Clustering Assignment - Smitan.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## We will be performing the below mentioned key steps to reach the conclusion of our analysis\n",
    "\n",
    "    - Read and understand the data\n",
    "    - Outlier analysis and decision based on outlier annalysis\n",
    "    - Perform PCA and get the data ready\n",
    "    - Perform the K means clustering\n",
    "    - Perform the Hierarchical analysis clustering\n",
    "    - Perform the analysis based on 3 keym etrics (gddp, child mortality, and income)\n",
    "    - Perform PCA and correlate it with the clusters we are able to find out\n",
    "    - Recommendation of 5 countries to be given"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 1. Read and understand the data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 186,
   "metadata": {},
   "outputs": [],
   "source": [
    "# import pandas and numpy\n",
    "\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 187,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "               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  \n",
      "(167, 10)\n"
     ]
    }
   ],
   "source": [
    "#reading the dataset\n",
    "\n",
    "country_df = pd.read_csv(\"Country-data.csv\")\n",
    "\n",
    "print(country_df.head())\n",
    "\n",
    "print(country_df.shape)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### So there are total 167 records and 10 columns describing those data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 188,
   "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>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": 188,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "country_df.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 189,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 167 entries, 0 to 166\n",
      "Data columns (total 10 columns):\n",
      "country       167 non-null object\n",
      "child_mort    167 non-null float64\n",
      "exports       167 non-null float64\n",
      "health        167 non-null float64\n",
      "imports       167 non-null float64\n",
      "income        167 non-null int64\n",
      "inflation     167 non-null float64\n",
      "life_expec    167 non-null float64\n",
      "total_fer     167 non-null float64\n",
      "gdpp          167 non-null int64\n",
      "dtypes: float64(7), int64(2), object(1)\n",
      "memory usage: 13.1+ KB\n"
     ]
    }
   ],
   "source": [
    "country_df.info()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### We can observe here that since none of the rows are empty (all have 167 records), we do not need to remove any data from the data set"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 2. Checking for outliers"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 190,
   "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>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>90%</th>\n",
       "      <td>100.220000</td>\n",
       "      <td>70.800000</td>\n",
       "      <td>10.940000</td>\n",
       "      <td>75.420000</td>\n",
       "      <td>41220.000000</td>\n",
       "      <td>16.640000</td>\n",
       "      <td>80.400000</td>\n",
       "      <td>5.322000</td>\n",
       "      <td>41840.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>95%</th>\n",
       "      <td>116.000000</td>\n",
       "      <td>80.570000</td>\n",
       "      <td>11.570000</td>\n",
       "      <td>81.140000</td>\n",
       "      <td>48290.000000</td>\n",
       "      <td>20.870000</td>\n",
       "      <td>81.400000</td>\n",
       "      <td>5.861000</td>\n",
       "      <td>48610.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>99%</th>\n",
       "      <td>153.400000</td>\n",
       "      <td>160.480000</td>\n",
       "      <td>13.474000</td>\n",
       "      <td>146.080000</td>\n",
       "      <td>84374.000000</td>\n",
       "      <td>41.478000</td>\n",
       "      <td>82.370000</td>\n",
       "      <td>6.563600</td>\n",
       "      <td>79088.000000</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",
       "90%    100.220000   70.800000   10.940000   75.420000   41220.000000   \n",
       "95%    116.000000   80.570000   11.570000   81.140000   48290.000000   \n",
       "99%    153.400000  160.480000   13.474000  146.080000   84374.000000   \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",
       "90%     16.640000   80.400000    5.322000   41840.000000  \n",
       "95%     20.870000   81.400000    5.861000   48610.000000  \n",
       "99%     41.478000   82.370000    6.563600   79088.000000  \n",
       "max    104.000000   82.800000    7.490000  105000.000000  "
      ]
     },
     "execution_count": 190,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# checking for outliers\n",
    "\n",
    "country_df.describe(percentiles = [0.25,0.5,0.75,0.9,0.95,.99])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Selecting outlier countries based on all the columns and have them appended into one dataframe"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 191,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>country</th>\n",
       "      <th>child_mort</th>\n",
       "      <th>exports</th>\n",
       "      <th>health</th>\n",
       "      <th>imports</th>\n",
       "      <th>income</th>\n",
       "      <th>inflation</th>\n",
       "      <th>life_expec</th>\n",
       "      <th>total_fer</th>\n",
       "      <th>gdpp</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <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>22.400</td>\n",
       "      <td>60.1</td>\n",
       "      <td>6.16</td>\n",
       "      <td>3530</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>Benin</td>\n",
       "      <td>111.0</td>\n",
       "      <td>23.8</td>\n",
       "      <td>4.10</td>\n",
       "      <td>37.2</td>\n",
       "      <td>1820</td>\n",
       "      <td>0.885</td>\n",
       "      <td>61.8</td>\n",
       "      <td>5.36</td>\n",
       "      <td>758</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>Burkina Faso</td>\n",
       "      <td>116.0</td>\n",
       "      <td>19.2</td>\n",
       "      <td>6.74</td>\n",
       "      <td>29.6</td>\n",
       "      <td>1430</td>\n",
       "      <td>6.810</td>\n",
       "      <td>57.9</td>\n",
       "      <td>5.87</td>\n",
       "      <td>575</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28</th>\n",
       "      <td>Cameroon</td>\n",
       "      <td>108.0</td>\n",
       "      <td>22.2</td>\n",
       "      <td>5.13</td>\n",
       "      <td>27.0</td>\n",
       "      <td>2660</td>\n",
       "      <td>1.910</td>\n",
       "      <td>57.3</td>\n",
       "      <td>5.11</td>\n",
       "      <td>1310</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>31</th>\n",
       "      <td>Central African Republic</td>\n",
       "      <td>149.0</td>\n",
       "      <td>11.8</td>\n",
       "      <td>3.98</td>\n",
       "      <td>26.5</td>\n",
       "      <td>888</td>\n",
       "      <td>2.010</td>\n",
       "      <td>47.5</td>\n",
       "      <td>5.21</td>\n",
       "      <td>446</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                     country  child_mort  exports  health  imports  income  \\\n",
       "3                     Angola       119.0     62.3    2.85     42.9    5900   \n",
       "17                     Benin       111.0     23.8    4.10     37.2    1820   \n",
       "25              Burkina Faso       116.0     19.2    6.74     29.6    1430   \n",
       "28                  Cameroon       108.0     22.2    5.13     27.0    2660   \n",
       "31  Central African Republic       149.0     11.8    3.98     26.5     888   \n",
       "\n",
       "    inflation  life_expec  total_fer  gdpp  \n",
       "3      22.400        60.1       6.16  3530  \n",
       "17      0.885        61.8       5.36   758  \n",
       "25      6.810        57.9       5.87   575  \n",
       "28      1.910        57.3       5.11  1310  \n",
       "31      2.010        47.5       5.21   446  "
      ]
     },
     "execution_count": 191,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "country_df_outlier1 = country_df[country_df.child_mort > 100]\n",
    "\n",
    "country_df_outlier1.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 192,
   "metadata": {},
   "outputs": [],
   "source": [
    "country_df_outlier2 = country_df[country_df.exports > 70]\n",
    "country_df_outlier3 = country_df[country_df.health > 11]\n",
    "country_df_outlier4 = country_df[country_df.imports > 76]\n",
    "country_df_outlier5 = country_df[country_df.income > 41220]\n",
    "country_df_outlier6 = country_df[country_df.inflation > 17]\n",
    "country_df_outlier7 = country_df[country_df.life_expec > 80]\n",
    "country_df_outlier8 = country_df[country_df.total_fer > 5.4]\n",
    "country_df_outlier9 = country_df[country_df.gdpp > 41840]\n",
    "\n",
    "country_df_outlier = pd.concat([country_df_outlier1,country_df_outlier2,country_df_outlier3,country_df_outlier4,country_df_outlier5,country_df_outlier6,\n",
    "                               country_df_outlier7,country_df_outlier8,country_df_outlier9])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 193,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>country</th>\n",
       "      <th>child_mort</th>\n",
       "      <th>exports</th>\n",
       "      <th>health</th>\n",
       "      <th>imports</th>\n",
       "      <th>income</th>\n",
       "      <th>inflation</th>\n",
       "      <th>life_expec</th>\n",
       "      <th>total_fer</th>\n",
       "      <th>gdpp</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <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>22.400</td>\n",
       "      <td>60.1</td>\n",
       "      <td>6.16</td>\n",
       "      <td>3530</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>Benin</td>\n",
       "      <td>111.0</td>\n",
       "      <td>23.8</td>\n",
       "      <td>4.10</td>\n",
       "      <td>37.2</td>\n",
       "      <td>1820</td>\n",
       "      <td>0.885</td>\n",
       "      <td>61.8</td>\n",
       "      <td>5.36</td>\n",
       "      <td>758</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>Burkina Faso</td>\n",
       "      <td>116.0</td>\n",
       "      <td>19.2</td>\n",
       "      <td>6.74</td>\n",
       "      <td>29.6</td>\n",
       "      <td>1430</td>\n",
       "      <td>6.810</td>\n",
       "      <td>57.9</td>\n",
       "      <td>5.87</td>\n",
       "      <td>575</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28</th>\n",
       "      <td>Cameroon</td>\n",
       "      <td>108.0</td>\n",
       "      <td>22.2</td>\n",
       "      <td>5.13</td>\n",
       "      <td>27.0</td>\n",
       "      <td>2660</td>\n",
       "      <td>1.910</td>\n",
       "      <td>57.3</td>\n",
       "      <td>5.11</td>\n",
       "      <td>1310</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>31</th>\n",
