Clustering_Assignment.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<h1 id=\"tocheading\">Table of Contents</h1>\n",
    "<div id=\"toc\"></div>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 75,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/javascript": [
       "$.getScript('https://kmahelona.github.io/ipython_notebook_goodies/ipython_notebook_toc.js')\n"
      ],
      "text/plain": [
       "<IPython.core.display.Javascript object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "%%javascript\n",
    "$.getScript('https://kmahelona.github.io/ipython_notebook_goodies/ipython_notebook_toc.js')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "import warnings\n",
    "warnings.filterwarnings('ignore')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "#importing necessary libraries\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Inspecting dataframe"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "#read the data frame and inspect\n",
    "df = pd.read_csv(\"Country-data.csv\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<div>\n",
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       "    .dataframe tbody tr th {\n",
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       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>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",
       "      <th>0</th>\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",
       "      <th>1</th>\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",
       "      <th>2</th>\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",
       "      <th>3</th>\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",
       "      <th>4</th>\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": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "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",
      " #   Column      Non-Null Count  Dtype  \n",
      "---  ------      --------------  -----  \n",
      " 0   country     167 non-null    object \n",
      " 1   child_mort  167 non-null    float64\n",
      " 2   exports     167 non-null    float64\n",
      " 3   health      167 non-null    float64\n",
      " 4   imports     167 non-null    float64\n",
      " 5   income      167 non-null    int64  \n",
      " 6   inflation   167 non-null    float64\n",
      " 7   life_expec  167 non-null    float64\n",
      " 8   total_fer   167 non-null    float64\n",
      " 9   gdpp        167 non-null    int64  \n",
      "dtypes: float64(7), int64(2), object(1)\n",
      "memory usage: 13.2+ KB\n"
     ]
    }
   ],
   "source": [
    "df.info()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##### Observation\n",
    "The data frame has 9 columns. The country name is the only object data type. The rest of the columns are numerical. There are no null values present. 167 records are present. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>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",
       "      <th>count</th>\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",
       "      <th>mean</th>\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",
       "      <th>std</th>\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",
       "      <th>min</th>\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",
       "      <th>25%</th>\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",
       "      <th>50%</th>\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",
       "      <th>75%</th>\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",
       "      <th>max</th>\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",
       "</div>"
      ],
      "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  "
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Inspecting the statistical parameters of the columns\n",
    "df.describe()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Converting Columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Converting the columns as per the data given about the data set \n",
    "#The columns exports,imports and health are represented as % of gdpp\n",
    "df.exports = df.exports*df.gdpp/100\n",
    "df.imports = df.imports*df.gdpp/100\n",
    "df.health = df.health*df.gdpp/100"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
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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",
       "      <th>count</th>\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",
       "      <th>mean</th>\n",
       "      <td>38.270060</td>\n",
       "      <td>7420.618847</td>\n",
       "      <td>1056.733204</td>\n",
       "      <td>6588.352108</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",
       "      <th>std</th>\n",
       "      <td>40.328931</td>\n",
       "      <td>17973.885795</td>\n",
       "      <td>1801.408906</td>\n",
       "      <td>14710.810418</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",
       "      <th>min</th>\n",
       "      <td>2.600000</td>\n",
       "      <td>1.076920</td>\n",
       "      <td>12.821200</td>\n",
       "      <td>0.651092</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",
       "      <th>25%</th>\n",
       "      <td>8.250000</td>\n",
       "      <td>447.140000</td>\n",
       "      <td>78.535500</td>\n",
       "      <td>640.215000</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",
       "      <th>50%</th>\n",
       "      <td>19.300000</td>\n",
       "      <td>1777.440000</td>\n",
       "      <td>321.886000</td>\n",
       "      <td>2045.580000</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",
       "      <th>75%</th>\n",
       "      <td>62.100000</td>\n",
       "      <td>7278.000000</td>\n",
       "      <td>976.940000</td>\n",
       "      <td>7719.600000</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",
       "      <th>max</th>\n",
       "      <td>208.000000</td>\n",
       "      <td>183750.000000</td>\n",
       "      <td>8663.600000</td>\n",
       "      <td>149100.000000</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",
       "</div>"
      ],
      "text/plain": [
       "       child_mort        exports       health        imports         income  \\\n",
       "count  167.000000     167.000000   167.000000     167.000000     167.000000   \n",
       "mean    38.270060    7420.618847  1056.733204    6588.352108   17144.688623   \n",
       "std     40.328931   17973.885795  1801.408906   14710.810418   19278.067698   \n",
       "min      2.600000       1.076920    12.821200       0.651092     609.000000   \n",
       "25%      8.250000     447.140000    78.535500     640.215000    3355.000000   \n",
       "50%     19.300000    1777.440000   321.886000    2045.580000    9960.000000   \n",
       "75%     62.100000    7278.000000   976.940000    7719.600000   22800.000000   \n",
       "max    208.000000  183750.000000  8663.600000  149100.000000  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  "
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Inspecting the statistical parameters of the columns\n",
    "df.describe()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##### Observation\n",
    "Columns child_mort, exports, imports, gdp, and total_fer have outliers. The univariate analysis is done to inspect each of the columns as below."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Univariate Analysis"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "columns = list(df.columns)\n",
    "num_columns=columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Considering only numeric columns\n",
    "num_columns.remove(\"country\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['child_mort',\n",
       " 'exports',\n",
       " 'health',\n",
       " 'imports',\n",
       " 'income',\n",
       " 'inflation',\n",
       " 'life_expec',\n",
       " 'total_fer',\n",
       " 'gdpp']"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "num_columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "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": [
    "#One glance numerical columns distribution\n",
    "fig,ax=plt.subplots(nrows=3,ncols=3,figsize=(15, 10))\n",
    "\n",
    "for col,i in zip(num_columns,range(1,len(num_columns)+1)):\n",
    "    plt.subplot(3,3,i)\n",
    "    sns.boxplot(df[col],color='orange')\n",
    "plt.suptitle(\"Observing the box plots for all numerical columns\",fontsize=16);      "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##### Observation \n",
    "1. Child mortality is very high in a few countries it cannot be considered as outliers since they may be countries that need the funds.\n",
    "2. In columns like income, gdpp, exports and imports the outliers are soft capped since if theses are showing a high number; it means indirectly that the countries may not be requiring monetary funds.\n",
    "3. Life expectancy has outliers below the 25 percentile. These may be countries which need help to better their healthcare facilities.  Hence they are also not removed.\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Handling Outliers\n",
    "The columns like income,imports exports and gdp have outliers that are greater than Q3. They are soft capped instead of deleting them as we have very few records."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [],
   "source": [
    "\n",
    "# soft capping (statistical) outliers\n",
    "# Columns gdpp, income, exports and imports which have outliers greater tha 99 percentile they are soft capped to 99percentile\n",
    "for col in [\"gdpp\",\"exports\",\"imports\",\"income\"]:\n",
    "    Q3 = df[col].quantile(0.99)\n",
    "    df[col][df[col]>Q3] = Q3\n",
    "#     IQR = Q3 - Q1\n",
    "#     df = df[(df[col] >= Q1 - 1.5*IQR) & (df[col] <= Q3 + 1.5*IQR)]\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "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": [
    "#One glance numerical columns distribution\n",
    "fig,ax=plt.subplots(nrows=3,ncols=3,figsize=(15, 10))\n",
    "\n",
    "for col,i in zip(num_columns,range(1,len(num_columns)+1)):\n",
    "    plt.subplot(3,3,i)\n",
    "    sns.boxplot(df[col],color='green')\n",
    "plt.suptitle(\"Observing the box plots for all numerical columns after outlier handling\",fontsize=16); "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "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",
      " #   Column      Non-Null Count  Dtype  \n",
      "---  ------      --------------  -----  \n",
      " 0   country     167 non-null    object \n",
      " 1   child_mort  167 non-null    float64\n",
      " 2   exports     167 non-null    float64\n",
      " 3   health      167 non-null    float64\n",
      " 4   imports     167 non-null    float64\n",
      " 5   income      167 non-null    float64\n",
      " 6   inflation   167 non-null    float64\n",
      " 7   life_expec  167 non-null    float64\n",
      " 8   total_fer   167 non-null    float64\n",
      " 9   gdpp        167 non-null    float64\n",
      "dtypes: float64(9), object(1)\n",
      "memory usage: 13.2+ KB\n"
     ]
    }
   ],
   "source": [
    "df.info()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Scaling\n",
    "The standard scaler is used to scale the numerical columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.preprocessing import StandardScaler\n",
    "scaler = StandardScaler()\n",
    "df_scaled = scaler.fit_transform(df[num_columns])\n",
    "# The scaler returns a  numpy object which has to be explicitly casted to a dataframe\n",
    "df_scaled = pd.DataFrame(df_scaled)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>0</th>\n",
       "      <th>1</th>\n",
       "      <th>2</th>\n",
       "      <th>3</th>\n",
       "      <th>4</th>\n",
       "      <th>5</th>\n",
       "      <th>6</th>\n",
       "      <th>7</th>\n",
       "      <th>8</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1.291532</td>\n",
       "      <td>-0.569622</td>\n",
       "      <td>-0.565040</td>\n",
       "      <td>-0.598741</td>\n",
       "      <td>-0.851668</td>\n",
       "      <td>0.157336</td>\n",
       "      <td>-1.619092</td>\n",
       "      <td>1.902882</td>\n",
       "      <td>-0.702259</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>-0.538949</td>\n",
       "      <td>-0.473858</td>\n",
       "      <td>-0.439218</td>\n",
       "      <td>-0.413584</td>\n",
       "      <td>-0.386946</td>\n",
       "      <td>-0.312347</td>\n",
       "      <td>0.647866</td>\n",
       "      <td>-0.859973</td>\n",
       "      <td>-0.498726</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>-0.272833</td>\n",
       "      <td>-0.424000</td>\n",
       "      <td>-0.484826</td>\n",
       "      <td>-0.476100</td>\n",
       "      <td>-0.221053</td>\n",
       "      <td>0.789274</td>\n",
       "      <td>0.670423</td>\n",
       "      <td>-0.038404</td>\n",
       "      <td>-0.477434</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2.007808</td>\n",
       "      <td>-0.381249</td>\n",
       "      <td>-0.532363</td>\n",
       "      <td>-0.463973</td>\n",
       "      <td>-0.612045</td>\n",
       "      <td>1.387054</td>\n",
       "      <td>-1.179234</td>\n",
       "      <td>2.128151</td>\n",
       "      <td>-0.530950</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>-0.695634</td>\n",
       "      <td>-0.086742</td>\n",
       "      <td>-0.178771</td>\n",
       "      <td>0.139728</td>\n",
       "      <td>0.125254</td>\n",
       "      <td>-0.601749</td>\n",
       "      <td>0.704258</td>\n",
       "      <td>-0.541946</td>\n",
       "      <td>-0.032042</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "          0         1         2         3         4         5         6  \\\n",
       "0  1.291532 -0.569622 -0.565040 -0.598741 -0.851668  0.157336 -1.619092   \n",
       "1 -0.538949 -0.473858 -0.439218 -0.413584 -0.386946 -0.312347  0.647866   \n",
       "2 -0.272833 -0.424000 -0.484826 -0.476100 -0.221053  0.789274  0.670423   \n",
       "3  2.007808 -0.381249 -0.532363 -0.463973 -0.612045  1.387054 -1.179234   \n",
       "4 -0.695634 -0.086742 -0.178771  0.139728  0.125254 -0.601749  0.704258   \n",
       "\n",
       "          7         8  \n",
       "0  1.902882 -0.702259  \n",
       "1 -0.859973 -0.498726  \n",
       "2 -0.038404 -0.477434  \n",
       "3  2.128151 -0.530950  \n",
       "4 -0.541946 -0.032042  "
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_scaled.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Hopkins Test"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Hopkins test to see if clustering is possible\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\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.8887309778938077"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Use the Hopkins Statistic function by passing the above dataframe as a paramter\n",
    "hopkins(df_scaled)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Modelling"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.cluster import KMeans\n",
    "from sklearn.metrics import silhouette_score"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "KMeans(max_iter=50, n_clusters=4)"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "### k-means with some arbitrary k\n",
    "from sklearn.cluster import KMeans\n",
    "chosen_clusters =4\n",
    "kmeans = KMeans(n_clusters=chosen_clusters, max_iter=50)\n",
    "kmeans.fit(df_scaled)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([1, 0, 0, 1, 0, 0, 0, 2, 2, 0, 2, 2, 0, 0, 0, 3, 0, 1, 0, 0, 0, 1,\n",
       "       0, 2, 0, 1, 1, 0, 1, 2, 0, 1, 1, 0, 0, 0, 1, 1, 1, 0, 1, 0, 2, 2,\n",
       "       3, 0, 0, 0, 0, 1, 1, 0, 0, 2, 2, 1, 1, 0, 2, 1, 2, 0, 0, 1, 1, 0,\n",
       "       1, 0, 2, 0, 0, 0, 1, 3, 2, 2, 0, 2, 0, 0, 1, 1, 2, 0, 1, 0, 0, 1,\n",
       "       1, 0, 0, 3, 0, 1, 1, 0, 0, 1, 2, 1, 0, 0, 0, 0, 0, 0, 1, 0, 1, 0,\n",
       "       3, 2, 1, 1, 3, 2, 1, 0, 0, 0, 0, 0, 2, 3, 0, 0, 1, 0, 2, 1, 0, 0,\n",
       "       1, 3, 2, 2, 1, 1, 2, 2, 0, 0, 1, 0, 2, 3, 0, 1, 0, 1, 1, 0, 0, 0,\n",
