Feature Engineering and Selection for the Life Expectancy Dataset.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "52a26d4c",
   "metadata": {},
   "source": [
    "# FEATURE ENGINEERING AND SELECTION"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "fe9953d2",
   "metadata": {},
   "source": [
    "### Importing Required Libraries"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "e9c527e7",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Surpress warnings from using older version of sklearn:\n",
    "def warn(*args, **kwargs):\n",
    "    pass\n",
    "import warnings\n",
    "warnings.warn = warn\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "\n",
    "import matplotlib.pyplot as plt\n",
    "%matplotlib inline\n",
    "import seaborn as sns\n",
    "import plotly.express as px\n",
    "\n",
    "from sklearn.preprocessing import StandardScaler\n",
    "from sklearn.decomposition import PCA"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "073e5e7b",
   "metadata": {},
   "source": [
    "### Reading and Understanding the data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "fc492ff5",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\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>Country</th>\n",
       "      <th>Year</th>\n",
       "      <th>Status</th>\n",
       "      <th>LifeExpectancy</th>\n",
       "      <th>Adult_Mortality</th>\n",
       "      <th>infant deaths</th>\n",
       "      <th>Alcohol</th>\n",
       "      <th>HIV/AIDS</th>\n",
       "      <th>GDP</th>\n",
       "      <th>Population</th>\n",
       "      <th>Measles</th>\n",
       "      <th>BMI</th>\n",
       "      <th>under-five deaths</th>\n",
       "      <th>Polio</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Afghanistan</td>\n",
       "      <td>2015</td>\n",
       "      <td>Developing</td>\n",
       "      <td>65.0</td>\n",
       "      <td>263.0</td>\n",
       "      <td>62</td>\n",
       "      <td>0.01</td>\n",
       "      <td>0.1</td>\n",
       "      <td>584.259210</td>\n",
       "      <td>33736494.0</td>\n",
       "      <td>1154</td>\n",
       "      <td>19.1</td>\n",
       "      <td>83</td>\n",
       "      <td>6.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Afghanistan</td>\n",
       "      <td>2014</td>\n",
       "      <td>Developing</td>\n",
       "      <td>59.9</td>\n",
       "      <td>271.0</td>\n",
       "      <td>64</td>\n",
       "      <td>0.01</td>\n",
       "      <td>0.1</td>\n",
       "      <td>612.696514</td>\n",
       "      <td>327582.0</td>\n",
       "      <td>492</td>\n",
       "      <td>18.6</td>\n",
       "      <td>86</td>\n",
       "      <td>58.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Afghanistan</td>\n",
       "      <td>2013</td>\n",
       "      <td>Developing</td>\n",
       "      <td>59.9</td>\n",
       "      <td>268.0</td>\n",
       "      <td>66</td>\n",
       "      <td>0.01</td>\n",
       "      <td>0.1</td>\n",
       "      <td>631.744976</td>\n",
       "      <td>31731688.0</td>\n",
       "      <td>430</td>\n",
       "      <td>18.1</td>\n",
       "      <td>89</td>\n",
       "      <td>62.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Afghanistan</td>\n",
       "      <td>2012</td>\n",
       "      <td>Developing</td>\n",
       "      <td>59.5</td>\n",
       "      <td>272.0</td>\n",
       "      <td>69</td>\n",
       "      <td>0.01</td>\n",
       "      <td>0.1</td>\n",
       "      <td>669.959000</td>\n",
       "      <td>3696958.0</td>\n",
       "      <td>2787</td>\n",
       "      <td>17.6</td>\n",
       "      <td>93</td>\n",
       "      <td>67.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Afghanistan</td>\n",
       "      <td>2011</td>\n",
       "      <td>Developing</td>\n",
       "      <td>59.2</td>\n",
       "      <td>275.0</td>\n",
       "      <td>71</td>\n",
       "      <td>0.01</td>\n",
       "      <td>0.1</td>\n",
       "      <td>63.537231</td>\n",
       "      <td>2978599.0</td>\n",
       "      <td>3013</td>\n",
       "      <td>17.2</td>\n",
       "      <td>97</td>\n",
       "      <td>68.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       Country  Year      Status  LifeExpectancy  Adult_Mortality  \\\n",
       "0  Afghanistan  2015  Developing            65.0            263.0   \n",
       "1  Afghanistan  2014  Developing            59.9            271.0   \n",
       "2  Afghanistan  2013  Developing            59.9            268.0   \n",
       "3  Afghanistan  2012  Developing            59.5            272.0   \n",
       "4  Afghanistan  2011  Developing            59.2            275.0   \n",
       "\n",
       "   infant deaths  Alcohol  HIV/AIDS         GDP  Population  Measles   BMI  \\\n",
       "0             62     0.01       0.1  584.259210  33736494.0     1154  19.1   \n",
       "1             64     0.01       0.1  612.696514    327582.0      492  18.6   \n",
       "2             66     0.01       0.1  631.744976  31731688.0      430  18.1   \n",
       "3             69     0.01       0.1  669.959000   3696958.0     2787  17.6   \n",
       "4             71     0.01       0.1   63.537231   2978599.0     3013  17.2   \n",
       "\n",
       "   under-five deaths   Polio  \n",
       "0                  83    6.0  \n",
       "1                  86   58.0  \n",
       "2                  89   62.0  \n",
       "3                  93   67.0  \n",
       "4                  97   68.0  "
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data = pd.read_csv(r'C:\\Users\\HP\\Desktop\\My documents\\Life_Expectancy data.csv')\n",
    "data.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "a5ddf5b1",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(2938, 14)"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "9c90e98e",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 2938 entries, 0 to 2937\n",
      "Data columns (total 14 columns):\n",
      " #   Column              Non-Null Count  Dtype  \n",
      "---  ------              --------------  -----  \n",
      " 0   Country             2938 non-null   object \n",
      " 1   Year                2938 non-null   int64  \n",
      " 2   Status              2938 non-null   object \n",
      " 3   LifeExpectancy      2938 non-null   float64\n",
