EDA_happiness_report_2019_V4.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "2e130cd7",
   "metadata": {},
   "source": [
    "# STEP 1"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "3c211975",
   "metadata": {},
   "source": [
    "<div class=\"alert alert-block alert-info\">\n",
    "<b>Load dataset and import librairies\n",
    "</div>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "39b725ed",
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "from pylab import rcParams\n",
    "rcParams['figure.figsize']=(12,6)\n",
    "sns.set\n",
    "import warnings\n",
    "warnings.filterwarnings(\"ignore\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f57471f0",
   "metadata": {},
   "source": [
    "# STEP 2"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "13d93f28",
   "metadata": {},
   "source": [
    "<div class=\"alert alert-block alert-info\">\n",
    "<b>Import the dataset\n",
    "</div>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "73b07843",
   "metadata": {},
   "outputs": [],
   "source": [
    "happiness_data=pd.read_csv('world_happiness_report_2019.csv')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "bd07e808",
   "metadata": {},
   "source": [
    "# STEP 3"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a19deca9",
   "metadata": {},
   "source": [
    "<div class=\"alert alert-block alert-info\">\n",
    "<b>We can observe the dataset using the head()function, which returns the first five records from the dataset\n",
    "</div>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "5a88b536",
   "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>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",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4</td>\n",
       "      <td>Iceland</td>\n",
       "      <td>7.494</td>\n",
       "      <td>1.380</td>\n",
       "      <td>1.624</td>\n",
       "      <td>1.026</td>\n",
       "      <td>0.591</td>\n",
       "      <td>0.354</td>\n",
       "      <td>0.118</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5</td>\n",
       "      <td>Netherlands</td>\n",
       "      <td>7.488</td>\n",
       "      <td>1.396</td>\n",
       "      <td>1.522</td>\n",
       "      <td>0.999</td>\n",
       "      <td>0.557</td>\n",
       "      <td>0.322</td>\n",
       "      <td>0.298</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",
       "3             4           Iceland  7.494           1.380           1.624   \n",
       "4             5       Netherlands  7.488           1.396           1.522   \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",
       "3                    1.026                         0.591       0.354   \n",
       "4                    0.999                         0.557       0.322   \n",
       "\n",
       "   Perceptions of corruption  \n",
       "0                      0.393  \n",
       "1                      0.410  \n",
       "2                      0.341  \n",
       "3                      0.118  \n",
       "4                      0.298  "
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "happiness_data.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a6bfe3f7",
   "metadata": {},
   "source": [
    "# STEP 4"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "20a880c2",
   "metadata": {},
   "source": [
    "<div class=\"alert alert-block alert-info\">\n",
    "<b>Using the shape function, we can observe the dimensions of the data\n",
    "</div>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "2d8166e1",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(156, 9)"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "happiness_data.shape"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "90767c83",
   "metadata": {},
   "source": [
    "<font color='green'>There are 9 columns and 156 observations</font>"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "2ebb4b6b",
   "metadata": {},
   "source": [
    "# STEP 5"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d865bf00",
   "metadata": {},
   "source": [
    "<div class=\"alert alert-block alert-info\">\n",
    "<b>The info() method shows some of the characteristics of the data such as Column Name\n",
    "</div>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "55fe78f5",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 156 entries, 0 to 155\n",
      "Data columns (total 9 columns):\n",
      " #   Column                        Non-Null Count  Dtype  \n",
      "---  ------                        --------------  -----  \n",
      " 0   Overall rank                  156 non-null    int64  \n",
      " 1   Country or region             156 non-null    object \n",
      " 2   Score                         156 non-null    float64\n",
      " 3   GDP per capita                156 non-null    float64\n",
      " 4   Social support                156 non-null    float64\n",
      " 5   Healthy life expectancy       156 non-null    float64\n",
      " 6   Freedom to make life choices  156 non-null    float64\n",
      " 7   Generosity                    156 non-null    float64\n",
      " 8   Perceptions of corruption     156 non-null    float64\n",
      "dtypes: float64(7), int64(1), object(1)\n",
      "memory usage: 11.1+ KB\n"
     ]
    }
   ],
   "source": [
    "happiness_data.info()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c565279d",