       "       0, 1, 0, 2, 2, 2, 0, 0, 0, 0, 0, 1, 1])"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "kmeans.labels_"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Choosing the right K"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Elbow Curve Method"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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685g8jivowl2vrKC6pjbqcEREJA6U9EJmxrRJhazesofnFqyPOhwREYkDJb0YU0bkc0x+Z6bPLqG21qMOR0REWpiSXoyUFOOGiYUsK9vFzMVlUYcjIiItTEmvnguP782A7llMn12Cu0p7IiLtiZJePWmpKVx/5hAWrC3n1eWbow5HRERakJJeAy4Z05feXTKY/nJJ1KGIiEgLUtJrQHpaClPPGMy7q7byzsdbog5HRERaiJJeIy47uT89Oqdzx2yV9kRE2gslvUZkpqdyzYTBvLZ8Mx+u2R51OCIi0gKU9A7h8nH9yclIY7pKeyIi7YKS3iFkZ3TgqvGDmLm4jKUbdkYdjoiIHCUlvcO4+rSBdEpPVWlPRKQdiGvSM7Nvm9kiM1toZo+aWYaZdTOzF81seficGzP9LWZWYmZLzezceMbWVLmd0rl83ACenb+OVZt3Rx2OiIgchbglPTMrAL4JFLn7KCAVuAy4GZjl7kOBWeEwZjYifH8kcB5wp5mlxiu+5rjm9EGkpaZw15wVUYciIiJHId7Vm2lAppmlAVnAOuAiYEb4/gzg4vD1RcBj7l7h7iuBEmBsnONrkp7ZGVx2cj+eer+Utdv3Rh2OiIgcobglPXdfC9wOfAKsB8rdfSaQ7+7rw2nWAz3DWQqANTGLKA3HJYTrzhyCO9z9ikp7IiJtVTyrN3MJSm+DgD5AJzO7/FCzNDDuoCs+m9lUMys2s+JNmza1TLBNUNA1k0vGFPDYe2vYtLOi1dYrIiItJ57Vm+cAK919k7tXAU8BpwFlZtYbIHzeGE5fCvSLmb8vQXXoAdz9bncvcveivLy8OIZ/sOsnFlJVU8u9r3/cqusVEZGWEc+k9wkwzsyyzMyAs4ElwDPAleE0VwJ/C18/A1xmZh3NbBAwFHg3jvE126Aenbjg+D48/NZqtu+pjDocERFppni26b0DPAnMAxaE67obuBWYbGbLgcnhMO6+CHgCWAw8D0xz95p4xXekpk0awu7KGu5/Y1XUoYiISDNZW75RalFRkRcXF7f6eq99sJh3V27ljZvPonPHtFZfv4iINM7M5rp7UUPv6YosR+Abkwop31vFw2+vjjoUERFpBiW9I3BCv66cPrQH9772MfuqEq4GVkREGqGkd4SmTSpk865KHnv3k6hDERGRJlLSO0KnDOrGyQNz+cOrH1NZXRt1OCIi0gRKekfIzJg2qZD15ft4+v3SqMMREZEmUNI7Cmcek8dxBV24a84KqmtU2hMRSXRKekchKO0NYdWWPTy3YH3U4YiIyGEo6R2lKSN6MbRnZ6bPLqG2tu2e8ygikgyU9I5SSkrQtresbBcvLimLOhwRETkEJb0WcOHxvenfLYvps0toy1e4ERFp75T0WkBaagrXTxzC/NJyXlu+OepwRESkEUp6LeSSMQX07pLBHS+XRB2KiIg0QkmvhXRMS2XqGYN5d9VW3l25NepwRESkAUp6Leiyk/vTvVM6d8xWaU9EJBEp6bWgzPRUrjl9EK8u28T80u1RhyMiIvUo6bWwr4wbQE5Gmtr2REQSkJJeC8vO6MBV4wcxc3EZSzfsjDocERGJoaQXB1efNpCs9FTunKPSnohIIlHSi4PcTulcPm4Af/9wHas27446HBERCSnpxcnXJgwiLTWFu+asiDoUEREJKenFSc+cDC47uR9PvV/Kuu17ow5HRERQ0our684cgjvc/erHUYciIiIo6cVVQddMPju6gEff/YRNOyuiDkdEJOkp6cXZ9ROHUFVTy72vq7QnIhI1Jb04G5zXmQuO78PDb61m+57KqMMREUlqSnqtYNqkIeyurOGBN1dFHYqISFJT0msFx/bK4Zzh+dz/xip2VVRHHY6ISNJS0msl3zirkPK9VTzy9uqoQxERSVpKeq3kxH5dOX1oD+55bSX7qmqiDkdEJCkp6bWiaZMK2byrgsffWxN1KCIiSUlJrxWdMqgbRQNy+cMrK6isro06HBGRpKOk14rMjGlnFbKufB9/fX9t1OGIiCQdJb1WNvGYPEYV5HDnnBKqa1TaExFpTXFLemY2zMw+iHnsMLObzKybmb1oZsvD59yYeW4xsxIzW2pm58YrtiiZGd+YVMiqLXt4bsH6qMMREUkqcUt67r7U3U909xOBk4A9wNPAzcAsdx8KzAqHMbMRwGXASOA84E4zS41XfFGaMqIXQ3t25s7ZK6it9ajDERFJGq1VvXk2sMLdVwMXATPC8TOAi8PXFwGPuXuFu68ESoCxrRRfq0pJMW6YNISlZTt5aUlZ1OGIiCSN1kp6lwGPhq/z3X09QPjcMxxfAMT25S8Nx7VLnz6+D/27ZXHH7BLcVdoTEWkNcU96ZpYOfAb48+EmbWDcQdnAzKaaWbGZFW/atKklQoxEWmoK108cwvzScl5bvjnqcEREkkJrlPQ+Bcxz97p6vDIz6w0QPm8Mx5cC/WLm6wusq78wd7/b3YvcvSgvLy+OYcffJWMK6JWTwR2zS6IORUQkKbRG0vsS/6zaBHgGuDJ8fSXwt5jxl5lZRzMbBAwF3m2F+CLTMS2VqWcM5t2VW3l35daowxERaffimvTMLAuYDDwVM/pWYLKZLQ/fuxXA3RcBTwCLgeeBae7e7i9S+aWx/eneKV2lPRGRVhDXpOfue9y9u7uXx4zb4u5nu/vQ8HlrzHs/c/ch7j7M3f8Rz9gSRWZ6KtecPohXl21ifun2qMMREWnXdEWWBPCVcQPIyUhjukp7IiJxpaSXALIzOnDVaQN5YVEZy8p2Rh2OiEi7paSXIK4eP4is9FSV9kRE4khJL0Hkdkrn8nED+PuH61i1eXfU4YiItEtKegnkaxMGkZaawu9fWRF1KCIi7ZKSXgLpmZPBF4v68Zd5pazbvjfqcERE2h0lvQRz3ZmDcYe7X/046lBERNodJb0E0zc3i8+OLuDRdz9h086KqMMREWlXlPQS0PUTh1BVU8t9r6+MOhQRkXZFSS8BDc7rzPnH9ebht1dTvqcq6nBERNoNJb0ENW1SIbsqqnngzVVRhyIi0m4o6SWo4b1zOGd4Pn98YyW7KqqjDkdEpF1Q0ktg3zirkPK9VTzy9uqoQxERaReU9BLYif26MqGwB/e8tpJ9Ve3+LksiInGnpJfgpk0qZPOuCp4oXhN1KCIibZ6SXoIbN7gbRQNy+f2cFVRW10YdjohIm6akl+DMjGlnFbKufB9/fX9t1OGIiLRpSnptwMRj8hhVkMNdr6ygptajDkdEpM1S0msDzIxpEwtZuXk3zy1YH3U4IiJtlpJeG3HuyF4U9uzM9JdLqFVpT0TkiCjptREpKca0SUNYWraTl5aURR2OiEibpKTXhnz6+D7075bF9NkluKu0JyLSXEp6bUhaagpfP3MIH5aW83rJ5qjDERFpc5T02pjPnVRAr5wM7ni5JOpQRETaHCW9NqZjWipTzxjMOyu38t6qrVGHIyLSpijptUFfGtuf7p3SVdoTEWkmJb02KDM9la9OGMQryzaxoLQ86nBERNoMJb026iunDiA7I43ps1XaExFpKiW9NionowNXnzaQ5xdtYFnZzqjDERFpE5T02rCrxw8iKz2VO1XaExFpEiW9Niy3UzpfPqU/z3y4jtVbdkcdjohIwlPSa+OuPX0waakp/P6VFVGHIiKS8JT02rieORl8sagfT84tZd32vVGHIyKS0OKa9Mysq5k9aWYfmdkSMzvVzLqZ2Ytmtjx8zo2Z/hYzKzGzpWZ2bjxja0+uO3Mw7nD3qx9HHYqISEKLd0nvN8Dz7n4scAKwBLgZmOXuQ4FZ4TBmNgK4DBgJnAfcaWapcY6vXeibm8XFowt47L1P2LyrIupwREQSVtySnpnlAGcA9wG4e6W7bwcuAmaEk80ALg5fXwQ85u4V7r4SKAHGxiu+9ub6iUOoqK7lvtdXRh2KiEjCimdJbzCwCbjfzN43s3vNrBOQ7+7rAcLnnuH0BcCamPlLw3HSBEPyOnPBcb156K3VlO+pijocEZGEFM+klwaMAe5y99HAbsKqzEZYA+MOummcmU01s2IzK960aVPLRNpOTJtUyK6Kah54c1XUoYiIJKR4Jr1SoNTd3wmHnyRIgmVm1hsgfN4YM32/mPn7AuvqL9Td73b3IncvysvLi1vwbdHw3jmcM7wn97+5kt0V1VGHIyKScOKW9Nx9A7DGzIaFo84GFgPPAFeG464E/ha+fga4zMw6mtkgYCjwbrzia6+mTSpk+54qHnlnddShiIgknLSmTmhmFxD0rMyoG+fuPznMbDcCj5hZOvAxcDVBon3CzK4BPgE+Hy5rkZk9QZAYq4Fp7l7TjG0RYHT/XCYU9uDuV1dyxakDyeigDrAiInWaVNIzs98DXyRIYkaQqAYcbj53/yCsijze3S92923uvsXdz3b3oeHz1pjpf+buQ9x9mLv/4wi3KelNm1TI5l0VPFG85vATi4gkkaZWb57m7lcA29z9x8CpHNj+Jglk3OBunDQglz+88jFVNbVRhyMikjCamvTqrm+1x8z6AFXAoPiEJEfLzPjGpELWbt/L0++vjTocEZGE0dSk96yZdQVuA+YBq4DH4hWUHL2Jw/IY2SeHu+asoKb2oDM/RESSUpOSnrv/1N23u/tfCNryjnX3/4hvaHI06kp7Kzfv5rkF66MOR0QkIRyy96aZneXuL5vZJQ28h7s/Fb/Q5GidO7IXhT07c+fsEi48rjcpKQ2d/y8ikjwOV9I7M3z+dAOPC+MYl7SAlBTjholD+GjDTmZ9tPHwM4iItHOHLOm5+w/Dlz8JLwK9X3gCuSS4z5zQh1+9tIw7ZpdwzvCemKm0JyLJq6kdWf7SwLgnWzIQiY+01BSuP7OQD9ds5/WSzVGHIyISqcO16R1LcBWWLvXa9XKIuTKLJLbPnVTArMUbSDFj7bY9pKel0r1Tutr4RCTpHO4yZMMI2u66ErTj1dkJXBuvoKRldUhJ4RtnD+XGR9+ndNte+uZmcs8VRQzLz1biE5Gkcrg2vb+Z2bPA9939v1spJmlhW3ZX7k94AKXb9nLtg8U8fcN48rI7RhydiEjrOWybXnjR58mtEIvESWV1zf6EV6d0214qq3U9bxFJLk29y8KbZnYH8DjBzWABcPd5cYlKWlR6Wip9czMPSHx9czPRhVpEJNk0+YLTBB1afgL8InzcHq+gpGV175TOPVcU0Tc3EwgS3v9eejzf/8uHzNb5eyKSRJpU0nP3SfEOROInJcUYlp/N0zeMp7K6hvS0VKpra9m+p5qvzniP70w+hhsmFqpTi4i0e029n16+md1nZv8Ih0eEN4GVNiIlxcjL7khBbhZ52R3p3SWTv1x/Ghed0IfbZy7j+kfmsquiOuowRUTiqqnVmw8ALwB9wuFlwE3xCEhaT2Z6Kr/64on8+wXDeWnJRi6e/gYfb9oVdVgiInHT1KTXw92fAGoB3L0aUNe/dsDM+Nrpg3noq2PZsquCi6a/wcsflUUdlohIXDQ16e02s+6AA5jZOKA8blFJqzutsAd/v3EC/btlcc2MYn47azm16t4pIu1MU5PevwLPAEPM7A3gQeDGuEUlkeibm8WTXw/a+X754jK+/rDa+USkfWnqTWTnEdxm6DTgOmCku8+PZ2ASjbp2vv+4cASzPgra+VaonU9E2ommlvQAxgInAGOAL5nZFfEJSaJmZlwzYRAPXTOWrbsrufiON5i1RO18ItL2NfWUhYcITkafAJwcPoriGJckgNOG9OCZb4xnQI+gne83L6mdT0TatqZehqwIGOHu+sdLMnXtfLc8tYBfvbSMRevK+cUXTiA7o0PUoYmINFtTqzcXAr3iGYgkrowOqfzyCyfwn2rnE5E2rsnn6QGLzewFM3um7hHPwCSxmBlfnTCIh685hW17qrj4jjd4abHa+USkbbGm1Fia2ZkNjXf3V1o8omYoKiry4uLiKENISmu37+W6h4pZuHYH3z7nGG48S9ftFJHEYWZz3b3BfidNveB0pMlNEktB10ye/Ppp/CBs51u4rpxfqp1PRNqAQ1ZvmtlOM9vRwGOnme1orSAl8WR0SOUXXziBH356BC9/tJGLpr9ByUa184lIYjtk0nP3bHfPaeCR7e45rRWkJCYz4+rxg3jka6dQvqeKixg2TDgAABsESURBVKe/wYtq5xORBNack9NFGjRucHeeuXECg3p04toHi/n1S8t0Pp+IJCQlPWkRBV0z+fPXT+WSMQX8+qXlTH1oLjv2VUUdlojIAZT0pMVkdEjlF58/gR99egSzlwbn86mdT0QSiZKetCgz4yq184lIgopr0jOzVWa2wMw+MLPicFw3M3vRzJaHz7kx099iZiVmttTMzo1nbBJf4wZ35+83TmBwXtDO96sX1c4nItFrjZLeJHc/MeZEwZuBWe4+FJgVDmNmI4DLgJHAecCdZpbaCvFJnPTpmskT153K58b05TezljP1oWK184lIpKKo3rwImBG+ngFcHDP+MXevcPeVQAnB7YykDcvokMrtnz+eH39mJHOWblI7n4hEKt5Jz4GZZjbXzKaG4/LdfT1A+NwzHF8ArImZtzQcJ22cmXHlaQMPaOd7YdGGqMMSkSQU76Q33t3HAJ8CppnZGYeYtqGLNx7UCGRmU82s2MyKN23a1FJxSis4JWznG5LXiesemssvZy5VO5+ItKq4Jj13Xxc+bwSeJqiuLDOz3gDh88Zw8lKgX8zsfYF1DSzzbncvcveivLy8eIYvcdCnayaPX3cql57Ul9++XMK1D6qdT0RaT9ySnpl1MrPsutfAFIL78j0DXBlOdiXwt/D1M8BlZtbRzAYBQ4F34xWfRCejQyq3XXo8P7loJK8s28TFd7xBycadUYclIkkgniW9fOB1M/uQIHk95+7PA7cCk81sOTA5HMbdFwFPAIuB54Fp7l4Tx/gkQmbGFacO5E/XjmPHviouukPtfCISf026n16i0v302of15Xv5+kNz+bC0nG+eVchN5xyj+/OJyBE71P30dEUWiVzvLkE73+fDdr6vPVhM+V6184lIy1PSk4SQ0SGV/730eH560UheXRacz7e8TO18ItKylPQkYZgZXzl1II9OHcfOfdVcPP0Nnl+odj4RaTlKepJwTh7Yjb/fOJ7C/Gy+/vBcfqHz+USkhSjpSULq3SWTx6eO4wtFffndyyVcM+M9tfOJyFFT0pOEldEhlf/53PH89OJRvLZ8Mxfd8TrL1M4nIkdBSU8SmpnxlXEDeHTqOHZV1PDZ6W/w/ML1UYclIm2Ukp60CScP7MazN05gaH42X394Hre/sJQatfOJSDMp6Umb0atLBo9fN44vFvXjjtklfE3tfCLSTEp60qZ0TEvl1s8dx39dPIrXS9TOJyLNo6QnbY6Zcfm4ATx6bdDOd/H0N/jHArXzicjhKelJm1UUtvMN65XN9Y/M47YXPlI7n4gckpKetGm9umTw2NRxXHZyP6bPXhGcz7dH7Xwi0jAlPWnzgna+4/nZZ0fxRslmvv+XD/lk6x7WbtvDpp0VupqLiOyXFnUAIi3ly6cMYHT/rpTvqeJf7nmb0m176ZubyT1XFDEsP1u3KxIRlfSkfcnrnMH3npxP6ba9AJRu28u1DxazZXdlxJGJSCJQ0pN2pbK6Zn/Cq1O6bS+bdu5jxz619YkkOyU9aVfS01Lpm5t5wLi+uZms2baXSbfN4ZF3VlNdUxtRdCISNSU9aVe6d0rnniuK9ie+uja9wT06MSSvM//v6YVc8NvXeW35pogjFZEomHvb7dlWVFTkxcXFUYchCaa21tmyu5LK6hrS01Lp3imdlBTD3Xl+4Qb++x9LWLN1L2cd25MfnD+cwp6dow5ZRFqQmc1196IG31PSk2Szr6qGB95cxR0vl7CvqobLxw3gpnOG0jUrPerQRKQFHCrpqXpTkk5Gh1S+fuYQ5nxvIl84uR8PvrWKM2+bw/1vrKRK7X0i7ZqSniStHp078t+fPY7/+9bpHFfQhR//fTHn/vpVZi0poy3XgIhI45T0JOkd2yuHh64Zy31XFoHDNTOK+cp97/LRhh1RhyYiLUxJT4Tgzg1nD8/nhW+fwQ8/PYIFa8s5/zev8YOnF7B5V0XU4YlIC1HSE4nRITWFq8cP4pXvTeSKUwfyxHtrmHTbHH7/ygoqqmuiDk9EjpKSnkgDumal86PPjOSFb5/B2EHduPUfHzH5l6/yjwXr1d4n0oYp6YkcwpC8ztx31ck8dM1YMjukcv0j8/ji3W+zoLQ86tBE5Ago6Yk0welD83jumxP42WdHsWLjLj4z/XW+88SHlO3YF3VoItIMSnoiTZSWmsKXTxnA7O9NZOoZg/n7h+uYeNscfjtrOXsr1d4n0hYo6Yk0U05GB2751HBe+tczmTgsj1++uIyzfjGHv76/VjesFUlwSnoiR6h/9yzuuvwkHp86ju6d07np8Q+45K43mbt6W9ShiUgjlPREjtIpg7vzzLQJ3Hbp8azbvpfP3fUmNz76PqXb9kQdmojUE/ekZ2apZva+mT0bDnczsxfNbHn4nBsz7S1mVmJmS83s3HjHJtJSUlKMzxf1Y/Z3J/LNswqZuWgDZ//iFW574SN2VVRHHZ6IhFqjpPctYEnM8M3ALHcfCswKhzGzEcBlwEjgPOBOM0tthfhEWkynjmn865RhzP7uRD41qhfTZ69g0u1zeOK9NdSovU8kcnFNembWF7gAuDdm9EXAjPD1DODimPGPuXuFu68ESoCx8YxPJF76dM3k15eN5ukbTqNvbib/9pf5fPp3r/PWii1RhyaS1OJd0vs18G9A7P1a8t19PUD43DMcXwCsiZmuNBwn0maN7p/LU9efxm+/NJrteyr50j1vM/XBYlZt3h11aCJJKW5Jz8wuBDa6+9ymztLAuIPqg8xsqpkVm1nxpk2bjipGkdZgZnzmhD68/N2JfHfKMbxespnJv3qFnz23mPK9VVGHJ5JU4lnSGw98xsxWAY8BZ5nZw0CZmfUGCJ83htOXAv1i5u8LrKu/UHe/292L3L0oLy8vjuGLtKyMDql846yhzPnuRD47uoB7X1/JpNvn8NDbq6nWzWtFWkXckp673+Lufd19IEEHlZfd/XLgGeDKcLIrgb+Fr58BLjOzjmY2CBgKvBuv+ESi0jMng/+99AT+/o0JDO3Zmf/460LO/+1rvLJMNRci8RbFeXq3ApPNbDkwORzG3RcBTwCLgeeBae6uaztJuzWqoAuPTR3H7y8/iYrqWq7847tcdf+7lGzcGXVoIu2WteXbpBQVFXlxcXHUYYgctYrqGma8uYrfzSphT1UNl5/Sn5vOOYbcTulRhybS5pjZXHcvaug9XZFFJAF0TEtl6hlDmPO9iXxpbD8eens1Z942m3tf+5jKarX3ibQUJT2RBNK9c0f+6+LjeP6mMzihX1f+67klnPvrV5m5aINuXivSApT0RBLQMfnZPPjVsdx/1cmkGEx9aC5fvvcdFq/bEXVoIm2akp5IgjIzJh3bk+dvOoMff2Yki9fv4ILfvcbNf5nPpp0VUYcn0iapI4tIG1G+p4rfvrycGW+uIqNDKjdMGsJXxw8iPTWFLbsrqayuIT0tle6d0klJaehaDyLJ4VAdWZT0RNqYjzft4r//7yNeWlLGlBE9mTZpKNP+NI/SbXvpm5vJPVcUMSw/W4lPkpaSnkg79EbJZmpqnR88vYDSbXv3j++bm8nTN4wnL7tjhNGJROdQSS+ttYMRkZYxvrAHpdv2HJDwAEq37WXr7gpqap1eXTIiik4kMSnpibRhHdNS6ZubeVBJb9WWPVz30Guc0LcLU0b2YsqIfAp7dsZMVZ6S3FS9KdKG1dY6S8t2cu2DxQe06WWlp/Ls/PXMXFzGh2u2AzCwe9b+BDi6fy6pavOTdkpteiLtWG2tH7L35obyfby4pIwXF5fx1orNVNU4PTqnc87wfCaPyGd8YQ8yOqRGuAUiLUtJT0QA2LGvijlLNzFz0QbmLN3EropqstJTOfOYPKaMzOesYfl0yeoQdZgiR0VJT0QOUlFdw9sfb2Xmog28uLiMjTsrSE0xThnUjSkj8pk8shcFXTOjDlOk2ZT0ROSQamudD0u38+LiMmYuLqNk4y4ARhXkMHl4L6aMzOfYXtnqCCNtgpKeiDTLik27ggS4aAPvr9mOO/TrlsmUEb2YPCKfogG5pKXqKoaSmJT0ROSIbdy5j1lLNjJz0QbeKNlCZU0tuVkdOHt4PlNG5HP60Dwy09URRhKHkp6ItIhdFdW8uizoCDPro43s3FdNRocUTh+ax5QR+Zw9PJ9uuvGtRExXZBGRFtG5YxrnH9eb84/rTVVNLe98vJWZi4OOMC8uLiPF4OSB3Zg8Ip9zR/aiX7esqEMWOYBKeiJy1NydhWt3MHPxBmYuKmNp2U4Aju2Vvf+E+JF9ctQRRlqFqjdFpFWt3rI77AhTRvHqrdQ6FHTNZPKIoB3w5EHd6KCOMBInSnoiEpktuyqY9dFGZi4q47Xlm6iorqVLZgfOOrYnU0bkc8YxeXTqqJYWaTlKeiKSEPZUVvPqss3MXLyBlz/ayPY9VaSnpXB6YQ+mjAw6wvTorFsiydFRRxYRSQhZ6WmcN6oX543qRXVNLe+t2ra/HXDWRxsxW8BJ/XOZMjKfySN6MahHp6hDlnZGJT0RiZy7s3j9jv3tgIvX7wBgaM/OTBmZz5QRvTiuoAvAIS+uLQKq3hSRNmbN1j37T4N4d9VWamqds4flcf2kQm56/IMDbqM0LD9biU8OoKQnIm3Wtt2VvPzRRvrmZvKdP3940A1z/3D5SeyurGFEnxw6q0OMoDY9EWnDcjul87mT+rJ2254DEh5A6ba97Kqo5ot3vw3A4B6dGNEnh1EFXRjVpwsj++SQqyvESAwlPRFpE9LTUumbm3lQSa9ftyzuv+pkFq4tZ+G6ct7/ZDvPzl+/f5qCrpmMKshhVJ8ujCrowsiCHHpmZ0SxCZIAlPREpE3o3imde64o4toHiw9o0+uVk0GfrplMOrbn/mm37a5k0bodLFxXzsK15Sxat4MXFpXtf79ndsewNJjDyIIgGfbpkqErxiQBtemJSJtRW+tH3Htz574qFq/bsT8ZLlq7g+Ubd1Ib/gXmZnVgVEGXoHo0LBUO6JalTjJtkNr0RKRdSEkx8rKP7OT17IwOnDK4O6cM7r5/3N7KGj7asIOF63awKKwe/ePrK6mqCTJh545pMUkwaCsc3KOT7iXYhinpiUjSykxPZXT/XEb3z90/rrK6lmVlO1m0rpyFa4NS4Z/eXc2+qloAMjqkMLz3PxPhyD5dOCY/m/Q0JcK2QNWbIiKHUV1Ty8ebdwedZcJEuHjdDnZVVAPQIdUY1is76DFaEPQaHd4rRzfXjYjO0xMRaWG1tc4nW/eEnWV2hCXDcrbtqQIgxaCwZ+f9iXBUnxxG9MkhO6NDxJG3f5EkPTPLAF4FOhJUoz7p7j80s27A48BAYBXwBXffFs5zC3ANUAN8091fONQ6lPREJJG4O+vK9wU9RteWs3DdDhauLWfjzor90wzq0YmRTTiX8Gg67SS7qJKeAZ3cfZeZdQBeB74FXAJsdfdbzexmINfdv29mI4BHgbFAH+Al4Bh3r2lsHUp6ItIWbNyxL+g1GnaWWbh2B2u3//N8w/rnEh7fN4eNOysPOj1Dl1xrmkh6b3qQTXeFgx3ChwMXARPD8TOAOcD3w/GPuXsFsNLMSggS4FvxilFEpDX0zMmgZ07GQecSLl5flwiD3qN15xL+4Ssn8dNnF+8/Eb90216ufbCYp28Yf8S9VyUQ196bZpYKzAUKgenu/o6Z5bv7egB3X29mdXtBAfB2zOyl4bj6y5wKTAXo379/PMMXEYmb3E7pjC/swfjCHvvH7dxXxZL1O8nJTGvwkmufbN3DL19cyuj+uZw0IJfBPTrphPpmimvSC6smTzSzrsDTZjbqEJM39M0dVPfq7ncDd0NQvdkigYqIJIDsjA6MHdSNTTsrGrzkWmV1Dc/NX8+j764BoGtWB8b0z2VM/66MGZDLCX276i70h9Eqn467bzezOcB5QJmZ9Q5Leb2BjeFkpUC/mNn6AutaIz4RkUTS2CXXhuVn88F/TmHFpl3M+2Qb81ZvZ+4n23j5o+BvNMVgeO8cxoQlwTH9c+nXLVOlwRjx7MiSB1SFCS8TmAn8D3AmsCWmI0s3d/83MxsJ/Il/dmSZBQxVRxYRSUbN6b1ZvqeKeWu28f7qbcz9ZBsffLKd3ZXBX2ePzh0Z079rkAQH5HJcQRcyOrTv8wejugxZb2BG2K6XAjzh7s+a2VvAE2Z2DfAJ8HkAd19kZk8Ai4FqYNqhEp6ISHvWnEuudcnqwKRhPZk0LOgiUVPrLN2wMywNbmPeJ9uYuTjoJNMh1RjRpwsn9c9lzIAgGfbukhm37Ug0OjldRCQJbN5VwfufbGdumATnl27ff2m13l0yGBNWh540IJcRvXPa9GXVdMFpEZEk16NzRyaPyGfyiHwAqmpqWbJ+B/NWb2PuJ9uZt3obz4X3IeyYlsLxfbsEnWTCZNheTpVQSU9ERAAo27EvSIJhaXDh2h1U1gSlwf7dsvb3Eh3TP5dje2Un7N0mdO1NERFptorqGhau3bG/XXDu6m37L6mWlZ7KCX277m8XHN0vt8HLqUVB1ZsiItJsHdNSOWlA0M4HwbVF127fy9zV2/a3D/7+lY+pCe/EOziv0wGnSwzt2TnhLpumkp6IiByxPZXVzC8tj+kpup2tuysByM5I48R+XfcnwRP7dyWngbtMtPTFtVXSExGRuMhKT2Pc4O6MC+9I7+6s2rLngCrR385aTq2DGRzTMztsFwyS4YBuWSzbuKvVLq6tkp6IiMTVzn1VfLimfH8SfP+TbezYF9yA974ri/jhM4sOuuTa0VxcWyU9ERGJTHZGByYM7cGEocHFtWtrff+l1HrmZDR4ce3K6vhcm0RJT0REWlVKijE0P5uh+dmNXlw7PS0+l0pLzJMsREQkKdRdXLtvbnAptLo2ve5xOv1BJT0REYlMSooxLD+bp28Y32K9Nw9FSU9ERCLVnItrH/W6WmUtIiIiCUBJT0REkoaSnoiIJA0lPRERSRpKeiIikjSU9EREJGko6YmISNJQ0hMRkaTRpu+yYGabgNUtsKgewOYWWE5boG1tv5Jpe7Wt7VNLbesAd89r6I02nfRaipkVN3YbivZG29p+JdP2alvbp9bYVlVviohI0lDSExGRpKGkF7g76gBakba1/Uqm7dW2tk9x31a16YmISNJQSU9ERJJGUic9M+tnZrPNbImZLTKzb0UdU7yYWYaZvWtmH4bb+uOoY4o3M0s1s/fN7NmoY4knM1tlZgvM7AMzK446nngys65m9qSZfRT+bk+NOqZ4MbNh4Xda99hhZjdFHVe8mNm3w/+mhWb2qJllxGU9yVy9aWa9gd7uPs/MsoG5wMXuvjji0FqcmRnQyd13mVkH4HXgW+7+dsShxY2Z/StQBOS4+4VRxxMvZrYKKHL3dn8ul5nNAF5z93vNLB3IcvftUccVb2aWCqwFTnH3ljg3OaGYWQHBf9IId99rZk8A/+fuD7T0upK6pOfu6919Xvh6J7AEKIg2qvjwwK5wsEP4aLdHPGbWF7gAuDfqWKRlmFkOcAZwH4C7VyZDwgudDaxojwkvRhqQaWZpQBawLh4rSeqkF8vMBgKjgXeijSR+wuq+D4CNwIvu3m63Ffg18G9AbdSBtAIHZprZXDObGnUwcTQY2ATcH1Zb32tmnaIOqpVcBjwadRDx4u5rgduBT4D1QLm7z4zHupT0ADPrDPwFuMndd0QdT7y4e427nwj0Bcaa2aioY4oHM7sQ2Ojuc6OOpZWMd/cxwKeAaWZ2RtQBxUkaMAa4y91HA7uBm6MNKf7CatzPAH+OOpZ4MbNc4CJgENAH6GRml8djXUmf9ML2rb8Aj7j7U1HH0xrCKqE5wHkRhxIv44HPhG1djwFnmdnD0YYUP+6+LnzeCDwNjI02orgpBUpjaiieJEiC7d2ngHnuXhZ1IHF0DrDS3Te5exXwFHBaPFaU1Ekv7NxxH7DE3X8ZdTzxZGZ5ZtY1fJ1JsJN9FG1U8eHut7h7X3cfSFAt9LK7x+WoMWpm1inshEVY1TcFWBhtVPHh7huANWY2LBx1NtDuOp014Eu046rN0CfAODPLCv+XzyboY9Hi0uKx0DZkPPAVYEHY1gXwA3f/vwhjipfewIywF1gK8IS7t+uu/EkiH3g6+J8gDfiTuz8fbUhxdSPwSFjl9zFwdcTxxJWZZQGTgeuijiWe3P0dM3sSmAdUA+8Tp6uzJPUpCyIiklySunpTRESSi5KeiIgkDSU9ERFJGkp6IiKSNJT0REQkaSjpiRwlM3Mz+0XM8HfN7EctsNyOZvZSeIX9Lzbw/nfDuw0sDO+ecUU4fo6ZFR3B+k40s/OPNm6RRKakJ3L0KoBLzKxHCy93NNDB3U9098dj3zCzrxOcvzXW3UcRXIjZjnJ9JwLNSnrhxYFF2gwlPZGjV01wIu23679hZgPMbJaZzQ+f+zcwTTcz+2s4zdtmdryZ9QQeBk4MS3pD6s32A+CGumvFunu5u89oYNm7Yl5famYPhK8/H1NCfDU82fsnwBfrSpbh1V7+aGbvhRd4viic9yoz+7OZ/Z3gQte9w2V8EC7z9CP7GEXiT0lPpGVMB75sZl3qjb8DeNDdjwceAX7bwLw/Bt4Pp/lBOP1G4GsE94470d1X1E0cXnYsO3bcEfhP4Fx3PwH4jLtXhuMejylZ/j+CS7idDEwCbou5q8GpwJXufhbwL8AL4cXMTwA+qL8ykUShpCfSAsIS14PAN+u9dSrwp/D1Q8CEBmafEL6Hu78MdG8gecYyjv5eiG8AD5jZtUBqI9NMAW4OL9E3B8gA6kqqL7r71vD1e8DVYTvmceG9KUUSkpKeSMv5NXANcKh7vDWUrBpqi2s0qYUJdreZDW5CTLHLyYhZxteBfwf6AR+YWfdG4vpcWPI70d37u3vdRYB3xyzrVYI2xbXAQ3UdakQSkZKeSAsJSz5PECS+Om8S3OkB4MvA6w3M+mr4HmY2EdjchPs6/hyYHt5NHDPLaeQGsmVmNtzMUoDP1o00syHu/o67/yewmSD57QSyY+Z9AbgxvOo9Zja6oUDMbADB/QvvIbhrSTLc7kfaKPW8EmlZvwC+ETP8TeCPZvY9grt+N3RXgB8R3A18PrAHuLIJ67kL6Ay8Z2ZVQFW47vpuBp4F1hDccqhzOP42MxtKUJqbBXxIcHuXuurMnwM/JSi9zg8T3yrgwgbWMRH4XhjHLkAlPUlYusuCiIgkDVVviohI0lDSExGRpKGkJyIiSUNJT0REkoaSnoiIJA0lPRERSRpKeiIikjSU9EREJGn8f/UGuj4OkULpAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 504x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "## Finding the Optimal Number of Clusters\n",
    "\n",
    "### SSD\n",
    "\n",
    "# elbow-curve/SSD\n",
    "ssd = []\n",
    "range_n_clusters = [2, 3, 4, 5, 6, 7, 8]\n",
    "for num_clusters in range_n_clusters:\n",
    "    kmeans = KMeans(n_clusters=num_clusters, max_iter=50)\n",
    "    kmeans.fit(df_scaled)\n",
    "    \n",
    "    ssd.append(kmeans.inertia_)\n",
    "    \n",
    "# plot the SSDs for each n_clusters\n",
    "# ssd\n",
    "\n",
    "# plt.plot(ssd)\n",
    "plt.figure(figsize=(7,5))\n",
    "ax= sns.lineplot(y=ssd,x= range_n_clusters,marker='o')\n",
    "ax.set(xlabel='No of Clusters', ylabel='Inertia');\n",
    "plt.suptitle(\"Plot of Elbow curve for different cluster numbers\",fontsize=14);"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "###### Observation\n",
    "From the above graph the inertia reduces sharply at 3 clusters or in other words the elbow of this graph is at 3 hence we will choose 3 or 4 as the number of clusters.\n",
    "Below the silhoutte analysis is done to cross verify the number of clusters required for this model."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Silhouette Analysis\n",
    "\n",
    "$$\\text{silhouette score}=\\frac{p-q}{max(p,q)}$$\n",
    "\n",
    "$p$ is the mean distance to the points in the nearest cluster that the data point is not a part of\n",
    "\n",
    "$q$ is the mean intra-cluster distance to all the points in its own cluster.\n",
    "\n",
    "* The value of the silhouette score range lies between -1 to 1. \n",
    "\n",
    "* A score closer to 1 indicates that the data point is very similar to other data points in the cluster, \n",
    "\n",
    "* A score closer to -1 indicates that the data point is not similar to the data points in its cluster."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "For n_clusters=2, the silhouette score is 0.47836991760560543\n",
      "For n_clusters=3, the silhouette score is 0.4224078601707397\n",
      "For n_clusters=4, the silhouette score is 0.4081398018579549\n",
      "For n_clusters=5, the silhouette score is 0.4145948046409389\n",
      "For n_clusters=6, the silhouette score is 0.31574306499482896\n",
      "For n_clusters=7, the silhouette score is 0.30011016450591443\n",
      "For n_clusters=8, the silhouette score is 0.32027088751175253\n"
     ]
    }
   ],
   "source": [
    "# silhouette analysis\n",
    "range_n_clusters = [2, 3, 4, 5, 6, 7, 8]\n",
    "silhouette_avg_list=[]\n",
    "for num_clusters in range_n_clusters:\n",
    "    \n",
    "    # intialise kmeans\n",
    "    kmeans = KMeans(n_clusters=num_clusters, max_iter=50)\n",
    "    kmeans.fit(df_scaled)\n",
    "    \n",
    "    cluster_labels = kmeans.labels_\n",
    "    \n",
    "    # silhouette score\n",
    "    silhouette_avg = silhouette_score(df_scaled, cluster_labels)\n",
    "    print(\"For n_clusters={0}, the silhouette score is {1}\".format(num_clusters, silhouette_avg))\n",
    "    silhouette_avg_list.append(silhouette_avg)\n",
    "    "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 504x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# plt.plot(ssd)\n",
    "plt.figure(figsize=(7,5))\n",
    "ax= sns.lineplot(y=silhouette_avg_list,x= range_n_clusters,marker='o')\n",
    "ax.set(xlabel='No of Clusters', ylabel='Silhouette Score');\n",
    "plt.suptitle(\"Plot of Silhouette Score for different cluster numbers\",fontsize=14);"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "###### Observation \n",
    "The sillhoutte score reduces with 6 clusters hence reassuring that 3 or 4 clusters is the most appropriate number of clusters"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Kmeans method with number of clusters = 3"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "KMeans(max_iter=50, n_clusters=3, random_state=0)"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# final model with k=3\n",
    "chosen_clusters =3\n",
    "kmeans = KMeans(n_clusters=chosen_clusters, max_iter=50,random_state=0)\n",
    "kmeans.fit(df_scaled)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "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>cluster_id</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\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>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\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>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\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>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\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>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\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>2</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  cluster_id  \n",
       "0       9.44        56.2       5.82    553.0           0  \n",
       "1       4.49        76.3       1.65   4090.0           2  \n",
       "2      16.10        76.5       2.89   4460.0           2  \n",
       "3      22.40        60.1       6.16   3530.0           0  \n",
       "4       1.44        76.8       2.13  12200.0           2  "
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# assign the label\n",
    "df['cluster_id'] = kmeans.labels_\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.title(\"Number of countries in each cluster with Kmeans method with 3 Clusters\",fontsize=14)\n",
    "sns.countplot(df['cluster_id']);"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 504x504 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# plot\n",
    "plt.figure(figsize=(7,7))\n",