      " 4   Adult_Mortality     2938 non-null   float64\n",
      " 5   infant deaths       2938 non-null   int64  \n",
      " 6   Alcohol             2938 non-null   float64\n",
      " 7   HIV/AIDS            2938 non-null   float64\n",
      " 8   GDP                 2938 non-null   float64\n",
      " 9   Population          2938 non-null   float64\n",
      " 10  Measles             2938 non-null   int64  \n",
      " 11  BMI                 2938 non-null   float64\n",
      " 12  under-five deaths   2938 non-null   int64  \n",
      " 13  Polio               2938 non-null   float64\n",
      "dtypes: float64(8), int64(4), object(2)\n",
      "memory usage: 321.5+ KB\n"
     ]
    }
   ],
   "source": [
    "data.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "a18454a4",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Country               0\n",
       "Year                  0\n",
       "Status                0\n",
       "LifeExpectancy        0\n",
       "Adult_Mortality       0\n",
       "infant deaths         0\n",
       "Alcohol               0\n",
       "HIV/AIDS              0\n",
       "GDP                   0\n",
       "Population            0\n",
       "Measles               0\n",
       "BMI                   0\n",
       "under-five deaths     0\n",
       "Polio                 0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.isnull().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "379490a5",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Year</th>\n",
       "      <th>LifeExpectancy</th>\n",
       "      <th>Adult_Mortality</th>\n",
       "      <th>infant deaths</th>\n",
       "      <th>Alcohol</th>\n",
       "      <th>HIV/AIDS</th>\n",
       "      <th>GDP</th>\n",
       "      <th>Population</th>\n",
       "      <th>Measles</th>\n",
       "      <th>BMI</th>\n",
       "      <th>under-five deaths</th>\n",
       "      <th>Polio</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>2938.000000</td>\n",
       "      <td>2938.000000</td>\n",
       "      <td>2938.000000</td>\n",
       "      <td>2938.000000</td>\n",
       "      <td>2938.000000</td>\n",
       "      <td>2938.000000</td>\n",
       "      <td>2938.000000</td>\n",
       "      <td>2.938000e+03</td>\n",
       "      <td>2938.000000</td>\n",
       "      <td>2938.000000</td>\n",
       "      <td>2938.000000</td>\n",
       "      <td>2938.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>2007.518720</td>\n",
       "      <td>69.234717</td>\n",
       "      <td>164.725664</td>\n",
       "      <td>30.303948</td>\n",
       "      <td>4.546875</td>\n",
       "      <td>1.742103</td>\n",
       "      <td>6611.523863</td>\n",
       "      <td>1.023085e+07</td>\n",
       "      <td>2419.592240</td>\n",
       "      <td>38.381178</td>\n",
       "      <td>42.035739</td>\n",
       "      <td>82.617767</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>4.613841</td>\n",
       "      <td>9.509115</td>\n",
       "      <td>124.086215</td>\n",
       "      <td>117.926501</td>\n",
       "      <td>3.921946</td>\n",
       "      <td>5.077785</td>\n",
       "      <td>13296.603449</td>\n",
       "      <td>5.402242e+07</td>\n",
       "      <td>11467.272489</td>\n",
       "      <td>19.935375</td>\n",
       "      <td>160.445548</td>\n",
       "      <td>23.367166</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>2000.000000</td>\n",
       "      <td>36.300000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.010000</td>\n",
       "      <td>0.100000</td>\n",
       "      <td>1.681350</td>\n",
       "      <td>3.400000e+01</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>3.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>2004.000000</td>\n",
       "      <td>63.200000</td>\n",
       "      <td>74.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.092500</td>\n",
       "      <td>0.100000</td>\n",
       "      <td>580.486996</td>\n",
       "      <td>4.189172e+05</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>19.400000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>78.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>2008.000000</td>\n",
       "      <td>72.100000</td>\n",
       "      <td>144.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>3.755000</td>\n",
       "      <td>0.100000</td>\n",
       "      <td>1766.947595</td>\n",
       "      <td>1.386542e+06</td>\n",
       "      <td>17.000000</td>\n",
       "      <td>43.500000</td>\n",
       "      <td>4.000000</td>\n",
       "      <td>93.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>2012.000000</td>\n",
       "      <td>75.600000</td>\n",
       "      <td>227.000000</td>\n",
       "      <td>22.000000</td>\n",
       "      <td>7.390000</td>\n",
       "      <td>0.800000</td>\n",
       "      <td>4779.405190</td>\n",
       "      <td>4.584371e+06</td>\n",
       "      <td>360.250000</td>\n",
       "      <td>56.100000</td>\n",
       "      <td>28.000000</td>\n",
       "      <td>97.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>2015.000000</td>\n",
       "      <td>89.000000</td>\n",
       "      <td>723.000000</td>\n",
       "      <td>1800.000000</td>\n",
       "      <td>17.870000</td>\n",
       "      <td>50.600000</td>\n",
       "      <td>119172.741800</td>\n",
       "      <td>1.293859e+09</td>\n",
       "      <td>212183.000000</td>\n",
       "      <td>87.300000</td>\n",
       "      <td>2500.000000</td>\n",
       "      <td>99.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "              Year  LifeExpectancy  Adult_Mortality  infant deaths  \\\n",
       "count  2938.000000     2938.000000      2938.000000    2938.000000   \n",
       "mean   2007.518720       69.234717       164.725664      30.303948   \n",
       "std       4.613841        9.509115       124.086215     117.926501   \n",
       "min    2000.000000       36.300000         1.000000       0.000000   \n",
       "25%    2004.000000       63.200000        74.000000       0.000000   \n",
       "50%    2008.000000       72.100000       144.000000       3.000000   \n",
       "75%    2012.000000       75.600000       227.000000      22.000000   \n",
       "max    2015.000000       89.000000       723.000000    1800.000000   \n",
       "\n",
       "           Alcohol     HIV/AIDS            GDP    Population        Measles  \\\n",
