\n",
       "0             1           Finland  7.632           1.305           1.592   \n",
       "1             2            Norway  7.594           1.456           1.582   \n",
       "2             3           Denmark  7.555           1.351           1.590   \n",
       "\n",
       "   Healthy life expectancy  Freedom to make life choices  Generosity  \\\n",
       "0                    0.874                         0.681       0.202   \n",
       "1                    0.861                         0.686       0.286   \n",
       "2                    0.868                         0.683       0.284   \n",
       "\n",
       "   Perceptions of corruption  \n",
       "0                      0.393  \n",
       "1                      0.340  \n",
       "2                      0.408  "
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "happiness_2018 = pd.read_csv(\"data/2018.csv\")\n",
    "happiness_2018.head(3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Overall rank</th>\n",
       "      <th>Country or region</th>\n",
       "      <th>Score</th>\n",
       "      <th>GDP per capita</th>\n",
       "      <th>Social support</th>\n",
       "      <th>Healthy life expectancy</th>\n",
       "      <th>Freedom to make life choices</th>\n",
       "      <th>Generosity</th>\n",
       "      <th>Perceptions of corruption</th>\n",
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       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>Finland</td>\n",
       "      <td>7.769</td>\n",
       "      <td>1.340</td>\n",
       "      <td>1.587</td>\n",
       "      <td>0.986</td>\n",
       "      <td>0.596</td>\n",
       "      <td>0.153</td>\n",
       "      <td>0.393</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>Denmark</td>\n",
       "      <td>7.600</td>\n",
       "      <td>1.383</td>\n",
       "      <td>1.573</td>\n",
       "      <td>0.996</td>\n",
       "      <td>0.592</td>\n",
       "      <td>0.252</td>\n",
       "      <td>0.410</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "      <td>Norway</td>\n",
       "      <td>7.554</td>\n",
       "      <td>1.488</td>\n",
       "      <td>1.582</td>\n",
       "      <td>1.028</td>\n",
       "      <td>0.603</td>\n",
       "      <td>0.271</td>\n",
       "      <td>0.341</td>\n",
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       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Overall rank Country or region  Score  GDP per capita  Social support  \\\n",
       "0             1           Finland  7.769           1.340           1.587   \n",
       "1             2           Denmark  7.600           1.383           1.573   \n",
       "2             3            Norway  7.554           1.488           1.582   \n",
       "\n",
       "   Healthy life expectancy  Freedom to make life choices  Generosity  \\\n",
       "0                    0.986                         0.596       0.153   \n",
       "1                    0.996                         0.592       0.252   \n",
       "2                    1.028                         0.603       0.271   \n",
       "\n",
       "   Perceptions of corruption  \n",
       "0                      0.393  \n",
       "1                      0.410  \n",
       "2                      0.341  "
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "happiness_2019 = pd.read_csv(\"data/2019.csv\")\n",
    "happiness_2019.head(3)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Make column names consistent"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "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>Country</th>\n",
       "      <th>Happiness.Rank</th>\n",
       "      <th>Happiness.Score</th>\n",
       "      <th>Whisker.high</th>\n",
       "      <th>Whisker.low</th>\n",
       "      <th>Economy..GDP.per.Capita.</th>\n",
       "      <th>Family</th>\n",
       "      <th>Health..Life.Expectancy.</th>\n",
       "      <th>Freedom</th>\n",
       "      <th>Generosity</th>\n",
       "      <th>Trust..Government.Corruption.</th>\n",
       "      <th>Dystopia.Residual</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Norway</td>\n",
       "      <td>1</td>\n",
       "      <td>7.537</td>\n",
       "      <td>7.594445</td>\n",
       "      <td>7.479556</td>\n",
       "      <td>1.616463</td>\n",
       "      <td>1.533524</td>\n",
       "      <td>0.796667</td>\n",
       "      <td>0.635423</td>\n",
       "      <td>0.362012</td>\n",
       "      <td>0.315964</td>\n",
       "      <td>2.277027</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Denmark</td>\n",
       "      <td>2</td>\n",
       "      <td>7.522</td>\n",
       "      <td>7.581728</td>\n",
       "      <td>7.462272</td>\n",
       "      <td>1.482383</td>\n",
       "      <td>1.551122</td>\n",
       "      <td>0.792566</td>\n",
       "      <td>0.626007</td>\n",
       "      <td>0.355280</td>\n",
       "      <td>0.400770</td>\n",
       "      <td>2.313707</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Iceland</td>\n",
       "      <td>3</td>\n",
       "      <td>7.504</td>\n",
       "      <td>7.622030</td>\n",
       "      <td>7.385970</td>\n",
       "      <td>1.480633</td>\n",
       "      <td>1.610574</td>\n",
       "      <td>0.833552</td>\n",
       "      <td>0.627163</td>\n",
       "      <td>0.475540</td>\n",
       "      <td>0.153527</td>\n",
       "      <td>2.322715</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Country  Happiness.Rank  Happiness.Score  Whisker.high  Whisker.low  \\\n",
       "0   Norway               1            7.537      7.594445     7.479556   \n",
       "1  Denmark               2            7.522      7.581728     7.462272   \n",
       "2  Iceland               3            7.504      7.622030     7.385970   \n",
       "\n",
       "   Economy..GDP.per.Capita.    Family  Health..Life.Expectancy.   Freedom  \\\n",
       "0                  1.616463  1.533524                  0.796667  0.635423   \n",
       "1                  1.482383  1.551122                  0.792566  0.626007   \n",
       "2                  1.480633  1.610574                  0.833552  0.627163   \n",
       "\n",
       "   Generosity  Trust..Government.Corruption.  Dystopia.Residual  \n",
       "0    0.362012                       0.315964           2.277027  \n",
       "1    0.355280                       0.400770           2.313707  \n",
       "2    0.475540                       0.153527           2.322715  "
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "happiness_2017.head(3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Country</th>\n",
       "      <th>Happiness Rank</th>\n",
       "      <th>Happiness Score</th>\n",
       "      <th>Upper Confidence Interval</th>\n",
       "      <th>Lower Confidence Interval</th>\n",
       "      <th>Economy (GDP per Capita)</th>\n",
       "      <th>Family</th>\n",
       "      <th>Health (Life Expectancy)</th>\n",
       "      <th>Freedom</th>\n",
       "      <th>Generosity</th>\n",
       "      <th>Trust (Government Corruption)</th>\n",
       "      <th>Dystopia Residual</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Norway</td>\n",
       "      <td>1</td>\n",
       "      <td>7.537</td>\n",
       "      <td>7.594445</td>\n",
       "      <td>7.479556</td>\n",
       "      <td>1.616463</td>\n",
       "      <td>1.533524</td>\n",
       "      <td>0.796667</td>\n",
       "      <td>0.635423</td>\n",
       "      <td>0.362012</td>\n",
       "      <td>0.315964</td>\n",
       "      <td>2.277027</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Denmark</td>\n",
       "      <td>2</td>\n",
       "      <td>7.522</td>\n",
       "      <td>7.581728</td>\n",
       "      <td>7.462272</td>\n",
       "      <td>1.482383</td>\n",