    "plt.title(\"Boxplot of child mortality for 3 clusters got by Kmeans\",fontdict={\"size\":16})\n",
    "sns.boxplot(x='cluster_id',y=\"child_mort\", data=df);"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##### Observation\n",
    "Cluster 0 has the highest child mortality rate. There is a slight overlap of clusters 1 and 2 and the whiskers of cluster 0 and 2. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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6Oum9XE8ahPRe0gjnB0jJ9PPAxZK+Q/ou/ANlreZNIekC4DnSAcUTeXkf57VzobVTj9FCY+FG9dGtT5C6C4+sMv+c/Aa/SEqOVwP75OdeT9qR/BwYV1ZnO9IJ7LvIP8Mgjxoknfu8h5Qs7gc+XLG+Mykb3ZrLdiSNEltFaj0+APwteQRZ2XxfIn2YS0eCrYO8DhNIO6CHSMnroTw9oWK+YY1uLZv//yElyofyNj5POp/xr1SMvCXtaEvvwTpSF/dtpKv1vLHKss9kE0YmVtTdaHRrLt8H+K/83r5EarnM2ZL15m29m7RzeJ60Y/4XYLdh1v9z0uCh/vwa/gH4RtnzF1duCynhXUM6Z/UY8E+kA6ny0a3vI50XWpWX+whp1OobypbzZ6RE9lyue2PFOi7Mn7FX8nK6geOHeq1IB2FX53W+nOv+J/m7NMTrMYv0XSq9Fp8mHYTcWTHfVNL5ztLrtqw8tjzPeFIvxROkJBFDrDuAs0gDxPryZ+QXFI8G/Uiu875N+GwGsLBK+Qk5xsWk7+vFQN9w6lNltDupR+pzpO/jS6TP/G9JLfqdyuY7hfTTjhdJ38f3kPZf5Z+Fw/Ly31Ow3tJnriPXfSK/Jw+SDi42aZTz5txKw2ttlFD6UXZLRLyj3rGYjWa5O74X+ElEnFjveMpJuozUFfymKD6FYluBu1vNrClIOpd0SuBR0mCuz5G6eguvQrS15VMGB5LOZ3/BCbL+nCTNrFlsB5zNayPBbyV1823yufMaupnUtd5J+s2k1Zm7W83MzAr4JyBmZmYFmq67dfLkydHa2lrvMMzMrIHcfvvt/RGx0RXLmi5Jtra20tMz1M+ZzMysmSj9V+tG3N1qZmZWwEnSzMysgJOkmZlZASdJMzOzAk6SZmZmBZwkzczMCjhJmpmZFXCSNDMzK1DTJCnpbyUtl3SPpO9L2k7SrpK6Ja3I97uUzX+apF5J90s6sqz8IEl35+cWSVIu31bSFbn8lvJ/hjczM9tSNUuSkqaR/q28PSL2J/1J6TzgVGBpRMwAluZpJO2bn9+P9AfF50kanxd3Pukf6Wfk25xcfiLwVES0kf6A8+xabY+ZmTWfWne3tgATJbUAryP9j9vRpL+BId8fkx8fDVweES9HxIOkP0M9WNJU0r9P3xzpL0suqahTWtZVwBGlVqaZmdmWqlmSjIg/Al8DHgZWAc9ExBJg94hYledZBeyWq0wDHilbRF8um5YfV5ZvUCci1gLPAJNqsT1mZtZ8atnduguppbcX6V/At5d0/GBVqpTFIOWD1amMZb6kHkk9q1evHjxwsybwwAMPMHfuXHp7e+sdillDq2V363uAByNidUQMAD8E/gp4PHehku+fyPP3AXuU1Z9O6p7ty48ryzeok7t0dwKerAwkIi6IiPaIaJ8yZaN/QjFrOgsXLuSFF17gK1/5Sr1DMWtotUySDwMzJb0unyc8ArgPWAx05Hk6gKvz48XAvDxidS/SAJ1bc5fsc5Jm5uWcUFGntKxjgevzeUszK/DAAw+wcuVKAFauXOnWpNkganlO8hbSYJo7gLvzui4AvgrMkrQCmJWniYjlwJXAvcB1wMkRsS4v7iTgQtJgnt8DXbn8ImCSpF7gC+SRsmZWbOHChRtMuzVpVkzN1vBqb28P/+myNbNDDz10o7KbbrqpDpGYNQ5Jt0dEe2W5r7hj1mRaW1sHnTaz1zhJmjWZ008/fYPpM844o06RmDU+J0mzJrP33nu/2npsbW2lra2tvgGZNTAnSbMmdPrpp7P99tu7FWk2hJZ6B2BmW9/ee+9NV1fX0DOaNTm3JM3MzAo4SZqZmRVwkjQzMyvgJGlmZlbASdLMzKyAk6SZmVkBJ0kzM7MCTpJmZmYFnCTNzMwKOEmaNaH+/n5OOeUU1qxZU+9QzBqak6RZE+rs7GTZsmV0dnbWOxSzhuYkadZk+vv76erqIiLo6upya9JsEE6SZk2ms7OTiABg/fr1bk2aDcJJ0qzJdHd3MzAwAMDAwABLliypc0RmjctJ0qzJzJo1iwkTJgAwYcIEZs+eXeeIzBqXk6RZk+no6EASAOPGjaOjo6POEZk1LidJsyYzefJk5s6diyTmzp3LpEmT6h2SWcNqqXcAZrb1dXR0sHLlSrcizYbgJGnWhCZPnsy5555b7zDMGp67W83MzAo4SZqZmRVwkjQzMyvgJGlmZlbASdLMzKyAk6SZmVkBJ0kzM7MCTpJmZmYFnCTNzMwKOEmaNaH+/n5OOeUU/+Gy2RCcJM2aUGdnJ8uWLfMfLpsNoWZJUtI+ku4quz0r6fOSdpXULWlFvt+lrM5pknol3S/pyLLygyTdnZ9bpPw/P5K2lXRFLr9FUmuttsdsrOjv76erq4uIoKury61Js0HULElGxP0RcWBEHAgcBPwJ+BFwKrA0ImYAS/M0kvYF5gH7AXOA8ySNz4s7H5gPzMi3Obn8ROCpiGgDvgGcXavtMRsrOjs7iQgA1q9f79ak2SC2VnfrEcDvI+Ih4Gig9K3sBI7Jj48GLo+IlyPiQaAXOFjSVGDHiLg50jf7koo6pWVdBRxRamWaWXXd3d0MDAwAMDAwwJIlS+ockVnj2lpJch7w/fx494hYBZDvd8vl04BHyur05bJp+XFl+QZ1ImIt8Azgf5A1G8SsWbOYMGECABMmTGD27Nl1jsiscdU8SUraBjgK+M+hZq1SFoOUD1anMob5knok9axevXqIMMzGto6ODkodLuPGjfMfL5sNYmu0JOcCd0TE43n68dyFSr5/Ipf3AXuU1ZsOPJrLp1cp36COpBZgJ+DJygAi4oKIaI+I9ilTpozIRpmNVpMnT2bu3LlIYu7cuUya5M4XsyJbI0l+hNe6WgEWA6VD1w7g6rLyeXnE6l6kATq35i7Z5yTNzOcbT6ioU1rWscD1URqRYGaFOjo6OOCAA9yKNBuCaplTJL2OdM7wTRHxTC6bBFwJ7Ak8DBwXEU/m574IfBJYC3w+IrpyeTtwMTAR6AJOiYiQtB1wKfBWUgtyXkT8YbCY2tvbo6enZ6Q31czMRjFJt0dE+0blzdbwcpI0M7NKRUnSV9wxMzMr4CRpZmZWwEnSzMysgJOkmZlZASdJMzOzAk6SZmZmBZwkzczMCjhJmpmZFXCSNDMzK+AkaWZmVsBJ0szMrICTpJmZWQEnSTMzswJOkmZmZgWcJM3MzAo4SZqZmRVwkjQzMyvgJGlmZlbASdLMzKyAk6SZmVkBJ0kzM7MCTpJmZmYFnCTNzMwKOEmamZkVcJI0MzMr4CRpZmZWwEnSzMysgJOkmZlZASdJMzOzAk6SZmZmBZwkzczMCjhJmpmZFXCSNDMzK+AkaWZmVsBJ0szMrEBNk6SknSVdJel3ku6T9N8k7SqpW9KKfL9L2fynSeqVdL+kI8vKD5J0d35ukSTl8m0lXZHLb5HUWsvtMTOz5lLrluQ5wHUR8RfAW4D7gFOBpRExA1iap5G0LzAP2A+YA5wnaXxezvnAfGBGvs3J5ScCT0VEG/AN4Owab4+ZmTWRmiVJSTsChwIXAUTEKxHxNHA00Jln6wSOyY+PBi6PiJcj4kGgFzhY0lRgx4i4OSICuKSiTmlZVwFHlFqZZmZmW6qWLck3AauB70i6U9KFkrYHdo+IVQD5frc8/zTgkbL6fblsWn5cWb5BnYhYCzwDTKoMRNJ8ST2SelavXj1S22dmZmNcLZNkC/A24PyIeCvwArlrtUC1FmAMUj5YnQ0LIi6IiPaIaJ8yZcrgUZuZmWW1TJJ9QF9E3JKnryIlzcdzFyr5/omy+fcoqz8deDSXT69SvkEdSS3ATsCTI74lZmbWlGqWJCPiMeARSfvkoiOAe4HFQEcu6wCuzo8XA/PyiNW9SAN0bs1dss9JmpnPN55QUae0rGOB6/N5SzMzsy3WUuPlnwJcJmkb4A/AX5MS85WSTgQeBo4DiIjlkq4kJdK1wMkRsS4v5yTgYmAi0JVvkAYFXSqpl9SCnFfj7TEzsyaiZmt4tbe3R09PT73DMDOzBiLp9ohoryz3FXfMzMwKOEmamZkVqPU5STPbBIsWLaK3t7fm6+nrSz89nj59+hBzbrm2tjYWLFhQ8/WY1YKTpFkTevHFF+sdgtmo4CRp1kC2VourtJ5FixZtlfWZjVY+J2lmZlbASdLMzKyAk6SZmVkBJ0kzM7MCTpJmZmYFnCTNzMwKOEmamZkVcJI0MzMr4CRpZmZWwEnSzMysgJOkmZlZASdJMzOzAk6SZmZmBZwkzczMCjhJmpmZFXCSNDMzK+AkaWZmVsBJ0szMrICTpJmZWQEnSTMzswJOkmZmZgWcJM3MzAo4SZqZmRVwkjQzMyvgJGlmZlbASdLMzKyAk6SZmVmBlnoHYDYaLFq0iN7e3nqHMWJWrFgBwIIFC+ocychpa2sbU9tjjcFJ0mwYent7eeCeO9hzh3X1DmVEbDOQOpFeWnlbnSMZGQ8/P77eIdgY5SRpNkx77rCO09ufr3cYVsXCnh3qHYKNUTU9JylppaS7Jd0lqSeX7SqpW9KKfL9L2fynSeqVdL+kI8vKD8rL6ZW0SJJy+baSrsjlt0hqreX2mJlZc9kaA3cOj4gDI6I9T58KLI2IGcDSPI2kfYF5wH7AHOA8SaU+lPOB+cCMfJuTy08EnoqINuAbwNlbYXvMzKxJ1GN069FAZ37cCRxTVn55RLwcEQ8CvcDBkqYCO0bEzRERwCUVdUrLugo4otTKNDMz21K1TpIBLJF0u6T5uWz3iFgFkO93y+XTgEfK6vblsmn5cWX5BnUiYi3wDDCpMghJ8yX1SOpZvXr1iGyYmZmNfbUeuHNIRDwqaTegW9LvBpm3WgswBikfrM6GBREXABcAtLe3b/S8mZlZNTVtSUbEo/n+CeBHwMHA47kLlXz/RJ69D9ijrPp04NFcPr1K+QZ1JLUAOwFP1mJbzMys+dQsSUraXtLrS4+B2cA9wGKgI8/WAVydHy8G5uURq3uRBujcmrtkn5M0M59vPKGiTmlZxwLX5/OWZmZmW6yW3a27Az/K42hagO9FxHWSbgOulHQi8DBwHEBELJd0JXAvsBY4OSJKv9w+CbgYmAh05RvARcClknpJLch5NdweMzNrMjVLkhHxB+AtVcrXAEcU1DkLOKtKeQ+wf5Xyl8hJ1szMbKT5AudmZmYFnCTNzMwKOEmamZkVcJI0MzMr4CRpZmZWwEnSzMysgJOkmZlZASdJMzOzAk6SZmZmBZwkzczMCtT6r7LMxoS+vj5eeG48C3t2qHcoVsVDz41n+76+oWc020RuSZqZmRVwS9JsGKZPn85La1dxevvz9Q7FqljYswPbTZ8+9Ixmm8gtSTMzswJOkmZmZgWcJM3MzAo4SZqZmRVwkjQzMyvgJGlmZlbASdLMzKyAk6SZmVkBJ0kzM7MCTpJmZmYFnCTNzMwKOEmamZkVcJI0MzMr4CRpZmZWwEnSzMysgJOkmZlZASdJMzOzAi3DmUnSdsDfAO8AAvglcH5EvFTD2MzMzOpqWEkSuAR4Djg3T38EuBQ4rhZBmZmZNYLhJsl9IuItZdM3SPptLQIyMzNrFMM9J3mnpJmlCUlvB35Vm5DMzMwaw3Bbkm8HTpD0cJ7eE7hP0t1ARMQBNYnOzMysjoabJOds7gokjQd6gD9GxPsl7QpcAbQCK4EPRcRTed7TgBOBdcCCiPhpLj8IuBiYCFwLfC4iQtK2pPOlBwFrgA9HxMrNjdXMzKzcsLpbI+IhYBJwNHAUMCkiHirdhqj+OeC+sulTgaURMQNYmqeRtC8wD9iPlJTPywkW4HxgPjAj30pJ+0TgqYhoA74BnD2c7TEzMxuOYSVJSWcAnaREORn4jqTTh1FvOvA+4MKy4qPzssj3x5SVXx4RL0fEg0AvcLCkqcCOEXFzRASp5XhMlWVdBRwhScPZJjMzs6EMt7v1I8BbS7+LlPRV4A5g4RD1vgn8L+D1ZWW7R8QqgIhYJWm3XD4N+E3ZfH25bCA/riwv1XkkL2utpGdIiby/PAhJ80ktUfbcc8+httXMzAwY/ujWlcB2ZdPbAr8frIKk9wNPRMTtw1xHtRZgDFI+WJ0NCyIuiIj2iGifMmXKMMMxM7NmN9yW5MvAckndpHjHursAABVcSURBVCQ0C/ilpEUAEbGgSp1DgKMkvZeUYHeU9F3gcUlTcytyKvBEnr8P2KOs/nTg0Vw+vUp5eZ0+SS3ATsCTw9wmMzOzQQ23Jfkj4O+BG4AbgS8CXcDt+baRiDgtIqZHRCtpQM71EXE8sBjoyLN1AFfnx4uBeZK2lbQXaYDOrblr9jlJM/P5xhMq6pSWdWxex0YtSTMzs80xrJZkRHQOPdewfRW4UtKJwMPkS9tFxHJJVwL3AmuBkyNiXa5zEq/9BKQr3wAuAi6V1EtqQc4bwTjNzKzJDZokSxcLKHp+uBcRiIgbSS1QImINcETBfGcBZ1Up7wH2r1L+Er5+rG0lDz8/noU9O9Q7jBHx+J9SJ9Lur1tf50hGxsPPj2fvegdhY9JQLcn35/uT8/2l+f5jwJ9qEpFZA2pra6t3CCPqlRUrANiudUadIxkZezP23iNrDBrOKTxJv4qIQ4YqGw3a29ujp6en3mGY1dWCBWms3aJFi+ociVljkHR7RLRXlg934M72kt5RtrBDgO1HKjgzM7NGNNyfgHySdJWdnUjnKJ8B/rpmUZmZmTWA4SbJw0iXf9sBeIGUKN8mKSLirhrFZmZmVlfD7W5tBz4D7Ai8gXSJt8OA/5D0v2oTmpmZWX0NtyU5CXhbRDwPIOlLpAuKH0q6mMD/rk14ZmZm9TPcluSewCtl0wPAGyPiRdIl68zMzMac4bYkvwf8RlLpcnAfAL4vaXvSFXLMzMzGnOFelu4fJV0LvIP0zxufyVfBgXRhATMzszFnuC1J8l9eDfdvr8zMzEa94Z6TNDMzazpOkmZmZgWcJM3MzAo4SZqZmRVwkjQzMyvgJGlmZlbASdLMzKyAk6SZmVkBJ0kzM7MCTpJmZmYFnCTNzMwKOEmamZkVcJI0MzMr4CRpZmZWwEnSzMysgJOkmZlZASdJMzOzAk6SZmZmBZwkzczMCjhJmpmZFXCSNDNrYg888ABz586lt7e33qE0JCdJM7MmtnDhQl544QW+8pWv1DuUhuQkaWbWpB544AFWrlwJwMqVK92arMJJ0sysSS1cuHCDabcmN1azJClpO0m3SvqtpOWSvpzLd5XULWlFvt+lrM5pknol3S/pyLLygyTdnZ9bJEm5fFtJV+TyWyS11mp7zMzGmlIrsmjaatuSfBl4d0S8BTgQmCNpJnAqsDQiZgBL8zSS9gXmAfsBc4DzJI3PyzofmA/MyLc5ufxE4KmIaAO+AZxdw+0xMxtTWltbB522GibJSJ7PkxPyLYCjgc5c3gkckx8fDVweES9HxINAL3CwpKnAjhFxc0QEcElFndKyrgKOKLUyzcxscKeffvoG02eccUadImlcNT0nKWm8pLuAJ4DuiLgF2D0iVgHk+93y7NOAR8qq9+WyaflxZfkGdSJiLfAMMKlKHPMl9UjqWb169UhtnpnZqLb33nu/2npsbW2lra2tvgE1oJomyYhYFxEHAtNJrcL9B5m9WgswBikfrE5lHBdERHtEtE+ZMmWosM3Mmsbpp5/O9ttv71ZkgZatsZKIeFrSjaRziY9LmhoRq3JX6hN5tj5gj7Jq04FHc/n0KuXldfoktQA7AU/WbEPMzMaYvffem66urnqH0bBqObp1iqSd8+OJwHuA3wGLgY48WwdwdX68GJiXR6zuRRqgc2vukn1O0sx8vvGEijqlZR0LXJ/PW5qZmW2xWrYkpwKdeYTqOODKiPixpJuBKyWdCDwMHAcQEcslXQncC6wFTo6IdXlZJwEXAxOBrnwDuAi4VFIvqQU5r4bbY2ZmTaZmSTIilgFvrVK+BjiioM5ZwFlVynuAjc5nRsRL5CRrZmabrr+/ny9/+cuceeaZTJq00bjHpucr7piZNbHOzk6WLVtGZ2fn0DM3ISdJM7Mm1d/fT1dXFxFBV1cXa9asqXdIDcdJ0sysSXV2dlIa67h+/Xq3JqtwkjQza1Ld3d0MDAwAMDAwwJIlS+ocUeNxkjQza1KzZs2ipSWN32xpaWH27Nl1jqjxOEmamTWpjo4O1q9fD6Tu1o6OjiFqNB8nSTMzswJOkmZmTaqzs5Nx41IaGDdunAfuVOEkaWbWpLq7u1m7di0Aa9eu9cCdKpwkzcya1KxZs5gwYQIAEyZM8MCdKpwkzcyaVEdHB6X/qR83bpwH7lThJGlm1qQmT57M3LlzkcTcuXN97dYqtsr/SZqZWWPq6Ohg5cqVbkUWcJI0M2tikydP5txzz613GA3L3a1mZmYFnCTNzMwKOEmamZkVcJI0MzMr4CRpZmZWwEnSzMysgJOkmZlZASdJMzOzAr6YgJlZA1q0aBG9vb01X09fXx8A06dPr/m62traWLBgQc3XM5KcJM3MmtiLL75Y7xAampOkmVkD2lotrtJ6Fi1atFXWN9r4nKSZmVkBJ0kzM7MCTpJmZmYFnCTNzMwKOEmamZkVcJI0MzMr4CRpZmZWwL+TNGsgW+sqKytWrAC2zm/xRuNVVsxKnCTNmtDEiRPrHYLZqOAkadZA3OIyayw1OycpaQ9JN0i6T9JySZ/L5btK6pa0It/vUlbnNEm9ku6XdGRZ+UGS7s7PLZKkXL6tpCty+S2SWmu1PWZm1nxqOXBnLfB3EfFmYCZwsqR9gVOBpRExA1iap8nPzQP2A+YA50kan5d1PjAfmJFvc3L5icBTEdEGfAM4u4bbY2ZmTaZmSTIiVkXEHfnxc8B9wDTgaKAzz9YJHJMfHw1cHhEvR8SDQC9wsKSpwI4RcXNEBHBJRZ3Ssq4Cjii1Ms3MzLbUVvkJSO4GfStwC7B7RKyClEiB3fJs04BHyqr15bJp+XFl+QZ1ImIt8Awwqcr650vqkdSzevXqkdkoMzMb82qeJCXtAPwA+HxEPDvYrFXKYpDywepsWBBxQUS0R0T7lClThgrZzMwMqHGSlDSBlCAvi4gf5uLHcxcq+f6JXN4H7FFWfTrwaC6fXqV8gzqSWoCdgCdHfkvMzKwZ1XJ0q4CLgPsi4utlTy0GOvLjDuDqsvJ5ecTqXqQBOrfmLtnnJM3Myzyhok5pWccC1+fzlmZmZluslr+TPAT4OHC3pLty2d8DXwWulHQi8DBwHEBELJd0JXAvaWTsyRGxLtc7CbgYmAh05RukJHyppF5SC3JeDbfHzMyaTM2SZET8kurnDAGOKKhzFnBWlfIeYP8q5S+Rk6yZmdlI8wXOzczMCjhJmpmZFXCSNDMzK+Ak2WT6+/s55ZRTWLNmTb1DMTNreE6STaazs5Nly5bR2dk59MxmZk3OSbKJ9Pf309XVRUTQ1dXl1qSZ2RCcJJtIZ2cnpWstrF+/3q1JM7MhOEk2ke7ubgYGBgAYGBhgyZIldY7IzKyxOUk2kVmzZjFhwgQAJkyYwOzZs+sckZlZY3OSbCIdHR2U/m5z3LhxdHR0DFHDzKy5OUk2kcmTJzN37lwkMXfuXCZN2uivN83MrEwtL3BuDaijo4OVK1e6FWlmNgxOkk1m8uTJnHvuufUOw8xsVHB3q5mZWQEnSTMzswJOkmZmZgWcJM3MzAo4SZqZmRVwkjQzMyvgJGlmZlbASdLMzKyAk6SZmVkBJ0kzM7MCTpJmZmYFnCTNzMwKOEmamZkVcJI0MzMr4CRpZmZWwEnSzMysgJOkmZlZgZZ6B2BmNlosWrSI3t7eeocxolasWAHAggUL6hzJyGlraxux7XGSNDMbpt7eXu5cfifsXO9IRtD6dHfnH++sbxwj5emRXZyTpJnZptgZ1h+2vt5RWIFxN47sWUSfkzQzMyvgJGlmZlagZklS0rclPSHpnrKyXSV1S1qR73cpe+40Sb2S7pd0ZFn5QZLuzs8tkqRcvq2kK3L5LZJaa7UtZmbWnGrZkrwYmFNRdiqwNCJmAEvzNJL2BeYB++U650kan+ucD8wHZuRbaZknAk9FRBvwDeDsmm2JmZk1pZolyYi4CXiyovhooDM/7gSOKSu/PCJejogHgV7gYElTgR0j4uaICOCSijqlZV0FHFFqZZqZmY2ErX1OcveIWAWQ73fL5dOAR8rm68tl0/LjyvIN6kTEWuAZYFK1lUqaL6lHUs/q1atHaFPMzGysa5SBO9VagDFI+WB1Ni6MuCAi2iOifcqUKZsZ4tjQ39/PKaecwpo1a+odiplZw9vaSfLx3IVKvn8il/cBe5TNNx14NJdPr1K+QR1JLcBObNy9axU6OztZtmwZnZ2dQ89sZtbktnaSXAx05McdwNVl5fPyiNW9SAN0bs1dss9JmpnPN55QUae0rGOB6/N5SyvQ399PV1cXEUFXV5dbk2ZmQ6jlT0C+D9wM7COpT9KJwFeBWZJWALPyNBGxHLgSuBe4Djg5ItblRZ0EXEgazPN7oCuXXwRMktQLfIE8UtaKdXZ2UjqOWL9+vVuTZmZDqNll6SLiIwVPHVEw/1nAWVXKe4D9q5S/BBy3JTE2m+7ubgYGBgAYGBhgyZIlfOELX6hzVGZmjatRBu7YVjBr1iwmTJgAwIQJE5g9e3adIzIza2xOkk2ko6OD0k9Jx40bR0dHxxA1zMyam5NkE5k8eTKHH344AIcffjiTJlX9WamZmWVOkmZmZgWcJJtIf38/N9xwAwA33HCDfwJiZjYEJ8km4p+AmJltGifJJlLtJyBmZlbMSbKJzJo169XRrZL8ExAzsyE4STaRD3zgA692t0YERx11VJ0jMjNrbE6STeSaa67ZoCW5ePHiOkdkZtbYnCSbSHd39wYtSZ+TNDMbnJNkE5k1axYtLelyvS0tLT4naWY2hJpd4NwaT0dHB9dccw2QfgLiy9KZbZq+vj54Bsbd6PZFw3oa+qJvxBbnd9rMzKyAW5JNpLOzc4OBO52dnf6rLLNNMH36dFZrNesPW1/vUKzAuBvHMX3a9JFb3ogtyRped3c369al/7Jet26dB+6YmQ3BSbKJvPOd79xg+tBDD61TJGZmo4OTpJmZWQEnySbyi1/8YoPpm266qU6RmJmNDk6STWTWrFkbTPt3kmZmg3OSbCKV5yTf9a531SkSM7PRwUmyiXzrW9/aYPqcc86pUyRmZqODk2QTWbly5aDTZma2IV9MoIm0trZukBhbW1vrFovZqPX0GLss3fP5foe6RjFyngamjdzinCSbyOmnn86nPvWpV6fPOOOMOkZjNvq0tbXVO4QRt2LFCgBmTJtR50hGyLSRfZ+cJBvEokWL6O3trfl6xo0bx/r169l2221ZtGhRTdfV1tbGggULaroOs61pLH6eS9tU6/3BaDWG+gxsOLbZZhsA3vjGN9Y5EjOzxueWZIPYWkeoPmo0Mxs+tyTNzMwKOEmamZkVcHfrELbWgJqtpTSSbSwNQPAAITOrFSfJIfT29nLn3fey/nW71juUEaFXAoDbf/9YnSMZGeP+9GS9QzCzMcxJchjWv25XXtr3/fUOw6rY7t4f1zsEMxvDnCSH0NfXx7g/PeOdcYMa96c19PWtrXcYZjZGjfokKWkOcA4wHrgwIr464itZt5Zxf1oz4outi/Xr0v248fWNY6Ssc4K0sWlrjYfYmuMURuP4gVGdJCWNB/4NmAX0AbdJWhwR947UOg477LAxOXBnxowxcgkqxualwsy2lokTJ9Y7hIY2qpMkcDDQGxF/AJB0OXA0MGJJcmsd9Yy1UbQwOo8azRqFvzuNYbT/TnIa8EjZdB8jev33sWfixIk+cjQzG6bR3pJUlbLYaCZpPjAfYM8996x1TJvFR41mZo1ntLck+4A9yqanA49WzhQRF0REe0S0T5kyZasFZ2Zmo9toT5K3ATMk7SVpG2AesLjOMZmZ2RgxqrtbI2KtpM8CPyX9BOTbEbG8zmGZmdkYMaqTJEBEXAtcW+84zMxs7Bnt3a1mZmY14yRpZmZWwEnSzMysgJOkmZlZASdJMzOzAk6SZmZmBZwkzczMCjhJmpmZFXCSNDMzK+AkaWZmVsBJ0szMrIAiNvr7xTFN0mrgoXrHUWeTgf56B2F158+BlfizAG+MiI3+S7HpkqSBpJ6IaK93HFZf/hxYiT8LxdzdamZmVsBJ0szMrICTZHO6oN4BWEPw58BK/Fko4HOSZmZmBdySNDMzK+AkaWZmVsBJsslImiPpfkm9kk6tdzy29Un6tqQnJN1T71isfiTtIekGSfdJWi7pc/WOqRH5nGQTkTQeeACYBfQBtwEfiYh76xqYbVWSDgWeBy6JiP3rHY/Vh6SpwNSIuEPS64HbgWO8P9iQW5LN5WCgNyL+EBGvAJcDR9c5JtvKIuIm4Ml6x2H1FRGrIuKO/Pg54D5gWn2jajxOks1lGvBI2XQf/lKYNT1JrcBbgVvqG0njcZJsLqpS5v52syYmaQfgB8DnI+LZesfTaJwkm0sfsEfZ9HTg0TrFYmZ1JmkCKUFeFhE/rHc8jchJsrncBsyQtJekbYB5wOI6x2RmdSBJwEXAfRHx9XrH06icJJtIRKwFPgv8lHSS/sqIWF7fqGxrk/R94GZgH0l9kk6sd0xWF4cAHwfeLemufHtvvYNqNP4JiJmZWQG3JM3MzAo4SZqZmRVwkjQzMyvgJGlmZlbASdLMzKyAk6SZmVkBJ0mzUUbSmZL+x2bU21nS34xgHNdK2rlK+WbFZ9aInCTNmsfOwCYlSSVV9xMR8d6IeHpEIjNrUE6SZg1O0gmSlkn6raRLK567UVJ7fjxZ0sr8eD9Jt+arqCyTNAP4KvDnuexf8nz/U9JteZ4v57LW/Ee85wF3sOH1fsvXvVLS5Pz4i/nPvH8G7FObV8Js62updwBmVkzSfsAXgUMiol/SrsCCYVT9DHBORFyWr9M7HjgV2D8iDszLng3MIP3PqIDF+Q+ZHyYlur+OiCFbnpIOIl0H+K2kfcodpD/wNRv1nCTNGtu7gasioh8gIp5M16Ue0s3AFyVNB34YESuq1Judb3fm6R1ISfNh4KGI+M0wY3wn8KOI+BOAJF8038YMd7eaNTYx+H9+ruW17/F2pcKI+B5wFPAi8FNJ7y5Y9j9HxIH51hYRF+XnXtjEOH0RaBuTnCTNGttS4EOSJgHk7tZyK4GD8uNjS4WS3gT8ISIWkf4O7QDgOeD1ZXV/Cnwy/+kukqZJ2m0zYrwJ+KCkiZJeD3xgM5Zh1pDc3WrWwCJiuaSzgJ9LWkfqGl1ZNsvXgCslfRy4vqz8w8DxkgaAx4Cv5K7aX0m6B+iKiP8p6c3Azbkr9nngeGDdJsZ4h6QrgLuAh4BfbM62mjUi/1WWmZlZAXe3mpmZFXB3q5kNStItwLYVxR+PiLvrEY/Z1uTuVjMzswLubjUzMyvgJGlmZlbASdLMzKyAk6SZmVmB/wuIv7Qe3dypuQAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 504x504 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(7,7))\n",
    "plt.title(\"Boxplot of GDP for 3 clusters got by Kmeans\",fontdict={\"size\":16})\n",
    "sns.boxplot(x='cluster_id',y=\"gdpp\", data=df);"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##### Observation\n",
    "Cluster 0 has the lowest gdpp. There is a slight overlap of clusters 1 and 2 and the whiskers of cluster 0 and 2."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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93it/dpQb7a9uvRf4DtXnhlqHP5bqMuu1VC+GrwIH14/tTtVd+T1gWtM4u1DtJH5MfSUZ1VWAP6A6X3ID1bvNW4DXtczvTJqubq3b9gD+kapr6DGqq8/+EoiW4d5F1T2zoV6ueROsh5lUl77fXm9Yt9f3Z7YM93ivbh1vM+zltFy2TnUU/3mqne6jVEdD57QMcwLVTvBRqh3tZ4D9WoZZScuVhe1qK9VH9eI6h+pjB4/V28YVNF1dWVj+za5ufbx1TzCvZ1BdrHFnPc176m3r9c3b4STTeA3VRUprqS6zvwp4RdPjmy0L1QVuN9Tj/AfVBVebPJdUXW3XUb1WHqJ6HSxqM52b6u0uabpym+oN4HfrmtZS7UQ/CRwy2bqi+ojUD+t1vJbq9XVm6zbdZrzpVG9cfl6PdznVTjuBt3S6P2h5fq6gugI3meD1w2/2R0fW03qQqpv+I5Q/pvEtqq7L4tXWhXkMt7TvRNWFuwH4k6bn/awOx78c+EFL2xDVlfc/o3r93E11desJTcPMobow6Rf17SKqC9Bat4ULmWT/QRX0H6M6eHiw3m6uaSzPltyinqB6JKoPXM/IzAk/2Cyp9yLitVRdkkdm+yvVe6L+wP0dwD9k5jt6Xc+OxO5WSWqjPmXycqqj6UeA5wKnUR2V/qCHpf1a3QV9MNVFZdOornTVFDIkJam9B6m6O0+hOrVxL9VR5OnZP11wL6c6h30HMJKZW/RxIU3O7lZJkgr8CIgkSQUD1906Z86cnDdvXq/LkCT1kR/96EerM3OzbzgbuJCcN28ey5ZN1RexSJJ2BBFxe7t2u1slSSowJCVJKjAkJUkqMCQlSSowJCVJKjAkJUkqMCQlSSowJCVJKjAkJUkqMCQlSSowJCVJKjAkJUkqMCQlSSowJCVJKjAkJUkqGLjfk5T62eLFixkbG+v6fMbHxwEYGhrq+ryGh4dZtGhR1+cjdYMhKQ2gtWvX9roEabtgSEp9ZFsdcTXms3jx4m0yP2l75TlJSZIKDElJkgoMSUmSCgxJSZIKDElJkgoMSUmSCgxJSZIKDElJkgoMSUmSCgxJSZIKDElJkgoMSUmSCgxJSZIKuhqSEfGXEXFjRNwQEZ+PiF0iYq+IWBoRK+q/T2oa/vSIGIuIWyLipU3tz42I6+vHFkdE1O07R8QX6/arImJeN5dHkjRYuhaSETEXWATMz8xDgenAQuA04LLMPBC4rL5PRBxSP/5M4FjgvIiYXk/uo8DJwIH17di6/STgF5k5DJwDfKBbyyNJGjzd7m6dAcyKiBnArsBdwPHAaP34KPCq+v/jgS9k5qOZeRswBhweEfsBe2TmlZmZwKdbxmlM68vAUY2jTEmSHq+uhWRm/gz4O+AO4G7g/sy8FHhyZt5dD3M3sE89ylzgzqZJjNdtc+v/W9s3GScz1wP3A7Nba4mIkyNiWUQsW7Vq1dQsoCRph9fN7tYnUR3pHQA8BXhCRJww0Sht2nKC9onG2bQh8/zMnJ+Z8/fee++JC5ckqdbN7taXALdl5qrMXAd8Bfh94J66C5X677318OPA/k3jD1F1z47X/7e2bzJO3aX7ROC+riyNJGngdDMk7wCOiIhd6/OERwE3A5cAI/UwI8BX6/8vARbWV6weQHWBztV1l+wDEXFEPZ03tozTmNZrgO/U5y0lSXrcZnRrwpl5VUR8GbgWWA9cB5wP7AZcHBEnUQXpa+vhb4yIi4Gb6uFPycwN9eTeDFwIzAKW1DeAC4DPRMQY1RHkwm4tjyRp8HQtJAEy813Au1qaH6U6qmw3/NnA2W3alwGHtml/hDpkJUmaan7jjiRJBYakJEkFhqQkSQWGpCRJBYakJEkFhqQkSQWGpCRJBYakJEkFhqQkSQWGpCRJBYakJEkFhqQkSQWGpCRJBYakJEkFhqQkSQWGpCRJBYakJEkFhqQkSQWGpCRJBYakJEkFhqQkSQWGpCRJBYakJEkFhqQkSQWGpCRJBYakJEkFhqQkSQWGpCRJBYakJEkFhqQkSQWGpCRJBYakJEkFhqQkSQWGpCRJBYakJEkFhqQkSQWGpCRJBYakJEkFhqQkSQWGpCRJBYakJEkFhqQkSQWGpCRJBYakJEkFhqQkSQWGpCRJBYakJEkFhqQkSQWGpCRJBYakJEkFhqQkSQWGpCRJBYakJEkFhqQkSQWGpCRJBYakJEkFhqQkSQWGpCRJBYakJEkFhqQkSQWGpCRJBYakJEkFhqQkSQWGpCRJBYakJEkFhqQkSQWGpCRJBYakJEkFhqQkSQWGpCRJBYakJEkFhqQkSQWGpCRJBV0NyYjYMyK+HBE/iYibI+L3ImKviFgaESvqv09qGv70iBiLiFsi4qVN7c+NiOvrxxZHRNTtO0fEF+v2qyJiXjeXR5I0WLp9JPlh4JuZ+Qzgd4GbgdOAyzLzQOCy+j4RcQiwEHgmcCxwXkRMr6fzUeBk4MD6dmzdfhLwi8wcBs4BPtDl5ZEkDZCuhWRE7AEcCVwAkJmPZeYvgeOB0XqwUeBV9f/HA1/IzEcz8zZgDDg8IvYD9sjMKzMzgU+3jNOY1peBoxpHmZIkPV7dPJJ8GrAK+FREXBcRn4iIJwBPzsy7Aeq/+9TDzwXubBp/vG6bW//f2r7JOJm5HrgfmN1aSEScHBHLImLZqlWrpmr5JEk7uG6G5AzgOcBHM/Mw4CHqrtWCdkeAOUH7RONs2pB5fmbOz8z5e++998RVS5JU62ZIjgPjmXlVff/LVKF5T92FSv333qbh928afwi4q24fatO+yTgRMQN4InDflC+JJGkgdS0kM/PnwJ0RcXDddBRwE3AJMFK3jQBfrf+/BFhYX7F6ANUFOlfXXbIPRMQR9fnGN7aM05jWa4Dv1OctJUl63GZ0efqnAhdFxE7AT4E/pQrmiyPiJOAO4LUAmXljRFxMFaTrgVMyc0M9nTcDFwKzgCX1DaqLgj4TEWNUR5ALu7w8kqQB0tWQzMwfA/PbPHRUYfizgbPbtC8DDm3T/gh1yEqSNNX8xh1JkgoMSUmSCgxJSZIKDElJkgoMSUmSCgxJSZIKDElJkgoMSUmSCrr9jTvSDmHx4sWMjY31uowps2LFCgAWLVrU40qmzvDw8A61POoPhqTUgbGxMW694VqeutuGyQfeDuy0rupEemTlNT2uZGrc8eD0yQeStoIhKXXoqbtt4Iz5D/a6DLVx1rLdel2CdlCek5QkqcCQlCSpwJCUJKnAkJQkqcCQlCSpwJCUJKnAkJQkqcCQlCSpwJCUJKnAkJQkqcCQlCSpwJCUJKnAkJQkqcCQlCSpwJCUJKnAkJQkqcCQlCSpwJCUJKnAkJQkqcCQlCSpwJCUJKnAkJQkqcCQlCSpwJCUJKnAkJQkqcCQlCSpwJCUJKnAkJQkqcCQlCSpwJCUJKnAkJQkqcCQlCSpwJCUJKnAkJQkqcCQlCSpwJCUJKnAkJQkqcCQlCSpwJCUJKnAkJQkqcCQlKQBduutt7JgwQLGxsZ6XUpf6igkI+KgiLgsIm6o7z8rIs7obmmSpG4766yzeOihh3jPe97T61L6UqdHkh8HTgfWAWTmcmBht4qSJHXfrbfeysqVKwFYuXKlR5NtdBqSu2bm1S1t66e6GEnStnPWWWdtct+jyc11GpKrI+LpQAJExGuAu7tWlSSp6xpHkaX76jwkTwE+BjwjIn4GvBV4c9eqkiR13bx58ya8rw5DMjN/mpkvAfYGnpGZL8jMlV2tTJLUVWecsen1l+985zt7VEn/mtHJQBGxJ/BGYB4wIyIAyMxFXatMktRVBx10EPPmzWPlypXMmzeP4eHhXpfUdzrtbv0GVUBeD/yo6SZJ2o6dccYZPOEJT/AosqCjI0lgl8z8q65WIkna5g466CCWLFnS6zL6VqdHkp+JiP8REftFxF6NW1crkySpxzo9knwM+CDwduqPgdR/n9aNoiRJ6gedhuRfAcOZubqbxUiS1E86DckbgYe7WYjUz8bHx3nogemctWy3XpeiNm5/YDpPGB/vdRnaAXUakhuAH0fEd4FHG41+BESStCPrNCT/pb5JA2loaIhH1t/NGfMf7HUpauOsZbuxy9BQr8vQDqijkMzM0YjYCTiobrolM9d1ryxJknqv09+TfDGwAvgIcB5wa0Qc2cW6JEnbwOrVqzn11FNZs2ZNr0vpS51+TvLvgWMy80WZeSTwUuCc7pUlSdoWRkdHWb58OaOjo70upS91GpIzM/OWxp3MvBWY2Z2SJEnbwurVq1myZAmZyZIlSzyabKPTkFwWERdExIvr28fxu1slabs2OjpKZvX9MBs3bvRoso1OQ/LNVJ+VXAS8BbgJ+PNuFSVJ6r6lS5eybl11Dea6deu49NJLe1xR/+k0JGcAH87MP8zMVwOLgendK0uS1G1HH300