       "count  2938.000000  2938.000000    2938.000000  2.938000e+03    2938.000000   \n",
       "mean      4.546875     1.742103    6611.523863  1.023085e+07    2419.592240   \n",
       "std       3.921946     5.077785   13296.603449  5.402242e+07   11467.272489   \n",
       "min       0.010000     0.100000       1.681350  3.400000e+01       0.000000   \n",
       "25%       1.092500     0.100000     580.486996  4.189172e+05       0.000000   \n",
       "50%       3.755000     0.100000    1766.947595  1.386542e+06      17.000000   \n",
       "75%       7.390000     0.800000    4779.405190  4.584371e+06     360.250000   \n",
       "max      17.870000    50.600000  119172.741800  1.293859e+09  212183.000000   \n",
       "\n",
       "               BMI  under-five deaths         Polio  \n",
       "count  2938.000000         2938.000000  2938.000000  \n",
       "mean     38.381178           42.035739    82.617767  \n",
       "std      19.935375          160.445548    23.367166  \n",
       "min       1.000000            0.000000     3.000000  \n",
       "25%      19.400000            0.000000    78.000000  \n",
       "50%      43.500000            4.000000    93.000000  \n",
       "75%      56.100000           28.000000    97.000000  \n",
       "max      87.300000         2500.000000    99.000000  "
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.describe()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "032fb79e",
   "metadata": {},
   "source": [
    "### Feature Transformation"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b111bfd9",
   "metadata": {},
   "source": [
    "Feature Transformation means transforming our features to the functions of the original features. For example, feature encoding, scaling, and discretization (the process of transforming continuous variables into discrete form, by creating bins or intervals) are the most common forms of data transformation.\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "2c9c1282",
   "metadata": {},
   "source": [
    "#### Dealing with Categorical  Variables"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "acee7169",
   "metadata": {},
   "source": [
    "Categorical variables represent qualitative data with no apparent inherent mathematical meaning. Therefore, for any machine learning analysis, all the categorical data must be transformed into the numerical data types. In this dataset only \"Country\" and \"Status\" columns contains categorical variables.I will use `unique()` method to obtain all the categories in these columns before proceeding with further transformations\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "f6e9b96b",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(['Afghanistan', 'Albania', 'Algeria', 'Angola',\n",
       "       'Antigua and Barbuda', 'Argentina', 'Armenia', 'Australia',\n",
       "       'Austria', 'Azerbaijan', 'Bahamas', 'Bahrain', 'Bangladesh',\n",
       "       'Barbados', 'Belarus', 'Belgium', 'Belize', 'Benin', 'Bhutan',\n",
       "       'Bolivia (Plurinational State of)', 'Bosnia and Herzegovina',\n",
       "       'Botswana', 'Brazil', 'Brunei Darussalam', 'Bulgaria',\n",
       "       'Burkina Faso', 'Burundi', \"Côte d'Ivoire\", 'Cabo Verde',\n",
       "       'Cambodia', 'Cameroon', 'Canada', 'Central African Republic',\n",
       "       'Chad', 'Chile', 'China', 'Colombia', 'Comoros', 'Congo',\n",
       "       'Cook Islands', 'Costa Rica', 'Croatia', 'Cuba', 'Cyprus',\n",
       "       'Czechia', \"Democratic People's Republic of Korea\",\n",
       "       'Democratic Republic of the Congo', 'Denmark', 'Djibouti',\n",
       "       'Dominica', 'Dominican Republic', 'Ecuador', 'Egypt',\n",
       "       'El Salvador', 'Equatorial Guinea', 'Eritrea', 'Estonia',\n",
       "       'Ethiopia', 'Fiji', 'Finland', 'France', 'Gabon', 'Gambia',\n",
       "       'Georgia', 'Germany', 'Ghana', 'Greece', 'Grenada', 'Guatemala',\n",
       "       'Guinea', 'Guinea-Bissau', 'Guyana', 'Haiti', 'Honduras',\n",
       "       'Hungary', 'Iceland', 'India', 'Indonesia',\n",
       "       'Iran (Islamic Republic of)', 'Iraq', 'Ireland', 'Israel', 'Italy',\n",
       "       'Jamaica', 'Japan', 'Jordan', 'Kazakhstan', 'Kenya', 'Kiribati',\n",
       "       'Kuwait', 'Kyrgyzstan', \"Lao People's Democratic Republic\",\n",
       "       'Latvia', 'Lebanon', 'Lesotho', 'Liberia', 'Libya', 'Lithuania',\n",
       "       'Luxembourg', 'Madagascar', 'Malawi', 'Malaysia', 'Maldives',\n",
       "       'Mali', 'Malta', 'Marshall Islands', 'Mauritania', 'Mauritius',\n",
       "       'Mexico', 'Micronesia (Federated States of)', 'Monaco', 'Mongolia',\n",
       "       'Montenegro', 'Morocco', 'Mozambique', 'Myanmar', 'Namibia',\n",
       "       'Nauru', 'Nepal', 'Netherlands', 'New Zealand', 'Nicaragua',\n",
       "       'Niger', 'Nigeria', 'Niue', 'Norway', 'Oman', 'Pakistan', 'Palau',\n",
       "       'Panama', 'Papua New Guinea', 'Paraguay', 'Peru', 'Philippines',\n",
       "       'Poland', 'Portugal', 'Qatar', 'Republic of Korea',\n",
       "       'Republic of Moldova', 'Romania', 'Russian Federation', 'Rwanda',\n",
       "       'Saint Kitts and Nevis', 'Saint Lucia',\n",
       "       'Saint Vincent and the Grenadines', 'Samoa', 'San Marino',\n",
       "       'Sao Tome and Principe', 'Saudi Arabia', 'Senegal', 'Serbia',\n",
       "       'Seychelles', 'Sierra Leone', 'Singapore', 'Slovakia', 'Slovenia',\n",
       "       'Solomon Islands', 'Somalia', 'South Africa', 'South Sudan',\n",
       "       'Spain', 'Sri Lanka', 'Sudan', 'Suriname', 'Swaziland', 'Sweden',\n",
       "       'Switzerland', 'Syrian Arab Republic', 'Tajikistan', 'Thailand',\n",
       "       'The former Yugoslav republic of Macedonia', 'Timor-Leste', 'Togo',\n",
       "       'Tonga', 'Trinidad and Tobago', 'Tunisia', 'Turkey',\n",
       "       'Turkmenistan', 'Tuvalu', 'Uganda', 'Ukraine',\n",
       "       'United Arab Emirates',\n",
       "       'United Kingdom of Great Britain and Northern Ireland',\n",
       "       'United Republic of Tanzania', 'United States of America',\n",
       "       'Uruguay', 'Uzbekistan', 'Vanuatu',\n",
       "       'Venezuela (Bolivarian Republic of)', 'Viet Nam', 'Yemen',\n",