       "      <td>Central African Republic</td>\n",
       "      <td>149.0</td>\n",
       "      <td>11.8</td>\n",
       "      <td>3.98</td>\n",
       "      <td>26.5</td>\n",
       "      <td>888</td>\n",
       "      <td>2.010</td>\n",
       "      <td>47.5</td>\n",
       "      <td>5.21</td>\n",
       "      <td>446</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>32</th>\n",
       "      <td>Chad</td>\n",
       "      <td>150.0</td>\n",
       "      <td>36.8</td>\n",
       "      <td>4.53</td>\n",
       "      <td>43.5</td>\n",
       "      <td>1930</td>\n",
       "      <td>6.390</td>\n",
       "      <td>56.5</td>\n",
       "      <td>6.59</td>\n",
       "      <td>897</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>37</th>\n",
       "      <td>Congo, Dem. Rep.</td>\n",
       "      <td>116.0</td>\n",
       "      <td>41.1</td>\n",
       "      <td>7.91</td>\n",
       "      <td>49.6</td>\n",
       "      <td>609</td>\n",
       "      <td>20.800</td>\n",
       "      <td>57.5</td>\n",
       "      <td>6.54</td>\n",
       "      <td>334</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>40</th>\n",
       "      <td>Cote d'Ivoire</td>\n",
       "      <td>111.0</td>\n",
       "      <td>50.6</td>\n",
       "      <td>5.30</td>\n",
       "      <td>43.3</td>\n",
       "      <td>2690</td>\n",
       "      <td>5.390</td>\n",
       "      <td>56.3</td>\n",
       "      <td>5.27</td>\n",
       "      <td>1220</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>49</th>\n",
       "      <td>Equatorial Guinea</td>\n",
       "      <td>111.0</td>\n",
       "      <td>85.8</td>\n",
       "      <td>4.48</td>\n",
       "      <td>58.9</td>\n",
       "      <td>33700</td>\n",
       "      <td>24.900</td>\n",
       "      <td>60.9</td>\n",
       "      <td>5.21</td>\n",
       "      <td>17100</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>63</th>\n",
       "      <td>Guinea</td>\n",
       "      <td>109.0</td>\n",
       "      <td>30.3</td>\n",
       "      <td>4.93</td>\n",
       "      <td>43.2</td>\n",
       "      <td>1190</td>\n",
       "      <td>16.100</td>\n",
       "      <td>58.0</td>\n",
       "      <td>5.34</td>\n",
       "      <td>648</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>64</th>\n",
       "      <td>Guinea-Bissau</td>\n",
       "      <td>114.0</td>\n",
       "      <td>14.9</td>\n",
       "      <td>8.50</td>\n",
       "      <td>35.2</td>\n",
       "      <td>1390</td>\n",
       "      <td>2.970</td>\n",
       "      <td>55.6</td>\n",
       "      <td>5.05</td>\n",
       "      <td>547</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>66</th>\n",
       "      <td>Haiti</td>\n",
       "      <td>208.0</td>\n",
       "      <td>15.3</td>\n",
       "      <td>6.91</td>\n",
       "      <td>64.7</td>\n",
       "      <td>1500</td>\n",
       "      <td>5.450</td>\n",
       "      <td>32.1</td>\n",
       "      <td>3.33</td>\n",
       "      <td>662</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>97</th>\n",
       "      <td>Mali</td>\n",
       "      <td>137.0</td>\n",
       "      <td>22.8</td>\n",
       "      <td>4.98</td>\n",
       "      <td>35.1</td>\n",
       "      <td>1870</td>\n",
       "      <td>4.370</td>\n",
       "      <td>59.5</td>\n",
       "      <td>6.55</td>\n",
       "      <td>708</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>106</th>\n",
       "      <td>Mozambique</td>\n",
       "      <td>101.0</td>\n",
       "      <td>31.5</td>\n",
       "      <td>5.21</td>\n",
       "      <td>46.2</td>\n",
       "      <td>918</td>\n",
       "      <td>7.640</td>\n",
       "      <td>54.5</td>\n",
       "      <td>5.56</td>\n",
       "      <td>419</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>112</th>\n",
       "      <td>Niger</td>\n",
       "      <td>123.0</td>\n",
       "      <td>22.2</td>\n",
       "      <td>5.16</td>\n",
       "      <td>49.1</td>\n",
       "      <td>814</td>\n",
       "      <td>2.550</td>\n",
       "      <td>58.8</td>\n",
       "      <td>7.49</td>\n",
       "      <td>348</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>113</th>\n",
       "      <td>Nigeria</td>\n",
       "      <td>130.0</td>\n",
       "      <td>25.3</td>\n",
       "      <td>5.07</td>\n",
       "      <td>17.4</td>\n",
       "      <td>5150</td>\n",
       "      <td>104.000</td>\n",
       "      <td>60.5</td>\n",
       "      <td>5.84</td>\n",
       "      <td>2330</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>132</th>\n",
       "      <td>Sierra Leone</td>\n",
       "      <td>160.0</td>\n",
       "      <td>16.8</td>\n",
       "      <td>13.10</td>\n",
       "      <td>34.5</td>\n",
       "      <td>1220</td>\n",
       "      <td>17.200</td>\n",
       "      <td>55.0</td>\n",
       "      <td>5.20</td>\n",
       "      <td>399</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>Belgium</td>\n",
       "      <td>4.5</td>\n",
       "      <td>76.4</td>\n",
       "      <td>10.70</td>\n",
       "      <td>74.7</td>\n",
       "      <td>41100</td>\n",
       "      <td>1.880</td>\n",
       "      <td>80.0</td>\n",
       "      <td>1.86</td>\n",
       "      <td>44400</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>38</th>\n",
       "      <td>Congo, Rep.</td>\n",
       "      <td>63.9</td>\n",
       "      <td>85.1</td>\n",
       "      <td>2.46</td>\n",
       "      <td>54.7</td>\n",
       "      <td>5190</td>\n",
       "      <td>20.700</td>\n",
       "      <td>60.4</td>\n",
       "      <td>4.95</td>\n",
       "      <td>2740</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>51</th>\n",
       "      <td>Estonia</td>\n",
       "      <td>4.5</td>\n",
       "      <td>75.1</td>\n",
       "      <td>6.03</td>\n",
       "      <td>68.7</td>\n",
       "      <td>22700</td>\n",
       "      <td>1.740</td>\n",
       "      <td>76.0</td>\n",
       "      <td>1.72</td>\n",
       "      <td>14600</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>67</th>\n",
       "      <td>Hungary</td>\n",
       "      <td>6.0</td>\n",
       "      <td>81.8</td>\n",
       "      <td>7.33</td>\n",
       "      <td>76.5</td>\n",
       "      <td>22300</td>\n",
       "      <td>2.330</td>\n",
       "      <td>74.5</td>\n",
       "      <td>1.25</td>\n",
       "      <td>13100</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>73</th>\n",
       "      <td>Ireland</td>\n",
       "      <td>4.2</td>\n",
       "      <td>103.0</td>\n",
       "      <td>9.19</td>\n",
       "      <td>86.5</td>\n",
       "      <td>45700</td>\n",
       "      <td>-3.220</td>\n",
       "      <td>80.4</td>\n",
       "      <td>2.05</td>\n",
       "      <td>48700</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>91</th>\n",
       "      <td>Luxembourg</td>\n",
       "      <td>2.8</td>\n",
       "      <td>175.0</td>\n",
       "      <td>7.77</td>\n",
       "      <td>142.0</td>\n",
       "      <td>91700</td>\n",
       "      <td>3.620</td>\n",
       "      <td>81.3</td>\n",
       "      <td>1.63</td>\n",
       "      <td>105000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>95</th>\n",
       "      <td>Malaysia</td>\n",
       "      <td>7.9</td>\n",
       "      <td>86.9</td>\n",
       "      <td>4.39</td>\n",
       "      <td>71.0</td>\n",
       "      <td>21100</td>\n",
       "      <td>7.270</td>\n",
       "      <td>74.5</td>\n",
       "      <td>2.15</td>\n",
       "      <td>9070</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>96</th>\n",
       "      <td>Maldives</td>\n",
       "      <td>13.2</td>\n",
       "      <td>77.6</td>\n",
       "      <td>6.33</td>\n",
       "      <td>65.4</td>\n",
       "      <td>10500</td>\n",
       "      <td>2.880</td>\n",
       "      <td>77.9</td>\n",
       "      <td>2.23</td>\n",
       "      <td>7100</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>98</th>\n",
       "      <td>Malta</td>\n",
       "      <td>6.8</td>\n",
       "      <td>153.0</td>\n",
       "      <td>8.65</td>\n",
       "      <td>154.0</td>\n",
       "      <td>28300</td>\n",
       "      <td>3.830</td>\n",
       "      <td>80.3</td>\n",
       "      <td>1.36</td>\n",
       "      <td>21100</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>110</th>\n",
       "      <td>Netherlands</td>\n",
       "      <td>4.5</td>\n",
       "      <td>72.0</td>\n",
       "      <td>11.90</td>\n",
       "      <td>63.6</td>\n",
       "      <td>45500</td>\n",
       "      <td>0.848</td>\n",
       "      <td>80.7</td>\n",
       "      <td>1.79</td>\n",
       "      <td>50300</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>131</th>\n",
       "      <td>Seychelles</td>\n",
       "      <td>14.4</td>\n",
       "      <td>93.8</td>\n",
       "      <td>3.40</td>\n",
       "      <td>108.0</td>\n",
       "      <td>20400</td>\n",
       "      <td>-4.210</td>\n",
       "      <td>73.4</td>\n",
       "      <td>2.17</td>\n",
       "      <td>10800</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>133</th>\n",
       "      <td>Singapore</td>\n",
       "      <td>2.8</td>\n",
       "      <td>200.0</td>\n",
       "      <td>3.96</td>\n",
       "      <td>174.0</td>\n",
       "      <td>72100</td>\n",
       "      <td>-0.046</td>\n",
       "      <td>82.7</td>\n",
       "      <td>1.15</td>\n",
       "      <td>46600</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>134</th>\n",
       "      <td>Slovak Republic</td>\n",
       "      <td>7.0</td>\n",
       "      <td>76.3</td>\n",
       "      <td>8.79</td>\n",
       "      <td>77.8</td>\n",
       "      <td>25200</td>\n",
       "      <td>0.485</td>\n",
       "      <td>75.5</td>\n",
       "      <td>1.43</td>\n",
       "      <td>16600</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>82</th>\n",
       "      <td>Kuwait</td>\n",
       "      <td>10.8</td>\n",
       "      <td>66.7</td>\n",
       "      <td>2.63</td>\n",
       "      <td>30.4</td>\n",
       "      <td>75200</td>\n",
       "      <td>11.200</td>\n",
       "      <td>78.2</td>\n",
       "      <td>2.21</td>\n",
       "      <td>38500</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>114</th>\n",
       "      <td>Norway</td>\n",
       "      <td>3.2</td>\n",
       "      <td>39.7</td>\n",
       "      <td>9.48</td>\n",
       "      <td>28.5</td>\n",
       "      <td>62300</td>\n",
       "      <td>5.950</td>\n",
       "      <td>81.0</td>\n",
       "      <td>1.95</td>\n",
       "      <td>87800</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>115</th>\n",
       "      <td>Oman</td>\n",
       "      <td>11.7</td>\n",
       "      <td>65.7</td>\n",
       "      <td>2.77</td>\n",
       "      <td>41.2</td>\n",
       "      <td>45300</td>\n",
       "      <td>15.600</td>\n",
       "      <td>76.1</td>\n",