   "metadata": {},
   "source": [
    "<font color='green'>We don’t have any missing values and numerical variables can have type 'int64' or 'float64'</font>"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f931f069",
   "metadata": {},
   "source": [
    "# STEP 6"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "3cf8d1cc",
   "metadata": {},
   "source": [
    "<div class=\"alert alert-block alert-info\">\n",
    "<b>We use describe() function, which shows basic statistical characteristics of each numerical feature\n",
    "</div>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "4c3cdf73",
   "metadata": {},
   "outputs": [
    {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Overall rank</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>count</th>\n",
       "      <td>156.000000</td>\n",
       "      <td>156.000000</td>\n",
       "      <td>156.000000</td>\n",
       "      <td>156.000000</td>\n",
       "      <td>156.000000</td>\n",
       "      <td>156.000000</td>\n",
       "      <td>156.000000</td>\n",
       "      <td>156.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>78.500000</td>\n",
       "      <td>5.407096</td>\n",
       "      <td>0.905147</td>\n",
       "      <td>1.208814</td>\n",
       "      <td>0.725244</td>\n",
       "      <td>0.392571</td>\n",
       "      <td>0.184846</td>\n",
       "      <td>0.110603</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>45.177428</td>\n",
       "      <td>1.113120</td>\n",
       "      <td>0.398389</td>\n",
       "      <td>0.299191</td>\n",
       "      <td>0.242124</td>\n",
       "      <td>0.143289</td>\n",
       "      <td>0.095254</td>\n",
       "      <td>0.094538</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>2.853000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>39.750000</td>\n",
       "      <td>4.544500</td>\n",
       "      <td>0.602750</td>\n",
       "      <td>1.055750</td>\n",
       "      <td>0.547750</td>\n",
       "      <td>0.308000</td>\n",
       "      <td>0.108750</td>\n",
       "      <td>0.047000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>78.500000</td>\n",
       "      <td>5.379500</td>\n",
       "      <td>0.960000</td>\n",
       "      <td>1.271500</td>\n",
       "      <td>0.789000</td>\n",
       "      <td>0.417000</td>\n",
       "      <td>0.177500</td>\n",
       "      <td>0.085500</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>117.250000</td>\n",
       "      <td>6.184500</td>\n",
       "      <td>1.232500</td>\n",
       "      <td>1.452500</td>\n",
       "      <td>0.881750</td>\n",
       "      <td>0.507250</td>\n",
       "      <td>0.248250</td>\n",
       "      <td>0.141250</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>156.000000</td>\n",
       "      <td>7.769000</td>\n",
       "      <td>1.684000</td>\n",
       "      <td>1.624000</td>\n",
       "      <td>1.141000</td>\n",
       "      <td>0.631000</td>\n",
       "      <td>0.566000</td>\n",
       "      <td>0.453000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       Overall rank       Score  GDP per capita  Social support  \\\n",
       "count    156.000000  156.000000      156.000000      156.000000   \n",
       "mean      78.500000    5.407096        0.905147        1.208814   \n",
       "std       45.177428    1.113120        0.398389        0.299191   \n",
       "min        1.000000    2.853000        0.000000        0.000000   \n",
       "25%       39.750000    4.544500        0.602750        1.055750   \n",
       "50%       78.500000    5.379500        0.960000        1.271500   \n",
       "75%      117.250000    6.184500        1.232500        1.452500   \n",
       "max      156.000000    7.769000        1.684000        1.624000   \n",
       "\n",
       "       Healthy life expectancy  Freedom to make life choices  Generosity  \\\n",
       "count               156.000000                    156.000000  156.000000   \n",
       "mean                  0.725244                      0.392571    0.184846   \n",
       "std                   0.242124                      0.143289    0.095254   \n",
       "min                   0.000000                      0.000000    0.000000   \n",
       "25%                   0.547750                      0.308000    0.108750   \n",
       "50%                   0.789000                      0.417000    0.177500   \n",
       "75%                   0.881750                      0.507250    0.248250   \n",
       "max                   1.141000                      0.631000    0.566000   \n",
       "\n",
       "       Perceptions of corruption  \n",
       "count                 156.000000  \n",
       "mean                    0.110603  \n",
       "std                     0.094538  \n",
       "min                     0.000000  \n",
       "25%                     0.047000  \n",
       "50%                     0.085500  \n",
       "75%                     0.141250  \n",
       "max                     0.453000  "
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "happiness_data.describe()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "410b0056",
   "metadata": {},
   "source": [
    "# STEP 7"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ac9eaf17",
   "metadata": {},
   "source": [
    "<div class=\"alert alert-block alert-info\">\n",