       "       'Zambia', 'Zimbabwe'], dtype=object)"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data['Country'].unique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "8816f06b",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(['Developing', 'Developed'], dtype=object)"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data['Status'].unique()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "79c534e0",
   "metadata": {},
   "source": [
    "### Categorical Variables Transformation with 'pandas get_dummies()'"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "82e632b3",
   "metadata": {},
   "source": [
    "Now, to be recognized by a machine learning algorithms, the categorical variables should be converted into numerical ones. One way to do this is through *one hot encoding*, *labels Encoder*, *Pandas get_dummies*, and among others.\n",
    "\n",
    "Here, I will be using, `get_dummies()` method to do this transformation. In the next cell, I will be transforming the 'country' variables into their numerical values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "02448e45",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
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       "\n",
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       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Year</th>\n",
       "      <th>Status</th>\n",
       "      <th>LifeExpectancy</th>\n",
       "      <th>Adult_Mortality</th>\n",
       "      <th>infant deaths</th>\n",
       "      <th>Alcohol</th>\n",
       "      <th>HIV/AIDS</th>\n",
       "      <th>GDP</th>\n",
       "      <th>Population</th>\n",
       "      <th>Measles</th>\n",
       "      <th>...</th>\n",
       "      <th>Country_United Republic of Tanzania</th>\n",
       "      <th>Country_United States of America</th>\n",
       "      <th>Country_Uruguay</th>\n",
       "      <th>Country_Uzbekistan</th>\n",
       "      <th>Country_Vanuatu</th>\n",
       "      <th>Country_Venezuela (Bolivarian Republic of)</th>\n",
       "      <th>Country_Viet Nam</th>\n",
       "      <th>Country_Yemen</th>\n",
       "      <th>Country_Zambia</th>\n",
       "      <th>Country_Zimbabwe</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2015</td>\n",
       "      <td>Developing</td>\n",
       "      <td>65.0</td>\n",
       "      <td>263.0</td>\n",
       "      <td>62</td>\n",
       "      <td>0.01</td>\n",
       "      <td>0.1</td>\n",
       "      <td>584.259210</td>\n",
       "      <td>33736494.0</td>\n",
       "      <td>1154</td>\n",
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       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2014</td>\n",
       "      <td>Developing</td>\n",
       "      <td>59.9</td>\n",
       "      <td>271.0</td>\n",
       "      <td>64</td>\n",
       "      <td>0.01</td>\n",
       "      <td>0.1</td>\n",
       "      <td>612.696514</td>\n",
       "      <td>327582.0</td>\n",
       "      <td>492</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
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       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2013</td>\n",
       "      <td>Developing</td>\n",
       "      <td>59.9</td>\n",
       "      <td>268.0</td>\n",
       "      <td>66</td>\n",
       "      <td>0.01</td>\n",
       "      <td>0.1</td>\n",
       "      <td>631.744976</td>\n",
       "      <td>31731688.0</td>\n",
       "      <td>430</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2012</td>\n",
       "      <td>Developing</td>\n",
       "      <td>59.5</td>\n",
       "      <td>272.0</td>\n",
       "      <td>69</td>\n",
       "      <td>0.01</td>\n",
       "      <td>0.1</td>\n",
       "      <td>669.959000</td>\n",
       "      <td>3696958.0</td>\n",
       "      <td>2787</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
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       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2011</td>\n",
       "      <td>Developing</td>\n",
       "      <td>59.2</td>\n",
       "      <td>275.0</td>\n",
       "      <td>71</td>\n",
       "      <td>0.01</td>\n",
       "      <td>0.1</td>\n",
       "      <td>63.537231</td>\n",
       "      <td>2978599.0</td>\n",
       "      <td>3013</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 206 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "   Year      Status  LifeExpectancy  Adult_Mortality  infant deaths  Alcohol  \\\n",
       "0  2015  Developing            65.0            263.0             62     0.01   \n",
       "1  2014  Developing            59.9            271.0             64     0.01   \n",
       "2  2013  Developing            59.9            268.0             66     0.01   \n",
       "3  2012  Developing            59.5            272.0             69     0.01   \n",
       "4  2011  Developing            59.2            275.0             71     0.01   \n",
       "\n",
       "   HIV/AIDS         GDP  Population  Measles  ...  \\\n",
       "0       0.1  584.259210  33736494.0     1154  ...   \n",
       "1       0.1  612.696514    327582.0      492  ...   \n",
       "2       0.1  631.744976  31731688.0      430  ...   \n",
       "3       0.1  669.959000   3696958.0     2787  ...   \n",
       "4       0.1   63.537231   2978599.0     3013  ...   \n",
       "\n",
       "   Country_United Republic of Tanzania  Country_United States of America  \\\n",
       "0                                    0                                 0   \n",
       "1                                    0                                 0   \n",
       "2                                    0                                 0   \n",
       "3                                    0                                 0   \n",
       "4                                    0                                 0   \n",
       "\n",
       "   Country_Uruguay  Country_Uzbekistan  Country_Vanuatu  \\\n",
       "0                0                   0                0   \n",
       "1                0                   0                0   \n",
       "2                0                   0                0   \n",
       "3                0                   0                0   \n",
       "4                0                   0                0   \n",
       "\n",
       "   Country_Venezuela (Bolivarian Republic of)  Country_Viet Nam  \\\n",