       "      <td>1.551122</td>\n",
       "      <td>0.792566</td>\n",
       "      <td>0.626007</td>\n",
       "      <td>0.355280</td>\n",
       "      <td>0.400770</td>\n",
       "      <td>2.313707</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Iceland</td>\n",
       "      <td>3</td>\n",
       "      <td>7.504</td>\n",
       "      <td>7.622030</td>\n",
       "      <td>7.385970</td>\n",
       "      <td>1.480633</td>\n",
       "      <td>1.610574</td>\n",
       "      <td>0.833552</td>\n",
       "      <td>0.627163</td>\n",
       "      <td>0.475540</td>\n",
       "      <td>0.153527</td>\n",
       "      <td>2.322715</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Country  Happiness Rank  Happiness Score  Upper Confidence Interval  \\\n",
       "0   Norway               1            7.537                   7.594445   \n",
       "1  Denmark               2            7.522                   7.581728   \n",
       "2  Iceland               3            7.504                   7.622030   \n",
       "\n",
       "   Lower Confidence Interval  Economy (GDP per Capita)    Family  \\\n",
       "0                   7.479556                  1.616463  1.533524   \n",
       "1                   7.462272                  1.482383  1.551122   \n",
       "2                   7.385970                  1.480633  1.610574   \n",
       "\n",
       "   Health (Life Expectancy)   Freedom  Generosity  \\\n",
       "0                  0.796667  0.635423    0.362012   \n",
       "1                  0.792566  0.626007    0.355280   \n",
       "2                  0.833552  0.627163    0.475540   \n",
       "\n",
       "   Trust (Government Corruption)  Dystopia Residual  \n",
       "0                       0.315964           2.277027  \n",
       "1                       0.400770           2.313707  \n",
       "2                       0.153527           2.322715  "
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "happiness_2017.rename(columns={\"Happiness.Rank\": \"Happiness Rank\",\n",
    "                               \"Happiness.Score\": \"Happiness Score\",\n",
    "                               \"Whisker.high\": \"Upper Confidence Interval\",\n",
    "                               \"Whisker.low\": \"Lower Confidence Interval\",\n",
    "                               \"Economy..GDP.per.Capita.\": \"Economy (GDP per Capita)\",\n",
    "                               \"Health..Life.Expectancy.\": \"Health (Life Expectancy)\",\n",
    "                               \"Trust..Government.Corruption.\": \"Trust (Government Corruption)\",\n",
    "                               \"Dystopia.Residual\": \"Dystopia Residual\"},\n",
    "                      inplace=True)\n",
    "\n",
    "happiness_2017.head(3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "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 rank</th>\n",
       "      <th>Country or region</th>\n",
       "      <th>Score</th>\n",
       "      <th>GDP per capita</th>\n",
       "      <th>Social support</th>\n",
       "      <th>Healthy life expectancy</th>\n",
       "      <th>Freedom to make life choices</th>\n",
       "      <th>Generosity</th>\n",
       "      <th>Perceptions of corruption</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>Finland</td>\n",
       "      <td>7.632</td>\n",
       "      <td>1.305</td>\n",
       "      <td>1.592</td>\n",
       "      <td>0.874</td>\n",
       "      <td>0.681</td>\n",
       "      <td>0.202</td>\n",
       "      <td>0.393</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>Norway</td>\n",
       "      <td>7.594</td>\n",
       "      <td>1.456</td>\n",
       "      <td>1.582</td>\n",
       "      <td>0.861</td>\n",
       "      <td>0.686</td>\n",
       "      <td>0.286</td>\n",
       "      <td>0.340</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "      <td>Denmark</td>\n",
       "      <td>7.555</td>\n",
       "      <td>1.351</td>\n",
       "      <td>1.590</td>\n",
       "      <td>0.868</td>\n",
       "      <td>0.683</td>\n",
       "      <td>0.284</td>\n",
       "      <td>0.408</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Overall rank Country or region  Score  GDP per capita  Social support  \\\n",
       "0             1           Finland  7.632           1.305           1.592   \n",
       "1             2            Norway  7.594           1.456           1.582   \n",
       "2             3           Denmark  7.555           1.351           1.590   \n",
       "\n",
       "   Healthy life expectancy  Freedom to make life choices  Generosity  \\\n",
       "0                    0.874                         0.681       0.202   \n",
       "1                    0.861                         0.686       0.286   \n",
       "2                    0.868                         0.683       0.284   \n",
       "\n",
       "   Perceptions of corruption  \n",
       "0                      0.393  \n",
       "1                      0.340  \n",
       "2                      0.408  "
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "happiness_2018.head(3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "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>Happiness Rank</th>\n",
       "      <th>Country</th>\n",
       "      <th>Happiness Score</th>\n",
       "      <th>Economy (GDP per Capita)</th>\n",
       "      <th>Social Support</th>\n",
       "      <th>Health (Life Expectancy)</th>\n",
       "      <th>Freedom</th>\n",
       "      <th>Generosity</th>\n",
       "      <th>Trust (Government Corruption)</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>Finland</td>\n",
       "      <td>7.632</td>\n",
       "      <td>1.305</td>\n",
       "      <td>1.592</td>\n",
       "      <td>0.874</td>\n",
       "      <td>0.681</td>\n",
       "      <td>0.202</td>\n",
       "      <td>0.393</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>Norway</td>\n",
       "      <td>7.594</td>\n",
       "      <td>1.456</td>\n",
       "      <td>1.582</td>\n",
       "      <td>0.861</td>\n",
       "      <td>0.686</td>\n",
       "      <td>0.286</td>\n",
       "      <td>0.340</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "      <td>Denmark</td>\n",
       "      <td>7.555</td>\n",
       "      <td>1.351</td>\n",
       "      <td>1.590</td>\n",
       "      <td>0.868</td>\n",
       "      <td>0.683</td>\n",
       "      <td>0.284</td>\n",
       "      <td>0.408</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Happiness Rank  Country  Happiness Score  Economy (GDP per Capita)  \\\n",
       "0               1  Finland            7.632                     1.305   \n",
       "1               2   Norway            7.594                     1.456   \n",
       "2               3  Denmark            7.555                     1.351   \n",
       "\n",
       "   Social Support  Health (Life Expectancy)  Freedom  Generosity  \\\n",
       "0           1.592                     0.874    0.681       0.202   \n",
       "1           1.582                     0.861    0.686       0.286   \n",
       "2           1.590                     0.868    0.683       0.284   \n",
       "\n",
       "   Trust (Government Corruption)  \n",
       "0                          0.393  \n",
       "1                          0.340  \n",
       "2                          0.408  "
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "happiness_2018.rename(columns={\"Overall rank\": \"Happiness Rank\",\n",
    "                               \"Country or region\": \"Country\",\n",
    "                               \"Score\": \"Happiness Score\",\n",
    "                               \"GDP per capita\": \"Economy (GDP per Capita)\",\n",
    "                               \"Social support\": \"Social Support\",\n",