M2dWZ85mzpzJMccc0+OK+k+nIXkZMKvp/izg252MGBHTI+K6iPhafX+viFgaESvqv09qGvb0iBiLiFsi4qVN7c+NiOvrxxZH/YOWEbFzRHyxbr8qIuZ1uDySNPBGRkZo/D7wtGnTGBkZ6XFF/afTkNwlM3/9Ker6/107HPctwM1N908DLsvMA6nC9zSAiDgEWAg8EzgWOC8iGkerHwVOBg6sb8fW7ScBv8jMYaqrbT/QYU2SNPDmzJnDggULiAgWLFjA7Nmze11S3+k0JB+KiOc07kTEc4G1k40UEUPAy4FPNDUfDzTODo8Cr2pq/0JmPpqZtwFjwOERsR+wR2ZemdUZ5k+3jNOY1peBoxpHmZKkyY2MjPCsZz3Lo8iCTr+W7q3AlyLirvr+fsDrOhjvH4D/Deze1PbkzLwbIDPvjoh96va5wA+bhhuv29bV/7e2N8a5s57W+oi4H5gNbPJrJRFxMtWRKE996lM7KFuSBsOcOXM499xze11G3+r0a+muiYhnAAcDAfxksq+li4hXAPdm5o/qb+yZTLsjwJygfaJxNm3IPB84H2D+/PmbPS5JUjudHkkCPA+YV49zWESQmZ+eYPjnA8dFxMuAXYA9IuKzwD0RsV99FLkfcG89/Diwf9P4Q8BddftQm/bmccYjYgbwROC+LVgmSZKKOv3u1s8Afwe8gCosnwfMn2iczDw9M4cycx7VBTnfycwTgEuARuf3CPDV+v9LgIX1FasHUF2gc3XdNftARBxRn298Y8s4jWm9pp6HR4qSpCnR6ZHkfOCQKQqg9wMXR8RJwB3AawEy88aIuJjqiwrWA6dk5oZ6nDcDF1J99GRJfQO4APhMRIxRHUEunIL6JEkCOg/JG4B9gbu3ZiaZeTlwef3/GuCownBnA2e3aV8GHNqm/RHqkJUkaap1GpJzgJsi4mrg0UZjZh7XlaokSeoDnYbkmd0sQpKkftTpR0C+1+1CpH53x4PTOWvZbr0uY0rc83B1zd6Td93Y40qmxh0PTuegXhehHdKEIRkRP8jMF0TEA2z6+cMAMjP36Gp1Up8YHh7udQlT6rEVKwDYZd6BPa5kahzEjvccqT9MGJKZ+YL67+4TDSft6BYtWtTrEqZUY3kWL17c40qk/tbpd7dKkjRwDElJkgoMSUmSCgxJSZIKDElJkgoMSUmSCgxJSZIKDElJkgoMSUmSCgxJSZIKDElJkgoMyQGzevVqTj31VNasWdPrUiSp7xmSA2Z0dJTly5czOjra61Ikqe8ZkgNk9erVLFmyhMxkyZIlHk1K0iQMyQEyOjpKZvWzoBs3bvRoUpImYUgOkKVLl7Ju3ToA1q1bx6WXXtrjiiSpvxmSA+Too49m5syZAMycOZNjjjmmxxVJUn8zJAfIyMgIEQHAtGnTGBkZ6XFFktTfDMkBMmfOHBYsWEBEsGDBAmbPnt3rkiSpr83odQHatkZGRli5cqVHkVKfW7x4MWNjY12fz/j4OABDQ0Ndn9fw8DCLFi3q+nymkiE5YObMmcO5557b6zIk9Ym1a9f2uoS+ZkhKUh/aVkdcjfksXrx4m8xve+M5SUmSCgxJSZIKDElJkgoMSUmSCgxJSZIKDElJkgoMSUmSCgxJSZIKDElJkgoMSUmSCgxJSZIKDElJkgoMSUmSCgxJSZIK/KksqY9sqx/aXbFiBbBtfo5pe/yhXanBkJQG0KxZs3pdgrRdMCSlPtzqcL8AAAx3SURBVOIRl9RfPCcpSVKBISlJUoEhKUlSgSEpSVKBISlJUoEhKUlSgSEpSVKBISlJUoEhKUlSgSEpSVKBISlJUoEhKUlSgSEpSVKBISlJUoEhKUlSgSEpSVKBISlJUoEhKUlSgSEpSVKBISlJUoEhKUlSgSEpSVKBISlJUoEhKUlSgSEpSVKBISlJUoEhKUlSgSEpSVKBISlJUkHXQjIi9o+I70bEzRFxY0S8pW7fKyKWRsSK+u+TmsY5PSLGIuKWiHhpU/tzI+L6+rHFERF1+84R8cW6/aqImNet5ZEkDZ5uHkmuB/46M38bOAI4JSIOAU4DLsvMA4HL6vvUjy0EngkcC5wXEdPraX0UOBk4sL4dW7efBPwiM4eBc4APdHF5JEkDpmshmZl3Z+a19f8PADcDc4HjgdF6sFHgVfX/xwNfyMxHM/M2YAw4PCL2A/bIzCszM4FPt4zTmNaXgaMaR5mSJD1e2+ScZN0NehhwFfDkzLwbqiAF9qkHmwvc2TTaeN02t/6/tX2TcTJzPXA/MLsbyyBJGjxdD8mI2A34Z+CtmfmriQZt05YTtE80TmsNJ0fEsohYtmrVqslKliQJ6HJIRsRMqoC8KDO/UjffU3ehUv+9t24fB/ZvGn0IuKtuH2rTvsk4ETEDeCJwX2sdmXl+Zs7PzPl77733VCyaJGkAzOjWhOtzgxcAN2fmh5oeugQYAd5f//1qU/vnIuJDwFOoLtC5OjM3RMQDEXEEVXftG4FzW6Z1JfAa4Dv1eUtJmnKLFy9mbGys12VMqRUrVgCwaNGiHlcydYaHh6dseboWksDzgTcA10fEj+u2t1GF48URcRJwB/BagMy8MSIuBm6iujL2lMzcUI/3ZuBCYBawpL5BFcKfiYgxqiPIhV1cHkkDbmxsjOtuvA727HUlU2hj9ee6n13X2zqmyi+ndnJdC8nM/AHtzxkCHFUY52zg7Dbty4BD27Q/Qh2ykrRN7AkbX7yx11WoYNrlU3sW0W/ckSSpwJCUJKnAkJQkqcCQlCSpwJCUJKnAkJQkqcCQlCSpwJCUJKnAkJQkqcCQlCSpwJAcMKtXr+bUU09lzZo1vS5FkvqeITlgRkdHWb58OaOjo70uRZL6niE5QFavXs2SJUvITJYsWeLRpCRNwpAcIKOjozR+bnPjxo0eTUrSJAzJAbJ06VLWrVsHwLp167j00kt7XJEk9TdDcoAcffTRzJw5E4CZM2dyzDHH9LgiSepvhuQAGRkZIaL6Hexp06YxMjLS44okqb8ZkgNkzpw5LFiwgIhgwYIFzJ49u9clSVJfMyQHzCtf+Up23XVXjjvuuF6XIkl9z5AcMP/6r//Kww8/zCWXXNLrUiSp7xmSA8TPSUrSljEkB8jo6CgbN24EYMOGDX5OUpImYUgOkKVLl7J+/XoA1q9f7+ckJWkShuQAeeELX7jJ/SOPPLJHlUjS9sGQlCSpwJAcIFdcccUm97///e/3qBJJ2j4YkgPE7lZJ2jKGpCRJBYbkALG7VZK2zIxeF6Bt5+ijj+brX/8669evZ8aMGf4KiLSFxsfH4X6YdrnHF33rlzCe41M2OZ/pATIyMsK0adVTPn36dH8FRJIm4ZHkAGn8Csgll1zir4BIW2FoaIhVsYqNL97Y61JUMO3yaQzNHZqy6RmSA2ZkZISVK1d6FClJHTAkB8ycOXM499xze12GJG0XPCcpSVKBISlJUoEhKUlSgSEpSVKBITlgVq9ezamnnsqaNWt6XYok9T1DcsCMjo6yfPlyRkdHe12KJPU9Q3KArF69miVLlpCZLFmyxKNJSZqEITlARkdHyUwANm7c6NGkJE3CkBwgS5cuZd26dQCsW7eOSy+9tMcVSVJ/MyQHyNFHH83MmTMBmDlzpr8CIkmTMCQHyMjICBEBwLRp0/z+VkmahCE5QBq/AhIR/gqIJHXALzgfMP4KiCR1zpAcMP4KiPQ4/bL6zcIdxoP13916WsXU+SUwd+omZ0hKUoeGh4d7XcKUW7FiBQAHzj2wx5VMkblT+zwZkpLUoUWLFvW6hCnXWKbFixf3uJL+tAP1GUiSNLUMSUmSCuxu7ROLFy9mbGys6/MZHx8HYGhoqOvzGh4e3iG7pyQNDkNywKxdu7bXJUjSdsOQ7BPb6ojLk/SS1DnPSUqSVGBISpJUYEhKklRgSEqSVGBISpJUYEhKklRgSEqSVODnJCexrb4JZ1tpfOP/jvRNOH6zj6RuMSQnMTY2xnXX38TGXffqdSlTIh5LAH70nz/vcSVTY9rD9/W6BEk7MEOyAxt33YtHDnlFr8tQG7vc9LVelyBpB+Y5SUmSCgxJSZIKDElJkgo8JzmJ8fFxpj18v+e++tS0h9cwPr6+12VIU25bXVm/La943x6vRDckJWmAzZo1q9cl9DVDchJDQ0Os+sWvel3GlIlHqmXJXfbocSVTJRgaGup1EdKU296OuHZUhuQkhoeHe13ClFqx4gEADnz6vj2uZKrsu8M9R5L6hyE5iR3t3VxjeRYvXtzjSiSp/3l1qyRJBdv9kWREHAt8GJgOfCIz39/jkraKV7JJUv/Zro8kI2I68BFgAXAI8McRcUhvq+pvs2bN8mo2SerQ9n4keTgwlpk/BYiILwDHAzf1tKqt4BGXJPWf7fpIEpgL3Nl0f7xu20REnBwRyyJi2apVq7ZZcZKk7dv2HpLRpi03a8g8PzPnZ+b8vffeexuUJUnaEWzvITkO7N90fwi4q0e1SJJ2MNt7SF4DHBgRB0TETsBC4JIe1yRJ2kFs1xfuZOb6iPgL4FtUHwH5ZGbe2OOyJEk7iO06JAEy8xvAN3pdhyRpx7O9d7dKktQ1hqQkSQWGpCRJBYakJEkFhqQkSQWGpCRJBYakJEkFhqQkSQWGpCRJBZG52Y9m7NAiYhVwe6/r6LE5wOpeF6GecztQg9sC/FZmbvYzUQMXkoKIWJaZ83tdh3rL7UANbgtldrdKklRgSEqSVGBIDqbze12A+oLbgRrcFgo8JylJUoFHkpIkFRiSkiQVGJIDJiKOjYhbImIsIk7rdT3a9iLikxFxb0Tc0Ota1DsRsX9EfDcibo6IGyPiLb2uqR95TnKARMR04FbgaGAcuAb448y8qaeFaZuKiCOBB4FPZ+ahva5HvRER+wH7Zea1EbE78CPgVe4PNuWR5GA5HBjLzJ9m5mPAF4Dje1yTtrHM/D5wX6/rUG9l5t2ZeW39/wPAzcDc3lbVfwzJwTIXuLPp/ji+KKSBFxHzgMOAq3pbSf8xJAdLtGmzv10aYBGxG/DPwFsz81e9rqffGJKDZRzYv+n+EHBXj2qR1GMRMZMqIC/KzK/0up5+ZEgOlmuAAyPigIjYCVgIXNLjmiT1QEQEcAFwc2Z+qNf19CtDcoBk5nrgL4BvUZ2kvzgzb+xtVdrWIuLzwJXAwRExHhEn9bom9cTzgTcAfxARP65vL+t1Uf3Gj4BIklTgkaQkSQWGpCRJBYakJEkFhqQkSQWGpCRJBYakJEkFhqS0nYmIMyPif23FeHtGxP+cwjq+ERF7tmnfqvqkfmRISoNjT2CLQjIqbfcTmfmyzPzllFQm9SlDUupzEfHGiFgeEf8REZ9peezyiJhf/z8nIlbW/z8zIq6uv0VleUQcCLwfeHrd9sF6uL+JiGvqYd5dt82rf4j3POBaNv2+3+Z5r4yIOfX/b69/zPvbwMHdWRPStjej1wVIKouIZwJvB56fmasjYi9gUQej/jnw4cy8qP6e3unAacChmfnsetrHAAdS/c5oAJfUP8h8B1XQ/WlmTnrkGRHPpfoe4MOo9inXUv2Ar7TdMySl/vYHwJczczVAZt5XfS/1pK4E3h4RQ8BXMnNFm/GOqW/X1fd3owrNO4DbM/OHHdb4QuD/ZebDABHhl+Zrh2F3q9Tfgol/83M9v3kd79JozMzPAccBa4FvRcQfFKb9vsx8dn0bzswL6sce2sI6/RJo7ZAMSam/XQb8UUTMBqi7W5utBJ5b//+aRmNEPA34aWYupvo5tGcBDwC7N437LeBN9Y/uEhFzI2Kfrajx+8CrI2JWROwOvHIrpiH1JbtbpT6WmTdGxNnA9yJiA1XX6MqmQf4OuDgi3gB8p6n9dcAJEbEO+Dnwnrqr9t8i4gZgSWb+TUT8NnBl3RX7IHACsGELa7w2Ir4I/Bi4Hbhia5ZV6kf+VJYkSQV2t0qSVGB3q6QJRcRVwM4tzW/IzOt7UY+0LdndKklSgd2tkiQVGJKSJBUYkpIkFRiSkiQV/H8uCrz0L+XNkwAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 504x504 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(7,7))\n",
    "plt.title(\"Boxplot of Income for 3 clusters got by Kmeans\",fontdict={\"size\":16})\n",
    "sns.boxplot(x='cluster_id',y=\"income\", data=df);"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##### Observation\n",
    "Cluster 0 has the lowest income. Looking at the boxplots above countries in cluster 0 need the aid."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0                   Afghanistan\n",
       "3                        Angola\n",
       "17                        Benin\n",
       "21                     Botswana\n",
       "25                 Burkina Faso\n",
       "26                      Burundi\n",
       "28                     Cameroon\n",
       "31     Central African Republic\n",
       "32                         Chad\n",
       "36                      Comoros\n",
       "37             Congo, Dem. Rep.\n",
       "38                  Congo, Rep.\n",
       "40                Cote d'Ivoire\n",
       "49            Equatorial Guinea\n",
       "50                      Eritrea\n",
       "55                        Gabon\n",
       "56                       Gambia\n",
       "59                        Ghana\n",
       "63                       Guinea\n",
       "64                Guinea-Bissau\n",
       "66                        Haiti\n",
       "72                         Iraq\n",
       "80                        Kenya\n",
       "81                     Kiribati\n",
       "84                          Lao\n",
       "87                      Lesotho\n",
       "88                      Liberia\n",
       "93                   Madagascar\n",
       "94                       Malawi\n",
       "97                         Mali\n",
       "99                   Mauritania\n",
       "106                  Mozambique\n",
       "108                     Namibia\n",
       "112                       Niger\n",
       "113                     Nigeria\n",
       "116                    Pakistan\n",
       "126                      Rwanda\n",
       "129                     Senegal\n",
       "132                Sierra Leone\n",
       "136             Solomon Islands\n",
       "137                South Africa\n",
       "142                       Sudan\n",
       "147                    Tanzania\n",
       "149                 Timor-Leste\n",
       "150                        Togo\n",
       "155                      Uganda\n",
       "165                       Yemen\n",
       "166                      Zambia\n",
       "Name: country, dtype: object"
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Contries that need aid\n",
    "df[df.cluster_id == 0].country"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "48"
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(df[df.cluster_id == 0].country)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "###### Observation\n",
    "When the number of clusters is chosen to be 3 there is a slight overlap of boxes in the boxplot as seen above. There seems to be some more room for better clustering with 4 clusters. "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Hierarchical Clustering"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [],
   "source": [
    "#importing libraries\n",
    "from scipy.cluster.hierarchy import linkage\n",
    "from scipy.cluster.hierarchy import dendrogram\n",
    "from scipy.cluster.hierarchy import cut_tree"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Single Linkage"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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LIURFkGALIURFkGALIURFkGALIURFkGALIURFkGALIURFkGALIURFkGALIURFkGALIURFkGALIURFkGALIURFkGALIURFkGALIURFkGALIURFkGALIURFkGALIURFkGALIURFkGALIURFkGALIURFkGALIURFaCrYZra2md1jZg+a2R/M7EuDYZgQQoi+jC2RZjnwdndfYmZdwB1mdr27/67DtgkhhMjRVLDd3YEl6WtXenknjRJCCLEmpWLYZjbGzB4AFgA3u/vdnTVLCCFEkVKC7e6r3H1vYBtgPzPbo5jGzKaZ2Qwzm9Hd3d1uO4UQYtTT0ioRd38FuBU4ssZvF7n7VHef2tXV1SbzhBBCZJRZJbKZmW2UPq8DHAY8Wir3GdNh+tEwf1a8ph8d24QQQrRMmVUiWwKXm9kYQuB/6u7Xlsp91pUwfxY/2S59nz8r3qd+uHVLhRBilFNmlchMYJ9+lzBpCnz4uvg8/eh+ZyOEEKOdwbnTsSc0MjNeCo0IIUTLlAmJDJwUGmHSnvFdoREhhGiZwRFsUGhECCEGiB7+JIQQFUGCLYQQFUGCLYQQFWFwBVurRYQQot8M3kVH6F0tMmEzWLoQ5twRwj3rSphyvFaNCCFEAwZXsCFWiwAsfQG2Pyg+a5mfEEI0ZfAFO0PL/IQQoiV00VEIISqCBFsIISqCBFsIISqCBFsIISrC0Aq21mULIURphm6VCOgpfkII0QJDHxKZNCVumgHAez1tedtCCNGHofWwM4qeNsjbFkKIAsNDsKHX0551ZdqQ87YzdPu6EGIUM/QhkTyZpw3hbRc97h4xF0KI0cfw8bAz8res59Ht60KIUU5TwTazbYHvAZOA1cBF7n5Bpw1jxvS+HvX8mbBqBZyzbXzPe98KlQghRgFlQiIrgU+5+67Am4FTzWy3zppF3/AIhECPGQcrlvZNp1CJEGKU0NTDdvd5wLz0ebGZPQJsDTzcYdvWDI9kYZFa24QQYoTT0kVHM5sM7APc3QljhBBC1Ke0YJvZesBVwN+7+6Iav08zsxlmNqO7u7udNgohhKDkKhEz6yLE+ofu/vNaadz9IuAigE2239XbZmEt8hck58+Md63XFkKMcJp62GZmwCXAI+7+n503qQRary2EGIWU8bAPBD4EzDKzB9K2z7n7LztnVgmyC5LF5X+6Q1IIMUIps0rkDsAGwZb+0fMckimweF7v9ixUsnxR7z+zF5GQCyEqxPC707E/ZN729KPj39izf2ZvhB4uJYSoGCNDsPPUu7W9iNZvCyEqxvB6+JMQQoi6SLCFEKIiSLCFEKIiSLCFEKIijLyLjlBjbXYNat0hWQst/RNCDBNGpoddfDRrLYp3SNZCd00KIYYRw9PDnjG9rwdcz8vNpyv+w3rZ5X2N0NI/IcQwYnh62LOuBCw84EZebpYOkycshBjxDE/Bhl4Pudldi5OmlLuzUQghKs7wFWwhhBB9kGALIURFkGALIURFkGALIURFGJ7L+tpFmRtoGlH25pp66KYbIUQbGdmCnd1AM2EiLF3Y/3wy4W6FRn+c0Gl0oBBiRDKyBRt6l/yV/WODqqM/ZhBixDLyBTujHXc+VgHdnSnEiGX0CHZGrbj24nkDC5kMJ1Ysjfdzth1aOzpJs2fAiOGFQnRtY3QJ9ozpcPNZEV8evwFM2AzW3zLEesVSGDdhqC0cOCOhDmLkoBBdW2kq2GZ2KXAMsMDd9+i8SR0ke/bI9gfFQFp/y94/74XRETIR7Wegq5FGNB4X3xWqq02LZx9l1mFfBhzZX3uGHWWfUSJEWco8zne0UuYxxqOVfjy+uamH7e63m9nkfpokxOhgtFzUFgOn54wsnX3MmF7ay9adjkIIMZhkZ2ST9qTVR0O37aKjmU0DpgGst+VO7cp2aFFsUpRhoHfEipFNtgpt0p4Rs4beM7IWx0zbBNvdLwIuAthk+129XfkOOnmRnj8zVpTYmPieX4GRrTARQjFa0YhsFVobrnOMrmV9Zeg5XZmS/vFmZu+SvwmbReMvXxTbFj0HY8b17lucuFp/KoRo45lXmWV9VwBvBSaa2VzgbHe/pG0WDEfyF5DyS/6mHx23uI/fIAR7VXe8xnTF+3Mz4t1XAw5z7oDrPtU372brpAfirekAIcSIpswqkRMHw5BBpdGf9zaj3nLA+bMAD8G2tcBXAZbecyxf1Dj/OXe0Zk9x3+IBolOMtBt0FNYYeuRwNGV0hkR6/ryX9l1UbCbkte6kXLE0BD2LkQsxEujPox76+3TLUSbyo1OwYfBunMmXU1ynqzssxUgkCx1OmlJevMdvEO+tPMq4k48wHqYHguEt2AMJXQxH8itQFs+Dl2fH5+xBTatW9I2JQ29IZezafS9wZtQ6lc9Pkuz3YToAxQglv2yt0aONB/LgtWYinz0IrVV8FTxzVzx3qEij1WHFedeBOTe8BbsToYuhJL8CJevYfJhkVXcMllWsGSpZ1V1bsGuRLSMa09W7NPGZu+CGM3vzyAu9xFx0kkZ3gTYT9IHQnz8eqUXmSPmqmFdLF9YW7vxD5Dr00KvhLdgw8p75UVwwnx/IxeU/tVaqlCGfz/xZtVe1zJ8ZA3Hla2uKOcRvxQNEtqyxpy5axijawHC/rX/60X3/uSpb1pu/GWbqh9dcUdYBhr9gj2ayEEr+TrpWRbHRxdAx40LAx03oFfTM48/EPX8GkPfcV3X3in7mfdQ6jWy0+kICP7oo3jlcKyyYUW/cDNWYyf9zVeYAwaA/PlaC3YhOxdCzfFetiIGav2U1T59nDtDewZEX8inH9wrt4V9e01vIKHruk6akenT3xhMzAe9JV+e0dPmiWLdeK064hq1N4vTFNDoQDE+K/7GaXyVV64wO+vZzPrSXdyYGs++LiwgG+XEEEuxGdCqGnuU7ZlzzW1YnTYlBmH+6V3887TL2ZJ/L5FsvtJOdPpYJZeXvIm2VfLxw1Yo1Qzw3n1VfBBTKGTrynup2+8fnbLzUCovkY9zZeMnO6IoM9p8lDMGiCAl2MzoVQ28l3x7PJMWQ59zRu5ypXWLTjnrmB3DRrnoP0ho3IQQ070nlY+X1rrYX44XFEA/UDutAba8t8/RribwEvv3Uupu4Wdpiuh5HhujTFUuiL7PwSqc97yFYFCHBzjOclxHmPZPtD4rPw+3vl2p56vk4fPGv2fITKu8x9+dqezHEM+vK3kcFFJc21vLaMooin3nv2eeVr8GcO3vvKPXVcWdrkUZnDa3eVakDRG0arbrK91ut9dqddHQ6qCMS7DxVWEZYK0TSwgPQO05xAOfj8IvnwUtPxvb1t+ydNNkAHzchYuhZ22ex9VbDQFmZmccNawp/o5BO8XsxXg+5ZZfe+zl7hgw0XgPc6s0h9dYED4R23opf6/GhMDgOUK1+zP936yY79Z6x1RPwdh8QO6gjEuwiVVhGmBfB7G+Ghotg5ymGSGZdmfNscwJab4Bn24vpy9DOi0P1TsmLQlFc/gW9wj2ma81QS/ECbV7w8/iq5s+gaUStRx8MZI1yZnf+CZa1rsU0E652rIKqRVbu+A1710rn13pnB5jsrK8T4t0hHZFgV5XM054/szMXIttBrQlbT/zqDfB+Pui9D2U9vVrpWvUQ86GrCRN7V0KsAjbYuu/NFpnHnp3GZ2lbJRPkWoJvY9r/HPeeMw2LvBrl10i4BmsVVPY9f9ZUFPCieA/GXOrHGYgEu8oMxANtRH4gNToQlBlwnYjxtbp/2VPUmun6eWpbFO6Xngyv7oDTetuyXjgGCjdqLKZHiDNxznvsPYI3s+86eegb0135Wu8NH2vYW+IRB8UbRNrBUIX4agn4Jjv1vagPnbWjH6ETCXbVKXra7YgVZgNpwsS+g7deup7PLeZfdr+iQPen3LKnqLU8s4GQF25obdlktt/49XuFeMy48ATHTFhzBU09wYe+oZd6yx2LDMKt1kD9EN9gLgLIt/cmO8UBtt3XDRqVWxIJ9kigk4+LzQtNo3T9zb8M9UIrVSJvbzF228ijLNZz/qxyq4SKHn7G8kVAV9+0EzbrtWWQb7XuY2+xnLLjupWQV7N2LzvuhwgJ9khhOKwX7ySdsGOolnEO5KJxqyGEWsK9YmmER7InQDbzpIdyuWuZfm8l5JXdz9DMgx4u475AjQWkQowSeia6Db4nlQlvXnTL0udiXUnbJ01JFwct7jAcvyFsPRU++2zks93+saSylj2D1U7ZgaE/ob1JU8qJbNYO4zdkSPp9gMjDFqObofSkBhLK6u+1i0b7NbJnMNppMO+DGKYedDMk2EL0l3aECgYiHJ246DvUQjbU5Q9zJNhC9JfhcGfsYFz0FcMGCbYQA0HCJwaRUhcdzexIM3vMzJ4wszM7bZQQQog1aSrYZjYG+BbwTmA34EQz263ThgkhhOhLGQ97P+AJd3/K3VcAPwaO7axZQgghipSJYW8NPJv7Phd4UzGRmU0DpqWvS376sQMe++nA7RNCiNHC9s0SlBFsq7FtjWdAuvtFwEUl8hNCCNEPyoRE5gL5vzTeBni+M+YIIYSoRxnBvhfY2cx2MLNxwAnA/++sWUIIIYo0DYm4+0oz+wRwIzAGuNTd/9Bxy4QQQvTB3Gv8JZEQQohhh57WJ4QQFUGCLYQQFUGCLYQQFWHYPPzJzDYCVrn74hJp13P3JW0oL+MfgJeA9YDlwMvACmBD4FV3v7hZPu7+ipmdSCx7vBDY391vbtGmS4CrgBvd1/z7bDPbAFjs7l6rvczsjUSfHgc84u7fr1POR4k7WAF+4e7Xpe2bEv/vXbMf0u8vAetndrRSv7Jk5eTzz7dzo3QN8lxj/wZpa44vM/t/6eOBwJ3u3tZ7wxqU22d7/nthHNesXzZuiMdLQNz49md3/89cmne4+4219qs33vpRv578cttqjePS/VrIv2e/Fvs7G9d/DWyeNv/J3S9vpfz+Uqtd6qZt55wzs7OIybwbMBH4AXAY8BSwCDgkJe0C3gD8BpgBHJk+nwwsBP437XMocD8wB9iLGGhLCVH8U/o8kbgTc1NCYJ8GniAE96fAP6cy3gfcB1wDfBZ4Ltm6QXq9AvwO+Avi7s4F6bUOcBewMtm/MTCB6ODN0m8bp7y2B34IbASMA7ZLdixI+f8FMIm48eixtL8RIrsqfb8V2CW102rgnpTv65L926c6v5rqPJs40KwGZqZ03wD+Btgi5TubGIgPAOumdvtjSrOSWGf/eMpnk9Q/s4FZwGtANzA1tct6qW6vAF8A/gB8Atg7lfdkqm93qufeybb5wKPAeOChtP29qYwHkp1vJFYijUn1WEmMgydTuVunPr49te1mqczZqV1vTG23PrBVquuc1JaW8tkitd9GKc3taf8XibHandpjfmqTLYC1U16LU7pngUtS27wrtdf+qb5dya5XgTvT+0Gpve4D/kyM87HEGCTZvBSYDDyT9lkn2bwI2DHZ81iq/18km3dM7deV2vhJop83SuXtnvJ/LtV5k5RHd6rPcuBuYCdgz7T91bTvxGTLFsR4H5PyuZVwKvL1PiC1yb2pfTL730Y4P2OJeb1uyu9eev7Chj2IMT07td/ylO/myQ5P72OJ+f8sMaeuTv23DDgx9dXGxDhZnPJ5OOWzVbJpw9RnlsrYJO23JPXZ2sBvCb04IbXvitQ3k1K6mYR2bUSM5ceJ+1K2Jcb1rkTkYh5wfbLjDGJ8fzS1VzehbQel7d8Ddnb3T9KEdgv2GUSHbQp8GjgvGfka8JfAd4hKn0hM0gXAEcAtwIeBc4nGfJEYLG8mxPxOorM/DXyQ6JRriAY/gBDaQ4kJuxXRoA8C7yHE+JeEV/SLZOoYQnTWJwbEBKJhl6TynfC6T0vb3ktMNtLv+6ey3gnckeqxH/Cau/+tmV2dylhEDNxxRIf/ihgouxJidDfxXJavEpP4eXf/hJmdQEzQC4CvA8cTB8GbCOG8NeW5BzEoxufynQzcTAjeLGLwHEQMqLlpv2eSvZcQB57jky27ERNgG+AG4GBCCEj5PQRMIdbhb5hsmU8M2J2JAZ8JwIrUVt2pLdZN+TyQ+mIjYqBOSv21HnHQPoQY7C+m7TcS4jCPmKwrU713Suknp3dSvpuk+kMcABYRYtBFiMUuqR27UxvcRgjbNqqbNUAAAAoOSURBVMB/EF7WBsC7iXGzLeF4HO7ubzezi4kxdxlwUqrn08QB5B7iQHRzsvOg1P6vpj5amdricWJOrEc4Il8mxvXGqW8ykbs12bNeaoOLk53zgUuBzwOnAP9IjNnsAHlTqtuzqU0uT++PEf17KDGuDicOIEYI29PJvgNT2/9Psu+PKc3ByfYdCvW+gej7icm+rM+Xp35YnuuvvdI2T3muSp+vJ+bjW4hx+gLR54cRc+xNhPO2dmqnRendCLHNHJs3pvZfL7X33oQDuDKV+27iYXZHEGPwnlSfbVP7TqN3jsxK7X5b6o8DU7/9JtX/IGJ8Hpzy70p1X5s4yGxOjPvrU3t8iOjb1em3n6Q+OAn4uLufThPaLdjbufszZrYt8Klk7D6El/RO4HziaJUZ30U0+FeJgfME8P5UqQuJhr2fEItl7v5pMzsY2N3dv21m2xGT8FxCwDdK3yEm6TqEqAC8gxDAG1Oe3yQafhnwc2BsCmtsDhzs7lelM4bZqfydiYm8ghCMJ4iJciAheDcTp2OXmNn27j7HzG4Bvk2vx/xJ4uByKbAvMbjnAH9FTKD5qdw3EhP8mtSOvyUOKtcB5xAT8v3ERNg45ftld/93M3sHIaofJybUAmJgPUqI44+AfyMG6IfoHWCZ4CwlDrK3p765nJj47yFE+jep/g8BS9x9Tur7N6c2nkscmNZP/TsHODXl/zTh/RxGTLitiTOlHZJ9s1O6R4kJMje107LUr7NS271KHLyfB34GHE1M6MXEAXvztM8zhEhdlerUTRz8D09ptiQOMOsQQnRAKnuiu3/FzA4gwksLgYfd/TozOzrVfzbhTDxKTPB1kg2vEpP3GEKILiDG4DOpL3Yg5sWS9H1+KustwMcIr/DvUx8/kvrjdfSGL59Otr9MiMe6xPxaTcy1xwhH42ni7Oe61C+rCRF8lRj/Y1LdHqHX49wlteGPUlvsRDhfJxPi/USyb39iTKxDHGwcOD3l8zTh7CwjzsDeRIyHlamfdk31/FUqezNijLxK71npI6lenyM82iVp2wpi3P3e3X+fQnt/R4z3f032Z/tuRRz0X0l1exNxMLiNGEdrE2P6ekJ7Xkuv+cQ8WEGcla9P6MqmqX7PEOPhS8T8vIWYn5sTY3QPQis2TeU+nup9HaF/U4kxdxzwfcKLXw687O4P0YSOrMM2s9OI0+1Licn6EOFBXE0MvO8RE+5o4AOEeFxLCNfrCc9iSkr7MHEqdii9p5mvERN5LaKyW6U0WxIe1o+ICbOUONU7nRDXRWl/Iwbknen39dPnjAPTdycafvv0vpzwxi5I79unvLZMNi9I318mBvN+hJfw+rTv/UQnvjH9fnWy8yupveYSArYWsF6ayGcR4nl4qvNDxMDsSnmuSr8fm9pyf+AN7v5VM7s+5ZnF1U9J3+cmOxcDO7j76WZ2drJ7X+LA9DgxeaBXfF8mJsMyQry/Q3gwf0lMZlJdf5BszLysHYnQUVbv61P7Z179JoT4PkqI+W7ERLXUt5el+v0q5buMEKA3pDrsQoSiDiE8q58TntQ/Ax9J9r9CTKiutP8q4uDzTkLIn0n9eT/hff2JOKBkXlMXMW4PSHWZRYztR4kJv1+y7/2EuI1NbTkm9c8BqdxsHO0M/BfR/wvTtu2IMbQVIWrnEwfu5cSEPyx9z8JIH0rf/5zaPjs73Qr472QfRP8vcPe3pX5eRN/xlYXXbkjt9Wyq/zqpn1cQZ2N/lfryaeDtub7J+nl8+n1eaq990rYXU16riLH9v6mdfkiI5m2p/a4nxtJtqW8fSnm/OeVzE6EDuxFn0Fm5y1IfbUGI62dSPbZPfXEkvSHMjxGO4ErCeTsq2XVTqus4es8Y/onQgOtSP12c7J1IeP37pnxW06sXBxAhlXz/35Bs3j+1zS0p3a8IZ+oM4prXVTSh3R729cCviSPZFKKTdyEmS+bJnUoMpkOAKwjv53RioAIc5+5TzOwLhBhcTky+BURjXUaI1zLg90RHX5HKu5towA+l928Sg/zXhCBe5u63JTsfITzO7YCfufsFKRQxD9g7fc/CLQcTneTEJJ5MDJgVxBH5ve5+WmqDtYgOMCKMclCq43uJo+vXiKP924iO24Y4AL3O3U80s88THsWLxAR5PpW3DTHYDkjp1yIG0jdSu346tfsOxKn8XqncfYF93f1UM3sbcJu7r052Hpf65Rxicmya2vUbye7VhBhsmWy+LrX97cSAPISYIP+Q0meD++uE1/dc6p//SXbNS/X+LHBmquNjKc3VxOnnQmD9dBDZjfCg9kn1+3LKd0HqkwuJCUwKRR1ITMDrCDHZi5j4M1MZD6V6fCLVcQJxIL+bOPhMJET83tSWtxMHqY0JD2imme2Z3g9OdXgXMUYnEl53Fv6YlOq2hPDEzkz9dF4aG0uJ8fwHYjLvR3jWXyRCax8xsy8Rwncv4TXPJMb/GfR6g9mB9DB3f0cK2SxP/Z2F1o4FPpX6/b3EWHrR3X9gZkfRe/3mS/SGp4yYN8ekuk0nBPGo1AfXEgfgZ4k5dl8aL0cRIZSvEY7PkYS4rSYOrgcRQr1n6p9r0v4TU52uIebKeoRjdDwheH9Itu0MbJbGx7HEwXV1aue5xPham5j/0wmP95zUbt2pXXcnRHt86v/Nkz1fSa8sDHg9cWBaTByIXiDG1YPE3PwFcSD4x5T/ZEIfLiXCvtkZ5V3EAWspMb6uI/RnN+KAcxfhOJ1CM9y9bS9gr/S+O3BK+nx07vdDcp9Py30+lph4xwKXpG2fL+S9K3A2cXq3MxH/+QJxSnwGcGou7e65z0fXszN9PpsQ83fkyun5XqLOa6RvVn6NPI4FDi3WO9eexwLnZb8X86/Xrv3ot6xdP0kIxBaEuJ1LHGh3z8pL78cC38zZdUgunyvr9U8/2rOY73dz5Z5fr79y42oL4qBxbmZHsn/nVNesvq3220DbPSu/3jgu2tenvNzv52TlF+1uZTzn2muvbBwSZ2X5+XJKMd9av+fnQbLzO1k96rVfmfasMz6ycXpGGqc151OD9i/aVW8+9iknZ8/Z9OrHKfXKqzHfDsm1Z6nx024P+wrilPID5E4h3f3Ywu9WZ3uz/bLfu6lxqtqsnGZ2Et5l3f0a1LdefdqVT0M7W7W7Qfn1yqnX7sVQQb30ffqnDe3QsP8b1KuWvQ+k5G3r/7Kk/R8gvLF647iufTn7G+3fn/q0NN6K3/sxr0uN47LzrVk+NdqfMu3WoJya9Wu1nUuNn1a9giZHjkNyR4490+c96xxR19jebL/s9+J72XKalddsv3r5tFp+q/k0s7PV8lptj3rtXjZ9u9uhbP7N7BloO3aq3evlX+97f+dBq+Og1XYbaP1aHR+t9mPZ/JqV099x3up+7m32sIUQQnQO3ZouhBAVQYIthBAVQYIthBAVQYIthBAVQYIthBAV4f8A/X8q5GI29xEAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# single linkage\n",