       "0                                           0                 0   \n",
       "1                                           0                 0   \n",
       "2                                           0                 0   \n",
       "3                                           0                 0   \n",
       "4                                           0                 0   \n",
       "\n",
       "   Country_Yemen  Country_Zambia  Country_Zimbabwe  \n",
       "0              0               0                 0  \n",
       "1              0               0                 0  \n",
       "2              0               0                 0  \n",
       "3              0               0                 0  \n",
       "4              0               0                 0  \n",
       "\n",
       "[5 rows x 206 columns]"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data1 = pd.get_dummies(data=data, columns = ['Country'])\n",
    "data1 = pd.DataFrame(data1)\n",
    "data1.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "a541f63d",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(2938, 14)"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "d2a18ff1",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(2938, 206)"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data1.shape"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "3ebc6f3a",
   "metadata": {},
   "source": [
    "As we can see, we went from 14 original features in our dataset to 206. This is because *Pandas* `get_dummies()` approach when applied to a column with different categories (e.g. different countries) will produce a new column (variable) for each unique categorical value (for each unique country). It will place a one in the column corresponding to the categorical value present for that observation.\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "7cd91012",
   "metadata": {},
   "source": [
    "### Categorical Variables Transformation with Label Encoder"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ff0bbefb",
   "metadata": {},
   "source": [
    "Since 'Status' is originally a categorical data type, we also need to convert it into numerical one. For this, we can perform a label encoding, where values are manually assigned to the corresponding keys, like \"0\" to a \"Developing\", using the `replace()` function.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "9f3724d4",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <td>0.1</td>\n",
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       "      <td>66</td>\n",
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       "      <th>3</th>\n",
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       "      <td>0</td>\n",
       "      <td>59.5</td>\n",
       "      <td>272.0</td>\n",
       "      <td>69</td>\n",
       "      <td>0.01</td>\n",
       "      <td>0.1</td>\n",
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       "      <th>4</th>\n",
       "      <td>2011</td>\n",
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       "      <td>59.2</td>\n",
       "      <td>275.0</td>\n",
       "      <td>71</td>\n",
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       "      <td>27</td>\n",
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       "      <td>454.366654</td>\n",
       "      <td>12777511.0</td>\n",
       "      <td>31</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2934</th>\n",
       "      <td>2003</td>\n",
       "      <td>0</td>\n",
       "      <td>44.5</td>\n",
       "      <td>715.0</td>\n",
       "      <td>26</td>\n",
       "      <td>4.06</td>\n",
       "      <td>36.7</td>\n",
       "      <td>453.351155</td>\n",
       "      <td>12633897.0</td>\n",
       "      <td>998</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2935</th>\n",
       "      <td>2002</td>\n",
       "      <td>0</td>\n",
       "      <td>44.8</td>\n",
       "      <td>73.0</td>\n",
       "      <td>25</td>\n",
       "      <td>4.43</td>\n",
       "      <td>39.8</td>\n",
       "      <td>57.348340</td>\n",
       "      <td>125525.0</td>\n",
       "      <td>304</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2936</th>\n",
       "      <td>2001</td>\n",
       "      <td>0</td>\n",
       "      <td>45.3</td>\n",
       "      <td>686.0</td>\n",
       "      <td>25</td>\n",
       "      <td>1.72</td>\n",
       "      <td>42.1</td>\n",
       "      <td>548.587312</td>\n",
       "      <td>12366165.0</td>\n",
       "      <td>529</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2937</th>\n",
       "      <td>2000</td>\n",
       "      <td>0</td>\n",
       "      <td>46.0</td>\n",
       "      <td>665.0</td>\n",
       "      <td>24</td>\n",
       "      <td>1.68</td>\n",
       "      <td>43.5</td>\n",
       "      <td>547.358878</td>\n",
       "      <td>12222251.0</td>\n",
       "      <td>1483</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>2938 rows × 206 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "      Year  Status  LifeExpectancy  Adult_Mortality  infant deaths  Alcohol  \\\n",
       "0     2015       0            65.0            263.0             62     0.01   \n",
       "1     2014       0            59.9            271.0             64     0.01   \n",
       "2     2013       0            59.9            268.0             66     0.01   \n",
       "3     2012       0            59.5            272.0             69     0.01   \n",
       "4     2011       0            59.2            275.0             71     0.01   \n",
       "...    ...     ...             ...              ...            ...      ...   \n",
       "2933  2004       0            44.3            723.0             27     4.36   \n",
       "2934  2003       0            44.5            715.0             26     4.06   \n",
       "2935  2002       0            44.8             73.0             25     4.43   \n",
       "2936  2001       0            45.3            686.0             25     1.72   \n",
       "2937  2000       0            46.0            665.0             24     1.68   \n",
       "\n",
       "      HIV/AIDS         GDP  Population  Measles  ...  \\\n",
       "0          0.1  584.259210  33736494.0     1154  ...   \n",
       "1          0.1  612.696514    327582.0      492  ...   \n",
