clustering_casestudy_v1.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Overall Objective:\n",
    "To categorise the countries using some socio-economic and health factors that determine the overall development of the country. Then we need to suggest the countries which the CEO needs to focus on the most."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Section 1: Data Inspection and EDA"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "\n",
    "import warnings\n",
    "warnings.filterwarnings('ignore')\n",
    "\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "\n",
    "from sklearn.cluster import KMeans\n",
    "\n",
    "from scipy.cluster.hierarchy import linkage\n",
    "from scipy.cluster.hierarchy import dendrogram\n",
    "from scipy.cluster.hierarchy import cut_tree"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>country</th>\n",
       "      <th>child_mort</th>\n",
       "      <th>exports</th>\n",
       "      <th>health</th>\n",
       "      <th>imports</th>\n",
       "      <th>income</th>\n",
       "      <th>inflation</th>\n",
       "      <th>life_expec</th>\n",
       "      <th>total_fer</th>\n",
       "      <th>gdpp</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>0</td>\n",
       "      <td>Afghanistan</td>\n",
       "      <td>90.2</td>\n",
       "      <td>10.0</td>\n",
       "      <td>7.58</td>\n",
       "      <td>44.9</td>\n",
       "      <td>1610</td>\n",
       "      <td>9.44</td>\n",
       "      <td>56.2</td>\n",
       "      <td>5.82</td>\n",
       "      <td>553</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>1</td>\n",
       "      <td>Albania</td>\n",
       "      <td>16.6</td>\n",
       "      <td>28.0</td>\n",
       "      <td>6.55</td>\n",
       "      <td>48.6</td>\n",
       "      <td>9930</td>\n",
       "      <td>4.49</td>\n",
       "      <td>76.3</td>\n",
       "      <td>1.65</td>\n",
       "      <td>4090</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2</td>\n",
       "      <td>Algeria</td>\n",
       "      <td>27.3</td>\n",
       "      <td>38.4</td>\n",
       "      <td>4.17</td>\n",
       "      <td>31.4</td>\n",
       "      <td>12900</td>\n",
       "      <td>16.10</td>\n",
       "      <td>76.5</td>\n",
       "      <td>2.89</td>\n",
       "      <td>4460</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>3</td>\n",
       "      <td>Angola</td>\n",
       "      <td>119.0</td>\n",
       "      <td>62.3</td>\n",
       "      <td>2.85</td>\n",
       "      <td>42.9</td>\n",
       "      <td>5900</td>\n",
       "      <td>22.40</td>\n",
       "      <td>60.1</td>\n",
       "      <td>6.16</td>\n",
       "      <td>3530</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>4</td>\n",
       "      <td>Antigua and Barbuda</td>\n",
       "      <td>10.3</td>\n",
       "      <td>45.5</td>\n",
       "      <td>6.03</td>\n",
       "      <td>58.9</td>\n",
       "      <td>19100</td>\n",
       "      <td>1.44</td>\n",
       "      <td>76.8</td>\n",
       "      <td>2.13</td>\n",
       "      <td>12200</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "               country  child_mort  exports  health  imports  income  \\\n",
       "0          Afghanistan        90.2     10.0    7.58     44.9    1610   \n",
       "1              Albania        16.6     28.0    6.55     48.6    9930   \n",
       "2              Algeria        27.3     38.4    4.17     31.4   12900   \n",
       "3               Angola       119.0     62.3    2.85     42.9    5900   \n",
       "4  Antigua and Barbuda        10.3     45.5    6.03     58.9   19100   \n",
       "\n",
       "   inflation  life_expec  total_fer   gdpp  \n",
       "0       9.44        56.2       5.82    553  \n",
       "1       4.49        76.3       1.65   4090  \n",
       "2      16.10        76.5       2.89   4460  \n",
       "3      22.40        60.1       6.16   3530  \n",
       "4       1.44        76.8       2.13  12200  "
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df = pd.read_csv('Country-data.csv')\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 167 entries, 0 to 166\n",
      "Data columns (total 10 columns):\n",
      "country       167 non-null object\n",
      "child_mort    167 non-null float64\n",
      "exports       167 non-null float64\n",
      "health        167 non-null float64\n",
      "imports       167 non-null float64\n",
      "income        167 non-null int64\n",
      "inflation     167 non-null float64\n",
      "life_expec    167 non-null float64\n",
      "total_fer     167 non-null float64\n",
      "gdpp          167 non-null int64\n",
      "dtypes: float64(7), int64(2), object(1)\n",
      "memory usage: 12.5+ KB\n"
     ]
    }
   ],
   "source": [
    "df.info()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "There seem to have no missing values in any of the columns, all of them have 167 row items. We have Country columns as string (object) type rest all are numeric."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>child_mort</th>\n",
       "      <th>exports</th>\n",
       "      <th>health</th>\n",
       "      <th>imports</th>\n",
       "      <th>income</th>\n",
       "      <th>inflation</th>\n",
       "      <th>life_expec</th>\n",
       "      <th>total_fer</th>\n",
       "      <th>gdpp</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>count</td>\n",
       "      <td>167.000000</td>\n",
       "      <td>167.000000</td>\n",
       "      <td>167.000000</td>\n",
       "      <td>167.000000</td>\n",
       "      <td>167.000000</td>\n",
       "      <td>167.000000</td>\n",
       "      <td>167.000000</td>\n",
       "      <td>167.000000</td>\n",
       "      <td>167.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>mean</td>\n",
       "      <td>38.270060</td>\n",
       "      <td>41.108976</td>\n",
       "      <td>6.815689</td>\n",
       "      <td>46.890215</td>\n",
       "      <td>17144.688623</td>\n",
       "      <td>7.781832</td>\n",
       "      <td>70.555689</td>\n",
       "      <td>2.947964</td>\n",
       "      <td>12964.155689</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>std</td>\n",
       "      <td>40.328931</td>\n",
       "      <td>27.412010</td>\n",
       "      <td>2.746837</td>\n",
       "      <td>24.209589</td>\n",
       "      <td>19278.067698</td>\n",
       "      <td>10.570704</td>\n",
       "      <td>8.893172</td>\n",
       "      <td>1.513848</td>\n",
       "      <td>18328.704809</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>min</td>\n",
       "      <td>2.600000</td>\n",
       "      <td>0.109000</td>\n",
       "      <td>1.810000</td>\n",
       "      <td>0.065900</td>\n",
       "      <td>609.000000</td>\n",
       "      <td>-4.210000</td>\n",
       "      <td>32.100000</td>\n",
       "      <td>1.150000</td>\n",
       "      <td>231.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>25%</td>\n",
       "      <td>8.250000</td>\n",
       "      <td>23.800000</td>\n",
       "      <td>4.920000</td>\n",
       "      <td>30.200000</td>\n",
       "      <td>3355.000000</td>\n",
       "      <td>1.810000</td>\n",
       "      <td>65.300000</td>\n",
       "      <td>1.795000</td>\n",
       "      <td>1330.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>50%</td>\n",
       "      <td>19.300000</td>\n",
       "      <td>35.000000</td>\n",
       "      <td>6.320000</td>\n",
       "      <td>43.300000</td>\n",
       "      <td>9960.000000</td>\n",
       "      <td>5.390000</td>\n",
       "      <td>73.100000</td>\n",
       "      <td>2.410000</td>\n",
       "      <td>4660.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>75%</td>\n",
       "      <td>62.100000</td>\n",
       "      <td>51.350000</td>\n",
       "      <td>8.600000</td>\n",
       "      <td>58.750000</td>\n",
       "      <td>22800.000000</td>\n",
       "      <td>10.750000</td>\n",
       "      <td>76.800000</td>\n",
       "      <td>3.880000</td>\n",
       "      <td>14050.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>max</td>\n",
       "      <td>208.000000</td>\n",
       "      <td>200.000000</td>\n",
       "      <td>17.900000</td>\n",
       "      <td>174.000000</td>\n",
       "      <td>125000.000000</td>\n",
       "      <td>104.000000</td>\n",
       "      <td>82.800000</td>\n",
       "      <td>7.490000</td>\n",
       "      <td>105000.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
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      ],
      "text/plain": [
       "       child_mort     exports      health     imports         income  \\\n",
       "count  167.000000  167.000000  167.000000  167.000000     167.000000   \n",
       "mean    38.270060   41.108976    6.815689   46.890215   17144.688623   \n",
       "std     40.328931   27.412010    2.746837   24.209589   19278.067698   \n",
       "min      2.600000    0.109000    1.810000    0.065900     609.000000   \n",
       "25%      8.250000   23.800000    4.920000   30.200000    3355.000000   \n",
       "50%     19.300000   35.000000    6.320000   43.300000    9960.000000   \n",
       "75%     62.100000   51.350000    8.600000   58.750000   22800.000000   \n",
       "max    208.000000  200.000000   17.900000  174.000000  125000.000000   \n",
       "\n",
       "        inflation  life_expec   total_fer           gdpp  \n",
       "count  167.000000  167.000000  167.000000     167.000000  \n",
       "mean     7.781832   70.555689    2.947964   12964.155689  \n",
       "std     10.570704    8.893172    1.513848   18328.704809  \n",
       "min     -4.210000   32.100000    1.150000     231.000000  \n",