    "mergings = linkage(df_scaled, method=\"single\", metric='euclidean')\n",
    "dendrogram(mergings)\n",
    "plt.title(\"Single Hierarchial Clustering\",fontdict={\"size\":16})\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#####  Observation\n",
    "The single linkage does not show a very clear clustering hence complete linkage for hierarchial clustering will be considered."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Complete Linkage"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# complete linkage\n",
    "mergings = linkage(df_scaled, method=\"complete\", metric='euclidean')\n",
    "dendrogram(mergings)\n",
    "plt.title(\"Complete Hierarchial Clustering\",fontdict={\"size\":16})\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Hierarchial method with number of clusters = 3"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([0, 0, 0, 0, 0, 0, 0, 1, 1, 0, 1, 1, 0, 0, 0, 1, 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, 0, 1, 1,\n",
       "       1, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 0, 0, 0, 1, 0, 1, 0, 0, 0, 0, 0,\n",
       "       0, 0, 1, 0, 0, 0, 0, 1, 1, 1, 0, 1, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0,\n",
       "       0, 1, 0, 1, 0, 0, 0, 1, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,\n",
       "       1, 1, 0, 2, 1, 1, 0, 0, 0, 0, 0, 0, 1, 1, 0, 0, 0, 0, 1, 0, 0, 0,\n",
       "       0, 1, 1, 1, 0, 0, 1, 1, 0, 0, 0, 0, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0,\n",
       "       0, 0, 0, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0])"
      ]
     },
     "execution_count": 38,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 3 clusters are chosen to keep the analysis comparable to the Kmeans clustering\n",
    "chosen_clusters = 3\n",
    "cluster_labels = cut_tree(mergings, n_clusters=chosen_clusters).reshape(-1,)\n",
    "cluster_labels"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "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>cluster_id</th>\n",
       "      <th>cluster_labels</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\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>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\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>2</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\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>2</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\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>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\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>2</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  cluster_id  cluster_labels  \n",
       "0       9.44        56.2       5.82    553.0           0               0  \n",
       "1       4.49        76.3       1.65   4090.0           2               0  \n",
       "2      16.10        76.5       2.89   4460.0           2               0  \n",
       "3      22.40        60.1       6.16   3530.0           0               0  \n",
       "4       1.44        76.8       2.13  12200.0           2               0  "
      ]
     },
     "execution_count": 39,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# assign cluster labels\n",
    "df['cluster_labels'] = cluster_labels\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.title(\"Number of countries in each cluster with Hierarchial method with 3 Clusters\",fontsize=14)\n",
    "sns.countplot(df['cluster_labels']);"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 504x504 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# plot\n",
    "plt.figure(figsize=(7,7))\n",
    "sns.boxplot(x='cluster_labels',y=\"child_mort\", data=df)\n",
    "plt.title(\"Boxplot of Child Mortality for 3 clusters got by Hierarchial method\",fontdict={\"size\":16});"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Observation \n",
    "Cluster 2 and 0 have the highest child mortality rate. There is a overlap among clusters 0 and 2. There is very small number  countries in cluster 2."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 504x504 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(7,7))\n",
    "sns.boxplot(x='cluster_labels',y=\"gdpp\", data=df)\n",
    "plt.title(\"Boxplot of GDPP for 3 clusters got by Hierarchial method\",fontdict={\"size\":16});"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##### Observation \n",
    "Cluster 2 and 0 have the lowest gdp. There is a overlap among clusters 0 and 2. There is very small number  countries in cluster 2."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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6w3ci4mTK9ZyPUgblfBogIv47pTv+o5l5aW07GTghIn6Umde3LO8u4OsR8RHKG4b3ALvW38NZQvlyiX+NiG0p79qHRj//U0uX4XX193sjYjFlp7yqU7dbZl4bEV8Djq1da/9FuQ7yIeBrmXnVCPX0wkcoIzv/KyI+TunCm0kZrXpIZj4ZER8GvhARX6Fcw5lJ6bpdxtMfvVtXJ1LO3H4c5aM+N1Cun+5OGbx44FgW1qu6I+INlIPMf1BGOz8LmEcJ5y+MMNaAzHwgIo4GToqIf6dcv3+A8oVCj2bmScPM+g3KpaMzI+IEykjWo3n6DH+otkuBcykB+SDlzcWLKD1WUF4Dq4D5EXEVJURvzsxVEfEB4HN1P19CeZ3NrMu4ODO/Osp2+TblTPVKypnjXpTrjV8YYXvcGxH/AvxDRDxACf6XUEbdQwmwIaMeD6rrgP8REW+kdFOuzMzlI9VOubb/z5TA2pvymjg9M9u/VOhUyvHmRZRR4T2Xmb+OiE8Cn43yUbsfUnpodqJcP/9S6ziYYZwPHFRfV98BXkoZhHpflzX8oO6vJ9RA+T7lo8L7At/NzIvH/sieUd+CiLiacvz5E8obtH74MOUN3I8i4rOUXpjnUN6QPi8z31Wnu45yRv0eSm/co5l59RjW0222nAj8FeW7Ko6lvIH4AGUU/OhGGw23Nj90HsF5N2XHeF2H6Q+gvCN5hPKC/TawW73vWZSu6R8Ck1rm2YxyIPsF9SNglNG9l1A+tnJN3Rg3AG9rW9+xtI0EpAwC+iyli+NxymjIv6FlFGad7iOUrrgnafv4SIfHNZXysZtbKKF/S709tW26dR213mlU5cW0fWSG0hvyNUowPEY5q2wfuXoI5UD9GCUMzgB2bJtmOW0jhjvVNlx9lBfMiZRrkI/XfePHtIyaHubxP2PU+rrWPcK6dqdcu7utLvOuum+9vXU/HGUZb6EMVnqEcr3tMuCNLfc/47FQBoVeU+f5JaWrdY3nktId+HPKa+UhyutgYYflXFf3uzVGzVIOJD+oNT1COah+GdhjtG1F+QjNT+s2foTy+jq2fZ/uMN9kypurO+t8F1MO4gm8r9vjQdvz82PKyPpkhNcPTx+P9q3LepDS5fk5hhn5TRn0dDsjfIpimHU84xMtnfZbOnz8rLYfWrfvQ7XO6ynHpVlt+81xHdYziXJ8ub1ulx9S3mgtZ82P2Y1W6wcpx7+hy5Hn8fTx+FV0fp0/4/F0eMwzKAMM760/Z1IGA7bvn6fR/aj144apY05b+8XAJW1tsyijx39TH+sdlJ6RQ1qm2YJyzBy67LN8LbZDt9nyEso+/Wit6UOUN7E52rYYGiK/UYjypRlTMnPEL6eQ1H8RcTBlJPm+2fkTKH0R5QtKbgX+JTM/1O96pNGs18FukiamennsDZReiUcp3b5HUc4+L+ljab9Tuz53owzEnEQZwS5t8AxySevDg5Su7fdSuhrvppyNH50bTrfgGyhjKm4FFmTmxvwPTbQR2ai61iVJmmjW9z9NkSRJ42jCda3PmDEjZ8+e3e8yJEkbkCuuuGJlZm7b7zrWxoQL8tmzZ7N06Xh9SZkkaWMQEbf0u4a1Zde6JEkNZpBLktRgBrkkSQ1mkEuS1GAGuSRJDWaQS5LUYAa5JEkNZpBLktRgBrkkSQ1mkEuS1GAGuSRJDWaQS5LUYAa5NAHdeOONzJs3j4GBgX6XImkdGeTSBHTcccfx0EMP8bGPfazfpUhaRwa5NMHceOONLF++HIDly5d7Vi41nEEuTTDHHXfcGrc9K5eazSCXJpihs/HhbktqFoNcmmBmz5494m1JzWKQSxPMMcccs8btD3/4w32qRNJ4MMilCWbXXXf93Vn47NmzmTNnTn8LkrRODHJpAjrmmGPYYostPBuXNgJT+l2ApPVv1113ZcmSJf0uQ9I48IxckqQGM8glSWowg1ySpAYzyCVJajCDXJKkBjPIpQlo5cqVHHnkkaxatarfpUhaRwa5NAEtXryYq666isWLF/e7FEnryCCXJpiVK1eyZMkSMpMlS5Z4Vi41nEEuTTCLFy8mMwF46qmnPCuXGq6nQR4RfxMR10bENRHxtYjYLCK2iYgLI2JZ/f2clumPjoiBiLghIl7X0v7SiLi63rcoIqK2bxoRX6/tl0XE7F4+HmljcOGFF7J69WoAVq9ezQUXXNDniiSti54FeUTMBBYCczNzT2AyMB84CrgoM3cBLqq3iYg96v0vAA4ATo6IyXVxnweOAHapPwfU9sOBezNzDnAi8MlePR5pY7HffvsxdepUAKZOncr+++/f54okrYted61PAaZFxBRgc+B24EBgqC9vMXBQ/ftA4KzMfCwzbwYGgL0jYkdgq8y8NEt/4Olt8wwt6xzgNUNn65I6W7BgAUMvk0mTJrFgwYI+VyRpXfQsyDPzN8CngFuBO4D7M/MCYPvMvKNOcwewXZ1lJnBbyyIGa9vM+nd7+xrzZOYTwP3A9PZaIuKIiFgaEUtXrFgxPg9QaqgZM2Ywb948IoJ58+YxffozXjKSGqSXXevPoZwx7wz8HrBFRBwy0iwd2nKE9pHmWbMh85TMnJuZc7fddtuRC5cmgAULFvDCF77Qs3FpI9DLf2P6WuDmzFwBEBHfBF4O3BURO2bmHbXb/O46/SCwU8v8syhd8YP17/b21nkGa/f9s4F7evR4pI3GjBkzOOmkk/pdhqRx0Mtr5LcC+0TE5vW69WuA64FzgaHTgAXAt+vf5wLz60j0nSmD2i6v3e8PRMQ+dTnvaJtnaFlvAb6fQ5+rkSRpAujZGXlmXhYR5wBXAk8APwdOAbYEzo6Iwylhf3Cd/tqIOBu4rk7/3sx8si7uPcBpwDRgSf0BOBU4IyIGKGfi83v1eCRJ2hDFRDuBnTt3bi5durTfZUiSNiARcUVmzu13HWvDb3aTJKnBDHJJkhrMIJckqcEMckmSGswglySpwQxySZIazCCXJKnBDHJJkhrMIJckqcEMckmSGswglyaglStXcuSRR7Jq1ap+lyJpHRnk0gS0ePFirrrqKhYvXtzvUiStI4NcmmBWrlzJkiVLyEyWLFniWbnUcAa5NMEsXryYof96+NRTT3lWLjWcQS5NMBdeeCGrV68GYPXq1VxwwQV9rkjSujDIpQlmv/32Y+rUqQBMnTqV/fffv88VSVoXBrk0wSxYsICIAGDSpEksWLCgzxVJWhcGuTTBzJgxg3nz5hERzJs3j+nTp/e7JEnrYEq/C5C0/i1YsIDly5d7Ni5tBAxyaQKaMWMGJ510Ur/LkDQO7FqXJKnBDHJJkhrMIJcmIL9rXdp4GOTSBOR3rUsbD4NcmmD8rnVp42KQSxOM37UubVwMcmmC8bvWpY2LQS5NMH7XurRxMcilCcbvWpc2Lga5NMH4XevSxsWvaJUmIL9rXdp4GOTSBOR3rUsbD7vWJUlqMINckqQGM8glSWowg1ySpAYzyCVJajCDXJKkBjPIJUlqMINckqQGM8glSWowg1ySpAYzyCVJajCDXJKkBjPIJUlqMINckqQGM8glSWowg1ySpAYzyCVJajCDXJKkBjPIJUlqMINckqQGM8glSWowg1ySpAYzyCVJajCDXJKkBjPIJUlqMINckqQGM8glSWowg1ySpAYzyCVJajCDXJKkBjPIJUlqMINckqQGM8glSWowg1ySpAab0u8CJD1t0aJFDAwM9Hw9g4ODAMyaNavn65ozZw4LFy7s+XqkicoglyagRx55pN8lSBonBrm0AVlfZ65D61m0aNF6WZ+k3vEauSRJDWaQS5LUYAa5JEkNZpBLktRgPQ3yiNg6Is6JiF9FxPUR8bKI2CYiLoyIZfX3c1qmPzoiBiLihoh4XUv7SyPi6nrfooiI2r5pRHy9tl8WEbN7+XgkSdrQ9PqM/DPA+Zm5O/Ai4HrgKOCizNwFuKjeJiL2AOYDLwAOAE6OiMl1OZ8HjgB2qT8H1PbDgXszcw5wIvDJHj8eSZI2KD0L8ojYCtgXOBUgMx/PzPuAA4HFdbLFwEH17wOBszLzscy8GRgA9o6IHYGtMvPSzEzg9LZ5hpZ1DvCaobN1SZImgl6ekT8PWAH8W0T8PCK+FBFbANtn5h0A9fd2dfqZwG0t8w/Wtpn17/b2NebJzCeA+4Hp7YVExBERsTQilq5YsWK8Hp8kSX3XyyCfArwE+Hxm7gU8RO1GH0anM+kcoX2kedZsyDwlM+dm5txtt9125KolSWqQXgb5IDCYmZfV2+dQgv2u2l1O/X13y/Q7tcw/C7i9ts/q0L7GPBExBXg2cM+4PxJJkjZQPQvyzLwTuC0idqtNrwGuA84FFtS2BcC369/nAvPrSPSdKYPaLq/d7w9ExD71+vc72uYZWtZbgO/X6+iSJE0Ivf6u9SOBMyNiE+Am4J2UNw9nR8ThwK3AwQCZeW1EnE0J+yeA92bmk3U57wFOA6YBS+oPlIF0Z0TEAOVMfH6PH48kSRuUngZ5Zv4CmNvhrtcMM/3xwPEd2pcCe3Zof5T6RkCSpInIb3aTJKnBDHJJkhrMIJckqcEMckmSGswglySpwQxySZIazCCXJKnBDHJJkhrMIJckqcEMckmSGswglySpwQxySZIazCCXJKnBDHJJkhrMIJckqcEMckmSGswglySpwQxySZIazCCXJKnBDHJJkhrMIJckqcEMckmSGswglySpwQxySZIazCCXJKnBDHJJkhrMIJckqcEMckmSGswglySpwQxySZIazCCXJKnBDHJJkhrMIJckqcEMckmSGswglySpwQxySZIazCCXJKnBDHJJkhrMIJckqcEMckmSGswglySpwQxySZIazCCXJKnBDHJJkhrMIJckqcEMckmSGswglySpwQxySZIazCCXJKnBDHJJkhrMIJckqcEMckmSGqyrII+IXSPiooi4pt5+YUQc09vSJEnSaKZ0Od0XgQ8AXwDIzKsi4qvAcb0qTNqQLFq0iIGBgX6XMW6WLVsGwMKFC/tcyfiZM2fORvV4pG51G+SbZ+blEdHa9kQP6pE2SAMDA9x4zZU8d8sn+13KuNhkdemMe3T5z/pcyfi49cHJ/S5B6ptug3xlRDwfSICIeAtwR8+qkjZAz93ySY6Z+2C/y1AHxy3dst8lSH3TbZC/FzgF2D0ifgPcDBzSs6okSVJXugryzLwJeG1EbAFMyswHeluWJEnqRldBHhFbA+8AZgNThq6VZ6YjSyRJ6qNuu9bPA34KXA081btyJEnSWHQb5Jtl5t/2tBJJkjRm3X6z2xkR8RcRsWNEbDP009PKJEnSqLo9I38c+Gfgg9SPoNXfz+tFUZIkqTvdBvnfAnMyc2Uvi5EkSWPTbdf6tcDDvSxEkiSNXbdn5E8Cv4iIHwCPDTX68TNJkvqr2yD/Vv2RJEkbkG6/2W1xRGwC7FqbbsjM1b0rS5IkdaPbb3Z7FbAYWA4EsFNELMjMH/WuNEmSNJpuu9Y/DeyfmTcARMSuwNeAl/aqMEmSNLpuR61PHQpxgMy8EZjam5IkSVK3uj0jXxoRpwJn1NtvB67oTUmSJKlb3Qb5eyj/k3wh5Rr5j4CTe1WUJEnqTrdBPgX4TGaeABARk4FNe1aVJEnqSrdBfhHwWuDBensacAHw8tFmrKG/FPhNZr6x/rOVr1P+t/ly4K2ZeW+d9mjgcMoX0CzMzO/V9pcCp9X1nge8LzMzIjYFTqcMulsFvC0zl3f5mKSuDQ4O8tADkzlu6Zb9LkUd3PLAZLYYHOx3GVJfdDvYbbPMHApx6t+bdznv+4DrW24fBVyUmbtQ3iAcBRARewDzgRcABwAn1zcBAJ8HjgB2qT8H1PbDgXszcw5wIvDJLmuSJGmj0O0Z+UMR8ZLMvBJ+d4b8yGgzRcQs4A3A8ZR/vAJwIPCq+vdi4GLg72v7WZn5GHBzRAwAe0fEcmCrzLy0LvN04CBgSZ3n2Lqsc4DPRkRk5tB/aJPGxaxZs3j0iTs4Zu6Do0+s9e64pVuy2axZ/S5D6otug/z9wDci4vZ6e0fgbV3M9y/A/wGe1dK2fWbeAZCZd0TEdrV9JvDTlukGa9vq+nd7+9A8t9VlPRER9wPTgTX+S1tEHEE5o+e5z31uF2VLktQM3X5F688iYndgNyNaZUcAAA7/SURBVMqo9V+N9hWtEfFG4O7MvKJ+M9xootOqR2gfaZ41GzJPAU4BmDt3rmfrkqSNRrdn5AB/SBmgNgXYKyLIzNNHmP4VwJsi4vXAZsBWEfEV4K6I2LGeje8I3F2nHwR2apl/FnB7bZ/Vob11nsGImAI8G7hnDI9JkqRG62qwW0ScAXwKeCUl0P8QmDvSPJl5dGbOyszZlEFs38/MQ4BzgQV1sgXAt+vf5wLzI2LTiNiZMqjt8toN/0BE7BMRAbyjbZ6hZb2lrsMzbknShNHtGflcYI9xCslPAGdHxOHArcDBAJl5bUScDVwHPAG8NzOfrPO8h6c/frak/gCcCpxRB8bdQ3nDIEnShNFtkF8D7ADcsTYrycyLKaPTycxVwGuGme54ygj39valwJ4d2h+lvhGQJGki6jbIZwDXRcTlwGNDjZn5pp5UJUmSutJtkB/byyIkSdLa6fbjZz/sdSGSJGnsRgzyiLgkM18ZEQ+w5uezA8jM3Kqn1UmSpBGNGOSZ+cr6+1kjTSdJkvqj23+aIkmSNkAGuSRJDWaQS5LUYAa5JEkNZpBLktRgBrkkSQ1mkEuS1GAGuSRJDWaQS5LUYAa5JEkNZpBLktRgBvkEs3LlSo488khWrVrV71IkSePAIJ9gFi9ezFVXXcXixYv7XYokaRwY5BPIypUrWbJkCZnJkiVLPCuXpI2AQT6BLF68mMzyb+Wfeuopz8olaSNgkE8gF154IatXrwZg9erVXHDBBX2uSJK0rgzyCWS//fZj6tSpAEydOpX999+/zxVJktaVQT6BLFiwgIgAYNKkSSxYsKDPFUmS1pVBPoHMmDGDefPmERHMmzeP6dOn97skSdI6mtLvArR+LViwgOXLl3s2LkkbCYN8gpkxYwYnnXRSv8uQJI0Tu9YlSWowg1ySpAYzyCVJajCDXJKkBjPIJUlqMINckqQGM8glSWowP0cudenWBydz3NIt+13GuLjr4fIefvvNn+pzJePj1gcns2u/i5D6xCCXujBnzpx+lzCuHl+2DIDNZu/S50rGx65sfM+R1C2DXOrCwoUL+13CuBp6PIsWLepzJZLWldfIJUlqMINckqQGM8glSWowg1ySpAYzyCVJajCDXJKkBjPIJUlqMINckqQGM8glSWowg1ySpAYzyCVJajCDXJKkBjPIJUlqMINckqQGM8glSWowg1ySpAYzyCVJajCDXJKkBjPIJUlqMINckqQGM8glSWowg1ySpAYzyCVJajCDXJKkBjPIJUlqMINckqQGM8glSWqwKf0uQMWiRYsYGBjo+XoGBwcBmDVrVs/XNWfOHBYuXNjz9UjSRGaQTzCPPPJIv0uQJI0jg3wDsb7OXIfWs2jRovWyPklSb3mNXJKkBjPIJUlqMINckqQGM8glSWowg1ySpAZz1Lq0AVlf3yewbNkyYP18WsLvE5B6yyCXJqBp06b1uwRJ46RnQR4ROwGnAzsATwGnZOZnImIb4OvAbGA58NbMvLfOczRwOPAksDAzv1fbXwqcBkwDzgPel5kZEZvWdbwUWAW8LTOX9+oxSb3mmaukserlNfIngL/LzD8A9gHeGxF7AEcBF2XmLsBF9Tb1vvnAC4ADgJMjYnJd1ueBI4Bd6s8Btf1w4N7MnAOcCHyyh49HkqQNTs+CPDPvyMwr698PANcDM4EDgcV1ssXAQfXvA4GzMvOxzLwZGAD2jogdga0y89LMTMoZeOs8Q8s6B3hNRESvHpMkSRua9TJqPSJmA3sBlwHbZ+YdUMIe2K5ONhO4rWW2wdo2s/7d3r7GPJn5BHA/ML0Xj0GSpA1Rz4M8IrYE/h14f2b+dqRJO7TlCO0jzdNewxERsTQilq5YsWK0kiVJaoyeBnlETKWE+JmZ+c3afFftLqf+vru2DwI7tcw+C7i9ts/q0L7GPBExBXg2cE97HZl5SmbOzcy522677Xg8NEmSNgg9C/J6rfpU4PrMPKHlrnOBBfXvBcC3W9rnR8SmEbEzZVDb5bX7/YGI2Kcu8x1t8wwt6y3A9+t1dEmSJoRefo78FcChwNUR8Yva9g/AJ4CzI+Jw4FbgYIDMvDYizgauo4x4f29mPlnnew9Pf/xsSf2B8kbhjIgYoJyJz+/h45EkaYPTsyDPzEvofA0b4DXDzHM8cHyH9qXAnh3aH6W+EZAkaSLyu9YlSWowg1ySpAYzyCVJajCDXJKkBjPIJUlqMINckqQGM8glSWowg1ySpAYzyCVJajCDXJKkBjPIJUlqMINckqQGM8glSWowg1ySpAYzyCVJajCDXJKkBjPIJUlqMINckqQGM8glSWowg1ySpAYzyCVJajCDXJKkBpvS7wI2dIsWLWJgYKDfZYybZcuWAbBw4cI+VzJ+5syZs1E9HkkaC4N8FAMDA/z86ut4avNt+l3KuIjHE4Arfn1nnysZH5MevqffJUhSXxnkXXhq8214dI839rsMdbDZdd/pdwmS1FdeI5ckqcEMckmSGswglySpwQxySZIazCCXJKnBDHJJkhrMIJckqcEMckmSGswglySpwQxySZIazCCXJKnBDHJJkhrMIJckqcEMckmSGswglySpwQxySZIazCCXJKnBDHJJkhrMIJckqcEMckmSGswglySpwQxySZIazCCXJKnBpvS7gA3d4OAgkx6+n82u+06/S1EHkx5exeDgE/0uQ5L6xjNySZIazDPyUcyaNYu7HpvCo3u8sd+lqIPNrvsOs2bt0O8yJKlvPCOXJKnBDHJJkhrMIJckqcEMckmSGszBbpLUpUWLFrFkyZL1sq6HH36YzFwv61pfIoLNN9+85+uZN28eCxcu7Pl6NhSekUuS1GCekUtSlxYuXDihzvTUDJ6RS5LUYAa5JEkNZpBLktRgBrkkSQ1mkEuS1GAGuSRJDWaQS5LUYAa5JEkN5hfCdGHSw/ew2XXf6XcZ4yIe/S0AudlWfa5kfEx6+B7A/0cuaeIyyEcxZ86cfpcwrpYtewCAXZ6/sYTfDhvdcyRJY2GQj2Jj+zrGocezaNGiPlciSRoPXiOXJKnBDHJJkhrMIJckqcEMckmSGswglySpwRo/aj0iDgA+A0wGvpSZn+hzSWtl0aJFDAwM9Hw9y5YtA9bPaPw5c+ZsdKP+JWlD0+gz8oiYDHwOmAfsAfxpROzR36o2bNOmTWPatGn9LkOSNE6afka+NzCQmTcBRMRZwIHAdX2tai145ipJWhuNPiMHZgK3tdwerG1riIgjImJpRCxdsWLFeitOkqRea3qQR4e2fEZD5imZOTcz52677bbroSxJktaPpgf5ILBTy+1ZwO19qkWSpPWu6UH+M2CXiNg5IjYB5gPn9rkmSZLWm0YPdsvMJyLir4HvUT5+9uXMvLbPZUmStN40OsgBMvM84Lx+1yFJUj80vWtdkqQJzSCXJKnBDHJJkhrMIJckqcEMckmSGswglySpwQxySZIazCCXJKnBDHJJkhosMp/xz8I2ahGxAril33X02QxgZb+LUN+5H2iI+wL8fmY28t9jTrggF0TE0syc2+861F/uBxrivtBsdq1LktRgBrkkSQ1mkE9Mp/S7AG0Q3A80xH2hwbxGLklSg3lGLklSgxnkkiQ1mEE+wUTEARFxQ0QMRMRR/a5H619EfDki7o6Ia/pdi/onInaKiB9ExPURcW1EvK/fNWnteI18AomIycCNwH7AIPAz4E8z87q+Fqb1KiL2BR4ETs/MPftdj/ojInYEdszMKyPiWcAVwEEeD5rHM/KJZW9gIDNvyszHgbOAA/tck9azzPwRcE+/61B/ZeYdmXll/fsB4HpgZn+r0towyCeWmcBtLbcH8YUrTXgRMRvYC7isv5VobRjkE0t0aPPaijSBRcSWwL8D78/M3/a7Ho2dQT6xDAI7tdyeBdzep1ok9VlETKWE+JmZ+c1+16O1Y5BPLD8DdomInSNiE2A+cG6fa5LUBxERwKnA9Zl5Qr/r0dozyCeQzHwC+Gvge5SBLWdn5rX9rUrrW0R8DbgU2C0iBiPi8H7XpL54BXAo8OqI+EX9eX2/i9LY+fEzSZIazDNySZIazCCXJKnBDHJJkhrMIJckqcEMckmSGswglySpwQxyaQMREcdGxP9ei/m2joi/Gsc6lkfEjFGmeXCMy1yrxyZpdAa51HxbA2MK8ih8/UsbAV/IUp9ExDsi4qqI+GVEnNF238URMbf+PSMilte/XxARl9dv4boqInYBPgE8v7b9c53uAxHxszrNR2vb7Ii4PiJOBq5kze/dH67Gb0XEFRFxbUQc0XbfpyPiyoi4KCK2rW3Pj4jz6zw/jojdOyxzYURcV2s7ay02naQWU/pdgDQRRcQLgA8Cr8jMlRGxDbCwi1nfDXwmM8+s35c/GTgK2DMzX1yXvT+wC+X/zwdwbkTsC9wK7Aa8MzO7PYN/V2beExHTgJ9FxL9n5ipgC+DKzPy7iPgw8BHK1/+eArw7M5dFxH8HTgZe3bbMo4CdM/OxiNi6yzokDcMgl/rj1cA5mbkSoIZlN/NdCnwwImYB36yB2T7N/vXn5/X2lpRgvxW4JTN/OoY6F0bEm+vfO9XlrAKeAr5e278CfLP+O8yXA99oqWnTDsu8CjgzIr4FfGsMtUjqwCCX+iMY+X/BP8HTl742G2rMzK9GxGXAG4DvRcSfAzd1WPY/ZeYX1miMmA081HWBEa8CXgu8LDMfjoiLW2tpk7Xe+4Z6BkbwBmBf4E3AhyLiBfUf+khaC14jl/rjIuCtETEdoHatt1oOvLT+/Zahxoh4HnBTZi6i/AvaFwIPAM9qmfd7wLvqGTIRMTMitluLGp8N3FtDfHdgn5b7JrXU9WfAJZn5W+DmiDi4rjci4kWtC6wD7HbKzB8A/4cyUG/LtahNUuUZudQHmXltRBwP/DAinqR0gy9vmeRTwNkRcSjw/Zb2twGHRMRq4E7gY7Vb/icRcQ2wJDM/EBF/AFxau7gfBA4BnhxjmecD746Iq4AbgNYu+YeAF0TEFcD9tS6AtwOfj4hjgKnAWcAvW+abDHwlIp5N6Tk4MTPvG2Ndklr4b0wlSWowu9YlSWowu9alCaoOmmsfVX5oZl7dj3okrR271iVJajC71iVJajCDXJKkBjPIJUlqMINckqQG+/9wA+fekRiuJQAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 504x504 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(7,7))\n",
    "sns.boxplot(x='cluster_labels',y=\"income\", data=df)\n",
    "plt.title(\"Boxplot of Income for 3 clusters got by Hierarchial method\",fontdict={\"size\":16});"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Observation \n",
    "Cluster 2 and 0 have the lowest income. There is a overlap among clusters 0 and 2."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1"
      ]
     },
     "execution_count": 44,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(df[df.cluster_labels == 2])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "125"
      ]
     },
     "execution_count": 45,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(df[df.cluster_labels == 0])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "###### Observation\n",
    "With the herachial method with the number of clusters are chosen to be 3 there is a overlap of boxes in the boxplot as seen above. Cluster 2 looks like the cluster of countries that need aid but there is only one country in that cluster and next cluster that needs aid is cluster 0 which has 125 countries which is too large a number. There seems to be some more room for better clustering with 4 clusters. "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Kmeans and Hierarchial  methods with 3 clusters are visually compared"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1080x360 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "##### Plotting both Hierarchial clustering and Kmeans clustering graphs to see if there is a visible difference\n",
    "color=['r','b','g','orange']\n",
    "clusters= df.cluster_id.unique()\n",
    "plt.figure(figsize=(15,5))\n",
    "\n",
    "ax=plt.subplot(1,2,1)\n",
    "ax.title.set_text(\"Hierarchial Clustering Child mortality vs income\")\n",
    "ax.set_xlabel(\"Child Mortality\")\n",
    "ax.set_ylabel(\"Income\")\n",
    "\n",
    "for k in range(chosen_clusters):\n",
    "    p1 = plt.scatter(df[df.cluster_labels == k].child_mort, df[df.cluster_labels == k].income,c=color[k],label=\"Cluster: \"+str(k))\n",
    "ax.legend(loc=1)  \n",
    "ax=plt.subplot(1,2,2)\n",
    "ax.title.set_text(\"KMeans Clustering Child mortality vs income\")\n",
    "ax.set_xlabel(\"Child Mortality\")\n",
    "ax.set_ylabel(\"Income\")\n",
    "\n",
    "for k in range(chosen_clusters):\n",