       "2          0.1  631.744976  31731688.0      430  ...   \n",
       "3          0.1  669.959000   3696958.0     2787  ...   \n",
       "4          0.1   63.537231   2978599.0     3013  ...   \n",
       "...        ...         ...         ...      ...  ...   \n",
       "2933      33.6  454.366654  12777511.0       31  ...   \n",
       "2934      36.7  453.351155  12633897.0      998  ...   \n",
       "2935      39.8   57.348340    125525.0      304  ...   \n",
       "2936      42.1  548.587312  12366165.0      529  ...   \n",
       "2937      43.5  547.358878  12222251.0     1483  ...   \n",
       "\n",
       "      Country_United Republic of Tanzania  Country_United States of America  \\\n",
       "0                                       0                                 0   \n",
       "1                                       0                                 0   \n",
       "2                                       0                                 0   \n",
       "3                                       0                                 0   \n",
       "4                                       0                                 0   \n",
       "...                                   ...                               ...   \n",
       "2933                                    0                                 0   \n",
       "2934                                    0                                 0   \n",
       "2935                                    0                                 0   \n",
       "2936                                    0                                 0   \n",
       "2937                                    0                                 0   \n",
       "\n",
       "      Country_Uruguay  Country_Uzbekistan  Country_Vanuatu  \\\n",
       "0                   0                   0                0   \n",
       "1                   0                   0                0   \n",
       "2                   0                   0                0   \n",
       "3                   0                   0                0   \n",
       "4                   0                   0                0   \n",
       "...               ...                 ...              ...   \n",
       "2933                0                   0                0   \n",
       "2934                0                   0                0   \n",
       "2935                0                   0                0   \n",
       "2936                0                   0                0   \n",
       "2937                0                   0                0   \n",
       "\n",
       "      Country_Venezuela (Bolivarian Republic of)  Country_Viet Nam  \\\n",
       "0                                              0                 0   \n",
       "1                                              0                 0   \n",
       "2                                              0                 0   \n",
       "3                                              0                 0   \n",
       "4                                              0                 0   \n",
       "...                                          ...               ...   \n",
       "2933                                           0                 0   \n",
       "2934                                           0                 0   \n",
       "2935                                           0                 0   \n",
       "2936                                           0                 0   \n",
       "2937                                           0                 0   \n",
       "\n",
       "      Country_Yemen  Country_Zambia  Country_Zimbabwe  \n",
       "0                 0               0                 0  \n",
       "1                 0               0                 0  \n",
       "2                 0               0                 0  \n",
       "3                 0               0                 0  \n",
       "4                 0               0                 0  \n",
       "...             ...             ...               ...  \n",
       "2933              0               0                 1  \n",
       "2934              0               0                 1  \n",
       "2935              0               0                 1  \n",
       "2936              0               0                 1  \n",
       "2937              0               0                 1  \n",
       "\n",
       "[2938 rows x 206 columns]"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data1.replace({\"Developing\":0,\"Developed\":1},inplace=True)\n",
    "data1"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "72e02a43",
   "metadata": {},
   "source": [
    "## Feature Selection"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "bc211be0",
   "metadata": {},
   "source": [
    "Here, I will be selecting only those attributes which best explain the relationship of the independent variables with respect to the target variable, 'Life Expectancy'. According to the course. there are many methods for feature selection, and building the heatmap and calculating the correlation coefficients scores are the most commonly used ones."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "e240e1fe",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['Year',\n",
       " 'Status',\n",
       " 'LifeExpectancy',\n",
       " 'Adult_Mortality',\n",
       " 'infant deaths',\n",
       " 'Alcohol',\n",
       " 'HIV/AIDS',\n",
       " 'GDP',\n",
       " 'Population',\n",
       " 'Measles',\n",
       " 'BMI',\n",
       " 'under-five deaths ',\n",
       " 'Polio',\n",
       " 'Country_Afghanistan',\n",
       " 'Country_Albania',\n",
       " 'Country_Algeria',\n",
       " 'Country_Angola',\n",
       " 'Country_Antigua and Barbuda',\n",
       " 'Country_Argentina',\n",
       " 'Country_Armenia',\n",
       " 'Country_Australia',\n",
       " 'Country_Austria',\n",
       " 'Country_Azerbaijan',\n",
       " 'Country_Bahamas',\n",
       " 'Country_Bahrain',\n",
       " 'Country_Bangladesh',\n",
       " 'Country_Barbados',\n",
       " 'Country_Belarus',\n",
       " 'Country_Belgium',\n",
       " 'Country_Belize',\n",
       " 'Country_Benin',\n",
       " 'Country_Bhutan',\n",
       " 'Country_Bolivia (Plurinational State of)',\n",
       " 'Country_Bosnia and Herzegovina',\n",
       " 'Country_Botswana',\n",