       "25%      1.810000   65.300000    1.795000    1330.000000  \n",
       "50%      5.390000   73.100000    2.410000    4660.000000  \n",
       "75%     10.750000   76.800000    3.880000   14050.000000  \n",
       "max    104.000000   82.800000    7.490000  105000.000000  "
      ]
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     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
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   ],
   "source": [
    "df.describe()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "- We can observe form above summary statistics that variable income and gdpp see a sudden spike in the values towards 75th percentile. However, rest all the other variables see a gradual increase in their values. This could mean that there are a group of very high income countries (having high gdp) sitting in the data. Potentially we could have other group of countries with low and medium level of income. Some visualizations below migh give us more insight and intuition about the data in hand.\n",
    "- import, export and health are percentage calculations derived from gdpp variable as described in the data definition file. We would have to bring them back to original values before proceeding with our analysis.\n",
    "\n",
    "Let us convert the values to original and look at the continuous variables in visualization."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Converting the import, export and health from percentage to a numberic. Since all these three variabels are Given as %age of the GDP per capita\n",
    "We can reverse calculate the actual value of each of three variabels using below formula:\n",
    "-  original_val = (value(in%) * gdpp) / 100"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>country</th>\n",
       "      <th>child_mort</th>\n",
       "      <th>exports</th>\n",
       "      <th>health</th>\n",
       "      <th>imports</th>\n",
       "      <th>income</th>\n",
       "      <th>inflation</th>\n",
       "      <th>life_expec</th>\n",
       "      <th>total_fer</th>\n",
       "      <th>gdpp</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>0</td>\n",
       "      <td>Afghanistan</td>\n",
       "      <td>90.2</td>\n",
       "      <td>55.30</td>\n",
       "      <td>7.58</td>\n",
       "      <td>44.9</td>\n",
       "      <td>1610</td>\n",
       "      <td>9.44</td>\n",
       "      <td>56.2</td>\n",
       "      <td>5.82</td>\n",
       "      <td>553</td>\n",
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       "    <tr>\n",
       "      <td>1</td>\n",
       "      <td>Albania</td>\n",
       "      <td>16.6</td>\n",
       "      <td>1145.20</td>\n",
       "      <td>6.55</td>\n",
       "      <td>48.6</td>\n",
       "      <td>9930</td>\n",
       "      <td>4.49</td>\n",
       "      <td>76.3</td>\n",
       "      <td>1.65</td>\n",
       "      <td>4090</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2</td>\n",
       "      <td>Algeria</td>\n",
       "      <td>27.3</td>\n",
       "      <td>1712.64</td>\n",
       "      <td>4.17</td>\n",
       "      <td>31.4</td>\n",
       "      <td>12900</td>\n",
       "      <td>16.10</td>\n",
       "      <td>76.5</td>\n",
       "      <td>2.89</td>\n",
       "      <td>4460</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>3</td>\n",
       "      <td>Angola</td>\n",
       "      <td>119.0</td>\n",
       "      <td>2199.19</td>\n",
       "      <td>2.85</td>\n",
       "      <td>42.9</td>\n",
       "      <td>5900</td>\n",
       "      <td>22.40</td>\n",
       "      <td>60.1</td>\n",
       "      <td>6.16</td>\n",
       "      <td>3530</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>4</td>\n",
       "      <td>Antigua and Barbuda</td>\n",
       "      <td>10.3</td>\n",
       "      <td>5551.00</td>\n",
       "      <td>6.03</td>\n",
       "      <td>58.9</td>\n",
       "      <td>19100</td>\n",
       "      <td>1.44</td>\n",
       "      <td>76.8</td>\n",
       "      <td>2.13</td>\n",
       "      <td>12200</td>\n",
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      ],
      "text/plain": [
       "               country  child_mort  exports  health  imports  income  \\\n",
       "0          Afghanistan        90.2    55.30    7.58     44.9    1610   \n",
       "1              Albania        16.6  1145.20    6.55     48.6    9930   \n",
       "2              Algeria        27.3  1712.64    4.17     31.4   12900   \n",
       "3               Angola       119.0  2199.19    2.85     42.9    5900   \n",
       "4  Antigua and Barbuda        10.3  5551.00    6.03     58.9   19100   \n",
       "\n",
       "   inflation  life_expec  total_fer   gdpp  \n",
       "0       9.44        56.2       5.82    553  \n",
       "1       4.49        76.3       1.65   4090  \n",
       "2      16.10        76.5       2.89   4460  \n",
       "3      22.40        60.1       6.16   3530  \n",
       "4       1.44        76.8       2.13  12200  "
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Exports\n",
    "\n",
    "df['exports'] = df.apply(lambda x: (df.exports*df.gdpp)/100)\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>country</th>\n",
       "      <th>child_mort</th>\n",
       "      <th>exports</th>\n",
       "      <th>health</th>\n",
       "      <th>imports</th>\n",
       "      <th>income</th>\n",
       "      <th>inflation</th>\n",
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       "    <tr>\n",
       "      <td>0</td>\n",
       "      <td>Afghanistan</td>\n",
       "      <td>90.2</td>\n",
       "      <td>55.30</td>\n",
       "      <td>7.58</td>\n",
       "      <td>248.297</td>\n",
       "      <td>1610</td>\n",
       "      <td>9.44</td>\n",
       "      <td>56.2</td>\n",
       "      <td>5.82</td>\n",
       "      <td>553</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>1</td>\n",
       "      <td>Albania</td>\n",
       "      <td>16.6</td>\n",
       "      <td>1145.20</td>\n",
       "      <td>6.55</td>\n",
       "      <td>1987.740</td>\n",
       "      <td>9930</td>\n",
       "      <td>4.49</td>\n",
       "      <td>76.3</td>\n",
       "      <td>1.65</td>\n",
       "      <td>4090</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2</td>\n",
       "      <td>Algeria</td>\n",
       "      <td>27.3</td>\n",
       "      <td>1712.64</td>\n",
       "      <td>4.17</td>\n",
       "      <td>1400.440</td>\n",
       "      <td>12900</td>\n",
       "      <td>16.10</td>\n",
       "      <td>76.5</td>\n",
       "      <td>2.89</td>\n",
       "      <td>4460</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>3</td>\n",
       "      <td>Angola</td>\n",
       "      <td>119.0</td>\n",
       "      <td>2199.19</td>\n",
       "      <td>2.85</td>\n",
       "      <td>1514.370</td>\n",
       "      <td>5900</td>\n",
       "      <td>22.40</td>\n",
       "      <td>60.1</td>\n",
       "      <td>6.16</td>\n",
       "      <td>3530</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>4</td>\n",
       "      <td>Antigua and Barbuda</td>\n",
       "      <td>10.3</td>\n",
       "      <td>5551.00</td>\n",
       "      <td>6.03</td>\n",
       "      <td>7185.800</td>\n",
       "      <td>19100</td>\n",
       "      <td>1.44</td>\n",
       "      <td>76.8</td>\n",
       "      <td>2.13</td>\n",
       "      <td>12200</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "               country  child_mort  exports  health   imports  income  \\\n",
       "0          Afghanistan        90.2    55.30    7.58   248.297    1610   \n",
       "1              Albania        16.6  1145.20    6.55  1987.740    9930   \n",
       "2              Algeria        27.3  1712.64    4.17  1400.440   12900   \n",
       "3               Angola       119.0  2199.19    2.85  1514.370    5900   \n",
       "4  Antigua and Barbuda        10.3  5551.00    6.03  7185.800   19100   \n",
       "\n",
       "   inflation  life_expec  total_fer   gdpp  \n",
       "0       9.44        56.2       5.82    553  \n",
       "1       4.49        76.3       1.65   4090  \n",
       "2      16.10        76.5       2.89   4460  \n",
       "3      22.40        60.1       6.16   3530  \n",
       "4       1.44        76.8       2.13  12200  "
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Imports\n",
    "\n",
    "df['imports'] = df.apply(lambda x: (df.imports*df.gdpp)/100)\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>country</th>\n",
       "      <th>child_mort</th>\n",
       "      <th>exports</th>\n",
       "      <th>health</th>\n",
       "      <th>imports</th>\n",
       "      <th>income</th>\n",
       "      <th>inflation</th>\n",
       "      <th>life_expec</th>\n",
       "      <th>total_fer</th>\n",
       "      <th>gdpp</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>0</td>\n",
       "      <td>Afghanistan</td>\n",
       "      <td>90.2</td>\n",
       "      <td>55.30</td>\n",
       "      <td>41.9174</td>\n",
       "      <td>248.297</td>\n",
       "      <td>1610</td>\n",
       "      <td>9.44</td>\n",
       "      <td>56.2</td>\n",
       "      <td>5.82</td>\n",
       "      <td>553</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>1</td>\n",
       "      <td>Albania</td>\n",
       "      <td>16.6</td>\n",
       "      <td>1145.20</td>\n",
       "      <td>267.8950</td>\n",
       "      <td>1987.740</td>\n",
       "      <td>9930</td>\n",
       "      <td>4.49</td>\n",
       "      <td>76.3</td>\n",
       "      <td>1.65</td>\n",
       "      <td>4090</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2</td>\n",
       "      <td>Algeria</td>\n",
       "      <td>27.3</td>\n",