       "      <td>2.90</td>\n",
       "      <td>19300</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>123</th>\n",
       "      <td>Qatar</td>\n",
       "      <td>9.0</td>\n",
       "      <td>62.3</td>\n",
       "      <td>1.81</td>\n",
       "      <td>23.8</td>\n",
       "      <td>125000</td>\n",
       "      <td>6.980</td>\n",
       "      <td>79.5</td>\n",
       "      <td>2.07</td>\n",
       "      <td>70300</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>128</th>\n",
       "      <td>Saudi Arabia</td>\n",
       "      <td>15.7</td>\n",
       "      <td>49.6</td>\n",
       "      <td>4.29</td>\n",
       "      <td>33.0</td>\n",
       "      <td>45400</td>\n",
       "      <td>17.200</td>\n",
       "      <td>75.1</td>\n",
       "      <td>2.96</td>\n",
       "      <td>19300</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>144</th>\n",
       "      <td>Sweden</td>\n",
       "      <td>3.0</td>\n",
       "      <td>46.2</td>\n",
       "      <td>9.63</td>\n",
       "      <td>40.7</td>\n",
       "      <td>42900</td>\n",
       "      <td>0.991</td>\n",
       "      <td>81.5</td>\n",
       "      <td>1.98</td>\n",
       "      <td>52100</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>Argentina</td>\n",
       "      <td>14.5</td>\n",
       "      <td>18.9</td>\n",
       "      <td>8.10</td>\n",
       "      <td>16.0</td>\n",
       "      <td>18700</td>\n",
       "      <td>20.900</td>\n",
       "      <td>75.8</td>\n",
       "      <td>2.37</td>\n",
       "      <td>10300</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>79</th>\n",
       "      <td>Kazakhstan</td>\n",
       "      <td>21.5</td>\n",
       "      <td>44.2</td>\n",
       "      <td>4.29</td>\n",
       "      <td>29.9</td>\n",
       "      <td>20100</td>\n",
       "      <td>19.500</td>\n",
       "      <td>68.4</td>\n",
       "      <td>2.60</td>\n",
       "      <td>9070</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>99</th>\n",
       "      <td>Mauritania</td>\n",
       "      <td>97.4</td>\n",
       "      <td>50.7</td>\n",
       "      <td>4.41</td>\n",
       "      <td>61.2</td>\n",
       "      <td>3320</td>\n",
       "      <td>18.900</td>\n",
       "      <td>68.2</td>\n",
       "      <td>4.98</td>\n",
       "      <td>1200</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>103</th>\n",
       "      <td>Mongolia</td>\n",
       "      <td>26.1</td>\n",
       "      <td>46.7</td>\n",
       "      <td>5.44</td>\n",
       "      <td>56.7</td>\n",
       "      <td>7710</td>\n",
       "      <td>39.200</td>\n",
       "      <td>66.2</td>\n",
       "      <td>2.64</td>\n",
       "      <td>2650</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>140</th>\n",
       "      <td>Sri Lanka</td>\n",
       "      <td>11.2</td>\n",
       "      <td>19.6</td>\n",
       "      <td>2.94</td>\n",
       "      <td>26.8</td>\n",
       "      <td>8560</td>\n",
       "      <td>22.800</td>\n",
       "      <td>74.4</td>\n",
       "      <td>2.20</td>\n",
       "      <td>2810</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>142</th>\n",
       "      <td>Sudan</td>\n",
       "      <td>76.7</td>\n",
       "      <td>19.7</td>\n",
       "      <td>6.32</td>\n",
       "      <td>17.2</td>\n",
       "      <td>3370</td>\n",
       "      <td>19.600</td>\n",
       "      <td>66.3</td>\n",
       "      <td>4.88</td>\n",
       "      <td>1480</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>149</th>\n",
       "      <td>Timor-Leste</td>\n",
       "      <td>62.6</td>\n",
       "      <td>2.2</td>\n",
       "      <td>9.12</td>\n",
       "      <td>27.8</td>\n",
       "      <td>1850</td>\n",
       "      <td>26.500</td>\n",
       "      <td>71.1</td>\n",
       "      <td>6.23</td>\n",
       "      <td>3600</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>163</th>\n",
       "      <td>Venezuela</td>\n",
       "      <td>17.1</td>\n",
       "      <td>28.5</td>\n",
       "      <td>4.91</td>\n",
       "      <td>17.6</td>\n",
       "      <td>16500</td>\n",
       "      <td>45.900</td>\n",
       "      <td>75.4</td>\n",
       "      <td>2.47</td>\n",
       "      <td>13500</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>165</th>\n",
       "      <td>Yemen</td>\n",
       "      <td>56.3</td>\n",
       "      <td>30.0</td>\n",
       "      <td>5.18</td>\n",
       "      <td>34.4</td>\n",
       "      <td>4480</td>\n",
       "      <td>23.600</td>\n",
       "      <td>67.5</td>\n",
       "      <td>4.67</td>\n",
       "      <td>1310</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>39</th>\n",
       "      <td>Costa Rica</td>\n",
       "      <td>10.2</td>\n",
       "      <td>33.2</td>\n",
       "      <td>10.90</td>\n",
       "      <td>35.0</td>\n",
       "      <td>13000</td>\n",
       "      <td>6.570</td>\n",
       "      <td>80.4</td>\n",
       "      <td>1.92</td>\n",
       "      <td>8200</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>60</th>\n",
       "      <td>Greece</td>\n",
       "      <td>3.9</td>\n",
       "      <td>22.1</td>\n",
       "      <td>10.30</td>\n",
       "      <td>30.7</td>\n",
       "      <td>28700</td>\n",
       "      <td>0.673</td>\n",
       "      <td>80.4</td>\n",
       "      <td>1.48</td>\n",
       "      <td>26900</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>68</th>\n",
       "      <td>Iceland</td>\n",
       "      <td>2.6</td>\n",
       "      <td>53.4</td>\n",
       "      <td>9.40</td>\n",
       "      <td>43.3</td>\n",
       "      <td>38800</td>\n",
       "      <td>5.470</td>\n",
       "      <td>82.0</td>\n",
       "      <td>2.20</td>\n",
       "      <td>41900</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>74</th>\n",
       "      <td>Israel</td>\n",
       "      <td>4.6</td>\n",
       "      <td>35.0</td>\n",
       "      <td>7.63</td>\n",
       "      <td>32.9</td>\n",
       "      <td>29600</td>\n",
       "      <td>1.770</td>\n",
       "      <td>81.4</td>\n",
       "      <td>3.03</td>\n",
       "      <td>30600</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75</th>\n",
       "      <td>Italy</td>\n",
       "      <td>4.0</td>\n",
       "      <td>25.2</td>\n",
       "      <td>9.53</td>\n",
       "      <td>27.2</td>\n",
       "      <td>36200</td>\n",
       "      <td>0.319</td>\n",
       "      <td>81.7</td>\n",
       "      <td>1.46</td>\n",
       "      <td>35800</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>77</th>\n",
       "      <td>Japan</td>\n",
       "      <td>3.2</td>\n",
       "      <td>15.0</td>\n",
       "      <td>9.49</td>\n",
       "      <td>13.6</td>\n",
       "      <td>35800</td>\n",
       "      <td>-1.900</td>\n",
       "      <td>82.8</td>\n",
       "      <td>1.39</td>\n",
       "      <td>44500</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>111</th>\n",
       "      <td>New Zealand</td>\n",
       "      <td>6.2</td>\n",
       "      <td>30.3</td>\n",
       "      <td>10.10</td>\n",
       "      <td>28.0</td>\n",
       "      <td>32300</td>\n",
       "      <td>3.730</td>\n",
       "      <td>80.9</td>\n",
       "      <td>2.17</td>\n",
       "      <td>33700</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>138</th>\n",
       "      <td>South Korea</td>\n",
       "      <td>4.1</td>\n",
       "      <td>49.4</td>\n",
       "      <td>6.93</td>\n",
       "      <td>46.2</td>\n",
       "      <td>30400</td>\n",
       "      <td>3.160</td>\n",
       "      <td>80.1</td>\n",
       "      <td>1.23</td>\n",
       "      <td>22100</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>139</th>\n",
       "      <td>Spain</td>\n",
       "      <td>3.8</td>\n",
       "      <td>25.5</td>\n",
       "      <td>9.54</td>\n",
       "      <td>26.8</td>\n",
       "      <td>32500</td>\n",
       "      <td>0.160</td>\n",
       "      <td>81.9</td>\n",
       "      <td>1.37</td>\n",
       "      <td>30700</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>158</th>\n",
       "      <td>United Kingdom</td>\n",
       "      <td>5.2</td>\n",
       "      <td>28.2</td>\n",
       "      <td>9.64</td>\n",
       "      <td>30.8</td>\n",
       "      <td>36200</td>\n",
       "      <td>1.570</td>\n",
       "      <td>80.3</td>\n",
       "      <td>1.92</td>\n",
       "      <td>38900</td>\n",
       "    </tr>\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>9.440</td>\n",
       "      <td>56.2</td>\n",
       "      <td>5.82</td>\n",
       "      <td>553</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>56</th>\n",
       "      <td>Gambia</td>\n",
       "      <td>80.3</td>\n",
       "      <td>23.8</td>\n",
       "      <td>5.69</td>\n",
       "      <td>42.7</td>\n",
       "      <td>1660</td>\n",
       "      <td>4.300</td>\n",
       "      <td>65.5</td>\n",
       "      <td>5.71</td>\n",
       "      <td>562</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>147</th>\n",
       "      <td>Tanzania</td>\n",
       "      <td>71.9</td>\n",
       "      <td>18.7</td>\n",
       "      <td>6.01</td>\n",
       "      <td>29.1</td>\n",
       "      <td>2090</td>\n",
       "      <td>9.250</td>\n",
       "      <td>59.3</td>\n",
       "      <td>5.43</td>\n",
       "      <td>702</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>155</th>\n",
       "      <td>Uganda</td>\n",
       "      <td>81.0</td>\n",
       "      <td>17.1</td>\n",
       "      <td>9.01</td>\n",
       "      <td>28.6</td>\n",
       "      <td>1540</td>\n",
       "      <td>10.600</td>\n",
       "      <td>56.8</td>\n",
       "      <td>6.15</td>\n",
       "      <td>595</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>53</th>\n",
       "      <td>Finland</td>\n",
       "      <td>3.0</td>\n",
       "      <td>38.7</td>\n",
       "      <td>8.95</td>\n",
       "      <td>37.4</td>\n",
       "      <td>39800</td>\n",
       "      <td>0.351</td>\n",
       "      <td>80.0</td>\n",
       "      <td>1.87</td>\n",
       "      <td>46200</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>83 rows × 10 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                      country  child_mort  exports  health  imports  income  \\\n",
       "3                      Angola       119.0     62.3    2.85     42.9    5900   \n",
       "17                      Benin       111.0     23.8    4.10     37.2    1820   \n",
       "25               Burkina Faso       116.0     19.2    6.74     29.6    1430   \n",
       "28                   Cameroon       108.0     22.2    5.13     27.0    2660   \n",
       "31   Central African Republic       149.0     11.8    3.98     26.5     888   \n",
       "32                       Chad       150.0     36.8    4.53     43.5    1930   \n",
       "37           Congo, Dem. Rep.       116.0     41.1    7.91     49.6     609   \n",