    "<b>We can check for duplicate values in our dataset\n",
    "</div>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "73ca18f9",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0\n"
     ]
    },
    {
     "data": {
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       "  <thead>\n",
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       "      <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",
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      ],
      "text/plain": [
       "Empty DataFrame\n",
       "Columns: [Overall rank, Country or region, Score, GDP per capita, Social support, Healthy life expectancy, Freedom to make life choices, Generosity, Perceptions of corruption]\n",
       "Index: []"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "duplicate_Values=happiness_data.duplicated()\n",
    "print(duplicate_Values.sum())\n",
    "happiness_data[duplicate_Values]"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "0644d57f",
   "metadata": {},
   "source": [
    "<font color='green'>We don’t have any duplicate values </font>"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "22cbca26",
   "metadata": {},
   "source": [
    "# STEP 8"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "85ac6409",
   "metadata": {},
   "source": [
    "<div class=\"alert alert-block alert-info\">\n",
    "<b>Let's have a look to the data type\n",
    "</div>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "c205c305",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Categorical Columns:  ['Country or region']\n",
      "Numerical Columns:  ['Overall rank', 'Score', 'GDP per capita', 'Social support', 'Healthy life expectancy', 'Freedom to make life choices', 'Generosity', 'Perceptions of corruption']\n",
      "Total columns: 9\n"
     ]
    }
   ],
   "source": [
    "cat_col = [col for col in happiness_data.columns if happiness_data[col].dtype ==\n",
    "           'object']  # Categorical columns\n",
    "num_col = [col for col in happiness_data.columns if happiness_data[col].dtype !=\n",
    "           'object']  # Numerical columns\n",
    "\n",
    "print('Categorical Columns: ', cat_col)\n",
    "print('Numerical Columns: ', num_col)\n",
    "\n",
    "# As it can be seen no column has been left behind\n",
    "print(f'Total columns: {len(cat_col) + len(num_col)}')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "bdf8d0d4",
   "metadata": {},
   "source": [
    "<font color='green'>It's mainly numerical variables and only 2 categorical variables </font>"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "edf98afc",
   "metadata": {},
   "source": [
    "# STEP 8"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "9bd2f4bd",
   "metadata": {},
   "source": [
    "<div class=\"alert alert-block alert-info\">\n",
    "<b>Handling the outliers in the data, i.e. the extreme values in the data. <br>We can find the outliers in our data using a Boxplot\n",
    "</div>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "d09f4f37",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<function matplotlib.pyplot.show(close=None, block=None)>"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 864x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "happiness_data.boxplot(column=['Score'])\n",
    "plt.show"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e2350c71",
   "metadata": {},
   "source": [
    "<font color='green'>Our dataframe is now free from outliers</font>"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "6b07da0a",
   "metadata": {},
   "source": [
    "# STEP 9"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "367953b3",
   "metadata": {},
   "source": [
    "<div class=\"alert alert-block alert-info\">\n",
    "<b>We can find the pairwise correlation between the different columns of the data using the corr() method. <br>  All non-numeric data type column will be ignored\n",
    "</div>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "e403feab",
   "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>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>Overall rank</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>-0.989096</td>\n",
       "      <td>-0.801947</td>\n",
       "      <td>-0.767465</td>\n",
       "      <td>-0.787411</td>\n",
       "      <td>-0.546606</td>\n",
       "      <td>-0.047993</td>\n",
       "      <td>-0.351959</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Score</th>\n",
       "      <td>-0.989096</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.793883</td>\n",
       "      <td>0.777058</td>\n",
       "      <td>0.779883</td>\n",
       "      <td>0.566742</td>\n",
       "      <td>0.075824</td>\n",
       "      <td>0.385613</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>GDP per capita</th>\n",
       "      <td>-0.801947</td>\n",
       "      <td>0.793883</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.754906</td>\n",
       "      <td>0.835462</td>\n",
       "      <td>0.379079</td>\n",
       "      <td>-0.079662</td>\n",
       "      <td>0.298920</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Social support</th>\n",
       "      <td>-0.767465</td>\n",
       "      <td>0.777058</td>\n",
       "      <td>0.754906</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.719009</td>\n",
       "      <td>0.447333</td>\n",
       "      <td>-0.048126</td>\n",