    "                               \"Healthy life expectancy\": \"Health (Life Expectancy)\",\n",
    "                               \"Freedom to make life choices\": \"Freedom\",\n",
    "                               \"Perceptions of corruption\": \"Trust (Government Corruption)\"},\n",
    "                      inplace=True)\n",
    "\n",
    "happiness_2018.head(3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
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       "\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 rank</th>\n",
       "      <th>Country or region</th>\n",
       "      <th>Score</th>\n",
       "      <th>GDP per capita</th>\n",
       "      <th>Social support</th>\n",
       "      <th>Healthy life expectancy</th>\n",
       "      <th>Freedom to make life choices</th>\n",
       "      <th>Generosity</th>\n",
       "      <th>Perceptions of corruption</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>Finland</td>\n",
       "      <td>7.769</td>\n",
       "      <td>1.340</td>\n",
       "      <td>1.587</td>\n",
       "      <td>0.986</td>\n",
       "      <td>0.596</td>\n",
       "      <td>0.153</td>\n",
       "      <td>0.393</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>Denmark</td>\n",
       "      <td>7.600</td>\n",
       "      <td>1.383</td>\n",
       "      <td>1.573</td>\n",
       "      <td>0.996</td>\n",
       "      <td>0.592</td>\n",
       "      <td>0.252</td>\n",
       "      <td>0.410</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "      <td>Norway</td>\n",
       "      <td>7.554</td>\n",
       "      <td>1.488</td>\n",
       "      <td>1.582</td>\n",
       "      <td>1.028</td>\n",
       "      <td>0.603</td>\n",
       "      <td>0.271</td>\n",
       "      <td>0.341</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Overall rank Country or region  Score  GDP per capita  Social support  \\\n",
       "0             1           Finland  7.769           1.340           1.587   \n",
       "1             2           Denmark  7.600           1.383           1.573   \n",
       "2             3            Norway  7.554           1.488           1.582   \n",
       "\n",
       "   Healthy life expectancy  Freedom to make life choices  Generosity  \\\n",
       "0                    0.986                         0.596       0.153   \n",
       "1                    0.996                         0.592       0.252   \n",
       "2                    1.028                         0.603       0.271   \n",
       "\n",
       "   Perceptions of corruption  \n",
       "0                      0.393  \n",
       "1                      0.410  \n",
       "2                      0.341  "
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "happiness_2019.head(3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "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>Happiness Rank</th>\n",
       "      <th>Country</th>\n",
       "      <th>Happiness Score</th>\n",
       "      <th>Economy (GDP per Capita)</th>\n",
       "      <th>Social Support</th>\n",
       "      <th>Health (Life Expectancy)</th>\n",
       "      <th>Freedom</th>\n",
       "      <th>Generosity</th>\n",
       "      <th>Trust (Government Corruption)</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>Finland</td>\n",
       "      <td>7.769</td>\n",
       "      <td>1.340</td>\n",
       "      <td>1.587</td>\n",
       "      <td>0.986</td>\n",
       "      <td>0.596</td>\n",
       "      <td>0.153</td>\n",
       "      <td>0.393</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>Denmark</td>\n",
       "      <td>7.600</td>\n",
       "      <td>1.383</td>\n",
       "      <td>1.573</td>\n",
       "      <td>0.996</td>\n",
       "      <td>0.592</td>\n",
       "      <td>0.252</td>\n",
       "      <td>0.410</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "      <td>Norway</td>\n",
       "      <td>7.554</td>\n",
       "      <td>1.488</td>\n",
       "      <td>1.582</td>\n",
       "      <td>1.028</td>\n",
       "      <td>0.603</td>\n",
       "      <td>0.271</td>\n",
       "      <td>0.341</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Happiness Rank  Country  Happiness Score  Economy (GDP per Capita)  \\\n",
       "0               1  Finland            7.769                     1.340   \n",
       "1               2  Denmark            7.600                     1.383   \n",
       "2               3   Norway            7.554                     1.488   \n",
       "\n",
       "   Social Support  Health (Life Expectancy)  Freedom  Generosity  \\\n",
       "0           1.587                     0.986    0.596       0.153   \n",
       "1           1.573                     0.996    0.592       0.252   \n",
       "2           1.582                     1.028    0.603       0.271   \n",
       "\n",
       "   Trust (Government Corruption)  \n",
       "0                          0.393  \n",
       "1                          0.410  \n",
       "2                          0.341  "
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "happiness_2019.rename(columns={\"Overall rank\": \"Happiness Rank\",\n",
    "                               \"Country or region\": \"Country\",\n",
    "                               \"Score\": \"Happiness Score\",\n",
    "                               \"GDP per capita\": \"Economy (GDP per Capita)\",\n",
    "                               \"Social support\": \"Social Support\",\n",
    "                               \"Healthy life expectancy\": \"Health (Life Expectancy)\",\n",
    "                               \"Freedom to make life choices\": \"Freedom\",\n",
    "                               \"Perceptions of corruption\": \"Trust (Government Corruption)\"},\n",
    "                      inplace=True)\n",
    "\n",
    "happiness_2019.head(3)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Understand the Data"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Check for NaNs, empty rows, missing values, etc."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Country                          0.0\n",
       "Region                           0.0\n",
       "Happiness Rank                   0.0\n",
       "Happiness Score                  0.0\n",
       "Standard Error                   0.0\n",
       "Economy (GDP per Capita)         0.0\n",
       "Family                           0.0\n",
       "Health (Life Expectancy)         0.0\n",
       "Freedom                          0.0\n",
       "Trust (Government Corruption)    0.0\n",
       "Generosity                       0.0\n",
       "Dystopia Residual                0.0\n",
       "dtype: float64"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "happiness_2015.isnull().sum().sort_values(ascending=False)/len(happiness_2015)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Country                          0.0\n",
       "Region                           0.0\n",
       "Happiness Rank                   0.0\n",
       "Happiness Score                  0.0\n",
       "Lower Confidence Interval        0.0\n",
       "Upper Confidence Interval        0.0\n",
       "Economy (GDP per Capita)         0.0\n",
       "Family                           0.0\n",
       "Health (Life Expectancy)         0.0\n",
       "Freedom                          0.0\n",
       "Trust (Government Corruption)    0.0\n",
       "Generosity                       0.0\n",
       "Dystopia Residual                0.0\n",
       "dtype: float64"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "happiness_2016.isnull().sum().sort_values(ascending=False)/len(happiness_2016)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Country                          0.0\n",
       "Happiness Rank                   0.0\n",