       "40              Cote d'Ivoire       111.0     50.6    5.30     43.3    2690   \n",
       "49          Equatorial Guinea       111.0     85.8    4.48     58.9   33700   \n",
       "63                     Guinea       109.0     30.3    4.93     43.2    1190   \n",
       "64              Guinea-Bissau       114.0     14.9    8.50     35.2    1390   \n",
       "66                      Haiti       208.0     15.3    6.91     64.7    1500   \n",
       "97                       Mali       137.0     22.8    4.98     35.1    1870   \n",
       "106                Mozambique       101.0     31.5    5.21     46.2     918   \n",
       "112                     Niger       123.0     22.2    5.16     49.1     814   \n",
       "113                   Nigeria       130.0     25.3    5.07     17.4    5150   \n",
       "132              Sierra Leone       160.0     16.8   13.10     34.5    1220   \n",
       "15                    Belgium         4.5     76.4   10.70     74.7   41100   \n",
       "38                Congo, Rep.        63.9     85.1    2.46     54.7    5190   \n",
       "51                    Estonia         4.5     75.1    6.03     68.7   22700   \n",
       "67                    Hungary         6.0     81.8    7.33     76.5   22300   \n",
       "73                    Ireland         4.2    103.0    9.19     86.5   45700   \n",
       "91                 Luxembourg         2.8    175.0    7.77    142.0   91700   \n",
       "95                   Malaysia         7.9     86.9    4.39     71.0   21100   \n",
       "96                   Maldives        13.2     77.6    6.33     65.4   10500   \n",
       "98                      Malta         6.8    153.0    8.65    154.0   28300   \n",
       "110               Netherlands         4.5     72.0   11.90     63.6   45500   \n",
       "131                Seychelles        14.4     93.8    3.40    108.0   20400   \n",
       "133                 Singapore         2.8    200.0    3.96    174.0   72100   \n",
       "134           Slovak Republic         7.0     76.3    8.79     77.8   25200   \n",
       "..                        ...         ...      ...     ...      ...     ...   \n",
       "82                     Kuwait        10.8     66.7    2.63     30.4   75200   \n",
       "114                    Norway         3.2     39.7    9.48     28.5   62300   \n",
       "115                      Oman        11.7     65.7    2.77     41.2   45300   \n",
       "123                     Qatar         9.0     62.3    1.81     23.8  125000   \n",
       "128              Saudi Arabia        15.7     49.6    4.29     33.0   45400   \n",
       "144                    Sweden         3.0     46.2    9.63     40.7   42900   \n",
       "5                   Argentina        14.5     18.9    8.10     16.0   18700   \n",
       "79                 Kazakhstan        21.5     44.2    4.29     29.9   20100   \n",
       "99                 Mauritania        97.4     50.7    4.41     61.2    3320   \n",
       "103                  Mongolia        26.1     46.7    5.44     56.7    7710   \n",
       "140                 Sri Lanka        11.2     19.6    2.94     26.8    8560   \n",
       "142                     Sudan        76.7     19.7    6.32     17.2    3370   \n",
       "149               Timor-Leste        62.6      2.2    9.12     27.8    1850   \n",
       "163                 Venezuela        17.1     28.5    4.91     17.6   16500   \n",
       "165                     Yemen        56.3     30.0    5.18     34.4    4480   \n",
       "39                 Costa Rica        10.2     33.2   10.90     35.0   13000   \n",
       "60                     Greece         3.9     22.1   10.30     30.7   28700   \n",
       "68                    Iceland         2.6     53.4    9.40     43.3   38800   \n",
       "74                     Israel         4.6     35.0    7.63     32.9   29600   \n",
       "75                      Italy         4.0     25.2    9.53     27.2   36200   \n",
       "77                      Japan         3.2     15.0    9.49     13.6   35800   \n",
       "111               New Zealand         6.2     30.3   10.10     28.0   32300   \n",
       "138               South Korea         4.1     49.4    6.93     46.2   30400   \n",
       "139                     Spain         3.8     25.5    9.54     26.8   32500   \n",
       "158            United Kingdom         5.2     28.2    9.64     30.8   36200   \n",
       "0                 Afghanistan        90.2     10.0    7.58     44.9    1610   \n",
       "56                     Gambia        80.3     23.8    5.69     42.7    1660   \n",
       "147                  Tanzania        71.9     18.7    6.01     29.1    2090   \n",
       "155                    Uganda        81.0     17.1    9.01     28.6    1540   \n",
       "53                    Finland         3.0     38.7    8.95     37.4   39800   \n",
       "\n",
       "     inflation  life_expec  total_fer    gdpp  \n",
       "3       22.400        60.1       6.16    3530  \n",
       "17       0.885        61.8       5.36     758  \n",
       "25       6.810        57.9       5.87     575  \n",
       "28       1.910        57.3       5.11    1310  \n",
       "31       2.010        47.5       5.21     446  \n",
       "32       6.390        56.5       6.59     897  \n",
       "37      20.800        57.5       6.54     334  \n",
       "40       5.390        56.3       5.27    1220  \n",
       "49      24.900        60.9       5.21   17100  \n",
       "63      16.100        58.0       5.34     648  \n",
       "64       2.970        55.6       5.05     547  \n",
       "66       5.450        32.1       3.33     662  \n",
       "97       4.370        59.5       6.55     708  \n",
       "106      7.640        54.5       5.56     419  \n",
       "112      2.550        58.8       7.49     348  \n",
       "113    104.000        60.5       5.84    2330  \n",
       "132     17.200        55.0       5.20     399  \n",
       "15       1.880        80.0       1.86   44400  \n",
       "38      20.700        60.4       4.95    2740  \n",
       "51       1.740        76.0       1.72   14600  \n",
       "67       2.330        74.5       1.25   13100  \n",
       "73      -3.220        80.4       2.05   48700  \n",
       "91       3.620        81.3       1.63  105000  \n",
       "95       7.270        74.5       2.15    9070  \n",
       "96       2.880        77.9       2.23    7100  \n",
       "98       3.830        80.3       1.36   21100  \n",
       "110      0.848        80.7       1.79   50300  \n",
       "131     -4.210        73.4       2.17   10800  \n",
       "133     -0.046        82.7       1.15   46600  \n",
       "134      0.485        75.5       1.43   16600  \n",
       "..         ...         ...        ...     ...  \n",
       "82      11.200        78.2       2.21   38500  \n",
       "114      5.950        81.0       1.95   87800  \n",
       "115     15.600        76.1       2.90   19300  \n",
       "123      6.980        79.5       2.07   70300  \n",
       "128     17.200        75.1       2.96   19300  \n",
       "144      0.991        81.5       1.98   52100  \n",
       "5       20.900        75.8       2.37   10300  \n",
       "79      19.500        68.4       2.60    9070  \n",
       "99      18.900        68.2       4.98    1200  \n",
       "103     39.200        66.2       2.64    2650  \n",
       "140     22.800        74.4       2.20    2810  \n",
       "142     19.600        66.3       4.88    1480  \n",
       "149     26.500        71.1       6.23    3600  \n",
       "163     45.900        75.4       2.47   13500  \n",
       "165     23.600        67.5       4.67    1310  \n",
       "39       6.570        80.4       1.92    8200  \n",
       "60       0.673        80.4       1.48   26900  \n",
       "68       5.470        82.0       2.20   41900  \n",
       "74       1.770        81.4       3.03   30600  \n",
       "75       0.319        81.7       1.46   35800  \n",
       "77      -1.900        82.8       1.39   44500  \n",
       "111      3.730        80.9       2.17   33700  \n",
       "138      3.160        80.1       1.23   22100  \n",
       "139      0.160        81.9       1.37   30700  \n",
       "158      1.570        80.3       1.92   38900  \n",
       "0        9.440        56.2       5.82     553  \n",
       "56       4.300        65.5       5.71     562  \n",
       "147      9.250        59.3       5.43     702  \n",
       "155     10.600        56.8       6.15     595  \n",
       "53       0.351        80.0       1.87   46200  \n",
       "\n",
       "[83 rows x 10 columns]"
      ]
     },
     "execution_count": 193,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "country_df_outlier.drop_duplicates()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 194,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(153, 10)"
      ]
     },
     "execution_count": 194,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "country_df_outlier.shape"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### There are only 167 records and out of which 153 records would consitutite outlier in one form or the other. Since it is important to consider all the variation in data to get a meaningful solution, we are not deleting any records based on outlier analysis"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 3. Perform PCA"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 3.1 Preparing the data for modelling"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 195,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Importing some of the important packages\n",
    "\n",
    "import sklearn\n",
    "from sklearn.preprocessing import StandardScaler\n",
    "from sklearn.cluster import KMeans\n",
    "from sklearn.metrics import silhouette_score"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 196,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(167, 3)"
      ]
     },
     "execution_count": 196,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 1 keeping only the require columns for clustering\n",
    "\n",
    "country_df_k = country_df[['gdpp','income','child_mort']]\n",
    "\n",
    "country_df_k.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 197,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(167, 3)"
      ]
     },
     "execution_count": 197,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 2 re-scaling the variables so that none of them exert more weight on the final model\n",
    "\n",
    "scaler = StandardScaler()\n",
    "\n",
    "#fit transform\n",
    "\n",
    "rfm_df_scaled = scaler.fit_transform(country_df_k)\n",
    "\n",
    "rfm_df_scaled.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 198,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(167, 3)"
      ]
     },
     "execution_count": 198,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "rfm_df_scaled = pd.DataFrame(rfm_df_scaled)\n",
    "\n",
    "rfm_df_scaled.columns = ['gdpp','income','child_mort']\n",