       "      <td>1712.64</td>\n",
       "      <td>185.9820</td>\n",
       "      <td>1400.440</td>\n",
       "      <td>12900</td>\n",
       "      <td>16.10</td>\n",
       "      <td>76.5</td>\n",
       "      <td>2.89</td>\n",
       "      <td>4460</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>3</td>\n",
       "      <td>Angola</td>\n",
       "      <td>119.0</td>\n",
       "      <td>2199.19</td>\n",
       "      <td>100.6050</td>\n",
       "      <td>1514.370</td>\n",
       "      <td>5900</td>\n",
       "      <td>22.40</td>\n",
       "      <td>60.1</td>\n",
       "      <td>6.16</td>\n",
       "      <td>3530</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>4</td>\n",
       "      <td>Antigua and Barbuda</td>\n",
       "      <td>10.3</td>\n",
       "      <td>5551.00</td>\n",
       "      <td>735.6600</td>\n",
       "      <td>7185.800</td>\n",
       "      <td>19100</td>\n",
       "      <td>1.44</td>\n",
       "      <td>76.8</td>\n",
       "      <td>2.13</td>\n",
       "      <td>12200</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "               country  child_mort  exports    health   imports  income  \\\n",
       "0          Afghanistan        90.2    55.30   41.9174   248.297    1610   \n",
       "1              Albania        16.6  1145.20  267.8950  1987.740    9930   \n",
       "2              Algeria        27.3  1712.64  185.9820  1400.440   12900   \n",
       "3               Angola       119.0  2199.19  100.6050  1514.370    5900   \n",
       "4  Antigua and Barbuda        10.3  5551.00  735.6600  7185.800   19100   \n",
       "\n",
       "   inflation  life_expec  total_fer   gdpp  \n",
       "0       9.44        56.2       5.82    553  \n",
       "1       4.49        76.3       1.65   4090  \n",
       "2      16.10        76.5       2.89   4460  \n",
       "3      22.40        60.1       6.16   3530  \n",
       "4       1.44        76.8       2.13  12200  "
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Health\n",
    "\n",
    "df['health'] = df.apply(lambda x: (df.health*df.gdpp)/100)\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1080x720 with 9 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize = (15,10))\n",
    "features = df.columns[1:]\n",
    "for i in enumerate(features):\n",
    "    plt.subplot(3,3,i[0]+1)\n",
    "    sns.distplot(df[i[1]])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "- First up we can notice that not all variables are normally distributed we can observe that almost all variabels distribution is skewed.\n",
    "- We can observe that the child mortality rate is in the range of 0  to 50 for most of the countries but there are still a lot of countries which have the values much higher that 50 reaching upto ~250.\n",
    "- Looking at the plot above we get an intuition that a group of countries which are doing well have higher life expectancy, inflation, income, imports, exports and gpd per capita.\n",
    "- while closely inscpecting the humps in the middle of graphs of income, life expectancy, gdp and inflation we can expect a secondary group of countries which are neither low performing nor high performing in terms of their socio-economic conditions. So we are expecting broadly 3 categories of countries.\n",
    "\n",
    "Lets look at the outliers in all these variabels."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Section 2: Outlier Treatment"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1080x720 with 9 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize = (15,10))\n",
    "features = df.columns[1:]\n",
    "for i in enumerate(features):\n",
    "    plt.subplot(3,3,i[0]+1)\n",
    "    sns.boxplot(df[i[1]])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "- First up we observer all variables have presence of outliers and point us towards the huntch we had made above that maybe a group of countries are present in our data which have high socio-economic condition than the rest.\n",
    "- We can treat outliers present in our data before we move ahead with the analysis\n",
    "    - We might use capping method for outlier treatment. However, we owuld not like to cap the lower end of data points present in variabels like life expectancy because we might loose our target countries which are performing bad and are in need of the aid."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "#print(df.describe())\n",
    "for i in features:\n",
    "    q4 = df[i].quantile(0.99)\n",
    "    df[i][df[i]>=q4] = q4\n",
    "#print(df.describe())"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Section 3.1: K-Means Clustering"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Calculating the Hopkins statistic\n",
    "from sklearn.neighbors import NearestNeighbors\n",
    "from random import sample\n",
    "from numpy.random import uniform\n",
    "import numpy as np\n",
    "from math import isnan\n",
    " \n",
    "def hopkins(X):\n",
    "    d = X.shape[1]\n",
    "    #d = len(vars) # columns\n",
    "    n = len(X) # rows\n",
    "    m = int(0.1 * n) \n",
    "    nbrs = NearestNeighbors(n_neighbors=1).fit(X.values)\n",
    " \n",
    "    rand_X = sample(range(0, n, 1), m)\n",
    " \n",
    "    ujd = []\n",
    "    wjd = []\n",
    "    for j in range(0, m):\n",
    "        u_dist, _ = nbrs.kneighbors(uniform(np.amin(X,axis=0),np.amax(X,axis=0),d).reshape(1, -1), 2, return_distance=True)\n",
    "        ujd.append(u_dist[0][1])\n",
    "        w_dist, _ = nbrs.kneighbors(X.iloc[rand_X[j]].values.reshape(1, -1), 2, return_distance=True)\n",
    "        wjd.append(w_dist[0][1])\n",
    " \n",
    "    H = sum(ujd) / (sum(ujd) + sum(wjd))\n",
    "    if isnan(H):\n",
    "        print(ujd, wjd)\n",
    "        H = 0\n",
    " \n",
    "    return H"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.9377001257042448"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "hopkins(df.drop('country',axis = 1))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We observe that the hopkins value is about 91 which is very significant because this informs us that the data we have is suitable for clustering analysis. Now perhaps we can move ahead and perform scaling (as different variales have different scales, which will drastically effect our results and interpretation of the results) the variables so that we have much better data on which we can perform our analysis. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [],
   "source": [
    "# scaling\n",
    "from sklearn.preprocessing import StandardScaler\n",
    "\n",
    "df1 = df.drop ('country', axis = 1)\n",
    "scaler = StandardScaler()\n",
    "df1 = scaler.fit_transform(df1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>country</th>\n",
       "      <th>child_mort</th>\n",
       "      <th>exports</th>\n",
       "      <th>health</th>\n",
       "      <th>imports</th>\n",
       "      <th>income</th>\n",
       "      <th>inflation</th>\n",
       "      <th>life_expec</th>\n",
       "      <th>total_fer</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>0</td>\n",
       "      <td>1.344006</td>\n",
       "      <td>-0.569622</td>\n",
       "      <td>-0.566958</td>\n",
       "      <td>-0.598741</td>\n",
       "      <td>-0.851668</td>\n",
       "      <td>0.265002</td>\n",
       "      <td>-1.619705</td>\n",
       "      <td>1.926396</td>\n",
       "      <td>-0.702259</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>1</td>\n",
       "      <td>-0.547497</td>\n",
       "      <td>-0.473858</td>\n",
       "      <td>-0.440393</td>\n",
       "      <td>-0.413584</td>\n",
       "      <td>-0.386946</td>\n",
       "      <td>-0.372075</td>\n",
       "      <td>0.648831</td>\n",
       "      <td>-0.865054</td>\n",
       "      <td>-0.498726</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2</td>\n",
       "      <td>-0.272510</td>\n",
       "      <td>-0.424000</td>\n",
       "      <td>-0.486271</td>\n",
       "      <td>-0.476100</td>\n",
       "      <td>-0.221053</td>\n",
       "      <td>1.122161</td>\n",
       "      <td>0.671403</td>\n",
       "      <td>-0.034983</td>\n",
       "      <td>-0.477434</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>3</td>\n",
       "      <td>2.084159</td>\n",
       "      <td>-0.381249</td>\n",
       "      <td>-0.534088</td>\n",
       "      <td>-0.463973</td>\n",
       "      <td>-0.612045</td>\n",
       "      <td>1.932987</td>\n",
       "      <td>-1.179541</td>\n",
       "      <td>2.153997</td>\n",
       "      <td>-0.530950</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>4</td>\n",
       "      <td>-0.709406</td>\n",
       "      <td>-0.086742</td>\n",
       "      <td>-0.178410</td>\n",
       "      <td>0.139728</td>\n",
       "      <td>0.125254</td>\n",
       "      <td>-0.764618</td>\n",
       "      <td>0.705262</td>\n",
       "      <td>-0.543736</td>\n",
       "      <td>-0.032042</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    country  child_mort   exports    health   imports    income  inflation  \\\n",
       "0  1.344006   -0.569622 -0.566958 -0.598741 -0.851668  0.265002  -1.619705   \n",
       "1 -0.547497   -0.473858 -0.440393 -0.413584 -0.386946 -0.372075   0.648831   \n",
       "2 -0.272510   -0.424000 -0.486271 -0.476100 -0.221053  1.122161   0.671403   \n",
       "3  2.084159   -0.381249 -0.534088 -0.463973 -0.612045  1.932987  -1.179541   \n",
       "4 -0.709406   -0.086742 -0.178410  0.139728  0.125254 -0.764618   0.705262   \n",
       "\n",
       "   life_expec  total_fer  \n",
       "0    1.926396  -0.702259  \n",
       "1   -0.865054  -0.498726  \n",
       "2   -0.034983  -0.477434  \n",
       "3    2.153997  -0.530950  \n",