       "Happiness Score                  0.0\n",
       "Upper Confidence Interval        0.0\n",
       "Lower Confidence Interval        0.0\n",
       "Economy (GDP per Capita)         0.0\n",
       "Family                           0.0\n",
       "Health (Life Expectancy)         0.0\n",
       "Freedom                          0.0\n",
       "Generosity                       0.0\n",
       "Trust (Government Corruption)    0.0\n",
       "Dystopia Residual                0.0\n",
       "dtype: float64"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "happiness_2017.isnull().sum().sort_values(ascending=False)/len(happiness_2017)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Trust (Government Corruption)    0.00641\n",
       "Happiness Rank                   0.00000\n",
       "Country                          0.00000\n",
       "Economy (GDP per Capita)         0.00000\n",
       "Happiness Score                  0.00000\n",
       "Social Support                   0.00000\n",
       "Health (Life Expectancy)         0.00000\n",
       "Freedom                          0.00000\n",
       "Generosity                       0.00000\n",
       "dtype: float64"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "happiness_2018.isnull().sum().sort_values(ascending=False)/len(happiness_2018)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "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>Happiness Rank</th>\n",
       "      <th>Country</th>\n",
       "      <th>Happiness Score</th>\n",
       "      <th>Economy (GDP per Capita)</th>\n",
       "      <th>Social Support</th>\n",
       "      <th>Health (Life Expectancy)</th>\n",
       "      <th>Freedom</th>\n",
       "      <th>Generosity</th>\n",
       "      <th>Trust (Government Corruption)</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>20</td>\n",
       "      <td>United Arab Emirates</td>\n",
       "      <td>6.774</td>\n",
       "      <td>2.096</td>\n",
       "      <td>0.776</td>\n",
       "      <td>0.67</td>\n",
       "      <td>0.284</td>\n",
       "      <td>0.186</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    Happiness Rank               Country  Happiness Score  \\\n",
       "19              20  United Arab Emirates            6.774   \n",
       "\n",
       "    Economy (GDP per Capita)  Social Support  Health (Life Expectancy)  \\\n",
       "19                     2.096           0.776                      0.67   \n",
       "\n",
       "    Freedom  Generosity  Trust (Government Corruption)  \n",
       "19    0.284       0.186                            NaN  "
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "happiness_2018[happiness_2018.isnull().any(axis=1)]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "United Arab Emirates has NaN for its perception of corruption so we will remove it."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "155"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "happiness_2018.dropna(inplace=True)\n",
    "len(happiness_2018)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Happiness Rank                   0.0\n",
       "Country                          0.0\n",
       "Happiness Score                  0.0\n",
       "Economy (GDP per Capita)         0.0\n",
       "Social Support                   0.0\n",
       "Health (Life Expectancy)         0.0\n",
       "Freedom                          0.0\n",
       "Generosity                       0.0\n",
       "Trust (Government Corruption)    0.0\n",
       "dtype: float64"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "happiness_2019.isnull().sum().sort_values(ascending=False)/len(happiness_2019)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Check for duplicated rows"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "np.int64(0)"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "happiness_2015.duplicated().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "np.int64(0)"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "happiness_2016.duplicated().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "np.int64(0)"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "happiness_2017.duplicated().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "np.int64(0)"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "happiness_2018.duplicated().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "np.int64(0)"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "happiness_2019.duplicated().sum()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Make sure numerical columns are of type float"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Country                           object\n",
       "Region                            object\n",
       "Happiness Rank                     int64\n",
       "Happiness Score                  float64\n",
       "Standard Error                   float64\n",
       "Economy (GDP per Capita)         float64\n",
       "Family                           float64\n",
       "Health (Life Expectancy)         float64\n",
       "Freedom                          float64\n",
       "Trust (Government Corruption)    float64\n",
       "Generosity                       float64\n",
       "Dystopia Residual                float64\n",
       "dtype: object"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "happiness_2015.dtypes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Country                           object\n",
       "Region                            object\n",
       "Happiness Rank                     int64\n",
       "Happiness Score                  float64\n",
       "Lower Confidence Interval        float64\n",
       "Upper Confidence Interval        float64\n",
       "Economy (GDP per Capita)         float64\n",
       "Family                           float64\n",
       "Health (Life Expectancy)         float64\n",
       "Freedom                          float64\n",
       "Trust (Government Corruption)    float64\n",
       "Generosity                       float64\n",
       "Dystopia Residual                float64\n",
       "dtype: object"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "happiness_2016.dtypes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Country                           object\n",
       "Happiness Rank                     int64\n",
       "Happiness Score                  float64\n",
       "Upper Confidence Interval        float64\n",
       "Lower Confidence Interval        float64\n",
       "Economy (GDP per Capita)         float64\n",
       "Family                           float64\n",
       "Health (Life Expectancy)         float64\n",
       "Freedom                          float64\n",
       "Generosity                       float64\n",
       "Trust (Government Corruption)    float64\n",
       "Dystopia Residual                float64\n",
       "dtype: object"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "happiness_2017.dtypes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Happiness Rank                     int64\n",
       "Country                           object\n",
       "Happiness Score                  float64\n",
       "Economy (GDP per Capita)         float64\n",
       "Social Support                   float64\n",
       "Health (Life Expectancy)         float64\n",
       "Freedom                          float64\n",
       "Generosity                       float64\n",
       "Trust (Government Corruption)    float64\n",
       "dtype: object"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "happiness_2018.dtypes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Happiness Rank                     int64\n",
       "Country                           object\n",
       "Happiness Score                  float64\n",
       "Economy (GDP per Capita)         float64\n",