       "      <td>0.181899</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Healthy life expectancy</th>\n",
       "      <td>-0.787411</td>\n",
       "      <td>0.779883</td>\n",
       "      <td>0.835462</td>\n",
       "      <td>0.719009</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.390395</td>\n",
       "      <td>-0.029511</td>\n",
       "      <td>0.295283</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Freedom to make life choices</th>\n",
       "      <td>-0.546606</td>\n",
       "      <td>0.566742</td>\n",
       "      <td>0.379079</td>\n",
       "      <td>0.447333</td>\n",
       "      <td>0.390395</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.269742</td>\n",
       "      <td>0.438843</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Generosity</th>\n",
       "      <td>-0.047993</td>\n",
       "      <td>0.075824</td>\n",
       "      <td>-0.079662</td>\n",
       "      <td>-0.048126</td>\n",
       "      <td>-0.029511</td>\n",
       "      <td>0.269742</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.326538</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Perceptions of corruption</th>\n",
       "      <td>-0.351959</td>\n",
       "      <td>0.385613</td>\n",
       "      <td>0.298920</td>\n",
       "      <td>0.181899</td>\n",
       "      <td>0.295283</td>\n",
       "      <td>0.438843</td>\n",
       "      <td>0.326538</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                              Overall rank     Score  GDP per capita  \\\n",
       "Overall rank                      1.000000 -0.989096       -0.801947   \n",
       "Score                            -0.989096  1.000000        0.793883   \n",
       "GDP per capita                   -0.801947  0.793883        1.000000   \n",
       "Social support                   -0.767465  0.777058        0.754906   \n",
       "Healthy life expectancy          -0.787411  0.779883        0.835462   \n",
       "Freedom to make life choices     -0.546606  0.566742        0.379079   \n",
       "Generosity                       -0.047993  0.075824       -0.079662   \n",
       "Perceptions of corruption        -0.351959  0.385613        0.298920   \n",
       "\n",
       "                              Social support  Healthy life expectancy  \\\n",
       "Overall rank                       -0.767465                -0.787411   \n",
       "Score                               0.777058                 0.779883   \n",
       "GDP per capita                      0.754906                 0.835462   \n",
       "Social support                      1.000000                 0.719009   \n",
       "Healthy life expectancy             0.719009                 1.000000   \n",
       "Freedom to make life choices        0.447333                 0.390395   \n",
       "Generosity                         -0.048126                -0.029511   \n",
       "Perceptions of corruption           0.181899                 0.295283   \n",
       "\n",
       "                              Freedom to make life choices  Generosity  \\\n",
       "Overall rank                                     -0.546606   -0.047993   \n",
       "Score                                             0.566742    0.075824   \n",
       "GDP per capita                                    0.379079   -0.079662   \n",
       "Social support                                    0.447333   -0.048126   \n",
       "Healthy life expectancy                           0.390395   -0.029511   \n",
       "Freedom to make life choices                      1.000000    0.269742   \n",
       "Generosity                                        0.269742    1.000000   \n",
       "Perceptions of corruption                         0.438843    0.326538   \n",
       "\n",
       "                              Perceptions of corruption  \n",
       "Overall rank                                  -0.351959  \n",
       "Score                                          0.385613  \n",
       "GDP per capita                                 0.298920  \n",
       "Social support                                 0.181899  \n",
       "Healthy life expectancy                        0.295283  \n",
       "Freedom to make life choices                   0.438843  \n",
       "Generosity                                     0.326538  \n",
       "Perceptions of corruption                      1.000000  "
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "happiness_data.corr()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b98fa5a6",
   "metadata": {},
   "source": [
    "# STEP 10"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "15a95cd1",
   "metadata": {},
   "source": [
    "<div class=\"alert alert-block alert-info\">To have a better outlook, we create a heatmap using Seaborn to visualize the correlation \n",
    "</div>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "c9ac8e16",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:>"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x432 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.heatmap(happiness_data.corr(),annot=True,cmap='RdYlGn')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a225dc9c",
   "metadata": {},
   "source": [
    "<font color='green'>There is a high correlation between:<br>GPD (economy) and Healthy life expenctancy = 0,84 <br>\n",