    "    p2 = plt.scatter(df[df.cluster_id == k].child_mort, df[df.cluster_id == k].income,c=color[k],label=\"Cluster: \"+str(k))\n",
    "ax.legend(loc=1);"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##### Observation\n",
    "The clustering done by Kmeans method seems to be having a better segregation of the clusters compared to the hierarchical method.   Cluster 0 in Kmeans method has the highest child mortality rate and low income. Hierarchial method has only one country in cluster 2 and 125 in cluster 0."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1080x360 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "##### Plotting both Hierarchial clustering and Kmeans clustering graphs to see if there is a visible difference\n",
    "color=['r','b','g','orange']\n",
    "plt.figure(figsize=(15,5))\n",
    "ax=plt.subplot(1,2,1)\n",
    "ax.title.set_text(\"Hierarchial Clustering Child mortality vs GDP\")\n",
    "ax.set_xlabel(\"Child Mortality\")\n",
    "ax.set_ylabel(\"GDP\")\n",
    "for k in range(chosen_clusters):\n",
    "    plt.scatter(df[df.cluster_labels == k].child_mort, df[df.cluster_labels == k].gdpp,c=color[k],label=\"Cluster: \"+str(k))\n",
    "ax.legend(loc=1)  \n",
    "        \n",
    "ax=plt.subplot(1,2,2)\n",
    "ax.title.set_text(\"KMeans Clustering Child mortality vs GDP\")\n",
    "ax.set_xlabel(\"Child Mortality\")\n",
    "ax.set_ylabel(\"GDP\")\n",
    "for k in range(chosen_clusters):\n",
    "    plt.scatter(df[df.cluster_id == k].child_mort, df[df.cluster_id == k].gdpp,c=color[k],label=\"Cluster: \"+str(k))\n",
    "ax.legend(loc=1);\n",
    "   "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##### Observation\n",
    "The clustering done by Kmeans method seems to be having a better segregation of the clusters compared to the hierarchical method.   Cluster 0 in Kmeans method has the highest child mortality rate and low GDP. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1080x360 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "##### Plotting both Hierarchial clustering and Kmeans clustering graphs to see if there is a visible difference\n",
    "color=['r','b','g','orange']\n",
    "plt.figure(figsize=(15,5))\n",
    "ax=plt.subplot(1,2,1)\n",
    "ax.title.set_text(\"Hierarchial Clustering Income vs GDP\")\n",
    "ax.set_xlabel(\"Income\")\n",
    "ax.set_ylabel(\"GDP\")\n",
    "for k in range(chosen_clusters):\n",
    "    plt.scatter(df[df.cluster_labels == k].income, df[df.cluster_labels == k].gdpp,c=color[k],label=\"Cluster: \"+str(k))\n",
    "ax.legend(loc=1)  \n",
    "    \n",
    "ax=plt.subplot(1,2,2)\n",
    "ax.title.set_text(\"KMeans Clustering Income vs GDP\")\n",
    "ax.set_xlabel(\"Income\")\n",
    "ax.set_ylabel(\"GDP\")\n",
    "for k in range(chosen_clusters):\n",
    "    plt.scatter(df[df.cluster_id == k].income, df[df.cluster_id == k].gdpp,c=color[k],label=\"Cluster: \"+str(k))\n",
    "ax.legend(loc=1) ; \n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##### Observation\n",
    "The clustering done by Kmeans method seems to be having a better segregation of the clusters compared to the hierarchical method.   Cluster 0 in Kmeans method has the lowest income and low GDP. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Kmeans with number of clusters = 4"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "KMeans(max_iter=50, n_clusters=4, random_state=0)"
      ]
     },
     "execution_count": 49,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# final model with k=4\n",
    "chosen_clusters =4\n",
    "kmeans = KMeans(n_clusters=chosen_clusters, max_iter=50,random_state=0)\n",
    "kmeans.fit(df_scaled)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "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>cluster_id</th>\n",
       "      <th>cluster_labels</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\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>2</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\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",
       "      <th>2</th>\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",
       "      <th>3</th>\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>2</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\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>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  cluster_id  cluster_labels  \n",
       "0       9.44        56.2       5.82    553.0           2               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           2               0  \n",
       "4       1.44        76.8       2.13  12200.0           0               0  "
      ]
     },
     "execution_count": 50,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# assign the label\n",
    "df['cluster_id'] = kmeans.labels_\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.title(\"Number of countries in each cluster with Kmeans method with 4 Clusters\",fontsize=14)\n",
    "sns.countplot(df['cluster_id']);"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 504x504 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# plot\n",
    "plt.figure(figsize=(7,7))\n",
    "plt.title(\"Boxplot of child mortality for 4 clusters got by Kmeans\",fontdict={\"size\":16})\n",
    "sns.boxplot(x='cluster_id',y=\"child_mort\", data=df);"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##### Observation \n",
    "Cluster 2 has the highest child mortality rate."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 504x504 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(7,7))\n",
    "plt.title(\"Boxplot of GDP for 4 clusters got by Kmeans\",fontdict={\"size\":16})\n",
    "sns.boxplot(x='cluster_id',y=\"gdpp\", data=df);"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##### Observation \n",
    "Cluster 2 has the lowest GDPP. There is a clear demarkation of the clusters there is a slight overlap with just the whiskers of the boxes."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 504x504 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(7,7))\n",
    "plt.title(\"Boxplot of Income for 4 clusters got by Kmeans\",fontdict={\"size\":16})\n",
    "sns.boxplot(x='cluster_id',y=\"income\", data=df);"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##### Observation \n",
    "Cluster 2 has the lowest income. There is a clear demarkation of the clusters there is a slight overlap with just the whiskers of the boxes.\n",
    "\n",
    "The countries in cluster 2 hence need the aid the most according to Kmeans clustering."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0                   Afghanistan\n",
       "3                        Angola\n",
       "17                        Benin\n",
       "21                     Botswana\n",
       "25                 Burkina Faso\n",
       "26                      Burundi\n",
       "28                     Cameroon\n",
       "31     Central African Republic\n",
       "32                         Chad\n",
       "36                      Comoros\n",
       "37             Congo, Dem. Rep.\n",
       "38                  Congo, Rep.\n",
       "40                Cote d'Ivoire\n",
       "49            Equatorial Guinea\n",
       "50                      Eritrea\n",
       "55                        Gabon\n",
       "56                       Gambia\n",
       "59                        Ghana\n",
       "63                       Guinea\n",
       "64                Guinea-Bissau\n",
       "66                        Haiti\n",
       "72                         Iraq\n",
       "80                        Kenya\n",
       "81                     Kiribati\n",
       "84                          Lao\n",
       "87                      Lesotho\n",
       "88                      Liberia\n",
       "93                   Madagascar\n",
       "94                       Malawi\n",
       "97                         Mali\n",
       "99                   Mauritania\n",
       "106                  Mozambique\n",
       "108                     Namibia\n",
       "112                       Niger\n",
       "113                     Nigeria\n",
       "116                    Pakistan\n",
       "126                      Rwanda\n",
       "129                     Senegal\n",
       "132                Sierra Leone\n",
       "136             Solomon Islands\n",
       "137                South Africa\n",
       "142                       Sudan\n",
       "147                    Tanzania\n",
       "149                 Timor-Leste\n",
       "150                        Togo\n",
       "155                      Uganda\n",
       "165                       Yemen\n",
       "166                      Zambia\n",
       "Name: country, dtype: object"
      ]
     },
     "execution_count": 55,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Countries that need aid by clustering using Kmeans method\n",
    "df[df.cluster_id == 2].country"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "48"
      ]
     },
     "execution_count": 56,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(df[df.cluster_id == 2].country)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Hierarchial Method with number of clusters = 4"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([0, 0, 0, 0, 0, 0, 0, 1, 1, 0, 1, 1, 0, 0, 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, 0, 1, 1,\n",
       "       2, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 0, 0, 0, 1, 0, 1, 0, 0, 0, 0, 0,\n",
       "       0, 0, 1, 0, 0, 0, 0, 2, 1, 1, 0, 1, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0,\n",
       "       0, 1, 0, 2, 0, 0, 0, 1, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,\n",
       "       2, 1, 0, 3, 2, 1, 0, 0, 0, 0, 0, 0, 1, 1, 0, 0, 0, 0, 1, 0, 0, 0,\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": 57,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 4 clusters are chosen to keep the analysis comparable to the Kmeans clustering\n",
    "chosen_clusters =4\n",
    "cluster_labels = cut_tree(mergings, n_clusters=chosen_clusters).reshape(-1,)\n",
    "cluster_labels"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "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>cluster_id</th>\n",
       "      <th>cluster_labels</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\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>2</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\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",
       "      <th>2</th>\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",
       "      <th>3</th>\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>2</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\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>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  cluster_id  cluster_labels  \n",
       "0       9.44        56.2       5.82    553.0           2               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           2               0  \n",
       "4       1.44        76.8       2.13  12200.0           0               0  "
      ]
     },
     "execution_count": 58,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# assign cluster labels\n",
    "df['cluster_labels'] = cluster_labels\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.title(\"Number of countries in each cluster with Hierarchial method with 4 Clusters\",fontsize=14)\n",
    "sns.countplot(df['cluster_labels']);"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 504x504 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# plot\n",
    "plt.figure(figsize=(7,7))\n",
    "sns.boxplot(x='cluster_labels',y=\"child_mort\", data=df)\n",
    "plt.title(\"Boxplot of Child Mortality for 4 clusters got by Hierarchial method\",fontdict={\"size\":16});"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Observation \n",
    "Cluster 3 and 0 have the highest child mortality rate. There is a overlap among clusters 0 and 3."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 504x504 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(7,7))\n",
    "sns.boxplot(x='cluster_labels',y=\"gdpp\", data=df)\n",
    "plt.title(\"Boxplot of GDPP for 4 clusters got by Hierarchial method\",fontdict={\"size\":16});"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Observation \n",
    "Cluster 3 and 0 have the lowest gdp. There is a overlap among clusters 0 and 3."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 504x504 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(7,7))\n",
    "sns.boxplot(x='cluster_labels',y=\"income\", data=df)\n",
    "plt.title(\"Boxplot of Income for 4 clusters got by Hierarchial method\",fontdict={\"size\":16});"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Observation \n",
    "Cluster 3 and 0 have the lowest income. There is a overlap among clusters 0 and 3."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "113    Nigeria\n",
       "Name: country, dtype: object"
      ]
     },
     "execution_count": 63,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Countries that need aid by using Hierarchial method in cluster 0\n",
    "df[df.cluster_labels == 3].country"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0              Afghanistan\n",
       "1                  Albania\n",
       "2                  Algeria\n",
       "3                   Angola\n",
       "4      Antigua and Barbuda\n",
       "              ...         \n",
       "162                Vanuatu\n",
       "163              Venezuela\n",
       "164                Vietnam\n",
       "165                  Yemen\n",
       "166                 Zambia\n",
       "Name: country, Length: 125, dtype: object"
      ]
     },
     "execution_count": 64,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Countries that need aid by using Hierarchial method in cluster 3\n",
    "df[df.cluster_labels == 0].country"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Observation \n",
    "The number of countries in cluster 3 is only Nigeria and hence cluster 0 was also examined. The number of countries are 125 hence not a very intutive cluster since the total number of countries is itslef only 167.\n",
    "\n",
    "Below both Kmeans and Hierarchial clusters are visually compared."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Kmeans and Hierarchial  methods with 4 clusters are visually compared"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1080x360 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "##### Plotting both Hierarchial clustering and Kmeans clustering graphs to see if there is a visible difference\n",
    "color=['r','b','g','orange']\n",
    "clusters= df.cluster_id.unique()\n",
    "plt.figure(figsize=(15,5))\n",
    "\n",
    "ax=plt.subplot(1,2,1)\n",
    "ax.title.set_text(\"Hierarchial Clustering Child mortality vs income\")\n",
    "ax.set_xlabel(\"Child Mortality\")\n",
    "ax.set_ylabel(\"Income\")\n",
    "\n",
    "for k in range(chosen_clusters):\n",
    "    p1 = plt.scatter(df[df.cluster_labels == k].child_mort, df[df.cluster_labels == k].income,c=color[k],label=\"Cluster: \"+str(k))\n",
    "ax.legend(loc=1)  \n",
    "ax=plt.subplot(1,2,2)\n",
    "ax.title.set_text(\"KMeans Clustering Child mortality vs income\")\n",
    "ax.set_xlabel(\"Child Mortality\")\n",
    "ax.set_ylabel(\"Income\")\n",
    "\n",
    "for k in range(chosen_clusters):\n",
    "    p2 = plt.scatter(df[df.cluster_id == k].child_mort, df[df.cluster_id == k].income,c=color[k],label=\"Cluster: \"+str(k))\n",
    "ax.legend(loc=1);"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##### Observation\n",
    "The clustering done by Kmeans method seems to be having a better segregation of the clusters compared to the hierarchical method.   Cluster 2 in Kmeans method has the highest child mortality rate and low income. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1080x360 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "##### Plotting both Hierarchial clustering and Kmeans clustering graphs to see if there is a visible difference\n",
    "color=['r','b','g','orange']\n",
    "plt.figure(figsize=(15,5))\n",
    "ax=plt.subplot(1,2,1)\n",
    "ax.title.set_text(\"Hierarchial Clustering Child mortality vs GDP\")\n",
    "ax.set_xlabel(\"Child Mortality\")\n",
    "ax.set_ylabel(\"GDP\")\n",
    "for k in range(chosen_clusters):\n",
    "    plt.scatter(df[df.cluster_labels == k].child_mort, df[df.cluster_labels == k].gdpp,c=color[k],label=\"Cluster: \"+str(k))\n",
    "ax.legend(loc=1)  \n",
    "        \n",
    "ax=plt.subplot(1,2,2)\n",
    "ax.title.set_text(\"KMeans Clustering Child mortality vs GDP\")\n",
    "ax.set_xlabel(\"Child Mortality\")\n",
    "ax.set_ylabel(\"GDP\")\n",
    "for k in range(chosen_clusters):\n",
    "    plt.scatter(df[df.cluster_id == k].child_mort, df[df.cluster_id == k].gdpp,c=color[k],label=\"Cluster: \"+str(k))\n",
    "ax.legend(loc=1);\n",
    "   "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##### Observation\n",
    "The clustering done by Kmeans method seems to be having a better segregation of the clusters compared to the hierarchical method.   Cluster 2 in Kmeans method has the highest child mortality rate and low GDP. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1080x360 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "##### Plotting both Hierarchial clustering and Kmeans clustering graphs to see if there is a visible difference\n",
    "color=['r','b','g','orange']\n",
    "plt.figure(figsize=(15,5))\n",
    "ax=plt.subplot(1,2,1)\n",
    "ax.title.set_text(\"Hierarchial Clustering Income vs GDP\")\n",
    "ax.set_xlabel(\"Income\")\n",
    "ax.set_ylabel(\"GDP\")\n",
    "for k in range(chosen_clusters):\n",
    "    plt.scatter(df[df.cluster_labels == k].income, df[df.cluster_labels == k].gdpp,c=color[k],label=\"Cluster: \"+str(k))\n",
    "ax.legend(loc=1)  \n",
    "    \n",
    "ax=plt.subplot(1,2,2)\n",
    "ax.title.set_text(\"KMeans Clustering Income vs GDP\")\n",
    "ax.set_xlabel(\"Income\")\n",
    "ax.set_ylabel(\"GDP\")\n",
    "for k in range(chosen_clusters):\n",
    "    plt.scatter(df[df.cluster_id == k].income, df[df.cluster_id == k].gdpp,c=color[k],label=\"Cluster: \"+str(k))\n",
    "ax.legend(loc=1) ; \n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##### Observation\n",
    "The clustering done by Kmeans method seems to be having a better segregation of the clusters compared to the hierarchical method.   Cluster 2 in Kmeans method has the lowest income and low GDP. "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Conclusion\n",