       "Social Support                   float64\n",
       "Health (Life Expectancy)         float64\n",
       "Freedom                          float64\n",
       "Generosity                       float64\n",
       "Trust (Government Corruption)    float64\n",
       "dtype: object"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "happiness_2019.dtypes"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Data Augmentation"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Add year columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [],
   "source": [
    "happiness_2015[\"Year\"] = 2015\n",
    "happiness_2016[\"Year\"] = 2016\n",
    "happiness_2017[\"Year\"] = 2017\n",
    "happiness_2018[\"Year\"] = 2018\n",
    "happiness_2019[\"Year\"] = 2019"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Concatenate the tables together"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [],
   "source": [
    "tables_list = [happiness_2015, happiness_2016, happiness_2017, happiness_2018, happiness_2019]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Analysis\n",
    "### Choropleth Plots"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "cf4e5598329047aaa1925d9b4c464949",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "Dropdown(description='Year', options=('2015', '2016', '2017', '2018', '2019'), value='2015')"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "fa4e159caa7543b598b74f15619fc2ab",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "Output()"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "<Figure size 640x480 with 0 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Download world map data\n",
    "url = \"https://naciscdn.org/naturalearth/110m/cultural/ne_110m_admin_0_countries.zip\"\n",
    "response = requests.get(url)\n",
    "world = gpd.read_file(io.BytesIO(response.content))\n",
    "\n",
    "# Create a dropdown widget so that a user can plot the choropleth maps per year\n",
    "years_list = [\"2015\", \"2016\", \"2017\", \"2018\", \"2019\"] # List of years\n",
    "dropdown = widgets.Dropdown(\n",
    "    options=years_list,\n",
    "    value=\"2015\",\n",
    "    description=\"Year\"\n",
    ")\n",
    "\n",
    "def plot_choropleth(year):\n",
    "    # Get the index of the selected year in years_list\n",
    "    df_index = years_list.index(year)\n",
    "    df = tables_list[df_index]  # Corresponding dataframe\n",
    "\n",
    "    # Merge happiness data with world map data\n",
    "    world_happiness = world.merge(df, how=\"left\", left_on=[\"SOVEREIGNT\"], right_on=[\"Country\"])\n",
    "\n",
    "    # Create figure and axis\n",
    "    fig, ax = plt.subplots(figsize=(15, 7))\n",
    "\n",
    "    # Plot the map\n",
    "    world_happiness.plot(column=\"Happiness Score\",\n",
    "                         ax=ax,\n",
    "                         legend=True,\n",
    "                         legend_kwds={\"label\": \"Happiness Score\"},\n",
    "                         cmap=\"viridis\",\n",
    "                         missing_kwds={\"color\": \"lightgrey\"})\n",
    "\n",
    "    # Remove axis\n",
    "    ax.axis(\"off\")\n",
    "\n",
    "    # Set title\n",
    "    plt.title(f\"World Happiness Report - {year}\", fontsize=16)\n",
    "\n",
    "    plt.savefig(f\"images_happiness/Happiness-report-{year}.png\", bbox_inches=\"tight\")\n",
    "    plt.show()\n",
    "\n",
    "# Create an output widget to display the plot\n",
    "output = widgets.Output()\n",
    "\n",
    "# Function to update the plot\n",
    "def update_plot(change):\n",
    "    year = change[\"new\"]\n",
    "\n",
    "    with output:\n",
    "        output.clear_output(wait=True)\n",
    "        plot_choropleth(year)\n",
    "\n",
    "# Connect the dropdown to the update function\n",
    "dropdown.observe(update_plot, names=\"value\")\n",
    "\n",
    "# Display the dropdown and initial plot\n",
    "display(dropdown, output)\n",
    "update_plot({\"new\": \"2015\"})\n",
    "plt.savefig(\"World Happiness Report\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "![Plot Name](images_happiness/Happiness-report-2015.png)\n",
    "![Plot Name](images_happiness/Happiness-report-2016.png)\n",
    "![Plot Name](images_happiness/Happiness-report-2017.png)\n",
    "![Plot Name](images_happiness/Happiness-report-2018.png)\n",
    "![Plot Name](images_happiness/Happiness-report-2019.png)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Correlations between happiness scores and other factors"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "df79004d8da644029f2475b329134a71",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "Dropdown(description='Year', options=('2015', '2016', '2017', '2018', '2019'), value='2015')"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "8abe41afe48a461ea061e93e1c6abefe",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "Output()"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Create a dropdown widget so that a user can plot the correlation between happiness scores\n",
    "# and other factors\n",
    "years_list = [\"2015\", \"2016\", \"2017\", \"2018\", \"2019\"] # List of years\n",
    "dropdown_years_heatmap = widgets.Dropdown(options=years_list, value=\"2015\", description=\"Year\")\n",
    "\n",
    "def update_heatmap(selected_year):\n",
    "    # Get the index of the selected year in years_list\n",
    "    df_index = years_list.index(selected_year)\n",
    "    df = tables_list[df_index]  # Corresponding dataframe based on selected_year\n",
    "\n",
    "    # Correlation matrix figure\n",
    "    plt.figure(figsize=(8,4))\n",
    "\n",
    "    correlation_matrix = df[[\"Happiness Score\", \"Economy (GDP per Capita)\", \"Health (Life Expectancy)\",\n",
    "                             \"Freedom\", \"Trust (Government Corruption)\", \"Generosity\"]].corr()\n",
    "\n",
    "    sns.heatmap(correlation_matrix, annot=True, cmap=\"coolwarm\")\n",
    "\n",
    "    plt.title(f\"Correlation Heatmap of Happiness Factors for {selected_year}\")\n",
    "\n",
    "    plt.savefig(f\"images_happiness/Correlation-factors-{selected_year}.png\", bbox_inches=\"tight\")\n",
    "    plt.show()\n",
    "\n",
    "# Create an output widget to display the plot\n",
    "output = widgets.Output()\n",
    "\n",
    "# Function to update the plot\n",
    "def update_plot_heatmap(change):\n",
    "    year = change[\"new\"]\n",
    "\n",
    "    with output:\n",
    "        output.clear_output(wait=True)\n",
    "        update_heatmap(year)\n",
    "\n",
    "# Connect the dropdown to the update function\n",
    "dropdown_years_heatmap.observe(update_plot_heatmap, names=\"value\")\n",
    "\n",
    "# Display the dropdown and initial plot\n",
    "display(dropdown_years_heatmap, output)\n",
    "update_plot_heatmap({\"new\": \"2015\"})"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "![Plot Name](images_happiness/Correlation-factors-2015.png)\n",
    "![Plot Name](images_happiness/Correlation-factors-2016.png)\n",
    "![Plot Name](images_happiness/Correlation-factors-2017.png)\n",
    "![Plot Name](images_happiness/Correlation-factors-2018.png)\n",
    "![Plot Name](images_happiness/Correlation-factors-2019.png)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Happiness Score over time per country"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "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>Country</th>\n",
       "      <th>Region</th>\n",
       "      <th>Happiness Rank</th>\n",
       "      <th>Happiness Score</th>\n",
       "      <th>Standard Error</th>\n",
       "      <th>Economy (GDP per Capita)</th>\n",
       "      <th>Family</th>\n",
       "      <th>Health (Life Expectancy)</th>\n",
       "      <th>Freedom</th>\n",
       "      <th>Trust (Government Corruption)</th>\n",