       "4   -0.543736  -0.032042  "
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df1 = pd.DataFrame(df1)\n",
    "df1.columns = df.columns[:-1]\n",
    "df1.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now that we have scaled the variables we are ready to perform next step of identification of value of 'k'.\n",
    "- Silhouette Score\n",
    "- Elbow Curve"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x12eaccf0>]"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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YGBNs7EawIHOspIwvNhQyZ/VOPl7zHYeOlxIbGcbwHm0Zld6ewWlxRIaHul2mMaYe1eUyUNOIRIaHMqJXO0b0asfx0jIW5+xmzqqdzFvzHe8vyyc6IpShPdpyZUYSP0iLc7tcY8wZZEcABoCSsnK+2rSHOat3Mi97J3uOFHPv8K5MHNrFLik1JsDVaSoIf2EB0DCOl5Zx/3urmL4sn3F9E/jTZek0CbNhIWMClQ0BGZ81CQvlqZ+eTUpcNE/N30DevqO8cF0/WkVHuF2aMaYe2aUfpkoiwp3D0nh2fF+W5+3n0ucWk7PrsNtlGWPqkQWAOa0xZ3dg2oSBHDleyrjnFrM4Z7fbJRlj6okFgKnWOR1bMv32QbRrHskNr3zDtG+2uV2SMaYeWAAYnyS1iuLdn5/P+V3iuP/9Vfxx9lrKygPnAgJjzMksAIzPmkWG88oNGVx/Xide/CKX2/6dRVFxqdtlGWNqyQLA1EhYaAiTxvbmdxf35JO13/HTf3xlC9obE6AsAEyt3DgohZdvOJfNhUcYO3kRq/MPuF2SMaaGLABMrf2oexve/fn5hIpwxQtfMS97p9slGWNqwALA1EmP9s34z8RBdG0Xy63/zuKlL3IJpLvLjQlmFgCmztrERvLWhIGM7t2ex2av5cHpqygpK3e7LGNMNWwqCFMvIsND+fv4vqTERfN/n+WwbW8Rz13dj+ZR4W6XZow5BTsCMPUmJET45Y+78ZcrzuabzXu59PnFbN1zxO2yjDGnYAFg6t3l/RL5980D2HukmEsmL+abzXvdLskYUwWfAkBERorIehHJEZH7q3j/NhFZJSLLRWSRiPR02q9x2ioe5SLSx3lvgdNnxXtt6vejGTcNSG3Nf24fRMuoCK6dsoT3v81zuyRjTCXVBoCIhAKTgVFAT2B8xRe8l6mqmq6qfYAngacAVPUNVe3jtF8HbFHV5V77XVPxvqruqo8PZPxHclw0799+Pv06teSet1fw13nrKbfpI4zxG74cAfQHclQ1V1WLgWnAWO8NVPWg18tooKr/y8cDb9a2UBOYWkRF8PpN/bkyI4m/f5rDL6Yt41hJmdtlGWPw7SqgBGC71+s8YEDljUTkDuAeIAIYWkU/V1IpOIBXRaQMeA/4g9oF5I1SRFgIj1+WTmp8NI9/tI78fUd56foM4mObuF2aMUHNlyOAqhaEPemLWlUnq2pn4D7goe91IDIAKFLV1V7N16hqOjDYeVxX5S8XmSAimSKSWVhY6EO5xh+JCLde0Jnnr+nHup0HuWTyYtbvPOR2WcYENV8CIA9I8nqdCOw4zfbTgEsqtV1FpeEfVc13fh4CpuIZajqJqr6oqhmqmhEfH+9DucafjezdjnduPZ+SsnIue/5LFqy3Uz/GuMWXAFgKpIlIiohE4Pkyn+G9gYikeb38CbDR670Q4Ao8wVDRFiYicc7zcOAiwPvowDRi6YnN+WDiIDq2iuKm15by+pdb3C7JmKBUbQCoaikwEZgLrAXeVtVsEZkkImOczSaKSLaILMdzHuAGry6GAHmqmuvV1gSYKyIrgeVAPvBS3T+OCRTtmzflndvOY2j3tjwyI5vfzcim1KaPMKZBSSCdd83IyNDMzEy3yzD1qKxc+dPstUxZtJkfdovn7+P7Ehtp00cYU59EJEtVMyq3253AxlWhIcJDF/Xkj5ems3Djbi5//ivy9hW5XZYxQcECwPiFqwd05PX/6c+OA0e5ZPKXLNu2z+2SjGn0LACM3/hBWhzTbz+fqIhQrnrxa2auPN3FZsaYurIAMH6lS5tYpt9+PukJzZk4dRn/9+lGW2DGmDPEAsD4ndYxTXjjZwO4tG8Cf5m3gXvfWcHxUps+wpj6ZgvCGL/UJCyUp356Nilx0Tw1fwN5e4/ywnX9aBUd4XZpxjQadgRg/JaIcOewNJ4d35flefu59LnFbCo87HZZxjQaFgDG7405uwPTJgzkyPFSrvzHV+w9Uux2ScY0ChYAJiCc07El/7p5APuLSvjDrDVul2NMo2ABYAJGj/bNuO2Czrz/bT4LN9rMsMbUlQWACSgTh3YhNS6aB6evoqi41O1yjAloFgAmoESGh/Kncels33uUv328sfodjDGnZAFgAs6A1NaM75/ElIW5rMo74HY5xgQsCwATkO4f1YPWMU24772VlNg00sbUigWACUjNm4YzaUwv1hQc5OVFm90ux5iAZAFgAtbI3u0Y0bMtT8/fwNY9R9wux5iAYwFgApaIMGlsbyJCQ3hw+iqbNM6YGrIAMAGtXfNI7hvVncU5e3g3K8/tcowJKD4FgIiMFJH1IpIjIvdX8f5tIrJKRJaLyCIR6em0J4vIUad9uYi84LVPP2efHBF5VkSk/j6WCSZX9+/Iuckt+cOstRQeOu52OcYEjGoDQERCgcnAKKAnML7iC97LVFVNV9U+wJPAU17vbVLVPs7jNq/254EJQJrzGFmHz2GCWEiI8Kdx6RwtLmPSTJsmwhhf+XIE0B/IUdVcVS0GpgFjvTdQ1YNeL6OB0w7Gikh7oJmqfqWegdt/ApfUqHJjvHRpE8sdP+rChyt28Om679wux5iA4EsAJADbvV7nOW3fIyJ3iMgmPEcAd3q9lSIiy0TkcxEZ7NWn94BtlX06/U4QkUwRySwstPlfzKn9/Ied6do2hoemr+bwcZsmwpjq+BIAVY3Nn/QXvqpOVtXOwH3AQ05zAdBRVfsC9wBTRaSZr306/b6oqhmqmhEfH+9DuSZYRYSF8KdxZ1Fw8Bh/mbve7XKM8Xu+BEAekOT1OhE43Wrd03CGc1T1uKrucZ5nAZuArk6fiTXo0xif9OvUkusHduL1r7bw7bZ9bpdjjF/zJQCWAmkikiIiEcBVwAzvDUQkzevlT4CNTnu8cxIZEUnFc7I3V1ULgEMiMtC5+ud64IM6fxpjgF+N7E67ZpE88N4qikttmghjTqXaAFDVUmAiMBdYC7ytqtkiMklExjibTRSRbBFZjmeo5wanfQiwUkRWAO8Ct6nqXue9nwNTgBw8RwZz6utDmeAW0ySMP1zSm/XfHeIfn29yuxxj/JYE0t2TGRkZmpmZ6XYZJkDcMfVb5md/x+y7BtOlTYzb5RjjGhHJUtWMyu12J7BptH53cS+aRoTy4PurKC8PnD90jGkoFgCm0YqPbcJvRvfgmy17mbZ0e/U7GBNkLABMo3ZFRiLnd27Nn2av5buDx9wuxxi/YgFgGjUR4Y+XplNcVs4jH2S7XY4xfsUCwDR6yXHR3H1hVz7K3slHq3e6XY4xfsMCwASFWwan0KN9M377wWoOHC1xuxxj/IIFgAkK4aEhPHFZOrsPH+eJj9a5XY4xfsECwASNsxJbcNOgFKYu2cY3m/dWv4MxjZwFgAkq94zoSmLLptz//kqOlZS5XY4xrrIAMEElKiKMP16aTm7hEZ77LMftcoxxlQWACTpDusYzrm8Czy3YxPqdh9wuxxjXWACYoPTQRT1p1jSc+95bSZlNE2GClAWACUqtoiN4+KIeLN++n399tcXtcoxxhQWACVqX9ElgSNd4/jx3Pfn7j7pdjjENzgLABC0R4bFLelOu8PB/VhNIU6MbUx8sAExQS2oVxb0juvLpul3MXFngdjnGNCgLABP0/mdQCmclNuf3H2azv6jY7XKMaTA+BYCIjBSR9SKSIyL3V/H+bSKySkSWi8giEenptA8XkSznvSwRGeq1zwKnz+XOo039fSxjfBcaIjw+7iz2FZXw2Ky1bpdjTIOpNgCcRd0nA6OAnsD4ii94L1NVNV1V+wBPAk857buBi1U1Hc86wf+qtN81qtrHeeyqywcxpi56dmjGhCGpvJOVx+Kc3W6XY0yD8OUIoD+Qo6q5qloMTAPGem+gqge9XkYD6rQvU9UdTns2ECkiTepetjH1765haSS3juLB6as4WmzTRJjGz5cASAC819PLc9q+R0TuEJFNeI4A7qyin8uAZap63KvtVWf452ERkRrUbUy9iwwP5Y/j0tm6p4i/fbLB7XKMOeN8CYCqvphPul5OVSeramfgPuCh73Ug0gt4ArjVq/kaZ2hosPO4rspfLjJBRDJFJLOwsNCHco2pvfM7x3FlRhJTFm5mdf4Bt8sx5ozyJQDygCSv14nAjlNsC54hoksqXohIIjAduF5VN1W0q2q+8/MQMBXPUNNJVPVFVc1Q1Yz4+HgfyjWmbh4c3YOWURHc//5KSsvK3S7HmDPGlwBYCqSJSIqIRABXATO8NxCRNK+XPwE2Ou0tgFnAA6q62Gv7MBGJc56HAxcBq+vyQYypL82jwvn9mF6szj/Iq4u3uF2OMWdMtQGgqqXARGAusBZ4W1WzRWSSiIxxNpsoItkishy4B88VPzj7dQEernS5ZxNgroisBJYD+cBL9frJjKmD0entuLBHG/46fz3b9hS5XY4xZ4QE0u3vGRkZmpmZ6XYZJkgUHDjK8Ke+oG/HFvzzpv7YdQomUIlIlqpmVG63O4GNOYX2zZvy65HdWLhxN+9/m+92OcbUOwsAY07j2gGdOKdjCx6dtYbdh49Xv4MxAcQCwJjTCAkRnrjsLI4cL+XRmWvcLseYemUBYEw10trGcvsPu/DB8h18tt5mLDGNhwWAMT64/Ued6dImhoemr+bI8VK3yzGmXlgAGOODJmGhPD4unfz9R/nrPJsmwjQOFgDG+CgjuRXXDuzIa19uZvn2/W6XY0ydWQAYUwO/HtmdNrGR3P/eSkpsmggT4CwAjKmBZpHhTBrbi3U7D/HiF7lul2NMnVgAGFNDI3q1Y3R6O575ZCO5hYfdLseYWrMAMKYWfndxL5qEhfDA+6soLw+c6VSM8WYBYEwttGkWyW9G92DJ5r28nbm9+h0MAEty9/DTF76i+8NzeGreelt5zWUWAMbU0pXnJjEgpRV/nL2WXQePuV2OX1uxfT/XvbyEK1/8mi17jvCDLnE8+2kOFz71OXNWFRBIk1I2JhYAxtSSiPCncekcKy3nvvdWssfmCjrJup0HmfDPTMZOXszq/AM8OLo7n//qR0y54VzemjCQ2Mgwfv7Gt1z38jfk7DrkdrlBx6aDNqaOXl60mUdnrqFJWAiX9Uvk5h+k0Dk+xu2yXLV59xGenr+BD1fuICYijFsGp3LTD5KJjQz/3nalZeW8sWQbf523nqLiMv5nUDJ3Dks7aTtTN6eaDtoCwJh6kLPrEC8v2sx73+ZTUlbOsO5tmTAklXOTWwbVOgL5+4/y7McbeffbPCJCQ7hxUDK3DkmlRVTEaffbc/g4f567nrcytxMX04QHRnXn0r4JQfVvdyZZABjTAAoPHedfX23hn19vZX9RCWcntWDC4FR+3KstYaGNd8R116FjPPfZJqYu2QbA1QM6cvuPOtMmNrJG/azYvp/fzshmxfb9ZHRqye/G9KJ3QvMzUXJQsQAwpgEVFZfyXlYeUxZtZuueIpJaNeXmQSlckZFEdJMwt8urN/uLinnh81xe/3ILxWXlXNEvkV8MSyOhRdNa91lerrz7bR5PzFnHvqJirh7QkXuHd6Nl9OmPIsyp1SkARGQk8AwQCkxR1ccrvX8bcAdQBhwGJqjqGue9B4CbnffuVNW5vvRZFQsAE2jKypX5a77jpYW5ZG3dR/Om4Vw7sCM3nJdMm2Y1++vYnxw6VsIri7YwZWEuh4tLGXN2B+6+sCspcdH19jsOHC3h6fkb+NfXW4mNDONXP+7GVed2JDTEhoVqqtYBICKhwAZgOJAHLAXGV3zBO9s0U9WDzvMxwO2qOlJEegJvAv2BDsDHQFdnt9P2WRULABPIsrbu5aUvNjN3zU7CQ0IY26cDPxuSSte2sW6X5rNjJWX886stPL9gE/uKShjRsy33jOhK93bNztjvXLfzII98kM2SzXvpndCM34/pRb9Orc7Y72uMThUAvhyL9gdyVDXX6WgaMBY48WVd8eXviAYqUmUsME1VjwObRSTH6Y/q+jSmsenXqRX9rmvFlt1HeHnRZt7J2s47WXn8sFs8Ewancl7n1n570rO4tJy3lm7j75/msOvQcYZ0jefe4V05O6nFGf/d3ds1Y9qEgcxcWcBjs9Zy2fNfMe6cBO4f1b3G5xjM9/kSAAmA962OecCAyhuJyB3APUAEMNRr384+5bUAAAySSURBVK8r7ZvgPK+2T6ffCcAEgI4dO/pQrjH+LTkumkcv6c3/Du/KG19v5fWvtnD1lCX06tCMCUNSGZ3ennA/OWFcWlbO+8vyeebjjeTvP0r/5Fb8fXxfBqS2btA6RISLz+7A0O5t+L/PcpiyMJd52d9x94Vp3HB+st/8ewUaX/7VqvqT5KRxI1WdrKqdgfuAh6rZ16c+nX5fVNUMVc2Ij4/3oVxjAkOr6Ah+MSyNRfcN5fFx6RwrKeOuacu54MnPeOmLXA4dK3GttvJy5cMVOxjxty/49bsraRUdwes39eetWwc2+Je/t+gmYdw3sjtz7x5CRnJL/jBrLaOfWcjinN2u1RTIfDkCyAOSvF4nAjtOs/004Hkf9q1Jn8Y0WpHhoVzVvyM/zUjis/W7eGlhLo/NXsuzn2xk/ICO3Hh+Mh3qcFVNTagqn6zdxV/nb2BtwUG6to3hhWv78eNebf1qeCo1PoZXbzyXT9buYtLMNVwzZQmj09vxm5/0rNMVSMHGl5PAYXhO2A4D8vGcsL1aVbO9tklT1Y3O84uBR1Q1Q0R6AVP570ngT4A0PEcAp+2zKnYS2ASLlXn7eWnhZmavKkCAi8/uwC2DU+jV4cxdE784Zzd/mbeeZdv206l1FP97YVcuPruD3191c6ykjJe+yGXyghwA7vhhF342JJXI8FCXK/Mfdb0MdDTwNzyXbL6iqo+JyCQgU1VniMgzwIVACbAPmFjxZS4ivwFuAkqBu1V1zqn6rK4OCwATbLbvLeLVxVuYtnQbRcVl/KBLHLcMTuGCrvH19hd51tZ9/GXuer7K3UP75pHcOSyNy/slBty4et6+Iv44ey2zV+2kY6soHrm4J8N6tHW7LL9gN4IZE8AOFJUw9ZttvPblZr47eJxubWO5ZXAKY/p0oElY7f7Szd5xgL/O28Cn63YRFxPB7T/swtUDOgb8X86Lc3bzyIxscnYd5kfd4vntxb3q9f6EQGQBYEwjUFxazocrdvDSwlzW7TxEm9gm3DgomWv6d6J5lG8TqOXsOszT8zcwa1UBzZuGc+sFqdx4fjJREY3nDuWSsnJe/3ILf/t4I8Wl5dwyOIWJQ7s0qs9YExYAxjQiqsrCjbt5aWEuCzfuJioilCvPTeKmQSkktYqqcp9te4p45pONTF+WR9PwUG7+QQo3D06ledPGO/PmrkPHeHzOOt7/Np/2zSN5cHQPLjqrvV+d0G4IFgDGNFJrdhxkyqJcZizfQbkqo9Pb87PBqSdu0tp54Bh//3Qjby3dTmiIcP15nbjtgs60jmnicuUNJ2vrXn77QTbZOw4yMLUVvx/Tm27tAucO7LqyADCmkSs4cJTXvtzC1K+3ceh4Kf1TWtG9XSxvLd1OuSpXnpvEL4am0TaA5yCqi7JyZdrSbfx57noOHSvl+vM6cfeFXRv1EVAFCwBjgsShYyW8tXQ7ry7eQsGBo4w7J5G7hqWdcmgo2Ow7Usxf5q1n6jfbaB0dwa9HdufycxIJ8fPLXevCAsCYIFNaVs6R4rKg+Au3NlbnH+CRGdlkbd1Hn6QWTBrbi7MSz/zcRm6wADDGmEpUlenL8vnj7HXsOXKcS/okkJHcktS4GFLjo2kT26RRnDCuy2ygxhjTKIkI485JZHjPtjz7yUb+/fU2pi/LP/F+dEQoKfHRJwIhJS6azvExJMdFE9MIFvaxIwBjjHGUlysFB4+RW3iYzbuPkFt4hNzdR8gtPEz+/qN4f122bdaElLhoUuNjSI2LdgIihqSWTf1u+U87AjDGmGqEhAgJLZqS0KIpg9O+P/vwsZIytu4pYvPuw2wq9ITD5t2Hmb2qgP1F/525NSxE6Ng66sRRQ2pc9ImgiIuJ8KshJQsAY4zxQWR4KN3axVZ5/8C+I8Xk7j584ohhc+ERcncf5ouNhRSXlp/YLjYyzDlaiHFCwRMOKXHRrtylbAFgjDF11DI6gn7RrU5aqrKsXNmx/+iJYSTPUcMRluTu+d65BoD2zSNPBEJqXAwp8dF0joshoWXTMzYjqwWAMcacIaEhQlKrKJJaRXFB1+8PKR0tLmPz7iPOuQbPOYdNu4/wwfIdHDpWemK7iNAQOrWO4vlr+9GlTUy91mcBYIwxLmgaEUrPDs3o2aHZ99pVlT1Hik+cY6gYVmodHVHvNVgAGGOMHxER4mKaEBfThP4prarfoQ7861olY4wxDcYCwBhjgpRPASAiI0VkvYjkiMj9Vbx/j4isEZGVIvKJiHRy2n8kIsu9HsdE5BLnvddEZLPXe33q96MZY4w5nWrPAYhIKDAZGA7kAUtFZIaqrvHabBmQoapFIvJz4EngSlX9DOjj9NMKyAHmee33K1V9t34+ijHGmJrw5QigP5CjqrmqWgxMA8Z6b6Cqn6lqkfPyayCxin4uB+Z4bWeMMcZFvgRAArDd63We03YqNwNzqmi/CnizUttjzrDR0yISPMsTGWOMH/AlAKq6Ba3KGeRE5FogA/hzpfb2QDow16v5AaA7cC7QCrjvFH1OEJFMEcksLCz0oVxjjDG+8CUA8oAkr9eJwI7KG4nIhcBvgDGqerzS2z8FpqvqiRmTVLVAPY4Dr+IZajqJqr6oqhmqmhEfH1/VJsYYY2rBlxvBlgJpIpIC5OMZyrnaewMR6Qv8Axipqruq6GM8nr/4vfdpr6oF4pka7xJgdXWFZGVl7RaRrT7UXJU4YHct9z2TrK6asbpqxuqqmcZaV6eqGn1aD0BERgN/A0KBV1T1MRGZBGSq6gwR+RjPEE+Bs8s2VR3j7JsMLAaSVLXcq89PgXg8Q0zLgdtU9XDtPlv1RCSzqvmw3WZ11YzVVTNWV80EW10+TQWhqrOB2ZXafuv1/MLT7LuFKk4aq+pQn6s0xhhT7+xOYGOMCVLBFAAvul3AKVhdNWN11YzVVTNBVVdArQlsjDGm/gTTEYAxxhgvjT4ARCRJRD4TkbUiki0id7ldE4CIRIrINyKywqnr927XVEFEQkVkmYjMdLsWbyKyRURWOZMHZrpdTwURaSEi74rIOue/s/P8oKZulSZiPCgid7tdF4CI/K/z3/xqEXlTRCLdrglARO5yasp2899KRF4RkV0istqrrZWIzBeRjc7PlvXxuxp9AAClwL2q2gMYCNwhIj1drgngODBUVc/GM2HeSBEZ6HJNFe4C1rpdxCn8SFX7+Nmles8AH6lqd+Bs/ODfTlXXO/9OfYB+QBEw3eWyEJEE4E48k0f2xnNp+VXuVgUi0hv4GZ4bUs8GLhKRNJfKeQ0YWantfuATVU0DPnFe11mjDwDnjuNvneeH8PzPebq5jBqEcxd0xX0P4c7D9RMyIpII/ASY4nYtgUBEmgFDgJcBVLVYVfe7W9VJhgGbVLW2N1HWtzCgqYiEAVFUMbOAC3oAX6tqkaqWAp8Dl7pRiKp+Aeyt1DwWeN15/jqem2frrNEHgDfnprS+wBJ3K/FwhlqWA7uA+arqD3X9Dfg1UF7dhi5QYJ6IZInIBLeLcaQChcCrzrDZFBGJdruoSqqaiNEVqpoP/AXYhufG0QOqOu/0ezWI1cAQEWktIlHAaL4/BY7b2qpqAXj+qAXa1EenQRMAIhIDvAfcraoH3a4HQFXLnEP0RKC/cxjqGhG5CNilqllu1nEag1T1HGAUnqG8IW4XhOev2XOA51W1L3CEejo8rw8iEgGMAd5xuxYAZ+x6LJACdACinUkkXaWqa4EngPnAR8AKPMPHjVpQBICIhOP58n9DVd93u57KnCGDBZw87tfQBgFjRGQLnnUfhorIv90t6b9UdYfzcxee8ewqJxBsYHlAntfR27t4AsFfjAK+VdXv3C7EcSGwWVULnckh3wfOd7kmAFT1ZVU9R1WH4BmC2eh2TV6+c2ZVrphduao512qs0QeAM9ncy8BaVX3K7XoqiEi8iLRwnjfF8z/GOjdrUtUHVDVRVZPxDBt8qqqu/3UGICLRIhJb8RwYgQ8TCJ5pqroT2C4i3ZymYcCa0+zS0MbjJ8M/jm3AQBGJcv7fHIYfnDQHEJE2zs+OwDj8699tBnCD8/wG4IP66NSnuYAC3CDgOmCVM94O8KAzv5Gb2gOvO0tuhgBvq6pfXXbpZ9oC0z3fGYQBU1X1I3dLOuEXwBvOcEsu8D8u1wOAM5Y9HLjV7VoqqOoSEXkX+BbPEMsy/Ofu2/dEpDVQAtyhqvvcKEJE3gR+CMSJSB7wCPA48LaI3IwnRK+ol99ldwIbY0xwavRDQMYYY6pmAWCMMUHKAsAYY4KUBYAxxgQpCwBjjAlSFgDGGBOkLACMMSZIWQAYY0yQ+n8A1gtaEX+ARgAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "from sklearn.metrics import silhouette_score\n",