    "\n",
    "rfm_df_scaled.shape"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 3.2 Perform modelling"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 199,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "PCA(copy=True, iterated_power='auto', n_components=None, random_state=50,\n",
       "    svd_solver='randomized', tol=0.0, whiten=False)"
      ]
     },
     "execution_count": 199,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# importing the PCA module\n",
    "\n",
    "from sklearn.decomposition import PCA\n",
    "\n",
    "pca = PCA(svd_solver='randomized', random_state = 50)\n",
    "\n",
    "# Performing the PCA\n",
    "\n",
    "pca.fit(rfm_df_scaled)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 200,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[ 0.61429738,  0.62343707, -0.48369924],\n",
       "       [ 0.37763998,  0.30595439,  0.87394505],\n",
       "       [-0.69283965,  0.71952632,  0.04748778]])"
      ]
     },
     "execution_count": 200,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# list of PCA components\n",
    "\n",
    "pca.components_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 201,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([0.7630789 , 0.20257426, 0.03434684])"
      ]
     },
     "execution_count": 201,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Let's check the variance ratios\n",
    "pca.explained_variance_ratio_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 202,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 864x576 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#Plotting the scree plot\n",
    "%matplotlib inline\n",
    "fig = plt.figure(figsize = (12,8))\n",
    "plt.plot(np.cumsum(pca.explained_variance_ratio_))\n",
    "plt.xlabel('number of components')\n",
    "plt.ylabel('cumulative explained variance')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### We see that the 96% variation in data can be explained by 2 principle components"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 203,
   "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>Feature</th>\n",
       "      <th>PC1</th>\n",
       "      <th>PC2</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>gdpp</td>\n",
       "      <td>0.614297</td>\n",
       "      <td>0.377640</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>income</td>\n",
       "      <td>0.623437</td>\n",
       "      <td>0.305954</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>child_mort</td>\n",
       "      <td>-0.483699</td>\n",
       "      <td>0.873945</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      Feature       PC1       PC2\n",
       "0        gdpp  0.614297  0.377640\n",
       "1      income  0.623437  0.305954\n",
       "2  child_mort -0.483699  0.873945"
      ]
     },
     "execution_count": 203,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Understanding how the original 3 variables are loaded on the principal components. It can be verified from above as well.\n",
    "\n",
    "colnames = list(rfm_df_scaled)\n",
    "\n",
    "pcs_df = pd.DataFrame({ 'Feature':colnames,'PC1':pca.components_[0],'PC2':pca.components_[1]})\n",
    "pcs_df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 204,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAjUAAAI4CAYAAACSixhGAAAABHNCSVQICAgIfAhkiAAAAAlwSFlzAAALEgAACxIB0t1+/AAAADh0RVh0U29mdHdhcmUAbWF0cGxvdGxpYiB2ZXJzaW9uMy4xLjAsIGh0dHA6Ly9tYXRwbG90bGliLm9yZy+17YcXAAAgAElEQVR4nO3de7yWZZ3v8c8PDCMDLKCDgEJJOiQgtkRTx0OmaQcktdBM0zJrSquxcW/aM1vJ9nSymaZGe804HdQ2pmaK7rJITTMsk0UghIqRkiJNkYqa4QH87T+eZ9HDcrF4Fmvd63Dxeb9evHju676e+/6t64X45boPV2QmkiRJA92gvi5AkiSpJxhqJElSEQw1kiSpCIYaSZJUBEONJEkqwg59XUBXjRo1KsePH9/XZUiSpB60aNGiP2Xm6O4cY8CFmvHjx9Pa2trXZUiSpB4UEb/r7jG8/CRJkopgqJEkSUUw1EiSpCJUGmoi4qiIWBERKyNidgf7d4uImyNiaUTcGhFjq6xHkiSVq7JQExGDgYuAo4FJwIkRMaldty8Bl2XmFOB84HNV1SNJkspW5UzNdGBlZt6fmc8CVwDHtOszCbi5/vmWDvZLkiQ1pcpQMwZ4qGF7db2t0V3AcfXP7wSGRcTICmuSJEmFqjLURAdt2W77H4BDImIxcAjwMLDhBQeKOCMiWiOide3atT1fqSRJGvCqDDWrgXEN22OBNY0dMnNNZh6bmdOAf6y3Pd7+QJl5cWa2ZGbL6NHdetmgJEkqVJWhZiEwMSImRMQQ4ATg+sYOETEqItpq+BTwzQrrkSRJBass1GTmBuBMYD5wD3BVZi6PiPMjYka926HAioi4D3gl8M9V1SNJksoWme1vc+nfWlpa0rWfJEkqS0QsysyW7hzDNwpLkqQiGGokSVIRDDWSJKkIhhpJklQEQ40kSSqCoUaSJBXBUCNJkopgqJEkSUUw1EiSpCIYaiRJUhEMNV1w6qmncvXVV7+gfc2aNRx//PEA3Hrrrbz97W/v8Pvjx4/nT3/6U6U1tlmyZAk33HBDr5xLkqT+wFDTA3bZZZcOw05f2bBhg6FGkrTdMdR04rLLLmPKlClMnTqVk08+GYDbbruNAw44gNe85jWbgsyqVavYa6+9XvD9Rx55hCOPPJJp06bxoQ99iM4WD121ahV77rknp59+OnvttRcnnXQSN910EwceeCATJ07kzjvvBODRRx9l5syZTJkyhf3335+lS5cCMGfOHM444wyOPPJITjnlFM4991yuvPJK9t57b6688sqeHhpJkvodQ0078xY/zIGf/wljPvA1PvTJf+Qf/m0ud911F1/5ylcA+P3vf8+CBQv4/ve/z+zZszs91qc//WkOOuggFi9ezIwZM3jwwQc77b9y5Uo+/vGPs3TpUu69914uv/xyFixYwJe+9CU++9nPAnDeeecxbdo0li5dymc/+1lOOeWUTd9ftGgR1113HZdffjnnn38+s2bNYsmSJcyaNauboyJJUv+3Q18X0J/MW/wwn7pmGeuf28j6B5cyZOIBfO6Whxn+spczc9oYAGbOnMmgQYOYNGkSf/jDHzo93m233cY111wDwNve9jZe9rKXddp/woQJTJ48GYDXv/71HH744UQEkydPZtWqVQAsWLCA733vewC86U1v4pFHHuHxxx8HYMaMGQwdOnSbf35JkgYyZ2oaXDB/Beuf21jbyASC9c9t5IL5Kzb12XHHHTd97uxyUpuIaPr8jcceNGjQpu1BgwaxYcOGLZ6z7Rw77bRT0+eSJKk0hpoGa9at3/T5xbtN5S/3/oyN659gzbr1PProo10+3sEHH8zcuXMB+OEPf8hjjz3W7Robj3nrrbcyatQohg8f/oJ+w4YN48knn+z2+SRJGigMNQ122fmvl26GjN6NEW+cxR8un80fL/0YZ599dpePd95553Hbbbexzz778OMf/5hdd9212zXOmTOH1tZWpkyZwuzZs7n00ks77HfYYYdx9913e6OwJGm7Ec1cQulPWlpasrW1tZJjN95T02boiwbzuWMnb7qnRpIk9byIWJSZLd05hjcKN2gLLhfMX8GadevZZeehnPOWPQw0kiQNAIaadmZOG1NpiHnkkUc4/PDDX9B+8803M3LkyMrOK0lS6Qw1vWzkyJEsWbKkr8uQJKk43igsSZKKYKiRJElFMNRIkqQiGGokSVIRDDWSJKkIhhpJklQEQ40kSSqCoUaSJBXBUCNJkopgqJEkSUUw1EiSpCIYaiRJUhEMNZIkqQiGGkmSVARDjSRJKoKhRpIkFcFQI0mSimCokSRJRTDUSJKkIhhqJElSEQw1kiSpCIYaSZJUBEONJEkqgqFGkiQVwVAjSZKKYKiRJElFMNRIkqQiGGokSVIRDDWSJKkIhhpJklQEQ40kSSqCoUaSJBXBUCNJkopgqJEkSUUw1EiSpCIYaiRJUhEMNZIkqQiGGkmSVARDjSRJKoKhRpIkFcFQI0mSimCokSRJRTDUSJKkIhhqJElSEQw1kiSpCIYaSZJUBEONJEkqgqFGkiQVwVAjSZKKYKiRJElFMNRIkqQiVBpqIuKoiFgRESsjYnYH+3eNiFsiYnFELI2It1ZZjyRJKldloSYiBgMXAUcDk4ATI2JSu27/BFyVmdOAE4CvVVWPJEkqW5UzNdOBlZl5f2Y+C1wBHNOuTwLD659HAGsqrEeSJBWsylAzBnioYXt1va3RHOC9EbEauAE4q6MDRcQZEdEaEa1r166tolZJkjTAVRlqooO2bLd9InBJZo4F3gp8OyJeUFNmXpyZLZnZMnr06ApKlSRJA12VoWY1MK5heywvvLz0AeAqgMz8BfBiYFSFNUmSpEJVGWoWAhMjYkJEDKF2I/D17fo8CBwOEBF/Qy3UeH1JkiR1WWWhJjM3AGcC84F7qD3ltDwizo+IGfVunwQ+GBF3Ad8BTs3M9peoJEmStmqHKg+emTdQuwG4se3chs93AwdWWYMkSdo++EZhSZJUBEONJEkqgqFGkiQVwVAjSZKKYKiRJElFMNRIkqQiGGokSVIRDDWSJKkIhhpJklQEQ40kSSqCoUaSJBXBUCNJkopgqJEkSUUw1EiSpCIYaiRJUhEMNZIkqQiGGkmSVARDjSRJKoKhRpIkFcFQI0mSimCokSRJRTDUSJKkIhhqJElSEQw1kiSpCIYaSZJUBEONJEkqgqFGkiQVwVAjSZKKYKiRJElFMNRIkqQiGGokSVIRDDWSJKkIhhpJklQEQ40kSSqCoUaSJBXBUCNJkopgqJEkSUUw1EiSpCIYaiRJUhEMNZIkqQiGGkmSVARDjSRJKoKhRpIkFcFQI0mSimCokSRJRTDUSJKkIhhqJElSEQw1kiSpCIYaSZJUBEONJEkqgqFGkiQVwVAjSZKKYKiRJElFMNRIkqQiGGokSVIRDDWSJKkIhhpJklQEQ40kSSqCoUaSJBXBUCNJkopgqJEkSUUw1EiSpCIYaiRJUhEMNZIkqQiGGkmSVARDjSRJKoKhRpIkFcFQI0mSimCokSRJRTDUSJKkIhhqJElSEQw1kiSpCIYaSZJUhEpDTUQcFRErImJlRMzuYP+XI2JJ/dd9EbGuynokSVK5dqjqwBExGLgIOAJYDSyMiOsz8+62Ppn59w39zwKmVVWPJEkqW5UzNdOBlZl5f2Y+C1wBHNNJ/xOB71RYjyRJKliVoWYM8FDD9up62wtExG7ABOAnW9h/RkS0RkTr2rVre7xQSZI08FUZaqKDttxC3xOAqzNzY0c7M/PizGzJzJbRo0f3WIGSJKkcVYaa1cC4hu2xwJot9D0BLz1JkqRuqDLULAQmRsSEiBhCLbhc375TROwBvAz4RYW1SJKkwlUWajJzA3AmMB+4B7gqM5dHxPkRMaOh64nAFZm5pUtTkiRJW1XZI90AmXkDcEO7tnPbbc+psgZJkrR98I3CkiSpCIYaSZJUBEONJEkqgqFGkiQVwVAjSZKKYKiRJElFMNRIkqQiGGokSVIRDDWSJKkIhhpJklQEQ40kSSqCoUaSJBXBUCNJkopgqJEkSUUw1EiSpCIYaiRJUhEMNZIkqQiGGkmSVARDjSRJKoKhRpIkFcFQI0mSimCokSRJRTDUSJKkIhhqJElSEQw1kiSpCIYaSZJUBEONJEkqgqFGkiQVwVAjSZKKsNVQExEv6qBtVDXlSJIkbZsthpqIOCwiVgNrIuLHETG+YfePqy5MkiSpKzqbqfki8JbMHA1cDNwYEfvX90XllUmSJHXBDp3sG5KZywEy8+qIuAe4JiJmA9kr1UmSJDWps1DzXES8KjP/GyAzl0fE4cD3gdf2SnWSJElN6uzy02zglY0NmbkaOAT4fJVFSZIkddUWZ2oy86YttD8O/HNlFUmSJG0D31MjSZKKYKiRJElFaOble+9qpk2SJKkvNTNT86km2yRJkvrMFm8UjoijgbcCYyLiqw27hgMbqi5MkiSpKzp7T80aoBWYASxqaH8S+Psqi5IkSeqqzh7pvgu4KyIuz8znerEmSZKkLutspqbN9IiYA+xW7x9AZuZrqixMkiSpK5oJNd+gdrlpEbCx2nIkSZK2TTOh5vHM/GHllUiSJHVDM6Hmloi4ALgGeKatMTN/VVlVkiRJXdRMqNmv/ntLQ1sCb+r5ciRJkrbNVkNNZh7WG4VIkiR1RzPLJLwyIr4RET+sb0+KiA9UX5okSVLzmlkm4RJgPrBLffs+4BNVFSRJkrQtmgk1ozLzKuB5gMzcgI92S5KkfqaZUPNURIykdnMwEbE/8HilVUmSJHVRM08/nQ1cD7w2Im4HRgPHV1qVJElSFzXz9NOvIuIQYA9qSySscC0oSZLU3zQzUwMwHRhf779PRJCZl1VWlSRJUhdtNdRExLeB1wJL+OsNwgkYaiRJUr/RzExNCzApM7PqYiRJkrZVM08//Rp4VdWFSJIkdUczMzWjgLsj4k42X9ByRmVVSZIkdVEzoWZO1UVIkiR1VzOPdP80Il4J7FtvujMz/1htWZIkSV3TzIKW7wbuBN4FvBv4ZUT48j1JktSvNHP56R+BfdtmZyJiNHATcHWVhUmSJHVFM08/DWp3uemRJr8nSZLUa5qZqflRRMwHvlPfngXcUF1JkiRJXdfMjcLnRMSxwEHU1n66ODOvrbwySZKkLmh27aefU1si4XlgYXXlSJIkbZtmnn46ndrTT+8EjgfuiIj3V12YJElSVzQzU3MOMC0zHwGIiJHUZm6+WWVhkiRJXdHMU0yrgScbtp8EHqqmHEmSpG3TzEzNw9ReuHcdkMAxwJ0RcTZAZv5rhfVJkiQ1pZlQ89v6rzbX1X8f1vPlSJIkbZtmHun+9LYePCKOAr4CDAa+npmf76DPu6ktmpnAXZn5nm09nyRJ2n5tNdRERAu1pRJ2a+yfmVO28r3BwEXAEdTuy1kYEddn5t0NfSYCnwIOzMzHIuIV2/RTSJKk7V4zl5/mUnsCahm199Q0azqwMjPvB4iIK6jdj3N3Q58PAhdl5mMArv4tSZK2VTOhZm1mXr8Nxx7D5k9JrQb2a9fndQARcTu1S1RzMvNH7Q8UEWcAZwDsuuuu21CKJEkqXTOh5ryI+DpwM/BMW2NmXrOV70UHbdnB+ScChwJjgZ9FxF6ZuW6zL2VeDFwM0NLS0v4YkiRJTYWa04A9gRfx18tPCWwt1KwGxjVsjwXWdNDnjsx8DnggIlZQCzkuxSBJkrqkmVAzNTMnb8OxFwITI2ICtXfdnAC0f7JpHnAicElEjKJ2Oer+bTiXJEnazjXzRuE7ImJSVw+cmRuAM4H5wD3AVZm5PCLOj4gZ9W7zgUci4m7gFuCctuUYJEmSuiIyO79FJSLuAV4LPEDtnpoAcmuPdFelpaUlW1tb++LUkiSpIhGxKDNbunOMZi4/HdWdE0iSJPWGrV5+yszfATsD76j/2rneJkmS1G9sNdRExMepvYDvFfVf/zcizqq6MEmSpK5o5vLTB4D9MvMpgIj4AvAL4N+rLEySJKkrmnn6KYCNDdsb6fjFepIkSX2mmZmabwG/jIhr69szgW9UV5IkSVLXbTXUZOa/RsStwEHUZmhOy8zFVRcmSZLUFVsMNRGxLzAqM3+Ymb8CflVvnxERgzJzUW8VKUmStDWd3VNzAbU3Abd3d32fJElSv9FZqBmZmavaN2bmSmBkZRVJkiRtg85CzdBO9u3U04VIkiR1R2eh5qaI+OeI2Ozx7Yj4NPCTasuSJEnqms6efvok8HVgZUQsqbdNBVqB06suTJIkqSu2GGrqbxA+MSJeA7y+3rw8M+/vlcokSZK6oJn31NwPGGQkSVK/1swyCZIkSf2eoUaSJBWhszcKv7yzL2bmoz1fjiRJ0rbp7J6aRUDS8YrcCbymkookSZK2QWdPP03ozUIkSZK6Y6tPPwFExMuAicCL29oy87aqipIkSeqqrYaaiDgd+DgwFlgC7A/8AnhTtaVJkiQ1r5mnnz4O7Av8LjMPA6YBayutSpIkqYuaCTVPZ+bTABGxY2beC+xRbVmSJEld08w9NasjYmdgHnBjRDwGrKm2LEmSpK5pZpmEd9Y/zomIW4ARwI8qrUqSJKmLmn36aR/gIGrvp7k9M5+ttCpJkqQu2uo9NRFxLnApMBIYBXwrIv6p6sIkSZK6opmZmhOBaQ03C38e+BXwf6osTJIkqSuaefppFQ0v3QN2BH5bSTWSJEnbqJmZmmeA5RFxI7V7ao4AFkTEVwEy82MV1idJktSUZkLNtfVfbW6tphRJkqRt18wj3Zf2RiGSJEndscVQExFXZea7I2IZtctOm8nMKZVWJkmS1AWdzdR8vP7723ujEEmSpO7YYqjJzN/XPw4Cft/wSPdQ4JW9UJskSVLTmnmk+7vA8w3bG+ttkiRJ/UYzoWaHxmUR6p+HVFeSJElS1zUTatZGxIy2jYg4BvhTdSVJkiR1XTPvqfkwMDciLgQCeAg4pdKqJEmSuqiZ99T8Ftg/Il4KRGY+WX1ZkiRJXbPVUBMROwLHAeOBHSICgMw8v9LKJEmSuqCZy0/XAY8Di6itAyVJktTvNBNqxmbmUZVXIkmS1A3NPP3084iYXHklkiRJ3dDMTM1BwKkR8QC1y08BpGs/SZKk/qSZUHN05VVIkiR1U2erdA/PzCcAH+GWJEn9XmczNZdTW6F7EZDULju1SeA1FdYlSZLUJZ2t0v32qL2U5pDMfLAXa5IkSeqyTp9+yswEru2lWiRJkrZZM4903xER+1ZeiSRJUjc08/TTYcCHI2IV8BQ+0i1JkvohH+mWJElF6OyR7hcDHwZ2B5YB38jMDb1VmCRJUld0dk/NpUALtUBzNPAvvVKRJEnSNujs8tOkzJwMEBHfAO7snZIkSZK6rrOZmufaPnjZSZIk9XedzdRMjYgn6p8DGFrfbnv6aXjl1UmSJDWpszcKD+7NQiRJkrqjmZfvSZIk9XuGGkmSVARDjSRJKoKhRpIkFcFQI0mSimCokSRJRTDUSJKkIhhqJElSEQw1kiSpCIYaSZJUBEONJEkqgqFGkiQVodJQExFHRcSKiFgZEbM72H9qRKyNiCX1X6dXWY8kSSrXFlfp7q6IGAxcBBwBrAYWRsT1mXl3u65XZuaZVdUhSZK2D1XO1EwHVmbm/Zn5LHAFcEyF55MkSduxKkPNGOChhu3V9bb2jouIpRFxdUSMq7AeSZJUsCpDTXTQlu22/x8wPjOnADcBl3Z4oIgzIqI1