       "      <th>Generosity</th>\n",
       "      <th>Dystopia Residual</th>\n",
       "      <th>Year</th>\n",
       "      <th>Lower Confidence Interval</th>\n",
       "      <th>Upper Confidence Interval</th>\n",
       "      <th>Social Support</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Switzerland</td>\n",
       "      <td>Western Europe</td>\n",
       "      <td>1</td>\n",
       "      <td>7.587</td>\n",
       "      <td>0.03411</td>\n",
       "      <td>1.39651</td>\n",
       "      <td>1.34951</td>\n",
       "      <td>0.94143</td>\n",
       "      <td>0.66557</td>\n",
       "      <td>0.41978</td>\n",
       "      <td>0.29678</td>\n",
       "      <td>2.51738</td>\n",
       "      <td>2015</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Iceland</td>\n",
       "      <td>Western Europe</td>\n",
       "      <td>2</td>\n",
       "      <td>7.561</td>\n",
       "      <td>0.04884</td>\n",
       "      <td>1.30232</td>\n",
       "      <td>1.40223</td>\n",
       "      <td>0.94784</td>\n",
       "      <td>0.62877</td>\n",
       "      <td>0.14145</td>\n",
       "      <td>0.43630</td>\n",
       "      <td>2.70201</td>\n",
       "      <td>2015</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Denmark</td>\n",
       "      <td>Western Europe</td>\n",
       "      <td>3</td>\n",
       "      <td>7.527</td>\n",
       "      <td>0.03328</td>\n",
       "      <td>1.32548</td>\n",
       "      <td>1.36058</td>\n",
       "      <td>0.87464</td>\n",
       "      <td>0.64938</td>\n",
       "      <td>0.48357</td>\n",
       "      <td>0.34139</td>\n",
       "      <td>2.49204</td>\n",
       "      <td>2015</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Norway</td>\n",
       "      <td>Western Europe</td>\n",
       "      <td>4</td>\n",
       "      <td>7.522</td>\n",
       "      <td>0.03880</td>\n",
       "      <td>1.45900</td>\n",
       "      <td>1.33095</td>\n",
       "      <td>0.88521</td>\n",
       "      <td>0.66973</td>\n",
       "      <td>0.36503</td>\n",
       "      <td>0.34699</td>\n",
       "      <td>2.46531</td>\n",
       "      <td>2015</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Canada</td>\n",
       "      <td>North America</td>\n",
       "      <td>5</td>\n",
       "      <td>7.427</td>\n",
       "      <td>0.03553</td>\n",
       "      <td>1.32629</td>\n",
       "      <td>1.32261</td>\n",
       "      <td>0.90563</td>\n",
       "      <td>0.63297</td>\n",
       "      <td>0.32957</td>\n",
       "      <td>0.45811</td>\n",
       "      <td>2.45176</td>\n",
       "      <td>2015</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       Country          Region  Happiness Rank  Happiness Score  \\\n",
       "0  Switzerland  Western Europe               1            7.587   \n",
       "1      Iceland  Western Europe               2            7.561   \n",
       "2      Denmark  Western Europe               3            7.527   \n",
       "3       Norway  Western Europe               4            7.522   \n",
       "4       Canada   North America               5            7.427   \n",
       "\n",
       "   Standard Error  Economy (GDP per Capita)   Family  \\\n",
       "0         0.03411                   1.39651  1.34951   \n",
       "1         0.04884                   1.30232  1.40223   \n",
       "2         0.03328                   1.32548  1.36058   \n",
       "3         0.03880                   1.45900  1.33095   \n",
       "4         0.03553                   1.32629  1.32261   \n",
       "\n",
       "   Health (Life Expectancy)  Freedom  Trust (Government Corruption)  \\\n",
       "0                   0.94143  0.66557                        0.41978   \n",
       "1                   0.94784  0.62877                        0.14145   \n",
       "2                   0.87464  0.64938                        0.48357   \n",
       "3                   0.88521  0.66973                        0.36503   \n",
       "4                   0.90563  0.63297                        0.32957   \n",
       "\n",
       "   Generosity  Dystopia Residual  Year  Lower Confidence Interval  \\\n",
       "0     0.29678            2.51738  2015                        NaN   \n",
       "1     0.43630            2.70201  2015                        NaN   \n",
       "2     0.34139            2.49204  2015                        NaN   \n",
       "3     0.34699            2.46531  2015                        NaN   \n",
       "4     0.45811            2.45176  2015                        NaN   \n",
       "\n",
       "   Upper Confidence Interval  Social Support  \n",
       "0                        NaN             NaN  \n",
       "1                        NaN             NaN  \n",
       "2                        NaN             NaN  \n",
       "3                        NaN             NaN  \n",
       "4                        NaN             NaN  "
      ]
     },
     "execution_count": 37,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Need to concatenate the tables\n",
    "tables_concat = pd.concat(objs=[happiness_2015, happiness_2016, happiness_2017, happiness_2018, happiness_2019])\n",
    "tables_concat.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "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>Country</th>\n",
       "      <th>Happiness Score</th>\n",
       "      <th>Year</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Afghanistan</td>\n",
       "      <td>[3.575, 3.36, 3.79399991035461, 3.632, 3.203]</td>\n",
       "      <td>[2015, 2016, 2017, 2018, 2019]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Albania</td>\n",
       "      <td>[4.959, 4.655, 4.64400005340576, 4.586, 4.719]</td>\n",
       "      <td>[2015, 2016, 2017, 2018, 2019]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Algeria</td>\n",
       "      <td>[5.605, 6.355, 5.87200021743774, 5.295, 5.211]</td>\n",
       "      <td>[2015, 2016, 2017, 2018, 2019]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Angola</td>\n",
       "      <td>[4.033, 3.866, 3.79500007629395, 3.795]</td>\n",
       "      <td>[2015, 2016, 2017, 2018]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Argentina</td>\n",
       "      <td>[6.574, 6.65, 6.59899997711182, 6.388, 6.086]</td>\n",
       "      <td>[2015, 2016, 2017, 2018, 2019]</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       Country                                 Happiness Score  \\\n",
       "0  Afghanistan   [3.575, 3.36, 3.79399991035461, 3.632, 3.203]   \n",
       "1      Albania  [4.959, 4.655, 4.64400005340576, 4.586, 4.719]   \n",
       "2      Algeria  [5.605, 6.355, 5.87200021743774, 5.295, 5.211]   \n",
       "3       Angola         [4.033, 3.866, 3.79500007629395, 3.795]   \n",
       "4    Argentina   [6.574, 6.65, 6.59899997711182, 6.388, 6.086]   \n",
       "\n",
       "                             Year  \n",
       "0  [2015, 2016, 2017, 2018, 2019]  \n",
       "1  [2015, 2016, 2017, 2018, 2019]  \n",
       "2  [2015, 2016, 2017, 2018, 2019]  \n",
       "3        [2015, 2016, 2017, 2018]  \n",
       "4  [2015, 2016, 2017, 2018, 2019]  "
      ]
     },
     "execution_count": 38,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Groupby country\n",
    "country_group = tables_concat[[\"Country\", \"Happiness Score\", \"Year\"]].groupby(\"Country\").agg({\n",
    "    \"Happiness Score\": lambda x: list(x),\n",
    "    \"Year\": lambda x: list(x)\n",
    "}).reset_index()\n",
    "\n",
    "country_group.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "e38d25a946fe41b0aeccd4dc373f6ad5",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "Dropdown(description='Country', index=145, options=('Afghanistan', 'Albania', 'Algeria', 'Angola', 'Argentina'…"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "cba03c178f9746ff9bdee8a76920164d",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "Output()"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Create a dropdown widget so that a user can plot the choropleth maps per year\n",