    "\n",
    "ss = []\n",
    "for k in range(2,11):\n",
    "    kmeans = KMeans(n_clusters = k).fit(df1)\n",
    "    ss.append([k, silhouette_score(df1, kmeans.labels_)])\n",
    "\n",
    "plt.plot(pd.DataFrame(ss)[0], pd.DataFrame(ss)[1])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x12eedeb0>]"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "ssd = []\n",
    "for k in range(2, 11):\n",
    "    kmean = KMeans(n_clusters = k).fit(df1)\n",
    "    ssd.append([k, kmean.inertia_])\n",
    "    \n",
    "plt.plot(pd.DataFrame(ssd)[0], pd.DataFrame(ssd)[1])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We can observer from above two plots that we have the first bend at value 3, meaning that possibolity of taking k=3.\n",
    "This suits our understanding of the data, i.e. possibilty of 3 clusters of countries with High socio-economic status, medium and low socio-economic status. Hence, it would be safe for us to go ahead and take k=3 to prepare 3 clusters."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "KMeans(algorithm='auto', copy_x=True, init='k-means++', max_iter=300,\n",
       "       n_clusters=3, n_init=10, n_jobs=None, precompute_distances='auto',\n",
       "       random_state=101, tol=0.0001, verbose=0)"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Providing K=3 to KMeans method\n",
    "\n",
    "kmean = KMeans(n_clusters = 3, random_state = 101)\n",
    "kmean.fit(df1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [],
   "source": [
    "label = pd.DataFrame(kmean.labels_, columns = ['label'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [],
   "source": [
    "df.kmean = df.copy()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Adding labels to the original dataframe\n",
    "\n",
    "df.kmean = pd.concat([df.kmean, label ], axis =1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>country</th>\n",
       "      <th>child_mort</th>\n",
       "      <th>exports</th>\n",
       "      <th>health</th>\n",
       "      <th>imports</th>\n",
       "      <th>income</th>\n",
       "      <th>inflation</th>\n",
       "      <th>life_expec</th>\n",
       "      <th>total_fer</th>\n",
       "      <th>gdpp</th>\n",
       "      <th>label</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>0</td>\n",
       "      <td>Afghanistan</td>\n",
       "      <td>90.2</td>\n",
       "      <td>55.30</td>\n",
       "      <td>41.9174</td>\n",
       "      <td>248.297</td>\n",
       "      <td>1610.0</td>\n",
       "      <td>9.44</td>\n",
       "      <td>56.2</td>\n",
       "      <td>5.82</td>\n",
       "      <td>553.0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>1</td>\n",
       "      <td>Albania</td>\n",
       "      <td>16.6</td>\n",
       "      <td>1145.20</td>\n",
       "      <td>267.8950</td>\n",
       "      <td>1987.740</td>\n",
       "      <td>9930.0</td>\n",
       "      <td>4.49</td>\n",
       "      <td>76.3</td>\n",
       "      <td>1.65</td>\n",
       "      <td>4090.0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2</td>\n",
       "      <td>Algeria</td>\n",
       "      <td>27.3</td>\n",
       "      <td>1712.64</td>\n",
       "      <td>185.9820</td>\n",
       "      <td>1400.440</td>\n",
       "      <td>12900.0</td>\n",
       "      <td>16.10</td>\n",
       "      <td>76.5</td>\n",
       "      <td>2.89</td>\n",
       "      <td>4460.0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>3</td>\n",
       "      <td>Angola</td>\n",
       "      <td>119.0</td>\n",
       "      <td>2199.19</td>\n",
       "      <td>100.6050</td>\n",
       "      <td>1514.370</td>\n",
       "      <td>5900.0</td>\n",
       "      <td>22.40</td>\n",
       "      <td>60.1</td>\n",
       "      <td>6.16</td>\n",
       "      <td>3530.0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>4</td>\n",
       "      <td>Antigua and Barbuda</td>\n",
       "      <td>10.3</td>\n",
       "      <td>5551.00</td>\n",
       "      <td>735.6600</td>\n",
       "      <td>7185.800</td>\n",
       "      <td>19100.0</td>\n",
       "      <td>1.44</td>\n",
       "      <td>76.8</td>\n",
       "      <td>2.13</td>\n",
       "      <td>12200.0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "               country  child_mort  exports    health   imports   income  \\\n",
       "0          Afghanistan        90.2    55.30   41.9174   248.297   1610.0   \n",
       "1              Albania        16.6  1145.20  267.8950  1987.740   9930.0   \n",
       "2              Algeria        27.3  1712.64  185.9820  1400.440  12900.0   \n",
       "3               Angola       119.0  2199.19  100.6050  1514.370   5900.0   \n",
       "4  Antigua and Barbuda        10.3  5551.00  735.6600  7185.800  19100.0   \n",
       "\n",
       "   inflation  life_expec  total_fer     gdpp  label  \n",
       "0       9.44        56.2       5.82    553.0      1  \n",
       "1       4.49        76.3       1.65   4090.0      0  \n",
       "2      16.10        76.5       2.89   4460.0      0  \n",
       "3      22.40        60.1       6.16   3530.0      1  \n",
       "4       1.44        76.8       2.13  12200.0      0  "
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.kmean.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0    90\n",
       "1    48\n",
       "2    29\n",
       "Name: label, dtype: int64"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Getting count of countries in each cluster\n",
    "\n",
    "df.kmean.label.value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x12433170>"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Plot\n",
    "sns.scatterplot(x = 'child_mort', y = 'gdpp', hue = 'label', data = df.kmean, palette = 'Set1')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x12f8e2b0>"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.scatterplot(x = 'income', y = 'gdpp', hue = 'label', data = df.kmean, palette = 'Set1')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x12bdcbf0>"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.scatterplot(x = 'income', y = 'child_mort', hue = 'label', data = df.kmean, palette = 'Set1')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We can observe that we have three clusters when we look at variables like child mortality, income and gdpp.\n",
    "Now let's look at the labels created by the algorithm versus some of the features."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x12c33c50>"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.boxplot(x='label', y='child_mort', data=df.kmean)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x11ef2530>"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAZEAAAEGCAYAAACkQqisAAAABHNCSVQICAgIfAhkiAAAAAlwSFlzAAALEgAACxIB0t1+/AAAADh0RVh0U29mdHdhcmUAbWF0cGxvdGxpYiB2ZXJzaW9uMy4xLjEsIGh0dHA6Ly9tYXRwbG90bGliLm9yZy8QZhcZAAAW4klEQVR4nO3df5Bd5X3f8fdXPwgSGGOQYoMWstQr7GKGjmGLSZzxEBMJRGMgHTzB44l3Mkw1dTGy68600GGMTSATT5s4XsahpoZ6cV0TSjz12pUCAkM6bh3M8qMCIaO9xTJcQ4wWGRCIHyvp2z/u2XCRVtLdoz179u6+XzN37j3Pec7Z7+UCn/ucc+55IjORJKmMBXUXIEnqXoaIJKk0Q0SSVJohIkkqzRCRJJW2qO4CZtqyZcuyt7e37jIkqWs89NBDY5m5fLJ18y5Eent7GRkZqbsMSeoaEfHzA63zcJYkqTRDRJJUmiEiSSrNEJEklWaISJJKM0QkSaUZIpKk0ubd70QkzU+Dg4M0Go1K9t1sNgHo6emZ9n339fWxbt26ad/vdDFEJOkwvfbaa3WXUBtDRNK8UOW3+Yl9Dw4OVvY3ZivPiUiSSjNEJEmlGSKSpNIMEUlSaYaIJKm0SkMkIv51RGyOiMcj4jsRcWREnBIRD0TEaET8VUQcUfT9tWK5UazvbdvP1UX7kxFxflv7BUVbIyKuqvK9SJL2V1mIRMQKYB3Qn5mnAwuBy4AvA1/JzJXAr4DLi00uB36VmX3AV4p+RMRpxXYfAC4A/jIiFkbEQuBrwBrgNOATRV9J0gyp+nDWImBJRCwClgLPAR8F7izWDwGXFK8vLpYp1p8XEVG0356Zb2Tmz4AGcHbxaGTmU5n5JnB70VeSNEMqC5HM/AXwH4GnaYXHS8BDwIuZubvo1gRWFK9XAM8U2+4u+h/f3r7PNgdqlyTNkCoPZ72L1sjgFOBE4Chah572lRObHGDdVNsnq2VtRIxExMj27dsPVbokqUNVHs76XeBnmbk9M8eB7wK/BRxbHN4C6AGeLV43gZMAivXvBHa0t++zzYHa95OZN2dmf2b2L1++fDremySJakPkaeCciFhanNs4D3gCuA+4tOgzAHyveD1cLFOs/2FmZtF+WXH11inASuAnwIPAyuJqryNonXwfrvD9SJL2UdkNGDPzgYi4E3gY2A08AtwM/E/g9oi4vmi7pdjkFuBbEdGgNQK5rNjP5oi4g1YA7QauyMw9ABHxGeAuWld+3ZqZm6t6P5Kk/VV6F9/MvBa4dp/mp2hdWbVv39eBjx9gPzcAN0zSvh5Yf/iVSpLK8BfrkqTSDBFJUmmGiCSpNENEklSaISJJKs0QkSSVZohIkkozRCRJpRkikqTSDBFJUmmGiCSpNENEklSaISJJKs0QkSSVZohIkkozRCRJpRkikqTSDBFJUmmGiCSpNENEklSaISJJKs0QkSSVZohIkkozRCRJpRkikqTSDBFJUmmGiCSpNENEklSaISJJKs0QkSSVZohIkkozRCRJpRkikqTSDBFJUmmGiCSpNENEklSaISJJKs0QkSSVZohIkkqrNEQi4tiIuDMifhoRWyLiNyPiuIjYGBGjxfO7ir4REYMR0YiITRFxZtt+Bor+oxEx0NZ+VkQ8VmwzGBFR5fuRJL1d1SORrwJ/k5nvB/4JsAW4Crg3M1cC9xbLAGuAlcVjLXATQEQcB1wLfAg4G7h2IniKPmvbtrug4vcjSWpTWYhExDHAR4BbADLzzcx8EbgYGCq6DQGXFK8vBm7Llr8Djo2IE4DzgY2ZuSMzfwVsBC4o1h2TmT/OzARua9uXJGkGVDkS+UfAduC/RMQjEfGNiDgKeHdmPgdQPP960X8F8Ezb9s2i7WDtzUna9xMRayNiJCJGtm/ffvjvTJIEVBsii4AzgZsy84PAq7x16Goyk53PyBLt+zdm3pyZ/ZnZv3z58oNXLUnqWJUh0gSamflAsXwnrVD5ZXEoiuL5+bb+J7Vt3wM8e4j2nknaJUkzpLIQycy/B56JiPcVTecBTwDDwMQVVgPA94rXw8Cniqu0zgFeKg533QWsjoh3FSfUVwN3Fet2RsQ5xVVZn2rblyRpBiyqeP9XAt+OiCOAp4A/ohVcd0TE5cDTwMeLvuuBC4EGsKvoS2buiIg/Bh4s+l2XmTuK158GvgksATYUD0nSDKk0RDLzUaB/klXnTdI3gSsOsJ9bgVsnaR8BTj/MMiVJJfmLdUlSaYaIJKk0Q0SSVFrVJ9YlaUoGBwdpNBp1lzElo6OjAKxbt67mSjrX19c3LfUaIpJmlUajwSObH4Fj665kCva2nh75xSP11tGpF6dvV4aIpNnnWNh77t66q5izFtw/fWcyPCciSSrNEJEklWaISJJKM0QkSaUZIpKk0gwRSVJphogkqTRDRJJUmiEiSSrNEJEklWaISJJKM0QkSaUZIpKk0gwRSVJpHYVIRJwaEfdGxOPF8hkRcU21pUmSZrtORyL/GbgaGAfIzE3AZVUVJUnqDp2GyNLM/Mk+bbunuxhJUnfpNETGIuK9QAJExKXAc5VVJUnqCp2GyBXA14H3R8QvgM8Bn66sKk3Z1q1bWbNmDY1Go+5SJM0jHYVIZj6Vmb8LLAfen5m/nZnbKq1MU3L99dfz6quvct1119VdiqR5ZFEnnSLiWOBTQC+wKCIAyMx1lVWmjm3dupVt27YBsG3bNhqNBn19ffUWJWle6PRw1npaAfIY8FDbQ7PA9ddf/7ZlRyOSZkpHIxHgyMz8fKWVqLSJUciBliWpKp2ORL4VEf8iIk6IiOMmHpVWpo719vYedFmSqtJpiLwJ/Afgx7x1KGukqqI0Nddc8/abB3zhC1+oqRJJ802nh7M+D/Rl5liVxaicU089ld7eXrZt20Zvb68n1SXNmE5HIpuBXVUWosNzzTXXcNRRRzkKkTSjOh2J7AEejYj7gDcmGr3Ed/Y49dRT2bBhQ91lSJpnOh2J/A/gBuD/4CW+s9LY2BhXXnklL7zwQt2lSJpHOv3F+hDwHd4Kj/9WtGmWGBoaYtOmTQwN+bFImjmdzidyLjAKfA34S2BrRHykwro0BWNjY2zYsIHMZMOGDY5GJM2YTs+J/BmwOjOfhNYkVbRGJmdVVZg6NzQ0RGYCsHfvXoaGhvj85/1tqLpTs9mEl2DB/U68WpkXoZnNadlVp5/S4okAAcjMrcDiTjaMiIUR8UhE/KBYPiUiHoiI0Yj4q4g4omj/tWK5UazvbdvH1UX7kxFxflv7BUVbIyKu6vC9zDkbN25kfHwcgPHxce6+++6aK5I0X3Q6EhmJiFuAbxXLn6TzE+ufBbYAxxTLXwa+kpm3R8R/Ai4Hbiqef5WZfRFxWdHvDyLiNFqzKH4AOBG4pxgJQevw2iqgCTwYEcOZ+USHdc0Zq1atYv369YyPj7N48WJWr15dd0lSaT09PWyP7ew9d2/dpcxZC+5fQM+KnunZV4f9Pk3rtyLraIXCE8C/PNRGEdED/DPgG8VyAB8F7iy6DAGXFK8vLpYp1p9X9L8YuD0z38jMnwEN4Ozi0ShuU/8mcHvRd94ZGBhg4s7KCxYsYGBgoOaKJM0XnYbIIuCrmfnPM/P3gUFgYQfb/QXwb4GJrxTHAy9m5sTUuk1gRfF6BfAMQLH+paL/P7Tvs82B2vcTEWsjYiQiRrZv395B2d1l2bJlrFmzhohgzZo1HH/88XWXJGme6DRE7gWWtC0vAe452AYR8XvA85nZftgrJumah1g31fb9GzNvzsz+zOxfvnz5QaruXgMDA5xxxhmOQiTNqKncCv6ViYXMfCUilh5imw8DF0XEhcCRtM6J/AVwbEQsKkYbPcCzRf8mcBLQjIhFwDuBHW3tE9q3OVD7vLNs2TJuvPHGusuQNM90OhJ5NSLOnFiIiLOA1w62QWZenZk9mdlL68T4DzPzk8B9wKVFtwHge8Xr4WKZYv0Ps3Xd6jBwWXH11inASuAnwIPAyuJqryOKvzHc4fuRJE2DTkcinwP+e0RMfNM/AfiDkn/z3wG3R8T1wCPALUX7LbTmLWnQGoFcBpCZmyPiDlon83cDV2TmHoCI+AxwF63zM7dm5uaSNUmSSugoRDLzwYh4P/A+WucifpqZ453+kcy8H7i/eP0UrSur9u3zOvDxA2x/A617d+3bvp7W1L2SpBp0OhIB+Ke05llfBHwwIsjM2yqpSpLUFToKkYj4FvBe4FFat4WH1pVQhogkzWOdjkT6gdNy4gZNklSlF7vs3lkT164eXWsVnXuRA/yqbuo6DZHHgfcAz03Pn5WkyXXj9M6jo6MArFyxsuZKOrRi+v45dxoiy4AnIuInvH1mw4umpQpJKqxb130Tpk7UPDg4WHMlM6/TEPlilUVI893Y2Bhf+tKX+OIXv+hta9RVOp3Z8G8ne1RdnDRfODOlutVBQyQiflQ874yIl9seOyPi5ZkpUZrbnJlS3eygIZKZv108vyMzj2l7vCMzjznYtpI6M9nMlFK36KJr6KS5yZkp1c0MEalmq1atYvHi1mzTzkypbmOISDVzZkp1M0NEqpkzU6qbTeUGjJIqMjAwwLZt2xyFqOsYItIs4MyU6lYezpIkleZIZIYNDg7SaDSmfb/NZhOAnp6ead83tG7W1o33NJJULUNkjnjttYNOeS9JlTBEZlhV3+bn811EJdXHcyKSpNIMEUlSaR7OkjQvVHVRC7w1s2EVh6tn+0UthogkHaYlS5bUXUJtDBFJ88Js/jbfzTwnIkkqzRCRJJVmiEiSSjNEJEmlGSKSpNIMEUlSaYaIJKk0Q0SSVJohIkkqzRCRJJVmiEiSSjNEJEmlGSKSpNIMEUlSaZWFSEScFBH3RcSWiNgcEZ8t2o+LiI0RMVo8v6toj4gYjIhGRGyKiDPb9jVQ9B+NiIG29rMi4rFim8GIiKrejyRpf1WORHYD/yYz/zFwDnBFRJwGXAXcm5krgXuLZYA1wMrisRa4CVqhA1wLfAg4G7h2IniKPmvbtrugwvcjSdpHZSGSmc9l5sPF653AFmAFcDEwVHQbAi4pXl8M3JYtfwccGxEnAOcDGzNzR2b+CtgIXFCsOyYzf5yZCdzWti9J0gyYkXMiEdELfBB4AHh3Zj4HraABfr3otgJ4pm2zZtF2sPbmJO2T/f21ETESESPbt28/3LcjSSpUHiIRcTTw18DnMvPlg3WdpC1LtO/fmHlzZvZnZv/y5csPVbIkqUOVzrEeEYtpBci3M/O7RfMvI+KEzHyuOCT1fNHeBE5q27wHeLZoP3ef9vuL9p5J+k+LwcFBGo3GdO2ucqOjo0D3zSPd19fXdTVLektlIVJcKXULsCUz/7xt1TAwAPxp8fy9tvbPRMTttE6iv1QEzV3An7SdTF8NXJ2ZOyJiZ0ScQ+sw2aeAG6er/kajwSOPPcHepcdN1y4rFW+2BmEP/b+/r7mSzi3YtaPuEiQdpipHIh8G/hB4LCIeLdr+Pa3wuCMiLgeeBj5erFsPXAg0gF3AHwEUYfHHwINFv+syc+L/Pp8GvgksATYUj2mzd+lxvH7a703nLtXmyCd+UHcJkg5TZSGSmT9i8vMWAOdN0j+BKw6wr1uBWydpHwFOP4wyJUmHwV+sS5JKM0SkWWBsbIwrr7ySF154oe5SpCkxRKRZYGhoiE2bNjE0NHToztIsYohINRsbG2PDhg1kJhs2bHA0oq5iiEg1GxoaonVdCezdu9fRiLqKISLVbOPGjYyPjwMwPj7O3XffXXNFUucMEalmq1atYtGi1tX2ixYtYvXq1TVXJHXOEJFqNjAwwJ49ewDYs2cPAwMDh9hCmj0MEWkWmDgnMvEsdQtDRKrZ17/+9YMuS7OZISLV7J577nnb8saNG2uqRJo6Q0SSVJohItXsxBNPPOiyNJsZIlLNxsbGDroszWaVzmzYzZrNJgt2veScFxVasOsFms3ddZdRu9WrVzM8PExmEhGcf/75dZckdcyRiFSzgYEBFi9eDMDixYv9nYi6iiORA+jp6eGXbyxyZsMKHfnED+jpeU/dZdRu2bJlrFmzhuHhYS688EKOP/74ukuSOuZIRJoFPvaxj7F06VIuuuiiukuRpsQQkWaB73//++zatYvh4eG6S5GmxBCRauZ8IupmhohUM+cTUTczRKSaOZ+IupkhItVs1apVb7vE1/lE1E0MEalmAwMDRAQACxYs8Hci6ir+TuQgFuza0TW/WI/XXwYgjzym5ko6t2DXDsDfibT/TmTNmjX+TkRdxRA5gL6+vrpLmJLR0Z0ArHxvN/1P+T1d98+5KgMDA2zbts1RiLpOzLeZ1Pr7+3NkZKTuMqbdunXrABgcHKy5krltcHCQRqMx7fttNptA604J062vr+8f/v2QyoiIhzKzf7J1jkSkWeC1116ruwSpFENEmoKqvtE7klS38uosSVJphogkqTRDRJJUmudENOdUdQVVlUZHR4HqzrlUxSu/ZIhozmk0Gmx9/GFOPnpP3aV07Ijx1kGB17c9WHMlnXv6lYV1l6BZwBDRnHTy0Xu4pv+VusuY064fObruEjQLeE5EklSaI5EZVtXx+qqPqXfTse9ms8mrOxf6TbliP9+5kKOKX9pr/jJE5oglS5bUXcKs8sae4Oc7u+eY/fje1l18Fy/ontsQvbEnOKruIlS7rg+RiLgA+CqwEPhGZv5pzSUdVLd8m+9m5557btdenbVy5cqaK5kab6Cprg6RiFgIfA1YBTSBByNiODOfqLcy1anKoO7Gy4e76VCkuk+3n1g/G2hk5lOZ+SZwO3BxzTVJU7ZkyRIPSaordfVIBFgBPNO23AQ+tG+niFgLrAU4+eSTZ6YyzUl+o5ferttHIjFJ235nJjPz5szsz8z+5cuXz0BZkjQ/dHuINIGT2pZ7gGdrqkWS5p1uD5EHgZURcUpEHAFcBgzXXJMkzRtdfU4kM3dHxGeAu2hd4ntrZm6uuSxJmje6OkQAMnM9sL7uOiRpPur2w1mSpBoZIpKk0gwRSVJpkdk9N3ybDhGxHfh53XVUZBkwVncRKs3Pr7vN5c/vNzJz0h/ZzbsQmcsiYiQz++uuQ+X4+XW3+fr5eThLklSaISJJKs0QmVturrsAHRY/v+42Lz8/z4lIkkpzJCJJKs0QkSSVZojMERFxQUQ8GRGNiLiq7nrUuYi4NSKej4jH665FUxMRJ0XEfRGxJSI2R8Rn665ppnlOZA4o5prfSttc88AnnGu+O0TER4BXgNsy8/S661HnIuIE4ITMfDgi3gE8BFwyn/7bcyQyNzjXfBfLzP8F7Ki7Dk1dZj6XmQ8Xr3cCW2hN2z1vGCJzw2Rzzc+rf5GlukVEL/BB4IF6K5lZhsjc0NFc85KqERFHA38NfC4zX667nplkiMwNzjUv1SQiFtMKkG9n5nfrrmemGSJzg3PNSzWIiABuAbZk5p/XXU8dDJE5IDN3AxNzzW8B7nCu+e4REd8Bfgy8LyKaEXF53TWpYx8G/hD4aEQ8WjwurLuomeQlvpKk0hyJSJJKM0QkSaUZIpKk0gwRSVJphogkqTRDRKpQRLxyiPW9U717b0R8MyIuPbzKpOlhiEiSSjNEpBkQEUdHxL0R8XBEPBYR7XdZXhQRQxGxKSLujIilxTZnRcTfRsRDEXFXcdtxaVYxRKSZ8Trw+5l5JvA7wJ8Vt8wAeB9wc2aeAbwM/Kvifkw3Apdm5lnArcANNdQtHdSiuguQ5okA/qSYgGovrVv1v7tY90xm/u/i9X8F1gF/A5wObCyyZiHw3IxWLHXAEJFmxieB5cBZmTkeEduAI4t1+957KGmFzubM/M2ZK1GaOg9nSTPjncDzRYD8DvAbbetOjoiJsPgE8CPgSWD5RHtELI6ID8xoxVIHDBFpZnwb6I+IEVqjkp+2rdsCDETEJuA44KZimuNLgS9HxP8FHgV+a4Zrlg7Ju/hKkkpzJCJJKs0QkSSVZohIkkozRCRJpRkikqTSDBFJUmmGiCSptP8P5h/qmGd7p6sAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.boxplot(x='label', y='income', data=df.kmean)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x123f94d0>"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.boxplot(x='label', y='gdpp', data=df.kmean)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We can see a clear image that above plot paints in front of us.\n",
    "- Countries with label = 2 as best performing\n",
    "- Countries with label = 1 as worst performing which are in dire need of getting AID\n",
    "- Countries with label = 0 as those which have child mortality better than the worst prerforming but worse than the best performing lot.\n",
    "\n",
    "This story is backed by the second and third plot which tells us that countries with label = 1 have Lowest gdp per capita, Highest child mortality and lowest income of all categories.\n",
    "\n",
    "It would be safe for us to say that the clustering activity is performed which is fits well with the intuition we have built while performing EDA.\n",
    "\n",
    "The next step would be to perform clustering using Hierarchical Clustering algorithm and compare the performance of both algorithms on same data."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Section 3.2: Hierarchical Clustering"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>country</th>\n",
       "      <th>child_mort</th>\n",
       "      <th>exports</th>\n",
       "      <th>health</th>\n",
       "      <th>imports</th>\n",
       "      <th>income</th>\n",
       "      <th>inflation</th>\n",
       "      <th>life_expec</th>\n",
       "      <th>total_fer</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>0</td>\n",
       "      <td>1.344006</td>\n",
       "      <td>-0.569622</td>\n",
       "      <td>-0.566958</td>\n",
       "      <td>-0.598741</td>\n",
       "      <td>-0.851668</td>\n",
       "      <td>0.265002</td>\n",
       "      <td>-1.619705</td>\n",
       "      <td>1.926396</td>\n",
       "      <td>-0.702259</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>1</td>\n",
       "      <td>-0.547497</td>\n",
       "      <td>-0.473858</td>\n",
       "      <td>-0.440393</td>\n",
       "      <td>-0.413584</td>\n",
       "      <td>-0.386946</td>\n",
       "      <td>-0.372075</td>\n",
       "      <td>0.648831</td>\n",
       "      <td>-0.865054</td>\n",
       "      <td>-0.498726</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2</td>\n",
       "      <td>-0.272510</td>\n",
       "      <td>-0.424000</td>\n",
       "      <td>-0.486271</td>\n",
       "      <td>-0.476100</td>\n",
       "      <td>-0.221053</td>\n",
       "      <td>1.122161</td>\n",
       "      <td>0.671403</td>\n",
       "      <td>-0.034983</td>\n",
       "      <td>-0.477434</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>3</td>\n",
       "      <td>2.084159</td>\n",
       "      <td>-0.381249</td>\n",
       "      <td>-0.534088</td>\n",
       "      <td>-0.463973</td>\n",
       "      <td>-0.612045</td>\n",
       "      <td>1.932987</td>\n",
       "      <td>-1.179541</td>\n",
       "      <td>2.153997</td>\n",
       "      <td>-0.530950</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>4</td>\n",
       "      <td>-0.709406</td>\n",
       "      <td>-0.086742</td>\n",
       "      <td>-0.178410</td>\n",
       "      <td>0.139728</td>\n",
       "      <td>0.125254</td>\n",
       "      <td>-0.764618</td>\n",
       "      <td>0.705262</td>\n",
       "      <td>-0.543736</td>\n",