IlrXrl3bw2VKkqQSVBlqVgONMy9jgTWNHTLzkcx8pr75X8AbOjpQZl6cmS2Z2TJ69OhKipUkSQNblaFmITAxIiZExBDgBOD6xg4R8eqGzRnAPRXWI0mSClbZ00+ZuSEizgTmA4OBb2bm8og4H2jNzOuBj0XEDGAD8ChwalX1SJKkskVm+9tc+reWlpZsbW3t6zIkSVIPiohFmdnSnWP4RmFJklQEQ40kSSqCoUaSJBXBUCNJkopgqJEkSUUw1EiSpCIYaiRJUhEMNZIkqQiGGkmSVARDjSRJKoKhRpIkFcFQI0mSimCokSRJRTDUSJKkIhhqJElSEQw1kiSpCIYaSZJUBEONJEkqgqFGkiQVwVAjSZKKYKiRJElFMNRIkqQiGGokSVIRDDWSJKkIhhpJklQEQ40kSSqCoUaSJBXBUCNJkopgqJEkSUUw1EiSpCIYaiRJUhEMNZIkqQiGGkmSVARDjSRJKoKhRpIkFcFQI0mSimCokSRJRTDUSJKkIhhqJElSEQw1kiSpCIYaSZJUBEONJEkqgqFGkiQVwVAjSZKKYKiRJElFMNRIkqQiGGokSVIRDDWSJKkIhhpJklQEQ40kSSqCoUaSJBXBUCNJkopgqJEkSUUw1EiSpCIYaiRJUhEMNZIkqQiGGkmSVARDjSRJKoKhRpIkFcFQI0mSimCokSRJRTDUSJKkIhhqJElSEQw1kiSpCIYaSZJUBEONJEkqgqFGkiQVwVAjSZKKYKiRJElFMNRIkqQiGGokSVIRDDWSJKkIhhpJklSESkNNRBwVESsiYmVEzO6k3/ERkRHRUmU9kiSpXJWFmogYDFwEHA1MAk6MiEkd9BsGfAz4ZVW1SJKk8lU5UzMdWJmZ92fms8AVwDEd9PsM8EXg6QprkSRJhasy1IwBHmrYXl1v2yQipgHjMvP7nR0oIs6IiNaIaF27dm3PVypJkga8KkNNdNCWm3ZGDAK+DHxyawfKzIszsyUzW0aPHt2DJUqSpFJUGWpWA+MatscCaxq2hwF7AbdGxCpgf+B6bxaWJEnbospQsxCYGBETImIIcAJwfdvOzHw8M0dl5vjMHA/cAczIzNYKa5IkSYWqLNRk5gbgTGA+cA9wVWYuj4jzI2JGVeeVJEnbpx2qPHhm3gDc0K7t3C30PbTKWiRJUtl8o7AkSSqCoUaSJBXBUCNJkopgqJEkSUUw1EiSpCIYaiRJUhEMNZIkqctWrVrFXnvt1ddlbMZQI0mSilDpy/ckSdLA9JnPfIa5c+cybtw4Ro0axRve8AYOO+ww3v/+9/OSl7yEgw46aFPfSy65hGuvvZZnnnmGBx54gPe85z2cd955rFq1iqOOOor99tuPxYsX87rXvY7LLruMl7zkJZXU7EyNJEnaTGtrK9/73vdYvHgx11xzDa2ttWUZTzvtNL761a/yi1/84gXfufPOO5k7dy5Llizhu9/97qbvrFixgjPOOIOlS5cyfPhwvva1r1VWt6FGkiQxb/HDHPj5nzBh9g9415xvscf0wxg6dCjDhg3jHe94B0899RTr1q3jkEMOAeDkk0/e7PtHHHEEI0eOZOjQoRx77LEsWLAAgHHjxnHggQcC8N73vndTexUMNZIkbefmLX6YT12zjIfXrSeBx9c/y833/pF5ix/e1GennXYiIrZ4jPb72ra31F4FQ40kSdu5C+avYP1zGzdt7zh2Ek/e90u+8P1l/PnPf+YHP/gBACNGjNg00zJ37tzNjnHjjTfy6KOPsn79eubNm7dpdubBBx/cdLnqO9/5zmb34vQ0Q40kSdu5NevWb7a946tfx9Ddp9P6b6dz7LHH0tLSwogRI/jWt77FRz/6Ud74xjcydOjQzb5z0EEHcfLJJ7P33ntz3HHH0dLSAsDf/M3fcOmllzJlyhQeffRR/u7v/q6yn8OnnyRJ2s7tsvNQHm4XbIZPP5bXv/105n1sfw4++GA++clPss8++3DXXXdt6jNnzpxNn1/xildw4YUXvuDYgwYN4j/+4z8qq32zc/XKWSRJUr91zlv2YOiLBm/W9viPL2LNt85in3324bjjjmOfffbpo+qaF5nZ1zV0SUtLS7Y9JiZJknrGvMUPc8H8FaxZt55ddh7KOW/Zg5nTxvTa+SNiUWa2dOcYXn6SJEnMnDamV0NMFbz8JEmSimCokSRJRTDUSJKkIhhqJElSEQw1kiSpCIYaSZJUBEONJEkqgqFGkiQVwVAjSZKKYKiRJElFMNRIkqQiGGokSVIRDDWSJKkIhhpJklQEQ40kSSqCoUaSJBXBUCNJkopgqJEkSUUw1EiSpCIYaiRJUhEMNZIkqQiGGkmSVARDjSRJKoKhRpIkFcFQI0mSimCokSRJRTDUSJKkIhhqJElSEQw1kiSpCIYaSZJUBEONJEkqgqFGkiQVwVAjSZKKYKiRJElFMNRIkqQiGGokSVIRDDWSJKkIhhpJklQEQ40kSSqCoUaSJBXBUCNJkopgqJEkSUUw1EiSpCIYaiRJUhEMNZIkqQiGGkmSVARDjSRJ6tQBBxzQ1yU0xVAjSZI69fOf/7yvS2iKoUaSJHXqpS99KQC33norhx56KMcffzx77rknJ510EpkJwMKFCznggAOYOnUq06dP58knn+Tpp5/mtNNOY/LkyUybNo1bbrkFgEsuuYSZM2fyjne8gwkTJnDhhRcCvDIiFkfEHRHxcoCIeG1E/CgiFkXEzyJiz87q3KHCMZAkSYVZvHgxy5cvZ5ddduHAAw/k9ttvZ/r06cyaNYsrr7ySfffdlyeeeIKhQ4fyla98BYBly5Zx7733cuSRR3LfffcB8Otf/5rFixfz9NNPs/vuuwM8n5nTIuLLwCnAvwEXAx/OzN9ExH7A14A3bak2Q40kSXqBeYsf5oL5K1izbj3rn9vIvMUPszMwffp0xo4dC8Dee+/NqlWrGDFiBK9+9avZd999ARg+fDgACxYs4KyzzgJgzz33ZLfddtsUag477DCGDRvGsGHDGDFiBE888cS6+qmXAVMi4qXAAcB3I6KtrB07q9lQI0mSNjNv8cN86pplrH9uIwCZ8KlrlnHSrk+y445/zRWDBw9mw4YNZCYNwWOTtktTHWk8zqBBgwDaOj9PLZ8MAtZl5t7N1u09NZIkaTMXzF+xKdC0Wf/cRq5Y+FCH/ffcc0/WrFnDwoULAXjyySfZsGEDBx98MHPnzgXgvvvu48EHH2SPPfZoqobMfAJ4ICLeBRA1Uzv7jqFGkiRtZs269R22/+nPz3TYPmTIEK688krOOusspk6dyhFHHMHTTz/NRz7yETZu3MjkyZOZNWsWl1xyyWYzNE04CfhARNwFLAeO6axzdDY11B+1tLRka2trX5chSVKxDvz8T3i4g2AzZpJCWv8AAArUSURBVOeh3D57i/fpdktELMrMlu4co9KZmog4KiJWRMTKiJjdwf4PR8SyiFgSEQsiYlKV9UiSpK075y17MPRFgzdrG/qiwZzzluYuHfWVykJNRAwGLgKOBiYBJ3YQWi7PzMn1m4C+CPxrVfVIkqTmzJw2hs8dO5kxOw8lqM3QfO7YycycNqavS+tUlU8/TQdWZub9ABFxBbVrYXe3dajfBNRmJ/5657MkSepDM6eN6fchpr0qQ80YoPE26dXAfu07RcRHgbOBIWzhhToRcQZwBsCuu+7a44VKkqSBr8p7al74wHoHMzGZeVFmvhb4n8A/dXSgzLw4M1sys2X06NE9XKYkSSpBlaFmNTCuYXsssKaT/lcAMyusR5IkFazKULMQmBgREyJiCHACcH1jh4iY2LD5NuA3FdYjSZIKVtk9NZm5ISLOBOYDg4FvZubyiDgfaM3M64EzI+LNwHPAY8D7qqpHkiSVrdK1nzLzBuCGdm3nNnz+eJXnlyRJ2w+XSZAkSUUw1EiSpCIYaiRJUhEMNZIkqQiGGkmSVARDjSRJKoKhRpIkFcFQI0mSimCokSRJRTDUSJKkIhhqJElSEQw1kiSpCIYaSZJUBEONJEkqgqFGkiQVwVAjSZKKYKiRJElFMNRIkqQiGGokSVIRIjP7uoYuiYi1wO/6uo5eNAr4U18XUQDHsfscw57hOPYMx7Fn9Kdx3C0zR3fnAAMu1GxvIqI1M1v6uo6BznHsPsewZziOPcNx7BmljaOXnyRJUhEMNZIkqQiGmv7v4r4uoBCOY/c5hj3DcewZjmPPKGocvadGkiQVwZkaSZJUBEONJEkqgqGmn4mIl0fEjRHxm/rvL+uk7/CIeDgiLuzNGgeCZsYxIvaOiF9ExPKIWBoRs/qi1v4mIo6KiBURsTIiZnewf8eIuLK+/5cRMb73q+z/mhjHsyPi7vqfvZsjYre+qLO/29o4NvQ7PiIyIop5PLmnNDOGEfHu+p/H5RFxeW/X2FMMNf3PbODmzJwI3Fzf3pLPAD/tlaoGnmbG8S/AKZn5euAo4N8iYuderLHfiYjBwEXA0cAk4MSImNSu2weAxzJzd+DLwBd6t8r+r8lxXAy0ZOYU4Grgi71bZf/X5DgSEcOAjwG/7N0K+79mxjAiJgKfAg6s/334iV4vtIcYavqfY4BL658vBWZ21Cki3gC8EvhxL9U10Gx1HDPzvsz8Tf3zGuCPQLfeZlmA6cDKzLw/M58FrqA2lo0ax/Zq4PCIiF6scSDY6jhm5i2Z+Zf65h3A2F6ucSBo5s8j1P6B90Xg6d4sboBoZgw/CFyUmY8BZOYfe7nGHmOo6X9emZm/B6j//or2HSJiEPAvwDm9XNtAstVxbBQR04EhwG97obb+bAzwUMP26npbh30ycwPwODCyV6obOJoZx0YfAH5YaUUD01bHMSKmAeMy8/u9WdgA0syfxdcBr4uI2yPijog4qteq62E79HUB26OIuAl4VQe7/rHJQ3wEuCEzH9qe/4HcA+PYdpxXA98G3peZz/dEbQNYR3+g2r/3oZk+27umxygi3gu0AIdUWtHA1Ok41v+B92Xg1N4qaABq5s/iDsBE4FBqM4Y/i4i9MnNdxbX1OENNH8jMN29pX0T8ISJenZm/r//PtqNpwDcCfxsRHwFeCgyJiD9nZmf33xSnB8aRiBgO/AD4p8y8o6JSB5LVwLiG7bHAmi30WR0ROwAjgEd7p7wBo5lxJCLeTC2EH5KZz/RSbQPJ1sZxGLAXcGv9H3ivAq6PiBmZ2dprVfZvzf43fUdmPgc8EBErqIWchb1TYs/x8lP/cz3wvvrn9wHXte+QmSdl5q6ZOR74B+Cy7S3QNGGr4xgRQ4BrqY3fd3uxtv5sITAxIibUx+cEamPZqHFsjwd+kr7Fs72tjmP9ssl/AjMG8j0MFet0HDPz8cwclZnj638f3kFtPA00f9XMf9PzgMMAImIUtctR9/dqlT3EUNP/fB44IiJ+AxxR3yYiWiLi631a2cDSzDi+GzgYODUiltR/7d035fYP9XtkzgTmA/cAV2Xm8og4PyJm1Lt9AxgZESuBs+n8Cb3tUpPjeAG1mdbv1v/stf8fzXavyXFUJ5ocw/nAIxFxN3ALcE5mPtI3FXePyyRIkqQiOFMjSZKKYKiRJElFMNRIkqQiGGokSVIRDDWSJKkIhhppAIuIjfXHgX8dEd+NiJdsod8N27JYZ0TsEhFXd6O+VfX3XrRvf2lE/GdE/La+KvBtEbHftp6nP4jaqu9v3cK+kRFxS0T8OSIu7O3apO2FoUYa2NZn5t6ZuRfwLPDhxp1RMygz37otrzzPzDWZeXxPFdvg69TeQjyxvirwqcALws8AszfQYaihttDi/6b2skxJFTHUSOX4GbB7RIyPiHsi4mvAr4BxbTMmDfv+qz5D8uOIGAoQEbtHxE0RcVdE/CoiXlvv/+v6/lMj4rqI+FFErIiI89pOHBHzImJR/ZhndFZkRLwW2I/a0hTPA9RXEP5Bff/Z9ZmnX0fEJ+pt4yPi3oj4er19bkS8ub4A32+itiApETEnIr4dET+pt3+w3h4RcUH9u8siYla9/dCIuDUirq4ff27U37cfEW+IiJ/Wf675UVtug3r/L0TEnRFxX0T8bf1NrecDs+ozZ7Maf+bMfCozF+Aq0lKlDDVSAaK2BtPRwLJ60x7Uln+Ylpm/a9d9InBRfYZkHXBcvX1uvX0qcADw+w5ONR04idqsxLsioqXe/v7MfAO1hRk/FhGdrdr9emBJZm7s4Od4A3AatdCzP/DBqC0nALA78BVgCrAn8B7gIGqzH/+r4TBTgLdRWyPt3IjYBTi2XvNU4M3ABW0hBZgGfAKYBLwGODAiXgT8O3B8/ef6JvDPDefYITOn1793XmY+C5wLXFmfObuyk59fUkVc0FIa2IZGxJL6559RW8JgF+B3nSzQ+UBmtn1nETA+IoYBYzLzWoDMfBogXrgK/I1tr0+PiGuohYpWakHmnfU+46gFp215zfpBwLWZ+VTDOf6W2lo1D2Tmsnr7cuDmzMyIWAaMbzjGdZm5HlgfEbdQC2IHAd+pB6k/RMRPgX2BJ4A7M3N1/bhL6sdaR22hxBvrYzCYzUPeNfXfF7U7t6Q+ZKiRBrb1mbnZelX1/wk/1cl3GleD3ggMBV6QXrag/boqGRGHUpv9eGNm/iUibgVe3MkxlgNT6/f6PN9uX2d1NNb9fMP282z+d9kLauzCcTfWjxXA8sx841a+09ZfUj/g5SdJZOYTwOqImAkQETtu4UmqIyLi5fX7cGYCtwMjgMfqgWZPapeNOjvXb6nN7ny64f6ViRFxDHAbMDMiXhIROwHvpDYD1RXHRMSL65fADqW2SvFt1O53GRwRo6ktZHpnJ8dYAYyOiDfW63tRRLx+K+d9EhjWxVol9SBDjaQ2J1O7jLQU+Dnwqg76LAC+DSwBvpeZrcCPgB3q3/sMsKXLXo1Orx9/Zf3y0X8BazLzV8Al1ALHL4GvZ+biLv4cdwI/qNfxmcxcA1wLLAXuAn4C/I/M/O8tHaB+j8zxwBci4q76z3vAVs57CzCpoxuFofZ4O/Cv1FaFXx0Rk7r4c0naClfpltSUiDgVaMnMM/u6li2JiDnAnzPzS31di6Te50yNJEkqgjM1kiSpCM7USJKkIhhqJElSEQw1kiSpCIYaSZJUBEONJEkqwv8HPnqYTmyAfpcAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 576x576 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Let's plot them to visualise how these features are loaded\n",
    "%matplotlib inline\n",
    "fig = plt.figure(figsize = (8,8))\n",
    "plt.scatter(pcs_df.PC1, pcs_df.PC2)\n",
    "plt.xlabel('Principal Component 1')\n",
    "plt.ylabel('Principal Component 2')\n",
    "for i, txt in enumerate(pcs_df.Feature):\n",
    "    plt.annotate(txt, (pcs_df.PC1[i],pcs_df.PC2[i]))\n",
    "plt.tight_layout()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 205,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Dimenstionality reduction using the two Principal Components\n",
    "from sklearn.decomposition import IncrementalPCA\n",
    "pca_final = IncrementalPCA(n_components=2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 206,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(167, 2)"
      ]
     },
     "execution_count": 206,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_pca = pca_final.fit_transform(rfm_df_scaled)\n",
    "df_pca.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 207,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Creating a transpose so that the each column is properly arranged\n",
    "pc = np.transpose(df_pca)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 208,
   "metadata": {},
   "outputs": [],
   "source": [
    "pcs_df2 = pd.DataFrame({'PC1':pc[0],'PC2':pc[1]})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 209,
   "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>PC1</th>\n",
       "      <th>PC2</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>-1.545850</td>\n",
       "      <td>0.624922</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>-0.271605</td>\n",
       "      <td>-0.769327</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>-0.291604</td>\n",
       "      <td>-0.481997</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>-1.653162</td>\n",
       "      <td>1.380589</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0.374234</td>\n",
       "      <td>-0.592688</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        PC1       PC2\n",
       "0 -1.545850  0.624922\n",
       "1 -0.271605 -0.769327\n",
       "2 -0.291604 -0.481997\n",
       "3 -1.653162  1.380589\n",
       "4  0.374234 -0.592688"
      ]
     },
     "execution_count": 209,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pcs_df2.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 210,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(167, 2)"
      ]
     },
     "execution_count": 210,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pcs_df2.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 211,
   "metadata": {
    "scrolled": true
   },
   "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>PC1</th>\n",
       "      <th>PC2</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>-1.545850</td>\n",
       "      <td>0.624922</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>-0.271605</td>\n",
       "      <td>-0.769327</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>-0.291604</td>\n",
       "      <td>-0.481997</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>-1.653162</td>\n",
       "      <td>1.380589</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0.374234</td>\n",
       "      <td>-0.592688</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>0.246840</td>\n",
       "      <td>-0.547132</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>-0.423637</td>\n",
       "      <td>-0.806002</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>2.498433</td>\n",
       "      <td>0.464325</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>2.394680</td>\n",
       "      <td>0.378394</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>-0.287864</td>\n",
       "      <td>-0.145593</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>0.986631</td>\n",