    "# List of countries in alphabetical order\n",
    "countries_list = sorted(list(country_group[\"Country\"].value_counts().index))\n",
    "dropdown_countries_happiness = widgets.Dropdown(options=countries_list,\n",
    "                                                value=\"Switzerland\",\n",
    "                                                description=\"Country\")\n",
    "\n",
    "def plot_happiness_over_time(selected_country):\n",
    "    # Get the dataframe corresponging to selected_country\n",
    "    filtered_country_group = country_group[country_group[\"Country\"] == selected_country]\n",
    "\n",
    "    # Create figure and axis\n",
    "    fig, ax = plt.subplots(figsize=(7, 5))\n",
    "\n",
    "    # Plot the happiness score over time\n",
    "    plt.bar(x=filtered_country_group[\"Year\"].values[0], height=filtered_country_group[\"Happiness Score\"].values[0], color=\"black\")\n",
    "\n",
    "    # Set title\n",
    "    plt.title(f\"Happiness Score Over Time For {selected_country}\")\n",
    "    plt.xlabel(\"Year\")\n",
    "    plt.ylabel(\"Happiness Score\")\n",
    "\n",
    "    # Annotate each bar with its value\n",
    "    for p in ax.patches:\n",
    "        height = p.get_height()\n",
    "        ax.text(p.get_x() + p.get_width() / 2., height, f\"{height:.2f}\", ha=\"center\", va=\"bottom\")\n",
    "\n",
    "    plt.ylim(0, 10)\n",
    "\n",
    "    plt.savefig(f\"images_happiness/Happiness-over-time-{selected_country}.png\", bbox_inches=\"tight\")\n",
    "    plt.show()\n",
    "\n",
    "# Create an output widget to display the plot\n",
    "output = widgets.Output()\n",
    "\n",
    "# Function to update the plot\n",
    "def update_plot_happiness(change):\n",
    "    selected_country = change[\"new\"]\n",
    "\n",
    "    with output:\n",
    "        output.clear_output(wait=True)\n",
    "        plot_happiness_over_time(selected_country)\n",
    "\n",
    "# Connect the dropdown to the update function\n",
    "dropdown_countries_happiness.observe(update_plot_happiness, names=\"value\")\n",
    "\n",
    "# Display the dropdown and initial plot\n",
    "display(dropdown_countries_happiness, output)\n",
    "update_plot_happiness({\"new\": \"Switzerland\"})"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "![Plot Name](images_happiness/Happiness-over-time-Japan.png)\n",
    "![Plot Name](images_happiness/Happiness-over-time-Netherlands.png)\n",
    "![Plot Name](images_happiness/Happiness-over-time-South-Africa.png)\n",
    "![Plot Name](images_happiness/Happiness-over-time-Switzerland.png)\n",
    "![Plot Name](images_happiness/Happiness-over-time-Zimbabwe.png)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Most contributing factor to the Happiness Score"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "4c17a66508c24a549987614bb3019ecd",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "Dropdown(description='Year', options=('2015', '2016', '2017', '2018', '2019'), value='2015')"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "d4cf423bfcb840cd86f8f50b59435e24",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "Output()"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Create a dropdown widget so that a user can plot the correlation between happiness scores\n",
    "# and other factors\n",
    "years_list = [\"2015\", \"2016\", \"2017\", \"2018\", \"2019\"] # List of years\n",
    "dropdown_years_factors = widgets.Dropdown(options=years_list, value=\"2015\", description=\"Year\")\n",
    "\n",
    "def update_contributing_factor(selected_year):\n",
    "    # Get the index of the selected year in years_list\n",
    "    df_index = years_list.index(selected_year)\n",
    "    df = tables_list[df_index]  # Corresponding dataframe based on selected_year\n",
    "\n",
    "    # Correlation matrix figure\n",
    "    fig, ax = plt.subplots(figsize=(6, 4))\n",
    "\n",
    "    av_contribution = df[[\"Happiness Score\", \"Economy (GDP per Capita)\", \"Health (Life Expectancy)\",\n",
    "                        \"Freedom\", \"Trust (Government Corruption)\", \"Generosity\"]].mean().sort_values(ascending=False)\n",
    "\n",
    "    colors = [\"red\", \"green\", \"blue\", \"purple\", \"orange\"]\n",
    "\n",
    "    # Plot the happiness score over time\n",
    "    av_contribution.plot(kind=\"bar\", color=colors)\n",
    "\n",
    "    # Set title\n",
    "    plt.title(\"Average Contribution of Factors to Happiness Score\")\n",
    "    plt.xlabel(\"Factor\")\n",
    "    plt.ylabel(\"Average Contribution\")\n",
    "\n",
    "    # Annotate each bar with its value\n",
    "    for p in ax.patches:\n",
    "        height = p.get_height()\n",
    "        ax.text(p.get_x() + p.get_width() / 2., height, f\"{height:.2f}\", ha=\"center\", va=\"bottom\")\n",
    "\n",
    "    plt.ylim(0, 10)\n",
    "\n",
    "    plt.savefig(f\"images_happiness/Av-factor-contribution-{selected_year}.png\", bbox_inches=\"tight\")\n",
    "    plt.show()\n",
    "\n",
    "# Create an output widget to display the plot\n",
    "output = widgets.Output()\n",
    "\n",
    "# Function to update the plot\n",
    "def update_plot_factors(change):\n",
    "    year = change[\"new\"]\n",
    "\n",
    "    with output:\n",
    "        output.clear_output(wait=True)\n",
    "        update_contributing_factor(year)\n",
    "\n",
    "# Connect the dropdown to the update function\n",
    "dropdown_years_factors.observe(update_plot_factors, names=\"value\")\n",
    "\n",
    "# Display the dropdown and initial plot\n",
    "display(dropdown_years_factors, output)\n",
    "update_plot_factors({\"new\": \"2015\"})"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "![Plot Name](images_happiness/Av-factor-contribution-2015.png)\n",
    "![Plot Name](images_happiness/Av-factor-contribution-2016.png)\n",
    "![Plot Name](images_happiness/Av-factor-contribution-2017.png)\n",
    "![Plot Name](images_happiness/Av-factor-contribution-2018.png)\n",
    "![Plot Name](images_happiness/Av-factor-contribution-2019.png)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Unsupervised Learning\n",
    "#### K-Means Clustering"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "4c17a66508c24a549987614bb3019ecd",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "Dropdown(description='Year', index=4, options=('2015', '2016', '2017', '2018', '2019'), value='2019')"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "f85d4b9068d94b17978a8cd6672f403e",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "Output()"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Create a dropdown widget so that a user can plot the correlation between happiness scores\n",
    "# and other factors\n",
    "years_list = [\"2015\", \"2016\", \"2017\", \"2018\", \"2019\"] # List of years\n",
    "dropdown_years_kmeans = widgets.Dropdown(options=years_list, value=\"2015\", description=\"Year\")\n",
    "\n",
    "def update_contributing_factor(selected_year):\n",
    "    # Get the index of the selected year in years_list\n",
    "    df_index = years_list.index(selected_year)\n",
    "    df = tables_list[df_index]  # Corresponding dataframe based on selected_year\n",
    "\n",
    "    # Prepare data\n",
    "    features = [\"Economy (GDP per Capita)\", \"Health (Life Expectancy)\", \"Freedom\",\n",
    "                \"Trust (Government Corruption)\", \"Generosity\"]\n",
    "\n",
    "    X = df[features]\n",
    "\n",
    "    # Normalize the features\n",
    "    scaler = StandardScaler()\n",
    "    X_scaled = scaler.fit_transform(X)\n",
    "\n",