       " 'Country_Brazil',\n",
       " 'Country_Brunei Darussalam',\n",
       " 'Country_Bulgaria',\n",
       " 'Country_Burkina Faso',\n",
       " 'Country_Burundi',\n",
       " 'Country_Cabo Verde',\n",
       " 'Country_Cambodia',\n",
       " 'Country_Cameroon',\n",
       " 'Country_Canada',\n",
       " 'Country_Central African Republic',\n",
       " 'Country_Chad',\n",
       " 'Country_Chile',\n",
       " 'Country_China',\n",
       " 'Country_Colombia',\n",
       " 'Country_Comoros',\n",
       " 'Country_Congo',\n",
       " 'Country_Cook Islands',\n",
       " 'Country_Costa Rica',\n",
       " 'Country_Croatia',\n",
       " 'Country_Cuba',\n",
       " 'Country_Cyprus',\n",
       " 'Country_Czechia',\n",
       " \"Country_Côte d'Ivoire\",\n",
       " \"Country_Democratic People's Republic of Korea\",\n",
       " 'Country_Democratic Republic of the Congo',\n",
       " 'Country_Denmark',\n",
       " 'Country_Djibouti',\n",
       " 'Country_Dominica',\n",
       " 'Country_Dominican Republic',\n",
       " 'Country_Ecuador',\n",
       " 'Country_Egypt',\n",
       " 'Country_El Salvador',\n",
       " 'Country_Equatorial Guinea',\n",
       " 'Country_Eritrea',\n",
       " 'Country_Estonia',\n",
       " 'Country_Ethiopia',\n",
       " 'Country_Fiji',\n",
       " 'Country_Finland',\n",
       " 'Country_France',\n",
       " 'Country_Gabon',\n",
       " 'Country_Gambia',\n",
       " 'Country_Georgia',\n",
       " 'Country_Germany',\n",
       " 'Country_Ghana',\n",
       " 'Country_Greece',\n",
       " 'Country_Grenada',\n",
       " 'Country_Guatemala',\n",
       " 'Country_Guinea',\n",
       " 'Country_Guinea-Bissau',\n",
       " 'Country_Guyana',\n",
       " 'Country_Haiti',\n",
       " 'Country_Honduras',\n",
       " 'Country_Hungary',\n",
       " 'Country_Iceland',\n",
       " 'Country_India',\n",
       " 'Country_Indonesia',\n",
       " 'Country_Iran (Islamic Republic of)',\n",
       " 'Country_Iraq',\n",
       " 'Country_Ireland',\n",
       " 'Country_Israel',\n",
       " 'Country_Italy',\n",
       " 'Country_Jamaica',\n",
       " 'Country_Japan',\n",
       " 'Country_Jordan',\n",
       " 'Country_Kazakhstan',\n",
       " 'Country_Kenya',\n",
       " 'Country_Kiribati',\n",
       " 'Country_Kuwait',\n",
       " 'Country_Kyrgyzstan',\n",
       " \"Country_Lao People's Democratic Republic\",\n",
       " 'Country_Latvia',\n",
       " 'Country_Lebanon',\n",
       " 'Country_Lesotho',\n",
       " 'Country_Liberia',\n",
       " 'Country_Libya',\n",
       " 'Country_Lithuania',\n",
       " 'Country_Luxembourg',\n",
       " 'Country_Madagascar',\n",
       " 'Country_Malawi',\n",
       " 'Country_Malaysia',\n",
       " 'Country_Maldives',\n",
       " 'Country_Mali',\n",
       " 'Country_Malta',\n",
       " 'Country_Marshall Islands',\n",
       " 'Country_Mauritania',\n",
       " 'Country_Mauritius',\n",
       " 'Country_Mexico',\n",
       " 'Country_Micronesia (Federated States of)',\n",
       " 'Country_Monaco',\n",
       " 'Country_Mongolia',\n",
       " 'Country_Montenegro',\n",
       " 'Country_Morocco',\n",
       " 'Country_Mozambique',\n",
       " 'Country_Myanmar',\n",
       " 'Country_Namibia',\n",
       " 'Country_Nauru',\n",
       " 'Country_Nepal',\n",
       " 'Country_Netherlands',\n",
       " 'Country_New Zealand',\n",
       " 'Country_Nicaragua',\n",
       " 'Country_Niger',\n",
       " 'Country_Nigeria',\n",
       " 'Country_Niue',\n",
       " 'Country_Norway',\n",
       " 'Country_Oman',\n",
       " 'Country_Pakistan',\n",
       " 'Country_Palau',\n",
       " 'Country_Panama',\n",
       " 'Country_Papua New Guinea',\n",
       " 'Country_Paraguay',\n",
       " 'Country_Peru',\n",
       " 'Country_Philippines',\n",
       " 'Country_Poland',\n",
       " 'Country_Portugal',\n",
       " 'Country_Qatar',\n",
       " 'Country_Republic of Korea',\n",
       " 'Country_Republic of Moldova',\n",
       " 'Country_Romania',\n",
       " 'Country_Russian Federation',\n",
       " 'Country_Rwanda',\n",
       " 'Country_Saint Kitts and Nevis',\n",
       " 'Country_Saint Lucia',\n",
       " 'Country_Saint Vincent and the Grenadines',\n",
       " 'Country_Samoa',\n",
       " 'Country_San Marino',\n",
       " 'Country_Sao Tome and Principe',\n",
       " 'Country_Saudi Arabia',\n",
       " 'Country_Senegal',\n",
       " 'Country_Serbia',\n",
       " 'Country_Seychelles',\n",
       " 'Country_Sierra Leone',\n",
       " 'Country_Singapore',\n",
       " 'Country_Slovakia',\n",
       " 'Country_Slovenia',\n",
       " 'Country_Solomon Islands',\n",
       " 'Country_Somalia',\n",
       " 'Country_South Africa',\n",
       " 'Country_South Sudan',\n",
       " 'Country_Spain',\n",
       " 'Country_Sri Lanka',\n",
       " 'Country_Sudan',\n",
       " 'Country_Suriname',\n",
       " 'Country_Swaziland',\n",
       " 'Country_Sweden',\n",
       " 'Country_Switzerland',\n",
       " 'Country_Syrian Arab Republic',\n",
       " 'Country_Tajikistan',\n",
       " 'Country_Thailand',\n",
       " 'Country_The former Yugoslav republic of Macedonia',\n",
       " 'Country_Timor-Leste',\n",
       " 'Country_Togo',\n",
       " 'Country_Tonga',\n",
       " 'Country_Trinidad and Tobago',\n",
       " 'Country_Tunisia',\n",
       " 'Country_Turkey',\n",
       " 'Country_Turkmenistan',\n",
       " 'Country_Tuvalu',\n",
       " 'Country_Uganda',\n",
       " 'Country_Ukraine',\n",
       " 'Country_United Arab Emirates',\n",
       " 'Country_United Kingdom of Great Britain and Northern Ireland',\n",
       " 'Country_United Republic of Tanzania',\n",
       " 'Country_United States of America',\n",
       " 'Country_Uruguay',\n",
       " 'Country_Uzbekistan',\n",
       " 'Country_Vanuatu',\n",
       " 'Country_Venezuela (Bolivarian Republic of)',\n",
       " 'Country_Viet Nam',\n",
       " 'Country_Yemen',\n",
       " 'Country_Zambia',\n",
       " 'Country_Zimbabwe']"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#printing all the columns in data1\n",
    "data1.columns.tolist()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "4f8b0af7",
   "metadata": {},
   "outputs": [],
   "source": [
    "new_data = data1.loc[:,['Year','Status','LifeExpectancy','Adult_Mortality','infant deaths','Alcohol','HIV/AIDS',\n",