    "The clustering method by Kmeans gave better clustering of countries as compared to Hierarchial method. The scatter plots for Kmeans with 4 clusters show that there is a better clustering with 4 clusters than 3 clusters. Hence the number of clusters are finalised as 4 and the shortlisting is done from the countries in cluster 2 of Kmeans clustering."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Countries that need aid"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "48"
      ]
     },
     "execution_count": 68,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Checking the number of Countries that need aid got by clustering using KMeans method\n",
    "countries_aid_Kmeans = df[df.cluster_id == 2].country\n",
    "len(countries_aid_Kmeans)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1"
      ]
     },
     "execution_count": 69,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Checking the number of Countries that need aid got by clustering using Hierarchial method\n",
    "countries_aid_Hierarchial1 = df[df.cluster_labels == 3].country\n",
    "len(countries_aid_Hierarchial1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 70,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "125"
      ]
     },
     "execution_count": 70,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Checking the number of Countries that need aid got by clustering using Hierarchial method\n",
    "countries_aid_Hierarchial2 = df[df.cluster_labels == 0].country\n",
    "len(countries_aid_Hierarchial2)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##### Observation\n",
    "The countries got by clustering using Kmeans is considered. The conclusion was arrived by lookins=g at the graphs comapring the hierarchial and Kmeans methods and listing the number of countries as in previous cells.Hence countries are better clustered with Kmeans method."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 71,
   "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>cluster_id</th>\n",
       "      <th>cluster_labels</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Afghanistan</td>\n",
       "      <td>90.2</td>\n",
       "      <td>55.3000</td>\n",
       "      <td>41.9174</td>\n",
       "      <td>248.297</td>\n",
       "      <td>1610.0</td>\n",
       "      <td>9.440</td>\n",
       "      <td>56.2</td>\n",
       "      <td>5.82</td>\n",
       "      <td>553.0</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Angola</td>\n",
       "      <td>119.0</td>\n",
       "      <td>2199.1900</td>\n",
       "      <td>100.6050</td>\n",
       "      <td>1514.370</td>\n",
       "      <td>5900.0</td>\n",
       "      <td>22.400</td>\n",
       "      <td>60.1</td>\n",
       "      <td>6.16</td>\n",
       "      <td>3530.0</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>Benin</td>\n",
       "      <td>111.0</td>\n",
       "      <td>180.4040</td>\n",
       "      <td>31.0780</td>\n",
       "      <td>281.976</td>\n",
       "      <td>1820.0</td>\n",
       "      <td>0.885</td>\n",
       "      <td>61.8</td>\n",
       "      <td>5.36</td>\n",
       "      <td>758.0</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>Botswana</td>\n",
       "      <td>52.5</td>\n",
       "      <td>2768.6000</td>\n",
       "      <td>527.0500</td>\n",
       "      <td>3257.550</td>\n",
       "      <td>13300.0</td>\n",
       "      <td>8.920</td>\n",
       "      <td>57.1</td>\n",
       "      <td>2.88</td>\n",
       "      <td>6350.0</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>Burkina Faso</td>\n",
       "      <td>116.0</td>\n",
       "      <td>110.4000</td>\n",
       "      <td>38.7550</td>\n",
       "      <td>170.200</td>\n",
       "      <td>1430.0</td>\n",
       "      <td>6.810</td>\n",
       "      <td>57.9</td>\n",
       "      <td>5.87</td>\n",
       "      <td>575.0</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26</th>\n",
       "      <td>Burundi</td>\n",
       "      <td>93.6</td>\n",
       "      <td>20.6052</td>\n",
       "      <td>26.7960</td>\n",
       "      <td>90.552</td>\n",
       "      <td>764.0</td>\n",
       "      <td>12.300</td>\n",
       "      <td>57.7</td>\n",
       "      <td>6.26</td>\n",
       "      <td>231.0</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28</th>\n",
       "      <td>Cameroon</td>\n",
       "      <td>108.0</td>\n",
       "      <td>290.8200</td>\n",
       "      <td>67.2030</td>\n",
       "      <td>353.700</td>\n",
       "      <td>2660.0</td>\n",
       "      <td>1.910</td>\n",
       "      <td>57.3</td>\n",
       "      <td>5.11</td>\n",
       "      <td>1310.0</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>31</th>\n",
       "      <td>Central African Republic</td>\n",
       "      <td>149.0</td>\n",
       "      <td>52.6280</td>\n",
       "      <td>17.7508</td>\n",
       "      <td>118.190</td>\n",
       "      <td>888.0</td>\n",
       "      <td>2.010</td>\n",
       "      <td>47.5</td>\n",
       "      <td>5.21</td>\n",
       "      <td>446.0</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>32</th>\n",
       "      <td>Chad</td>\n",
       "      <td>150.0</td>\n",
       "      <td>330.0960</td>\n",
       "      <td>40.6341</td>\n",
       "      <td>390.195</td>\n",
       "      <td>1930.0</td>\n",
       "      <td>6.390</td>\n",
       "      <td>56.5</td>\n",
       "      <td>6.59</td>\n",
       "      <td>897.0</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>36</th>\n",
       "      <td>Comoros</td>\n",
       "      <td>88.2</td>\n",
       "      <td>126.8850</td>\n",
       "      <td>34.6819</td>\n",
       "      <td>397.573</td>\n",
       "      <td>1410.0</td>\n",
       "      <td>3.870</td>\n",
       "      <td>65.9</td>\n",
       "      <td>4.75</td>\n",
       "      <td>769.0</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>37</th>\n",
       "      <td>Congo, Dem. Rep.</td>\n",
       "      <td>116.0</td>\n",
       "      <td>137.2740</td>\n",
       "      <td>26.4194</td>\n",
       "      <td>165.664</td>\n",
       "      <td>609.0</td>\n",
       "      <td>20.800</td>\n",
       "      <td>57.5</td>\n",
       "      <td>6.54</td>\n",
       "      <td>334.0</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>38</th>\n",
       "      <td>Congo, Rep.</td>\n",
       "      <td>63.9</td>\n",
       "      <td>2331.7400</td>\n",
       "      <td>67.4040</td>\n",
       "      <td>1498.780</td>\n",
       "      <td>5190.0</td>\n",
       "      <td>20.700</td>\n",
       "      <td>60.4</td>\n",
       "      <td>4.95</td>\n",
       "      <td>2740.0</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>40</th>\n",
       "      <td>Cote d'Ivoire</td>\n",
       "      <td>111.0</td>\n",
       "      <td>617.3200</td>\n",
       "      <td>64.6600</td>\n",
       "      <td>528.260</td>\n",
       "      <td>2690.0</td>\n",
       "      <td>5.390</td>\n",
       "      <td>56.3</td>\n",
       "      <td>5.27</td>\n",
       "      <td>1220.0</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>49</th>\n",
       "      <td>Equatorial Guinea</td>\n",
       "      <td>111.0</td>\n",
       "      <td>14671.8000</td>\n",
       "      <td>766.0800</td>\n",
       "      <td>10071.900</td>\n",
       "      <td>33700.0</td>\n",
       "      <td>24.900</td>\n",
       "      <td>60.9</td>\n",
       "      <td>5.21</td>\n",
       "      <td>17100.0</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50</th>\n",
       "      <td>Eritrea</td>\n",
       "      <td>55.2</td>\n",
       "      <td>23.0878</td>\n",
       "      <td>12.8212</td>\n",
       "      <td>112.306</td>\n",
       "      <td>1420.0</td>\n",
       "      <td>11.600</td>\n",
       "      <td>61.7</td>\n",
       "      <td>4.61</td>\n",
       "      <td>482.0</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>55</th>\n",
       "      <td>Gabon</td>\n",
       "      <td>63.7</td>\n",
       "      <td>5048.7500</td>\n",
       "      <td>306.2500</td>\n",
       "      <td>1653.750</td>\n",
       "      <td>15400.0</td>\n",
       "      <td>16.600</td>\n",
       "      <td>62.9</td>\n",
       "      <td>4.08</td>\n",
       "      <td>8750.0</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>56</th>\n",
       "      <td>Gambia</td>\n",
       "      <td>80.3</td>\n",
       "      <td>133.7560</td>\n",
       "      <td>31.9778</td>\n",
       "      <td>239.974</td>\n",
       "      <td>1660.0</td>\n",
       "      <td>4.300</td>\n",
       "      <td>65.5</td>\n",
       "      <td>5.71</td>\n",
       "      <td>562.0</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>59</th>\n",
       "      <td>Ghana</td>\n",
       "      <td>74.7</td>\n",
       "      <td>386.4500</td>\n",
       "      <td>68.3820</td>\n",
       "      <td>601.290</td>\n",
       "      <td>3060.0</td>\n",
       "      <td>16.600</td>\n",
       "      <td>62.2</td>\n",
       "      <td>4.27</td>\n",
       "      <td>1310.0</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>63</th>\n",
       "      <td>Guinea</td>\n",
       "      <td>109.0</td>\n",
       "      <td>196.3440</td>\n",
       "      <td>31.9464</td>\n",
       "      <td>279.936</td>\n",
       "      <td>1190.0</td>\n",
       "      <td>16.100</td>\n",
       "      <td>58.0</td>\n",
       "      <td>5.34</td>\n",
       "      <td>648.0</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>64</th>\n",
       "      <td>Guinea-Bissau</td>\n",
       "      <td>114.0</td>\n",
       "      <td>81.5030</td>\n",
       "      <td>46.4950</td>\n",
       "      <td>192.544</td>\n",
       "      <td>1390.0</td>\n",
       "      <td>2.970</td>\n",
       "      <td>55.6</td>\n",
       "      <td>5.05</td>\n",
       "      <td>547.0</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>66</th>\n",
       "      <td>Haiti</td>\n",
       "      <td>208.0</td>\n",
       "      <td>101.2860</td>\n",
       "      <td>45.7442</td>\n",
       "      <td>428.314</td>\n",
       "      <td>1500.0</td>\n",
       "      <td>5.450</td>\n",
       "      <td>32.1</td>\n",
       "      <td>3.33</td>\n",
       "      <td>662.0</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>72</th>\n",
       "      <td>Iraq</td>\n",
       "      <td>36.9</td>\n",
       "      <td>1773.0000</td>\n",
       "      <td>378.4500</td>\n",
       "      <td>1534.500</td>\n",
       "      <td>12700.0</td>\n",
       "      <td>16.600</td>\n",
       "      <td>67.2</td>\n",
       "      <td>4.56</td>\n",
       "      <td>4500.0</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>80</th>\n",
       "      <td>Kenya</td>\n",
       "      <td>62.2</td>\n",
       "      <td>200.1690</td>\n",
       "      <td>45.9325</td>\n",
       "      <td>324.912</td>\n",
       "      <td>2480.0</td>\n",
       "      <td>2.090</td>\n",
       "      <td>62.8</td>\n",
       "      <td>4.37</td>\n",
       "      <td>967.0</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>81</th>\n",
       "      <td>Kiribati</td>\n",
       "      <td>62.7</td>\n",
       "      <td>198.1700</td>\n",
       "      <td>168.3700</td>\n",
       "      <td>1190.510</td>\n",
       "      <td>1730.0</td>\n",
       "      <td>1.520</td>\n",
       "      <td>60.7</td>\n",
       "      <td>3.84</td>\n",
       "      <td>1490.0</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>84</th>\n",
       "      <td>Lao</td>\n",
       "      <td>78.9</td>\n",
       "      <td>403.5600</td>\n",
       "      <td>50.9580</td>\n",
       "      <td>562.020</td>\n",
       "      <td>3980.0</td>\n",
       "      <td>9.200</td>\n",
       "      <td>63.8</td>\n",
       "      <td>3.15</td>\n",
       "      <td>1140.0</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>87</th>\n",
       "      <td>Lesotho</td>\n",
       "      <td>99.7</td>\n",
       "      <td>460.9800</td>\n",
       "      <td>129.8700</td>\n",
       "      <td>1181.700</td>\n",
       "      <td>2380.0</td>\n",
       "      <td>4.150</td>\n",
       "      <td>46.5</td>\n",
       "      <td>3.30</td>\n",
       "      <td>1170.0</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>88</th>\n",
       "      <td>Liberia</td>\n",
       "      <td>89.3</td>\n",
       "      <td>62.4570</td>\n",
       "      <td>38.5860</td>\n",
       "      <td>302.802</td>\n",
       "      <td>700.0</td>\n",
       "      <td>5.470</td>\n",
       "      <td>60.8</td>\n",
       "      <td>5.02</td>\n",
       "      <td>327.0</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>93</th>\n",
       "      <td>Madagascar</td>\n",
       "      <td>62.2</td>\n",
       "      <td>103.2500</td>\n",
       "      <td>15.5701</td>\n",
       "      <td>177.590</td>\n",
       "      <td>1390.0</td>\n",
       "      <td>8.790</td>\n",
       "      <td>60.8</td>\n",
       "      <td>4.60</td>\n",
       "      <td>413.0</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>94</th>\n",
       "      <td>Malawi</td>\n",
       "      <td>90.5</td>\n",
       "      <td>104.6520</td>\n",
       "      <td>30.2481</td>\n",
       "      <td>160.191</td>\n",
       "      <td>1030.0</td>\n",
       "      <td>12.100</td>\n",
       "      <td>53.1</td>\n",
       "      <td>5.31</td>\n",
       "      <td>459.0</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>97</th>\n",
       "      <td>Mali</td>\n",
       "      <td>137.0</td>\n",
       "      <td>161.4240</td>\n",
       "      <td>35.2584</td>\n",
       "      <td>248.508</td>\n",
       "      <td>1870.0</td>\n",
       "      <td>4.370</td>\n",
       "      <td>59.5</td>\n",
       "      <td>6.55</td>\n",
       "      <td>708.0</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>99</th>\n",
       "      <td>Mauritania</td>\n",
       "      <td>97.4</td>\n",
       "      <td>608.4000</td>\n",
       "      <td>52.9200</td>\n",
       "      <td>734.400</td>\n",
       "      <td>3320.0</td>\n",
       "      <td>18.900</td>\n",
       "      <td>68.2</td>\n",
       "      <td>4.98</td>\n",
       "      <td>1200.0</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>106</th>\n",
       "      <td>Mozambique</td>\n",
       "      <td>101.0</td>\n",
       "      <td>131.9850</td>\n",
       "      <td>21.8299</td>\n",
       "      <td>193.578</td>\n",
       "      <td>918.0</td>\n",
       "      <td>7.640</td>\n",
       "      <td>54.5</td>\n",
       "      <td>5.56</td>\n",
       "      <td>419.0</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>108</th>\n",
       "      <td>Namibia</td>\n",
       "      <td>56.0</td>\n",
       "      <td>2480.8200</td>\n",
       "      <td>351.8820</td>\n",
       "      <td>3150.330</td>\n",
       "      <td>8460.0</td>\n",
       "      <td>3.560</td>\n",
       "      <td>58.6</td>\n",
       "      <td>3.60</td>\n",
       "      <td>5190.0</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>112</th>\n",
       "      <td>Niger</td>\n",
       "      <td>123.0</td>\n",
       "      <td>77.2560</td>\n",
       "      <td>17.9568</td>\n",
       "      <td>170.868</td>\n",
       "      <td>814.0</td>\n",
       "      <td>2.550</td>\n",
       "      <td>58.8</td>\n",
       "      <td>7.49</td>\n",
       "      <td>348.0</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>113</th>\n",
       "      <td>Nigeria</td>\n",
       "      <td>130.0</td>\n",
       "      <td>589.4900</td>\n",
       "      <td>118.1310</td>\n",
       "      <td>405.420</td>\n",
       "      <td>5150.0</td>\n",
       "      <td>104.000</td>\n",
       "      <td>60.5</td>\n",
       "      <td>5.84</td>\n",
       "      <td>2330.0</td>\n",
       "      <td>2</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>116</th>\n",
       "      <td>Pakistan</td>\n",
       "      <td>92.1</td>\n",
       "      <td>140.4000</td>\n",
       "      <td>22.8800</td>\n",
       "      <td>201.760</td>\n",
       "      <td>4280.0</td>\n",
       "      <td>10.900</td>\n",
       "      <td>65.3</td>\n",
       "      <td>3.85</td>\n",
       "      <td>1040.0</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>126</th>\n",
       "      <td>Rwanda</td>\n",
       "      <td>63.6</td>\n",
       "      <td>67.5600</td>\n",
       "      <td>59.1150</td>\n",
       "      <td>168.900</td>\n",
       "      <td>1350.0</td>\n",
       "      <td>2.610</td>\n",
       "      <td>64.6</td>\n",
       "      <td>4.51</td>\n",
       "      <td>563.0</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>129</th>\n",
       "      <td>Senegal</td>\n",
       "      <td>66.8</td>\n",
       "      <td>249.0000</td>\n",
       "      <td>56.6000</td>\n",
       "      <td>403.000</td>\n",
       "      <td>2180.0</td>\n",
       "      <td>1.850</td>\n",
       "      <td>64.0</td>\n",
       "      <td>5.06</td>\n",
       "      <td>1000.0</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>132</th>\n",
       "      <td>Sierra Leone</td>\n",
       "      <td>160.0</td>\n",
       "      <td>67.0320</td>\n",
       "      <td>52.2690</td>\n",
       "      <td>137.655</td>\n",
       "      <td>1220.0</td>\n",
       "      <td>17.200</td>\n",
       "      <td>55.0</td>\n",
       "      <td>5.20</td>\n",
       "      <td>399.0</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>136</th>\n",
       "      <td>Solomon Islands</td>\n",
       "      <td>28.1</td>\n",
       "      <td>635.9700</td>\n",
       "      <td>110.2950</td>\n",
       "      <td>1047.480</td>\n",
       "      <td>1780.0</td>\n",
       "      <td>6.810</td>\n",
       "      <td>61.7</td>\n",
       "      <td>4.24</td>\n",
       "      <td>1290.0</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>137</th>\n",
       "      <td>South Africa</td>\n",
       "      <td>53.7</td>\n",
       "      <td>2082.0800</td>\n",
       "      <td>650.8320</td>\n",
       "      <td>1994.720</td>\n",
       "      <td>12000.0</td>\n",
       "      <td>6.350</td>\n",
       "      <td>54.3</td>\n",
       "      <td>2.59</td>\n",
       "      <td>7280.0</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>142</th>\n",
       "      <td>Sudan</td>\n",
       "      <td>76.7</td>\n",
       "      <td>291.5600</td>\n",
       "      <td>93.5360</td>\n",
       "      <td>254.560</td>\n",
       "      <td>3370.0</td>\n",
       "      <td>19.600</td>\n",
       "      <td>66.3</td>\n",
       "      <td>4.88</td>\n",
       "      <td>1480.0</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>147</th>\n",
       "      <td>Tanzania</td>\n",
       "      <td>71.9</td>\n",
       "      <td>131.2740</td>\n",
       "      <td>42.1902</td>\n",
       "      <td>204.282</td>\n",
       "      <td>2090.0</td>\n",
       "      <td>9.250</td>\n",
       "      <td>59.3</td>\n",
       "      <td>5.43</td>\n",