    "    # Perform K-means clustering\n",
    "    n_clusters = len(features)\n",
    "    kmeans = KMeans(n_clusters=n_clusters, random_state=42)\n",
    "    cluster_labels = kmeans.fit_predict(X_scaled)\n",
    "\n",
    "    # Perform PCA to reduce to 2 dimensions for visualization\n",
    "    pca = PCA(n_components=2)\n",
    "    X_pca = pca.fit_transform(X_scaled)\n",
    "\n",
    "    # Visualize clusters\n",
    "    fig, ax = plt.subplots(figsize=(8, 8))\n",
    "\n",
    "    # Create a scatter plot for each cluster and store the color used\n",
    "    cluster_colors = {}\n",
    "\n",
    "    for i in range(n_clusters):\n",
    "        cluster_points = X_pca[cluster_labels == i]\n",
    "\n",
    "        # Scatter Plot\n",
    "        scatter = plt.scatter(cluster_points[:, 0], cluster_points[:, 1], label=features[i])\n",
    "        cluster_colors[i] = scatter.get_facecolor()[0]\n",
    "\n",
    "    # Plot centroids\n",
    "    centroids_pca = pca.transform(kmeans.cluster_centers_)\n",
    "    plt.scatter(centroids_pca[:, 0], centroids_pca[:, 1], s=300, c=\"black\", marker=\"*\", label=\"Centroids\")\n",
    "\n",
    "    # Highlighter countries\n",
    "    highlight_countries = [\"Switzerland\", \"United States\", \"Japan\", \"Brazil\", \"India\", \"South Africa\"]\n",
    "\n",
    "    # Overlay highlighted countries with different markers\n",
    "    for i, country in enumerate(df[\"Country\"]):\n",
    "\n",
    "        if country in highlight_countries:\n",
    "            # Get the color of the cluster\n",
    "            cluster_index = cluster_labels[i]\n",
    "            cluster_color = cluster_colors[cluster_index]\n",
    "\n",
    "            # Diamond marker 'D' for highlighted countries\n",
    "            plt.scatter(X_pca[i, 0], X_pca[i, 1], color=cluster_color, marker=\"D\", s=50)\n",
    "            plt.annotate(country, (X_pca[i, 0], X_pca[i, 1]), xytext=(5, 5), textcoords=\"offset points\")\n",
    "\n",
    "    # Customize the plot\n",
    "    plt.title(\"Clusters of Countries based on Happiness Factors\", fontsize=16)\n",
    "    plt.xlabel(\"First Principal Component\", fontsize=12)\n",
    "    plt.ylabel(\"Second Principal Component\", fontsize=12)\n",
    "\n",
    "    plt.legend()\n",
    "    plt.grid(True, linestyle=\"--\", alpha=0.7)\n",
    "    plt.tight_layout()\n",
    "\n",
    "    plt.savefig(f\"images_happiness/kmeans-{selected_year}.png\")\n",
    "    plt.show()\n",
    "\n",
    "    # Print the variance explained by the first two principal components\n",
    "    print(f\"Variance explained: {pca.explained_variance_ratio_.sum():.2f}\")\n",
    "\n",
    "\n",
    "# Create an output widget to display the plot\n",
    "output = widgets.Output()\n",
    "\n",
    "# Function to update the plot\n",
    "def update_plot_factors(change):\n",
    "    year = change[\"new\"]\n",
    "\n",
    "    with output:\n",
    "        output.clear_output(wait=True)\n",
    "        update_contributing_factor(year)\n",
    "\n",
    "# Connect the dropdown to the update function\n",
    "dropdown_years_factors.observe(update_plot_factors, names=\"value\")\n",
    "\n",
    "# Display the dropdown and initial plot\n",
    "display(dropdown_years_factors, output)\n",
    "update_plot_factors({\"new\": \"2015\"})"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "![Plot Name](images_happiness/kmeans-2015.png)\n",
    "![Plot Name](images_happiness/kmeans-2016.png)\n",
    "![Plot Name](images_happiness/kmeans-2017.png)\n",
    "![Plot Name](images_happiness/kmeans-2018.png)\n",
    "![Plot Name](images_happiness/kmeans-2019.png)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Supervised Learning\n",
    "#### Random Forest Regressor"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "51d65f9b4ce7498385ef3985f357d9e3",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "Dropdown(description='Year', options=('2015', '2016', '2017', '2018', '2019'), value='2015')"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "a554727218584df69f4e249a66540aba",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "Output()"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Create a dropdown widget so that a user can plot the correlation between happiness scores\n",
    "# and other factors\n",
    "years_list = [\"2015\", \"2016\", \"2017\", \"2018\", \"2019\"] # List of years\n",
    "dropdown_years_rforest = widgets.Dropdown(options=years_list, value=\"2015\", description=\"Year\")\n",
    "\n",
    "def update_random_forest(selected_year):\n",
    "    # Get the index of the selected year in years_list\n",
    "    df_index = years_list.index(selected_year)\n",
    "    df = tables_list[df_index]  # Corresponding dataframe based on selected_year\n",
    "\n",
    "    # Prepare data\n",
    "    features = [\"Economy (GDP per Capita)\", \"Health (Life Expectancy)\", \"Freedom\",\n",
    "                \"Trust (Government Corruption)\", \"Generosity\"]\n",
    "\n",
    "    # Features and target\n",
    "    X = df[features]\n",
    "    y = df[\"Happiness Score\"]\n",
    "\n",
    "    # Train test split the data\n",
    "    X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)\n",
    "\n",
    "    # Initialize Random Forest Regressor and fit to the data\n",
    "    model = RandomForestRegressor(n_estimators=100, random_state=42)\n",
    "    model.fit(X_train, y_train)\n",
    "\n",
    "    # Predicted happiness score\n",
    "    y_pred = model.predict(X_test)\n",
    "\n",
    "    # Mean squared error\n",
    "    mse = mean_squared_error(y_test, y_pred)\n",
    "\n",
    "    # R-squared\n",
    "    r2 = r2_score(y_test, y_pred)\n",
    "\n",
    "    # Feature importance\n",
    "    importance = pd.DataFrame({\"Feature\": features, \"Importance\": model.feature_importances_})\n",
    "    importance = importance.sort_values(\"Importance\", ascending=False)\n",
    "\n",
    "    # Plotting\n",
    "    plt.figure(figsize=(9,7))\n",
    "\n",
    "    colors = [\"red\", \"green\", \"blue\", \"purple\", \"orange\"]\n",
    "    sns.barplot(x=\"Feature\", y=\"Importance\", data=importance, palette=colors, hue=\"Feature\")\n",
    "\n",
    "    plt.title(f\"Feature Importance in Predicting Happiness Score for {selected_year}\")\n",
    "\n",
    "    # Rotate x-axis labels\n",
    "    plt.xticks(rotation=90, ha=\"right\")\n",
    "\n",
    "    plt.ylim(0,1)\n",
    "\n",
    "    plt.savefig(f\"images_happiness/random-forest-{selected_year}.png\", bbox_inches=\"tight\")\n",
    "    plt.tight_layout()\n",
    "    plt.show()\n",
    "\n",
    "    print(\"Mean Squared Error: %.3f\"%(mse))\n",
    "    print(\"R-squared Score: %.3f\"%(r2))\n",
    "\n",
    "# Create an output widget to display the plot\n",
    "output = widgets.Output()\n",
    "\n",
    "# Function to update the plot\n",
    "def update_plot_rforest(change):\n",
    "    year = change[\"new\"]\n",
    "\n",
    "    with output:\n",
    "        output.clear_output(wait=True)\n",
    "        update_random_forest(year)\n",
    "\n",
    "# Connect the dropdown to the update function\n",
    "dropdown_years_rforest.observe(update_plot_rforest, names=\"value\")\n",
    "\n",
    "# Display the dropdown and initial plot\n",
    "display(dropdown_years_rforest, output)\n",
    "update_plot_rforest({\"new\": \"2015\"})"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "![Plot Name](images_happiness/Random-forest-2015.png)\n",
    "![Plot Name](images_happiness/Random-forest-2016.png)\n",
    "![Plot Name](images_happiness/Random-forest-2017.png)\n",
    "![Plot Name](images_happiness/Random-forest-2018.png)\n",
    "![Plot Name](images_happiness/Random-forest-2019.png)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": []
  }
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