       "      <td>-0.128892</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>1.393840</td>\n",
       "      <td>-0.104448</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>-1.021147</td>\n",
       "      <td>-0.244367</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>0.331879</td>\n",
       "      <td>-0.489464</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>0.130482</td>\n",
       "      <td>-0.870884</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>2.240117</td>\n",
       "      <td>0.297614</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>-0.356138</td>\n",
       "      <td>-0.748857</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>-1.782410</td>\n",
       "      <td>1.084548</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>-0.763670</td>\n",
       "      <td>-0.297366</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>-0.850067</td>\n",
       "      <td>-0.232776</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>-0.144220</td>\n",
       "      <td>-0.972692</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>-0.518283</td>\n",
       "      <td>0.111189</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>0.077155</td>\n",
       "      <td>-0.479928</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>3.142672</td>\n",
       "      <td>0.866088</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>0.064769</td>\n",
       "      <td>-0.753191</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>-1.861368</td>\n",
       "      <td>1.183233</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26</th>\n",
       "      <td>-1.625017</td>\n",
       "      <td>0.678719</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27</th>\n",
       "      <td>-0.957453</td>\n",
       "      <td>-0.351183</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28</th>\n",
       "      <td>-1.700516</td>\n",
       "      <td>1.044115</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29</th>\n",
       "      <td>2.314791</td>\n",
       "      <td>0.377347</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>137</th>\n",
       "      <td>-0.543599</td>\n",
       "      <td>0.135918</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>138</th>\n",
       "      <td>1.148123</td>\n",
       "      <td>-0.342988</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>139</th>\n",
       "      <td>1.509011</td>\n",
       "      <td>-0.137962</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>140</th>\n",
       "      <td>-0.294096</td>\n",
       "      <td>-0.934947</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>141</th>\n",
       "      <td>-0.249299</td>\n",
       "      <td>-0.636030</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>142</th>\n",
       "      <td>-1.295183</td>\n",
       "      <td>0.378660</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>143</th>\n",
       "      <td>-0.081826</td>\n",
       "      <td>-0.451337</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>144</th>\n",
       "      <td>2.575451</td>\n",
       "      <td>0.453148</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>145</th>\n",
       "      <td>3.722540</td>\n",
       "      <td>1.151944</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>146</th>\n",
       "      <td>-1.068615</td>\n",
       "      <td>-0.184818</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>147</th>\n",
       "      <td>-1.305094</td>\n",
       "      <td>0.237911</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>148</th>\n",
       "      <td>-0.102107</td>\n",
       "      <td>-0.729118</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>149</th>\n",
       "      <td>-1.103531</td>\n",
       "      <td>0.092046</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>150</th>\n",
       "      <td>-1.562209</td>\n",
       "      <td>0.619402</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>151</th>\n",
       "      <td>-0.459887</td>\n",
       "      <td>-0.841663</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>152</th>\n",
       "      <td>-0.264310</td>\n",
       "      <td>-0.743448</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>153</th>\n",
       "      <td>0.182248</td>\n",
       "      <td>-0.449976</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>154</th>\n",
       "      <td>-0.805756</td>\n",
       "      <td>0.224763</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>155</th>\n",
       "      <td>-1.436015</td>\n",
       "      <td>0.424738</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>156</th>\n",
       "      <td>-0.318725</td>\n",
       "      <td>-0.932505</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>157</th>\n",
       "      <td>2.409693</td>\n",
       "      <td>0.453683</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>158</th>\n",
       "      <td>1.887872</td>\n",
       "      <td>0.121104</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>159</th>\n",
       "      <td>2.610052</td>\n",
       "      <td>0.573043</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>160</th>\n",
       "      <td>0.295688</td>\n",
       "      <td>-0.624116</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>161</th>\n",
       "      <td>-0.784252</td>\n",
       "      <td>-0.487622</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>162</th>\n",
       "      <td>-0.687187</td>\n",
       "      <td>-0.629440</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>163</th>\n",
       "      <td>0.251829</td>\n",
       "      <td>-0.459211</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>164</th>\n",
       "      <td>-0.622086</td>\n",
       "      <td>-0.767635</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>165</th>\n",
       "      <td>-1.019460</td>\n",
       "      <td>-0.050605</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>166</th>\n",
       "      <td>-1.375777</td>\n",
       "      <td>0.515909</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>167 rows × 2 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "          PC1       PC2\n",
       "0   -1.545850  0.624922\n",
       "1   -0.271605 -0.769327\n",
       "2   -0.291604 -0.481997\n",
       "3   -1.653162  1.380589\n",
       "4    0.374234 -0.592688\n",
       "5    0.246840 -0.547132\n",
       "6   -0.423637 -0.806002\n",
       "7    2.498433  0.464325\n",
       "8    2.394680  0.378394\n",
       "9   -0.287864 -0.145593\n",
       "10   0.986631 -0.128892\n",
       "11   1.393840 -0.104448\n",
       "12  -1.021147 -0.244367\n",
       "13   0.331879 -0.489464\n",
       "14   0.130482 -0.870884\n",
       "15   2.240117  0.297614\n",
       "16  -0.356138 -0.748857\n",
       "17  -1.782410  1.084548\n",
       "18  -0.763670 -0.297366\n",
       "19  -0.850067 -0.232776\n",
       "20  -0.144220 -0.972692\n",
       "21  -0.518283  0.111189\n",
       "22   0.077155 -0.479928\n",
       "23   3.142672  0.866088\n",
       "24   0.064769 -0.753191\n",
       "25  -1.861368  1.183233\n",
       "26  -1.625017  0.678719\n",
       "27  -0.957453 -0.351183\n",
       "28  -1.700516  1.044115\n",
       "29   2.314791  0.377347\n",
       "..        ...       ...\n",
       "137 -0.543599  0.135918\n",
       "138  1.148123 -0.342988\n",
       "139  1.509011 -0.137962\n",
       "140 -0.294096 -0.934947\n",
       "141 -0.249299 -0.636030\n",
       "142 -1.295183  0.378660\n",
       "143 -0.081826 -0.451337\n",
       "144  2.575451  0.453148\n",
       "145  3.722540  1.151944\n",
       "146 -1.068615 -0.184818\n",
       "147 -1.305094  0.237911\n",
       "148 -0.102107 -0.729118\n",
       "149 -1.103531  0.092046\n",
       "150 -1.562209  0.619402\n",
       "151 -0.459887 -0.841663\n",
       "152 -0.264310 -0.743448\n",
       "153  0.182248 -0.449976\n",
       "154 -0.805756  0.224763\n",
       "155 -1.436015  0.424738\n",
       "156 -0.318725 -0.932505\n",
       "157  2.409693  0.453683\n",
       "158  1.887872  0.121104\n",
       "159  2.610052  0.573043\n",
       "160  0.295688 -0.624116\n",
       "161 -0.784252 -0.487622\n",
       "162 -0.687187 -0.629440\n",
       "163  0.251829 -0.459211\n",
       "164 -0.622086 -0.767635\n",
       "165 -1.019460 -0.050605\n",
       "166 -1.375777  0.515909\n",
       "\n",
       "[167 rows x 2 columns]"
      ]
     },
     "execution_count": 211,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pcs_df2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 212,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x20b9d953048>"
      ]
     },
     "execution_count": 212,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#Visualising the points on the PCs.\n",
    "\n",
    "sns.scatterplot(x='PC1',y='PC2',data=pcs_df2)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### We can see that some of the distinct clusters are available, hence we can go ahead with clustering"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 4. Perform K means clustering"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 4.1 Calculate Hopkins statistic"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 213,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.9099790979274591"
      ]
     },
     "execution_count": 213,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "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",
    "\n",
    "hopkins(pcs_df2)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Since the hopkins statistics is> 0.7 we can conclude that the dataset has a good tendency to form clusters"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 214,
   "metadata": {},
   "outputs": [],
   "source": [
    "dat3_1 = pcs_df2"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 4.2 Checking Silhoutte score"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 215,
   "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": [
    "#First we'll do the silhouette score analysis\n",
    "from sklearn.metrics import silhouette_score\n",
    "sse_ = []\n",
    "for k in range(2, 10):\n",
    "    kmeans = KMeans(n_clusters=k).fit(dat3_1)\n",
    "    sse_.append([k, silhouette_score(dat3_1, kmeans.labels_)])\n",
    "    \n",
    "plt.plot(pd.DataFrame(sse_)[0], pd.DataFrame(sse_)[1]);"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### From the above mentioned graph, we notice that for both 2 and 4 clusters we see a good score. We will check the score from elbow analysis and decide the number of clusters accordingly"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 4.3 Checking Elbow Curve method"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 216,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x20b9c8405c0>]"
      ]
     },
     "execution_count": 216,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "ssd = []\n",
    "for num_clusters in list(range(1,10)):\n",
    "    model_clus = KMeans(n_clusters = num_clusters, max_iter=50)\n",
    "    model_clus.fit(dat3_1)\n",