       "      <td>-0.032042</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    country  child_mort   exports    health   imports    income  inflation  \\\n",
       "0  1.344006   -0.569622 -0.566958 -0.598741 -0.851668  0.265002  -1.619705   \n",
       "1 -0.547497   -0.473858 -0.440393 -0.413584 -0.386946 -0.372075   0.648831   \n",
       "2 -0.272510   -0.424000 -0.486271 -0.476100 -0.221053  1.122161   0.671403   \n",
       "3  2.084159   -0.381249 -0.534088 -0.463973 -0.612045  1.932987  -1.179541   \n",
       "4 -0.709406   -0.086742 -0.178410  0.139728  0.125254 -0.764618   0.705262   \n",
       "\n",
       "   life_expec  total_fer  \n",
       "0    1.926396  -0.702259  \n",
       "1   -0.865054  -0.498726  \n",
       "2   -0.034983  -0.477434  \n",
       "3    2.153997  -0.530950  \n",
       "4   -0.543736  -0.032042  "
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Taking a look again at our Scaled data\n",
    "df1.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "data": {
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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# single linkage\n",
    "mergings = linkage(df1, method=\"single\", metric='euclidean')\n",
    "dendrogram(mergings)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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WqOp5xsnVqweTyCa60ci6cWVEO5cUtnP5YJFgOmx3HpZdqyAMgz0vpEDeD9Xz2QOax26luoLVVqjq4NTLGyZ6dQVM9fryrKPolRHtXFKY9/LBToLpMN15OApa5ecg82s0A3mjwDmogFk99dKPeewezNnnDTz1pmkg36iyV1fAVKejmW6ctO32JYUKppLHaAbyeoGzk4BZGxgnJ9tfT6P57HanYeoFadg6UA9qzp6tg/GaTZuoTE/z8DPP5B5Z6goYkfaNZiCHbQNnJ/PW1YGxUonf6wXFbgT8dtKSqReoBzhnX32VwLpNmzhujz22PMeinSmC2tH95OrVIzlf22xuvdVcuk5GCoxyIM8rzwgX8j0SIG/A71TeTqrNjqUXgbOTS7+qR/dFOoKi+n1LeLO59VYP94LOT0Y26kiadSKt8iHvjTiz5YadXlMgzzvCzavbz4DpRJsdy6ACZzOddASNAnKrYNHuXH03Lt8sMrferSmnRh1Jo04kTweS90acojfsDFMHMAyX745+IM9z4q+b0zDDps2OpZ3AWXtZ4qCDfq16AbkXc/WtLt8sgzwdSW3H2Cpgjc2bx8SCBVSmp5suX6SzHqY7Njst/250BKMfyAd44q+uVk9ZHIJLEfOqvSxxGANXFiTaDUJFtwOje3K2SMDKG3DrDQpaBbiig468Zd/OFFsn5d+NgUDHL5YoIntHZ2VtpbN3dFa/YKLZyyWyUenUVHtvpe+F6qMD2PbKmtrnpzQzORkdQJ486JFWL2mAmUZUmZ4e2A1FtaPzvC8RmFy9mvGVKxlfuXKrkeUgb4zK0jSIPM0C1sSCBQC58iMbmTdbvt69CkXLrJ4iL5Co7kRqj+oafb+6rrdTT7J8nTr66EKPKB7IiLxr7+isDnrtBMBG+nHVCRS/FDHruLKf+3VytUO9GLkXmf8uMmrqZHomT9pr01+t0b40O5eR5zC9G8/8aHdqI8/yjU72dutIp8gUTruXwzbaz17P6Q9saqVr7+js5gg7C4wLFsBtt8GZZ3Zv3Y00u+uzdpqlXsc1TCdXm+jGixCqVQezBTvswLpNm7jy4Yd7Ml9a25g7DSjduBO1UVDKc5jerWd+tBsYh+XhVb1Wbz97Pac/+nPk7co6hmzE0o2RfjON7vpsNJc/6KmhLmj3JGmj5bt1vXq3tHPSqt5Ir95Iuchlj3lGse0+82PYn7czKO3kSy87stkVyGunJmDb3zP9DJjVUy21I/QBPFqgaKPN+712p1ryLN+P0V6r6+yzvxU9QhjmN+F063k73e4Q+n0ZYm36G+VLvzu+0Q7k9eaUM7Uj7Twj73odQTfTt3Tp1iP0NWvgyiu3PjnaB0UbbTvfqw5UeW5E6vbUTBF5rrPP0ln0CGGQjyjIc5VIp4rWrUaBsVtTFnmPEuulv16+dGM/2+mYRjuQN5pTrtbOyDvvydW8Ab92fdmou3reO88UT7c7GIo32iLfa/dGpF6O6vIcHldfG91o+XrLtXqEbqvL72rX0Sr41NuvPEcUvb4ePk8dyTvyzdZX9N6HeutsdRI9bx0v2hay77aT/6MdyKH7UyR51tco4NcLuK3W18n2SqSdxtjp4Ww7QaKd7edZrl4gbdSRVU/V3PbEE5x5551N11/b4BsFp1ZHFNl193kub+zV1EbekW831pupXn+vTk7Wq6O1vxeZJhz9QD4o9QJwLwNus4Bf24G85CXd3fYAtHM4mydw11tfs46hyKis2ZFHo5F+9VRN7X7lSUe92+7rBYminVsvr8ZoJ3C3e1SVd/vdPu/SLF87mWNXIO+3QVx1UtuBDGkg78aUSd4An6cxd+sEX7VmwSHvaLGRokck2ei/dtuDDHjt6kVZQW9OWuZ5pk27+6NAPkx6MNe9RQkuW+xVY4T+zvm30o2Rfj1Fp3y6cTJzGC5P7EVZ9bJOttLO/nQUyM3sJOBcYA5wnrt/ppP1zXojMNfdqWG4OqXXhqHD6sUNWvV+HgVlqJOFA7mZzQG+AJwArAJ+a2Y/cvebupW4WakEI2fpXBmCQ7tGcZ/KopOHZr0UuMPd73L3TcB3gFO7kywREcmrk6mVZwH3Vf2+CnhZ7UJmthTIbkucvvKPr7y16Aat6Bdlay960aBTIENE7aoUntPsj50E8nrl79t84D4J9P/5qiIis0QnUyurgAOrfn82oKfpiIj0WSeB/LfAIWb2XDPbAXgH8KPuJEtERPIqPLXi7k+b2QeBS4nLD7/m7jd2LWUiIpKLuW8zrS0iIiUykHd2iohI9yiQi4iUnAK5iEjJDeyhWWa2J4C7P2Rm89x9ugvre8bdH231uZntDjzqNScIzOzLwA+BS939marP/xL4PTAP+H/AF919uno9bW7nbenHY4BfuvsFLfYLd3+ozt+2Wn+9fGz2/Trrezdxf8AewBPufl76fHt3f8rMtgd2rd3Pqu3U7v+u7v5Yne3MS/m3D/BM+jcnS2f6fCOwd/p/T+Ah4AjgHnd/xMysNl/rbYeo4zu4+/pW+99gHbnzr853DTgq/XodsGva7+rP765ev5mdCPwKeBTY3923uaS3Nn/q1K+6+Z4jvbnrcPrbyenHlwEPu/s56fP/Qtz5DfBDd/9xzffeSVyufDVwNHAAcDNwIWyd19m+VtXxA9x9dfr8QOBpd7+hZv1Gg7wpqqrOZvfPvAi4rbYdVC2/O2mg3KzupDr6WLY+4C3AfunP69z967nS14+TnWb2UeJmoTcBtwCHAPcTAWMOsBdwJ7AeeIpotC9Lf9uV2NF9gd2ADcCvicB6HLAjcAdRMY4EfgFcQATc/wk8kb7/S+Bw4HnAGmB/4EkicG0CFqY0/l8iwAI8Py27BjgYuBh4cUr/GmBZ+vkRIsjsC9xONNJngO8DBwGfACaA1wMPpn34CfBBYGVa187A+cAfpPS8HliU8mIaWAB8M6372LT+e9J+PJ7ybTERGA4k7rqdk/Jp15TXN6b9eBJ4LhHkbk9lcXDKswXAvSk99wG7EHfxLkr5fEBK040pf54BbiXuPHt++u7PU14vAnYigs7CVFbXAYel7c4jGtwuKR9gprPcG/hZWseOKX//hagXm1P6nwDWAZW0zD0pf15GlPmitG+HAdemn9ekclpA1JfVwKfS/nwyleectL6VKW3zUjkuAC5L5TcH2D3l/05EPZpO+bNLSsv2Kf9J312YyueQtG/bE412PlEv9kjLrSHq4u6pDLZP+3xNVZ7uTdT5jURAfIAIBnNSmWTlbaksD0nr2jWl8Qng8pSP64HXpL8/O/1tXiqr+9K+Pwi8OZXbXkRdWEKU/VNpn3+Tvv9y4G+JerjJ3c8xs38GricC1c7E1W5vBla7+7Fm9lOiPb0j7e9KZtro/PTzg8y0sb3Tfu4CPAzcRdSbp1I+XJjW/wBwBvBvwHdTmh4Axom681fuXjGzlcS9MXcQncuTwFXAyURMyGLX4rS9O4k6/mQqqxekst1AtMesnBam8lyZtnt8KqPNaT/np3Xdmta3YyrP5Skv7k55+Oc00a+plXuISjhN7NQviQb0L+mzTen35xAZcAQRPH5KNISfEgHiy+nn/YiHdf0CuJKojHOIBn0b8H7gY0SG3EgE76OJwl9IFLoDVxAVtkJU6hXESOifgL9J67qRaHT3ERXwZqIi3Qi8jQjsryYa3dyUlkvSfr+OKPjLgHen/ZwHTAFvTb9vBN6Qlr0C+ADwnrTeXxKVZ3XKv9OB11atP9uPfyAq13Lg74iA9Sai8qwnKsnVRKXZG7gB+AFRcX9e9fnfERX+auLIZCFRGS8iOs+bUl44cGjaj6uJhvNUyqOfAv8ReAXRWDcTAeHytI4n0v7cQHSANxEV/N70/74pX3YDTkn5uX/6/PUpPQ8T9zHcmdL+SqL8s/z5FLA27d8H0rKXEw19E9FpHQZ8hQg+f0PcfbxfKq8pohEfnPJgfSqb64i6fEj69zQR1C5N670+/XxP2rdriUa9kuj8svK5h3g20Q7p53OIundFWvac9L11qRyniFH6GPCSqvyZSut8DlH/LOXXTak8jiICzH5pmfVEILuIKPdjiE7vn1NaHkn79JP0/w7M1OEDiSBzChE89yQC3IpUBl8iyvwBoj29iwiCB6aB3GFEHd4E3Onuf0q0pQcJvyfq1rVEnVpIdBRZR/RXqfzWEXXnIaKtPpbyYTeiDu+X9ulIogPaCfjPRL37LNH+nGgjxwBnpE5kbkr3VUSd3Rv4R6JO35G2s4ao3/sTg5adiHq7B9Ge7iY6yVcQdfx+YjC3oqqMfpLy+R/S3+8i6kC2vq8S8eSjxED7Y0Rn0VS/RuRvJzL1k0RF/hzweeB7ROV/E/Bj4FXAiUTD/HZa5nlE4NyPaMB7EoHvCSJj9kk/H5D+fYEo9DOIIPh2IpPXEIWza9rOg0RFu4do7JcCf5rWvYKotJekdBwCXOHunzaz/YhAsSPwH4hGsDfRaNYRDfCLaX/3TGnYMS3zkbTMv6d9udTdb0wVfS9iNHYL0YDmEhVyCdEov5v2dQPx6OCDiI7kVqLzu4UIoD8kKvXjRKPfTFTYLxEd52FEQFiU8uvXKW8WESOER4mGejfRmDamcsqmQU5O+b4r0WH+mGh4f0l0rD8gbgx7DdGY5qTt7ENU5uyIahERRB8jOoXH034vSvluxEPYfpe29QQxylpHNMZ7gbOJRrQxpfstxMBgBTOj3Z+l/b8L+K27X2lmrwT+kGhstwM3uPs9ZnZF2pcXpHXsSQwork/lkQWLI1N+L0jl8CjRse+T9uPeVC5fA95JBNb7gbOIevdgKtNb0vpI6dyLCKCLUr58IK3neCKY7ZLWtTcRLB5LeXAwUZ/mEx3YvwL/NZXz+rQ/m4g6emHa/qNp399AjFwniYHdzUSde5Rofy9N6XwYOCnlyZ6pLK9J5bIylfN3iI7ljJRfXyHa+qVER7AzUWdXuftXzeyIbFrEzPYmjrAXpvU8Abwv1YFLU9ouJEb8O6eyuJ+IKVcQwfkCoi5NEG18PdH5Voi28GQqo68Rg483Eh3CVen726Vy+XNigPjNVBb3AX9NdDITwGeIevhgytN9U978NVFvD2ZmYHoVEUOeR3SKf5HS9DhRj3cnOozdiTixOZXhb4iR+M3V+dRIvwL5WURj+AxRGA8Dr3T3U83srURjOIIYhZ5PFMJdxM5vT/RgexAj8jGiwC8kKvg+RIB8H9GwbiAKcxXRCLcnRpk7E5lzAlE5NxG96ylEoe1AZOIOzASLp4lO5a3ECD+bfz4tpXOCmQeHHe7uB5nZJ4nA8ioiCB1KBKJsJF69vkOIRvAaohFvZKbRvJQIKnOJQ9VVRCVemb63S0rf74kRwkpmpoKOIyrDM0QQPZ9oEBNEADgk5edY+t7had/fmdL1aMrX1URlf4AYDR1IVNQF6W8XEZ3ZQUQQf0VK0/7MBLqdiY7glFQOb3f3l9fk001pfy8mOqPLq/JtAdFA38jMYexjKT0HESPVF6XtXJzy9/PEUc0BKe2/IgLWfKKDWkzUizvSdj6e8uJlRP05Iq3/ipRv2ehv31ROD6X9v5OZDjfr+N9ETKW9jwg2D1Xlw2+JoPLutPzDhMvT3zPvYmZK5dK0779J6do9fTaXmSOMOWm/f5aWXZD2c8eUB/+aymk+EbwvSOl7NnHUuZYYCU4Q7fQ04E/c/V4zOxN4YcrjbCruUOABd39FKsdHiPr9SNrOKqKePEx02q8n6vw3iU71UOLI6RXu/j2AtB1L+35v2v/NRAy4IaWVlP9ZHbyXqG+3EwEXot4eQNS1MWAnd3+1mf3U3Y9PsWgTUY/en/br34gA/lRa5wkpHyeIacwLiA5me+LIfkdmBh4XEXV7PdHBbiLa03tTHj+LqDsHEXXm56m8jknl+JK03QNTXj2V1nsJMUI/lpgrP5cm+nZDkJm9mmjEHzSzi4me/2Qi2FxHFMTNRAHeR/R+xxO95PZEJTqOGA38BVGpbyF6+HlEBpxLVOirieB8Rvr+a9P/c4kK8X9IJxrT71el719CjCzXEwHyWmIU9XuiMJ9DBMqsod1DNMyDiQI7nah0exKNbC5RuN8hDpf+rGZ9O7j7lSl/TiMq+x8AXyeCxl1EBfozosDfTARMB05w95PM7CNUQOEAAAQqSURBVDyigf6CGEW+iqgsvyBGYu8nKt+ZxJTRg8So+RQikP6CqIz/i2jEFxAN5ddpuzcSjfOlxBTRS4mK/ANiRLRzKp8xIsBuJEbGlxAd4ouJCnxQKpMjgT+qyadH0v4+SRyuvy3l22eJwL4HcURyMNG4PaXjQmIqYQ3RGZ6d8vf5ROfw90SdmEd09jumcphK+XAKcURxTkrLR4j684O0DyemfHsPgLufnsrp2LTMSal8vp/yaRciiGRTGr9L6byNCDgL0z6uTXn2IHC8u59oZp8gAtKatA+nEJ3CkUT7OCNtZzNxVLWAOOJYnvbrWUSnfCNRv15NBPD3p23fmr53BnFE+QJ3f6uZTRLt7vVER/Bcog6tSr9nR293pP14LG3zZHf/uJm9hWhDv8/qg7tfZGbbAce5+88AzGzc3afM7GMpz98APNfdT09/f0fNvn8q7cOfpzLZjugYtkt5uldabjyld3P6+23EgPFnab82uvt3zewzRPA9IaV/H6IDPieVybuJOnsN8I/uviTtwxeJtv6StN6TUtldTIywHyTq9veJgcdOqSxOTPn+nbTc3qnOPErU/2tSeWwmBoj3pLw/m4hBmwHc/RNm9nF3/zRNDOTOTjM7yt2vM7OjgJe7+5dTz34fcL+7X5qWOzxNPRxHNMa5RMX/UPXOmdmpwLS7X17zefb9o4jKNhcYd/cz8mROg7QfRow8t6Szzn4dRzTkq4DXuvvnzeyNtWfv66z7uKrA/iF3/3zN37P9ORxY7O4/NrM3Andnj0eot51svSkfjk3pOR24qsX3DiE6292I4HQ10YDurrf/VflzIjHqv4M4PPxCs3xK5f8h4HcpnYdX5xvROE8mgv1hROeZpafe+reqD0TnlF1hsTnL16p8OZX69appeTSSbZ/orM4jGunN7v6FtJ+XpPQsJE72bakfKf9OJQYNt1Snt852jnL369LPpxNHENvsZ6P0VbeXOuv6ddXvWdvcjggwdcs+Z940zNOafZ8LPJmVScrHtxJBdC8iKG/JI6KMs79vUyeqtrHVfrr7l9PPhzdrC1Xfz9oE5M/jzxGder2YsaBeuhvFtEYGFci/TYweJogdeCr9/w3SlEvB5azI9wuke6vttLtfXVz/N4jRyuM9/F6FmHJpuT+NtlMgPfU+L5IOq/k+NK8fuepVK63qQZ39abr/jbabdz9bfS9NcdZbV9faTrNt13zeLM/y/L1pWeXYfp7vt5PHedtCofRs4e59/0ccckFMlRyZ/Z8+O7LockW/3266G32/0+21u/5suX59r9X+NNpOu+lp9Xne9bfaz1bpbZU/Ret3u/uft77kTW+95Rqtq2hdbmfbRfKs3bqcd/tFv9+qDuStq0Xrnh6aJSJScrpFX0Sk5BTIRURKToFcRKTkFMhFREpOgVxEpOT+PyBcB34n8cp1AAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# complete linkage\n",
    "mergings = linkage(df1, method=\"complete\", metric='euclidean')\n",
    "dendrogram(mergings)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We can see that out of both linkage methods, we have better segregated clusters using complete linkage, which is expected and we would be able to make a cut the tree to get less branches which is intuitive in this case. It might be significant to make the cut at values where we get about three clusters, i.e. between 8-10."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([0, 0, 0, 0, 1, 0, 0, 1, 1, 0, 1, 1, 0, 1, 0, 2, 0, 0, 0, 0, 0, 0,\n",
       "       0, 1, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 1,\n",
       "       2, 0, 0, 0, 0, 0, 0, 1, 0, 1, 1, 0, 0, 0, 1, 0, 1, 0, 0, 0, 0, 0,\n",