    "Score and GPD(economy) = 0,79 <br> Score and social support = 0,78</font>"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "5b32e583",
   "metadata": {},
   "source": [
    "#### We can practice the corr() function with selected columns <br>For example 'generosity', 'overall rank' and 'corruption'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "id": "bbe969b4",
   "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>Generosity</th>\n",
       "      <th>Overall rank</th>\n",
       "      <th>Perceptions of corruption</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Generosity</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>-0.047993</td>\n",
       "      <td>0.326538</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Overall rank</th>\n",
       "      <td>-0.047993</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>-0.351959</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Perceptions of corruption</th>\n",
       "      <td>0.326538</td>\n",
       "      <td>-0.351959</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                           Generosity  Overall rank  Perceptions of corruption\n",
       "Generosity                   1.000000     -0.047993                   0.326538\n",
       "Overall rank                -0.047993      1.000000                  -0.351959\n",
       "Perceptions of corruption    0.326538     -0.351959                   1.000000"
      ]
     },
     "execution_count": 50,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "happiness_data[['Generosity', 'Overall rank', 'Perceptions of corruption']].corr()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a174b3e6",
   "metadata": {},
   "source": [
    "# STEP 11"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "946c7bdc",
   "metadata": {},
   "source": [
    "<div class=\"alert alert-block alert-info\">Using Seaborn, we visualize the relation between Economy (GDP per Capita) and Happiness Score by using a regression plot <br>We use \"regplot\" which plots the scartterplot plus the fitted regression libe for the data.\n",
    "</div>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 90,
   "id": "2e489c8c",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='GDP per capita', ylabel='Score'>"
      ]
     },
     "execution_count": 90,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.regplot(x='GDP per capita', y='Score', data=happiness_data)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "4f95e0ab",
   "metadata": {},
   "source": [
    "<font color='green'> As the Economy increases, the Happiness Score increases as well as denoting a positive correlation between \n",
    "those two variables <br> GPD per capita seems to be a pretty good predictor of happiness score <br>The regression line is almost a perfect diagonal line</font>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "id": "9ab6d159",
   "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>GDP per capita</th>\n",
       "      <th>Score</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>GDP per capita</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.793883</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Score</th>\n",
       "      <td>0.793883</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                GDP per capita     Score\n",
       "GDP per capita        1.000000  0.793883\n",
       "Score                 0.793883  1.000000"
      ]
     },
     "execution_count": 63,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "happiness_data[['GDP per capita', 'Score']].corr()\n",
    "\n",
    "# The correlation is approximately 0.79"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "75bed566",
   "metadata": {},
   "source": [
    "<font color='green'>Be careful to distinguish correlation (interdependence) vs. causation (cause/effect relationship)  </font>"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "8ce0e365",
   "metadata": {},
   "source": [
    "# Step 12"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "07c9c56a",
   "metadata": {},
   "source": [
    "<div class=\"alert alert-block alert-info\">The P-value is to know the significance of the correlation estimate\n",
    "when p-value is <br> < 0.001 leads to strong evidence <br> < 0.05 leads to moderate evidence <br> < 0.1 leads to weak evidence <br> > 0.1 leads to no evidence </div>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 92,
   "id": "ca1c4345",
   "metadata": {},
   "outputs": [],
   "source": [
    "from scipy import stats"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 93,
   "id": "39c1655a",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The Pearson Correlation coefficient is 0.7938828678781276  with a P-value of P = 4.315480913319409e-35\n"
     ]
    }
   ],
   "source": [
    "pearson_coef, p_value = stats.pearsonr(happiness_data['GDP per capita'], happiness_data['Score'])\n",
    "print (\"The Pearson Correlation coefficient is\", pearson_coef, \" with a P-value of P =\", p_value)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d336f20e",
   "metadata": {},
   "source": [
    "<font color='green'>The linear relationship is strong (app. 0.79) and the correlation is statistically significant</font>\n"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
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 },
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