    "        'GDP','Population','Measles','BMI','under-five deaths ','Polio']]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "1156a4cc",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1296x1296 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#constructing heatmap using seaborn\n",
    "plt.figure(figsize=(18,18))\n",
    "sns.heatmap(new_data.corr(),annot=True,cmap='RdYlGn')\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "181b7ad2",
   "metadata": {},
   "source": [
    "From the heatmap above, extreme green means highly positively correlated features (relationship between two variables in which both variables move in the same direction), extreme red means negatively correlated features (relationship between two variables in which an increase in one variable is associated with a decrease in the other)."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a8cae9a5",
   "metadata": {},
   "source": [
    "Now, we can use the `corr()` function to calculate and list the correlation between all independent variables and the 'Life Expectancy'."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "f1689ff5",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Adult_Mortality        -0.696390\n",
       "HIV/AIDS               -0.556703\n",
       "under-five deaths      -0.222738\n",
       "infant deaths          -0.196769\n",
       "Country_Sierra Leone   -0.179963\n",
       "                          ...   \n",
       "GDP                     0.430461\n",
       "Polio                   0.458399\n",
       "Status                  0.481415\n",
       "BMI                     0.556901\n",
       "LifeExpectancy          1.000000\n",
       "Name: LifeExpectancy, Length: 206, dtype: float64"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "features = data1.corr()['LifeExpectancy'].sort_values()\n",
    "features"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ccebae77",
   "metadata": {},
   "source": [
    "## Feature Extraction using Principal Component Analysis "
   ]
  },
  {
   "cell_type": "markdown",
   "id": "9c69ed9f",
   "metadata": {},
   "source": [
    "### PCA with Scikit-Learn"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "558bc45f",
   "metadata": {},
   "source": [
    "According to the course, Dimentionality Reduction is part of the feature extraction process that combines the existing features to produce more useful ones. The goal of dimensionality reduction is to simplify the data without loosing too much information. Principal Component Analysis (PCA) is one of the most popular dimensionality reduction algorithms. First, it identifies the hyperplane that lies closest to the data, and then it projects the data onto it. In this way, a few multidimensional features are merged into one.\n",
    "\n",
    "In the following portion of the lab, I will be using `scikit-learn` library to perform some PCA on the data."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "7b3502aa",
   "metadata": {},
   "outputs": [],
   "source": [
    "x = data1.drop('LifeExpectancy', axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "56aee891",
   "metadata": {},
   "outputs": [],
   "source": [
    "y = data1['LifeExpectancy']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "b5b48edf",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[ 1.6217623 , -0.45939851,  0.79211913, ..., -0.07399798,\n",
       "        -0.07399798, -0.07399798],\n",
       "       [ 1.40498625, -0.45939851,  0.85660141, ..., -0.07399798,\n",
       "        -0.07399798, -0.07399798],\n",
       "       [ 1.1882102 , -0.45939851,  0.83242056, ..., -0.07399798,\n",
       "        -0.07399798, -0.07399798],\n",
       "       ...,\n",
       "       [-1.19632639, -0.45939851, -0.73933497, ..., -0.07399798,\n",
       "        -0.07399798, 13.51388175],\n",
       "       [-1.41310244, -0.45939851,  4.20161958, ..., -0.07399798,\n",
       "        -0.07399798, 13.51388175],\n",
       "       [-1.62987849, -0.45939851,  4.0323536 , ..., -0.07399798,\n",
       "        -0.07399798, 13.51388175]])"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "scaler = StandardScaler()\n",
    "x=scaler.fit_transform(x.astype(np.float64))\n",
    "x"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "d247d4bd",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[ 2.28217919, -1.36130111],\n",
       "       [ 1.69680591, -1.26872268],\n",
       "       [ 1.81516893, -1.08436141],\n",
       "       ...,\n",
       "       [ 1.85812008, -2.93599106],\n",
       "       [ 3.21288328, -4.50507   ],\n",
       "       [ 3.2317695 , -4.50503841]])"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#reducing the dimensionality of the dataset down to two dimensions.\n",
    "pca = PCA(n_components = 2)\n",
    "pca.fit_transform(x)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "27ef4463",
   "metadata": {},
   "source": [
    "### Explained Variance Ratio"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "4354bd0c",
   "metadata": {},
   "source": [
    "According to the course, another useful piece of information in PCA is the explained variance ratio of each principal component, available via the `explained_variance_ratio_` function. The ratio indicates the proportion of the dataset's variance that lies along each principal component. Let's look at the explained variance ratio of each of our two components.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "e8525a2d",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([0.02033928, 0.01547936])"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "explained_variance=pca.explained_variance_ratio_\n",
    "explained_variance"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "a64f141d",
   "metadata": {},
   "outputs": [],
   "source": [
    "# visualizing the result of pca and explained variance ratio\n",
    "with plt.style.context('dark_background'):\n",
    "    plt.figure(figsize=(6, 4))\n",
    "\n",
    "\n",
    "plt.bar(range(7), explained_variance, alpha=0.5, align='center',\n",
    "    label='individual explained variance')\n",
    "plt.ylabel('Explained variance ratio')\n",
    "plt.xlabel('Principal components')\n",
    "plt.legend(loc='best')\n",
    "plt.tight_layout() "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "4acdb5b6",
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
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