       "       0, 1, 1, 0, 0, 0, 0, 2, 1, 1, 0, 1, 0, 0, 0, 0, 1, 0, 0, 1, 0, 0,\n",
       "       0, 1, 1, 2, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,\n",
       "       2, 1, 0, 0, 2, 1, 0, 0, 0, 0, 0, 1, 1, 1, 0, 0, 0, 0, 1, 0, 0, 1,\n",
       "       0, 2, 1, 1, 0, 0, 1, 1, 0, 0, 0, 0, 1, 2, 0, 0, 0, 0, 0, 0, 0, 0,\n",
       "       0, 0, 0, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0])"
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Considering 3 clusters\n",
    "cluster_labels = cut_tree(mergings, n_clusters=3).reshape(-1, )\n",
    "cluster_labels"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>country</th>\n",
       "      <th>child_mort</th>\n",
       "      <th>exports</th>\n",
       "      <th>health</th>\n",
       "      <th>imports</th>\n",
       "      <th>income</th>\n",
       "      <th>inflation</th>\n",
       "      <th>life_expec</th>\n",
       "      <th>total_fer</th>\n",
       "      <th>gdpp</th>\n",
       "      <th>label</th>\n",
       "      <th>cluster_labels</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>0</td>\n",
       "      <td>Afghanistan</td>\n",
       "      <td>90.2</td>\n",
       "      <td>55.30</td>\n",
       "      <td>41.9174</td>\n",
       "      <td>248.297</td>\n",
       "      <td>1610.0</td>\n",
       "      <td>9.44</td>\n",
       "      <td>56.2</td>\n",
       "      <td>5.82</td>\n",
       "      <td>553.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>1</td>\n",
       "      <td>Albania</td>\n",
       "      <td>16.6</td>\n",
       "      <td>1145.20</td>\n",
       "      <td>267.8950</td>\n",
       "      <td>1987.740</td>\n",
       "      <td>9930.0</td>\n",
       "      <td>4.49</td>\n",
       "      <td>76.3</td>\n",
       "      <td>1.65</td>\n",
       "      <td>4090.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2</td>\n",
       "      <td>Algeria</td>\n",
       "      <td>27.3</td>\n",
       "      <td>1712.64</td>\n",
       "      <td>185.9820</td>\n",
       "      <td>1400.440</td>\n",
       "      <td>12900.0</td>\n",
       "      <td>16.10</td>\n",
       "      <td>76.5</td>\n",
       "      <td>2.89</td>\n",
       "      <td>4460.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>3</td>\n",
       "      <td>Angola</td>\n",
       "      <td>119.0</td>\n",
       "      <td>2199.19</td>\n",
       "      <td>100.6050</td>\n",
       "      <td>1514.370</td>\n",
       "      <td>5900.0</td>\n",
       "      <td>22.40</td>\n",
       "      <td>60.1</td>\n",
       "      <td>6.16</td>\n",
       "      <td>3530.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>4</td>\n",
       "      <td>Antigua and Barbuda</td>\n",
       "      <td>10.3</td>\n",
       "      <td>5551.00</td>\n",
       "      <td>735.6600</td>\n",
       "      <td>7185.800</td>\n",
       "      <td>19100.0</td>\n",
       "      <td>1.44</td>\n",
       "      <td>76.8</td>\n",
       "      <td>2.13</td>\n",
       "      <td>12200.0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "               country  child_mort  exports    health   imports   income  \\\n",
       "0          Afghanistan        90.2    55.30   41.9174   248.297   1610.0   \n",
       "1              Albania        16.6  1145.20  267.8950  1987.740   9930.0   \n",
       "2              Algeria        27.3  1712.64  185.9820  1400.440  12900.0   \n",
       "3               Angola       119.0  2199.19  100.6050  1514.370   5900.0   \n",
       "4  Antigua and Barbuda        10.3  5551.00  735.6600  7185.800  19100.0   \n",
       "\n",
       "   inflation  life_expec  total_fer     gdpp  label  cluster_labels  \n",
       "0       9.44        56.2       5.82    553.0      1               0  \n",
       "1       4.49        76.3       1.65   4090.0      0               0  \n",
       "2      16.10        76.5       2.89   4460.0      0               0  \n",
       "3      22.40        60.1       6.16   3530.0      1               0  \n",
       "4       1.44        76.8       2.13  12200.0      0               1  "
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# assign cluster labels to each country in the dataframe\n",
    "df.kmean['cluster_labels'] = cluster_labels\n",
    "df.kmean.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now that we have assigned both label (using Kmeans) and cluster_labels (using Hierarchical) to each country in our dataframe. It would be nice to compare the labels assigned using both methods.\n",
    "First, looking at the cluster_labels. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x123f53f0>"
      ]
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.boxplot(x='cluster_labels', y='child_mort', data=df.kmean)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x123f5190>"
      ]
     },
     "execution_count": 36,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.boxplot(x='cluster_labels', y='income', data=df.kmean)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x128866b0>"
      ]
     },
     "execution_count": 37,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.boxplot(x='cluster_labels', y='gdpp', data=df.kmean)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We can observe that the three clusters created using Hierarchical method have following meaning of labels.\n",
    "- Cluster_labels = 2 for Best performing countries\n",
    "- Cluster_labels = 0 for Worst performing countries which are in dire need of the AID\n",
    "- Cluster_labels = 1 for countries which are performing somewhere in between the best and the worst perfroming countries\n",
    "\n",
    "### Identifying top 5 worst performing countries"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>country</th>\n",
       "      <th>child_mort</th>\n",
       "      <th>exports</th>\n",
       "      <th>health</th>\n",
       "      <th>imports</th>\n",
       "      <th>income</th>\n",
       "      <th>inflation</th>\n",
       "      <th>life_expec</th>\n",
       "      <th>total_fer</th>\n",
       "      <th>gdpp</th>\n",
       "      <th>label</th>\n",
       "      <th>cluster_labels</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>132</td>\n",
       "      <td>Sierra Leone</td>\n",
       "      <td>153.4</td>\n",
       "      <td>67.032</td>\n",
       "      <td>52.2690</td>\n",
       "      <td>137.655</td>\n",
       "      <td>1220.0</td>\n",
       "      <td>17.20</td>\n",
       "      <td>55.0</td>\n",
       "      <td>5.2000</td>\n",
       "      <td>399.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>66</td>\n",
       "      <td>Haiti</td>\n",
       "      <td>153.4</td>\n",
       "      <td>101.286</td>\n",
       "      <td>45.7442</td>\n",
       "      <td>428.314</td>\n",
       "      <td>1500.0</td>\n",
       "      <td>5.45</td>\n",
       "      <td>32.1</td>\n",
       "      <td>3.3300</td>\n",
       "      <td>662.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>32</td>\n",
       "      <td>Chad</td>\n",
       "      <td>150.0</td>\n",
       "      <td>330.096</td>\n",
       "      <td>40.6341</td>\n",
       "      <td>390.195</td>\n",
       "      <td>1930.0</td>\n",
       "      <td>6.39</td>\n",
       "      <td>56.5</td>\n",
       "      <td>6.5636</td>\n",
       "      <td>897.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>31</td>\n",
       "      <td>Central African Republic</td>\n",
       "      <td>149.0</td>\n",
       "      <td>52.628</td>\n",
       "      <td>17.7508</td>\n",
       "      <td>118.190</td>\n",
       "      <td>888.0</td>\n",
       "      <td>2.01</td>\n",
       "      <td>47.5</td>\n",
       "      <td>5.2100</td>\n",
       "      <td>446.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>97</td>\n",
       "      <td>Mali</td>\n",
       "      <td>137.0</td>\n",
       "      <td>161.424</td>\n",
       "      <td>35.2584</td>\n",
       "      <td>248.508</td>\n",
       "      <td>1870.0</td>\n",
       "      <td>4.37</td>\n",
       "      <td>59.5</td>\n",
       "      <td>6.5500</td>\n",
       "      <td>708.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                      country  child_mort  exports   health  imports  income  \\\n",
       "132              Sierra Leone       153.4   67.032  52.2690  137.655  1220.0   \n",
       "66                      Haiti       153.4  101.286  45.7442  428.314  1500.0   \n",
       "32                       Chad       150.0  330.096  40.6341  390.195  1930.0   \n",
       "31   Central African Republic       149.0   52.628  17.7508  118.190   888.0   \n",
       "97                       Mali       137.0  161.424  35.2584  248.508  1870.0   \n",
       "\n",
       "     inflation  life_expec  total_fer   gdpp  label  cluster_labels  \n",
       "132      17.20        55.0     5.2000  399.0      1               0  \n",
       "66        5.45        32.1     3.3300  662.0      1               0  \n",
       "32        6.39        56.5     6.5636  897.0      1               0  \n",
       "31        2.01        47.5     5.2100  446.0      1               0  \n",
       "97        4.37        59.5     6.5500  708.0      1               0  "
      ]
     },
     "execution_count": 38,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.kmean[df.kmean['label'] == 1].sort_values(by = ['child_mort', 'income', 'gdpp'], ascending = [False, True, True]).head(5)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Based on this exercise of clustering, we can give a recommendation of top 5 countries which should be helped on priority.\n",
    "- Sierra Leone\n",
    "- Haiti\n",
    "- Chad\n",
    "- Central African Republic\n",
    "- Mali"
   ]
  }
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