       "      <td>702.0</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>149</th>\n",
       "      <td>Timor-Leste</td>\n",
       "      <td>62.6</td>\n",
       "      <td>79.2000</td>\n",
       "      <td>328.3200</td>\n",
       "      <td>1000.800</td>\n",
       "      <td>1850.0</td>\n",
       "      <td>26.500</td>\n",
       "      <td>71.1</td>\n",
       "      <td>6.23</td>\n",
       "      <td>3600.0</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>150</th>\n",
       "      <td>Togo</td>\n",
       "      <td>90.3</td>\n",
       "      <td>196.1760</td>\n",
       "      <td>37.3320</td>\n",
       "      <td>279.624</td>\n",
       "      <td>1210.0</td>\n",
       "      <td>1.180</td>\n",
       "      <td>58.7</td>\n",
       "      <td>4.87</td>\n",
       "      <td>488.0</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>155</th>\n",
       "      <td>Uganda</td>\n",
       "      <td>81.0</td>\n",
       "      <td>101.7450</td>\n",
       "      <td>53.6095</td>\n",
       "      <td>170.170</td>\n",
       "      <td>1540.0</td>\n",
       "      <td>10.600</td>\n",
       "      <td>56.8</td>\n",
       "      <td>6.15</td>\n",
       "      <td>595.0</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>165</th>\n",
       "      <td>Yemen</td>\n",
       "      <td>56.3</td>\n",
       "      <td>393.0000</td>\n",
       "      <td>67.8580</td>\n",
       "      <td>450.640</td>\n",
       "      <td>4480.0</td>\n",
       "      <td>23.600</td>\n",
       "      <td>67.5</td>\n",
       "      <td>4.67</td>\n",
       "      <td>1310.0</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>166</th>\n",
       "      <td>Zambia</td>\n",
       "      <td>83.1</td>\n",
       "      <td>540.2000</td>\n",
       "      <td>85.9940</td>\n",
       "      <td>451.140</td>\n",
       "      <td>3280.0</td>\n",
       "      <td>14.000</td>\n",
       "      <td>52.0</td>\n",
       "      <td>5.40</td>\n",
       "      <td>1460.0</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                      country  child_mort     exports    health    imports  \\\n",
       "0                 Afghanistan        90.2     55.3000   41.9174    248.297   \n",
       "3                      Angola       119.0   2199.1900  100.6050   1514.370   \n",
       "17                      Benin       111.0    180.4040   31.0780    281.976   \n",
       "21                   Botswana        52.5   2768.6000  527.0500   3257.550   \n",
       "25               Burkina Faso       116.0    110.4000   38.7550    170.200   \n",
       "26                    Burundi        93.6     20.6052   26.7960     90.552   \n",
       "28                   Cameroon       108.0    290.8200   67.2030    353.700   \n",
       "31   Central African Republic       149.0     52.6280   17.7508    118.190   \n",
       "32                       Chad       150.0    330.0960   40.6341    390.195   \n",
       "36                    Comoros        88.2    126.8850   34.6819    397.573   \n",
       "37           Congo, Dem. Rep.       116.0    137.2740   26.4194    165.664   \n",
       "38                Congo, Rep.        63.9   2331.7400   67.4040   1498.780   \n",
       "40              Cote d'Ivoire       111.0    617.3200   64.6600    528.260   \n",
       "49          Equatorial Guinea       111.0  14671.8000  766.0800  10071.900   \n",
       "50                    Eritrea        55.2     23.0878   12.8212    112.306   \n",
       "55                      Gabon        63.7   5048.7500  306.2500   1653.750   \n",
       "56                     Gambia        80.3    133.7560   31.9778    239.974   \n",
       "59                      Ghana        74.7    386.4500   68.3820    601.290   \n",
       "63                     Guinea       109.0    196.3440   31.9464    279.936   \n",
       "64              Guinea-Bissau       114.0     81.5030   46.4950    192.544   \n",
       "66                      Haiti       208.0    101.2860   45.7442    428.314   \n",
       "72                       Iraq        36.9   1773.0000  378.4500   1534.500   \n",
       "80                      Kenya        62.2    200.1690   45.9325    324.912   \n",
       "81                   Kiribati        62.7    198.1700  168.3700   1190.510   \n",
       "84                        Lao        78.9    403.5600   50.9580    562.020   \n",
       "87                    Lesotho        99.7    460.9800  129.8700   1181.700   \n",
       "88                    Liberia        89.3     62.4570   38.5860    302.802   \n",
       "93                 Madagascar        62.2    103.2500   15.5701    177.590   \n",
       "94                     Malawi        90.5    104.6520   30.2481    160.191   \n",
       "97                       Mali       137.0    161.4240   35.2584    248.508   \n",
       "99                 Mauritania        97.4    608.4000   52.9200    734.400   \n",
       "106                Mozambique       101.0    131.9850   21.8299    193.578   \n",
       "108                   Namibia        56.0   2480.8200  351.8820   3150.330   \n",
       "112                     Niger       123.0     77.2560   17.9568    170.868   \n",
       "113                   Nigeria       130.0    589.4900  118.1310    405.420   \n",
       "116                  Pakistan        92.1    140.4000   22.8800    201.760   \n",
       "126                    Rwanda        63.6     67.5600   59.1150    168.900   \n",
       "129                   Senegal        66.8    249.0000   56.6000    403.000   \n",
       "132              Sierra Leone       160.0     67.0320   52.2690    137.655   \n",
       "136           Solomon Islands        28.1    635.9700  110.2950   1047.480   \n",
       "137              South Africa        53.7   2082.0800  650.8320   1994.720   \n",
       "142                     Sudan        76.7    291.5600   93.5360    254.560   \n",
       "147                  Tanzania        71.9    131.2740   42.1902    204.282   \n",
       "149               Timor-Leste        62.6     79.2000  328.3200   1000.800   \n",
       "150                      Togo        90.3    196.1760   37.3320    279.624   \n",
       "155                    Uganda        81.0    101.7450   53.6095    170.170   \n",
       "165                     Yemen        56.3    393.0000   67.8580    450.640   \n",
       "166                    Zambia        83.1    540.2000   85.9940    451.140   \n",
       "\n",
       "      income  inflation  life_expec  total_fer     gdpp  cluster_id  \\\n",
       "0     1610.0      9.440        56.2       5.82    553.0           2   \n",
       "3     5900.0     22.400        60.1       6.16   3530.0           2   \n",
       "17    1820.0      0.885        61.8       5.36    758.0           2   \n",
       "21   13300.0      8.920        57.1       2.88   6350.0           2   \n",
       "25    1430.0      6.810        57.9       5.87    575.0           2   \n",
       "26     764.0     12.300        57.7       6.26    231.0           2   \n",
       "28    2660.0      1.910        57.3       5.11   1310.0           2   \n",
       "31     888.0      2.010        47.5       5.21    446.0           2   \n",
       "32    1930.0      6.390        56.5       6.59    897.0           2   \n",
       "36    1410.0      3.870        65.9       4.75    769.0           2   \n",
       "37     609.0     20.800        57.5       6.54    334.0           2   \n",
       "38    5190.0     20.700        60.4       4.95   2740.0           2   \n",
       "40    2690.0      5.390        56.3       5.27   1220.0           2   \n",
       "49   33700.0     24.900        60.9       5.21  17100.0           2   \n",
       "50    1420.0     11.600        61.7       4.61    482.0           2   \n",
       "55   15400.0     16.600        62.9       4.08   8750.0           2   \n",
       "56    1660.0      4.300        65.5       5.71    562.0           2   \n",
       "59    3060.0     16.600        62.2       4.27   1310.0           2   \n",
       "63    1190.0     16.100        58.0       5.34    648.0           2   \n",
       "64    1390.0      2.970        55.6       5.05    547.0           2   \n",
       "66    1500.0      5.450        32.1       3.33    662.0           2   \n",
       "72   12700.0     16.600        67.2       4.56   4500.0           2   \n",
       "80    2480.0      2.090        62.8       4.37    967.0           2   \n",
       "81    1730.0      1.520        60.7       3.84   1490.0           2   \n",
       "84    3980.0      9.200        63.8       3.15   1140.0           2   \n",
       "87    2380.0      4.150        46.5       3.30   1170.0           2   \n",
       "88     700.0      5.470        60.8       5.02    327.0           2   \n",
       "93    1390.0      8.790        60.8       4.60    413.0           2   \n",
       "94    1030.0     12.100        53.1       5.31    459.0           2   \n",
       "97    1870.0      4.370        59.5       6.55    708.0           2   \n",
       "99    3320.0     18.900        68.2       4.98   1200.0           2   \n",
       "106    918.0      7.640        54.5       5.56    419.0           2   \n",
       "108   8460.0      3.560        58.6       3.60   5190.0           2   \n",
       "112    814.0      2.550        58.8       7.49    348.0           2   \n",
       "113   5150.0    104.000        60.5       5.84   2330.0           2   \n",
       "116   4280.0     10.900        65.3       3.85   1040.0           2   \n",
       "126   1350.0      2.610        64.6       4.51    563.0           2   \n",
       "129   2180.0      1.850        64.0       5.06   1000.0           2   \n",
       "132   1220.0     17.200        55.0       5.20    399.0           2   \n",
       "136   1780.0      6.810        61.7       4.24   1290.0           2   \n",
       "137  12000.0      6.350        54.3       2.59   7280.0           2   \n",
       "142   3370.0     19.600        66.3       4.88   1480.0           2   \n",
       "147   2090.0      9.250        59.3       5.43    702.0           2   \n",
       "149   1850.0     26.500        71.1       6.23   3600.0           2   \n",
       "150   1210.0      1.180        58.7       4.87    488.0           2   \n",
       "155   1540.0     10.600        56.8       6.15    595.0           2   \n",
       "165   4480.0     23.600        67.5       4.67   1310.0           2   \n",
       "166   3280.0     14.000        52.0       5.40   1460.0           2   \n",
       "\n",
       "     cluster_labels  \n",
       "0                 0  \n",
       "3                 0  \n",
       "17                0  \n",
       "21                0  \n",
       "25                0  \n",
       "26                0  \n",
       "28                0  \n",
       "31                0  \n",
       "32                0  \n",
       "36                0  \n",
       "37                0  \n",
       "38                0  \n",
       "40                0  \n",
       "49                0  \n",
       "50                0  \n",
       "55                0  \n",
       "56                0  \n",
       "59                0  \n",
       "63                0  \n",
       "64                0  \n",
       "66                0  \n",
       "72                0  \n",
       "80                0  \n",
       "81                0  \n",
       "84                0  \n",
       "87                0  \n",
       "88                0  \n",
       "93                0  \n",
       "94                0  \n",
       "97                0  \n",
       "99                0  \n",
       "106               0  \n",
       "108               0  \n",
       "112               0  \n",
       "113               3  \n",
       "116               0  \n",
       "126               0  \n",
       "129               0  \n",
       "132               0  \n",
       "136               0  \n",
       "137               0  \n",
       "142               0  \n",
       "147               0  \n",
       "149               0  \n",
       "150               0  \n",
       "155               0  \n",
       "165               0  \n",
       "166               0  "
      ]
     },
     "execution_count": 71,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "aid_reqd_df = df[df.country.isin(countries_aid_Kmeans)]\n",
    "aid_reqd_df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 72,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Sorting the countries in descending order of child mortality and ascending order of life expectancy\n",
    "aid_reqd_df_sorted = aid_reqd_df.sort_values(by =['child_mort','income','gdpp'], ascending=[False, True, True])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 76,
   "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>cluster_id</th>\n",
       "      <th>cluster_labels</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>66</th>\n",
       "      <td>Haiti</td>\n",
       "      <td>208.0</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>3.33</td>\n",
       "      <td>662.0</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>132</th>\n",
       "      <td>Sierra Leone</td>\n",
       "      <td>160.0</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>5.20</td>\n",
       "      <td>399.0</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>32</th>\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>6.59</td>\n",
       "      <td>897.0</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>31</th>\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>5.21</td>\n",
       "      <td>446.0</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>97</th>\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>6.55</td>\n",
       "      <td>708.0</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>113</th>\n",
       "      <td>Nigeria</td>\n",
       "      <td>130.0</td>\n",
       "      <td>589.490</td>\n",
       "      <td>118.1310</td>\n",
       "      <td>405.420</td>\n",
       "      <td>5150.0</td>\n",
       "      <td>104.00</td>\n",
       "      <td>60.5</td>\n",
       "      <td>5.84</td>\n",
       "      <td>2330.0</td>\n",
       "      <td>2</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>112</th>\n",
       "      <td>Niger</td>\n",
       "      <td>123.0</td>\n",
       "      <td>77.256</td>\n",
       "      <td>17.9568</td>\n",
       "      <td>170.868</td>\n",
       "      <td>814.0</td>\n",
       "      <td>2.55</td>\n",
       "      <td>58.8</td>\n",
       "      <td>7.49</td>\n",
       "      <td>348.0</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Angola</td>\n",
       "      <td>119.0</td>\n",
       "      <td>2199.190</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>2</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>37</th>\n",
       "      <td>Congo, Dem. Rep.</td>\n",
       "      <td>116.0</td>\n",
       "      <td>137.274</td>\n",
       "      <td>26.4194</td>\n",
       "      <td>165.664</td>\n",
       "      <td>609.0</td>\n",
       "      <td>20.80</td>\n",
       "      <td>57.5</td>\n",
       "      <td>6.54</td>\n",
       "      <td>334.0</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>Burkina Faso</td>\n",
       "      <td>116.0</td>\n",
       "      <td>110.400</td>\n",
       "      <td>38.7550</td>\n",
       "      <td>170.200</td>\n",
       "      <td>1430.0</td>\n",
       "      <td>6.81</td>\n",
       "      <td>57.9</td>\n",
       "      <td>5.87</td>\n",
       "      <td>575.0</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                      country  child_mort   exports    health   imports  \\\n",
       "66                      Haiti       208.0   101.286   45.7442   428.314   \n",
       "132              Sierra Leone       160.0    67.032   52.2690   137.655   \n",
       "32                       Chad       150.0   330.096   40.6341   390.195   \n",
       "31   Central African Republic       149.0    52.628   17.7508   118.190   \n",
       "97                       Mali       137.0   161.424   35.2584   248.508   \n",
       "113                   Nigeria       130.0   589.490  118.1310   405.420   \n",
       "112                     Niger       123.0    77.256   17.9568   170.868   \n",
       "3                      Angola       119.0  2199.190  100.6050  1514.370   \n",
       "37           Congo, Dem. Rep.       116.0   137.274   26.4194   165.664   \n",
       "25               Burkina Faso       116.0   110.400   38.7550   170.200   \n",
       "\n",
       "     income  inflation  life_expec  total_fer    gdpp  cluster_id  \\\n",
       "66   1500.0       5.45        32.1       3.33   662.0           2   \n",
       "132  1220.0      17.20        55.0       5.20   399.0           2   \n",
       "32   1930.0       6.39        56.5       6.59   897.0           2   \n",
       "31    888.0       2.01        47.5       5.21   446.0           2   \n",
       "97   1870.0       4.37        59.5       6.55   708.0           2   \n",
       "113  5150.0     104.00        60.5       5.84  2330.0           2   \n",
       "112   814.0       2.55        58.8       7.49   348.0           2   \n",
       "3    5900.0      22.40        60.1       6.16  3530.0           2   \n",
       "37    609.0      20.80        57.5       6.54   334.0           2   \n",
       "25   1430.0       6.81        57.9       5.87   575.0           2   \n",
       "\n",
       "     cluster_labels  \n",
       "66                0  \n",
       "132               0  \n",
       "32                0  \n",
       "31                0  \n",
       "97                0  \n",
       "113               3  \n",
       "112               0  \n",
       "3                 0  \n",
       "37                0  \n",
       "25                0  "
      ]
     },
     "execution_count": 76,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# List of 10 countries that have been shortlisted\n",
    "\n",
    "aid_reqd_df_sorted.head(10)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 74,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "66                        Haiti\n",
       "132                Sierra Leone\n",
       "32                         Chad\n",
       "31     Central African Republic\n",
       "97                         Mali\n",
       "Name: country, dtype: object"
      ]
     },
     "execution_count": 74,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "aid_reqd_df_sorted.country.head(5)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##  List of 5 countries that have been shortlisted\n",
    "1. Haiti, \n",
    "2. Sierra Leone \n",
    "3. Chad\n",
    "4. Central African Republic\n",
    "5. Mali"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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