    "    ssd.append(model_clus.inertia_)\n",
    "\n",
    "plt.plot(ssd)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### We notice that the bend around 2 is the last jerk that we are seeing, after which we notice a gradual slope. Hence we will be considering only 2 clusters"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 4.3 Performing K means clustering using K = 2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 217,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "KMeans(algorithm='auto', copy_x=True, init='k-means++', max_iter=50,\n",
       "       n_clusters=2, n_init=10, n_jobs=None, precompute_distances='auto',\n",
       "       random_state=50, tol=0.0001, verbose=0)"
      ]
     },
     "execution_count": 217,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model_clus2 = KMeans(n_clusters = 2, max_iter=50,random_state = 50)\n",
    "model_clus2.fit(dat3_1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 218,
   "metadata": {
    "scrolled": true
   },
   "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>PC1</th>\n",
       "      <th>PC2</th>\n",
       "      <th>ClusterID</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>-1.545850</td>\n",
       "      <td>0.624922</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>-0.271605</td>\n",
       "      <td>-0.769327</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>-0.291604</td>\n",
       "      <td>-0.481997</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>-1.653162</td>\n",
       "      <td>1.380589</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0.374234</td>\n",
       "      <td>-0.592688</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>0.246840</td>\n",
       "      <td>-0.547132</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>-0.423637</td>\n",
       "      <td>-0.806002</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>2.498433</td>\n",
       "      <td>0.464325</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>2.394680</td>\n",
       "      <td>0.378394</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>-0.287864</td>\n",
       "      <td>-0.145593</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>0.986631</td>\n",
       "      <td>-0.128892</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>1.393840</td>\n",
       "      <td>-0.104448</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>-1.021147</td>\n",
       "      <td>-0.244367</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>0.331879</td>\n",
       "      <td>-0.489464</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>0.130482</td>\n",
       "      <td>-0.870884</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>2.240117</td>\n",
       "      <td>0.297614</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>-0.356138</td>\n",
       "      <td>-0.748857</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>-1.782410</td>\n",
       "      <td>1.084548</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>-0.763670</td>\n",
       "      <td>-0.297366</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>-0.850067</td>\n",
       "      <td>-0.232776</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>-0.144220</td>\n",
       "      <td>-0.972692</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>-0.518283</td>\n",
       "      <td>0.111189</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>0.077155</td>\n",
       "      <td>-0.479928</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>3.142672</td>\n",
       "      <td>0.866088</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>0.064769</td>\n",
       "      <td>-0.753191</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>-1.861368</td>\n",
       "      <td>1.183233</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26</th>\n",
       "      <td>-1.625017</td>\n",
       "      <td>0.678719</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27</th>\n",
       "      <td>-0.957453</td>\n",
       "      <td>-0.351183</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28</th>\n",
       "      <td>-1.700516</td>\n",
       "      <td>1.044115</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29</th>\n",
       "      <td>2.314791</td>\n",
       "      <td>0.377347</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>137</th>\n",
       "      <td>-0.543599</td>\n",
       "      <td>0.135918</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>138</th>\n",
       "      <td>1.148123</td>\n",
       "      <td>-0.342988</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>139</th>\n",
       "      <td>1.509011</td>\n",
       "      <td>-0.137962</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>140</th>\n",
       "      <td>-0.294096</td>\n",
       "      <td>-0.934947</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>141</th>\n",
       "      <td>-0.249299</td>\n",
       "      <td>-0.636030</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>142</th>\n",
       "      <td>-1.295183</td>\n",
       "      <td>0.378660</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>143</th>\n",
       "      <td>-0.081826</td>\n",
       "      <td>-0.451337</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>144</th>\n",
       "      <td>2.575451</td>\n",
       "      <td>0.453148</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>145</th>\n",
       "      <td>3.722540</td>\n",
       "      <td>1.151944</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>146</th>\n",
       "      <td>-1.068615</td>\n",
       "      <td>-0.184818</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>147</th>\n",
       "      <td>-1.305094</td>\n",
       "      <td>0.237911</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>148</th>\n",
       "      <td>-0.102107</td>\n",
       "      <td>-0.729118</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>149</th>\n",
       "      <td>-1.103531</td>\n",
       "      <td>0.092046</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>150</th>\n",
       "      <td>-1.562209</td>\n",
       "      <td>0.619402</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>151</th>\n",
       "      <td>-0.459887</td>\n",
       "      <td>-0.841663</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>152</th>\n",
       "      <td>-0.264310</td>\n",
       "      <td>-0.743448</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>153</th>\n",
       "      <td>0.182248</td>\n",
       "      <td>-0.449976</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>154</th>\n",
       "      <td>-0.805756</td>\n",
       "      <td>0.224763</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>155</th>\n",
       "      <td>-1.436015</td>\n",
       "      <td>0.424738</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>156</th>\n",
       "      <td>-0.318725</td>\n",
       "      <td>-0.932505</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>157</th>\n",
       "      <td>2.409693</td>\n",
       "      <td>0.453683</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>158</th>\n",
       "      <td>1.887872</td>\n",
       "      <td>0.121104</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>159</th>\n",
       "      <td>2.610052</td>\n",
       "      <td>0.573043</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>160</th>\n",
       "      <td>0.295688</td>\n",
       "      <td>-0.624116</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>161</th>\n",
       "      <td>-0.784252</td>\n",
       "      <td>-0.487622</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>162</th>\n",
       "      <td>-0.687187</td>\n",
       "      <td>-0.629440</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>163</th>\n",
       "      <td>0.251829</td>\n",
       "      <td>-0.459211</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>164</th>\n",
       "      <td>-0.622086</td>\n",
       "      <td>-0.767635</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>165</th>\n",
       "      <td>-1.019460</td>\n",
       "      <td>-0.050605</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>166</th>\n",
       "      <td>-1.375777</td>\n",
       "      <td>0.515909</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>167 rows × 3 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "          PC1       PC2  ClusterID\n",
       "0   -1.545850  0.624922          1\n",
       "1   -0.271605 -0.769327          1\n",
       "2   -0.291604 -0.481997          1\n",
       "3   -1.653162  1.380589          1\n",
       "4    0.374234 -0.592688          1\n",
       "5    0.246840 -0.547132          1\n",
       "6   -0.423637 -0.806002          1\n",
       "7    2.498433  0.464325          0\n",
       "8    2.394680  0.378394          0\n",
       "9   -0.287864 -0.145593          1\n",
       "10   0.986631 -0.128892          0\n",
       "11   1.393840 -0.104448          0\n",
       "12  -1.021147 -0.244367          1\n",
       "13   0.331879 -0.489464          1\n",
       "14   0.130482 -0.870884          1\n",
       "15   2.240117  0.297614          0\n",
       "16  -0.356138 -0.748857          1\n",
       "17  -1.782410  1.084548          1\n",
       "18  -0.763670 -0.297366          1\n",
       "19  -0.850067 -0.232776          1\n",
       "20  -0.144220 -0.972692          1\n",
       "21  -0.518283  0.111189          1\n",
       "22   0.077155 -0.479928          1\n",
       "23   3.142672  0.866088          0\n",
       "24   0.064769 -0.753191          1\n",
       "25  -1.861368  1.183233          1\n",
       "26  -1.625017  0.678719          1\n",
       "27  -0.957453 -0.351183          1\n",
       "28  -1.700516  1.044115          1\n",
       "29   2.314791  0.377347          0\n",
       "..        ...       ...        ...\n",
       "137 -0.543599  0.135918          1\n",
       "138  1.148123 -0.342988          0\n",
       "139  1.509011 -0.137962          0\n",
       "140 -0.294096 -0.934947          1\n",
       "141 -0.249299 -0.636030          1\n",
       "142 -1.295183  0.378660          1\n",
       "143 -0.081826 -0.451337          1\n",
       "144  2.575451  0.453148          0\n",
       "145  3.722540  1.151944          0\n",
       "146 -1.068615 -0.184818          1\n",
       "147 -1.305094  0.237911          1\n",
       "148 -0.102107 -0.729118          1\n",
       "149 -1.103531  0.092046          1\n",
       "150 -1.562209  0.619402          1\n",
       "151 -0.459887 -0.841663          1\n",
       "152 -0.264310 -0.743448          1\n",
       "153  0.182248 -0.449976          1\n",
       "154 -0.805756  0.224763          1\n",
       "155 -1.436015  0.424738          1\n",
       "156 -0.318725 -0.932505          1\n",
       "157  2.409693  0.453683          0\n",
       "158  1.887872  0.121104          0\n",
       "159  2.610052  0.573043          0\n",
       "160  0.295688 -0.624116          1\n",
       "161 -0.784252 -0.487622          1\n",
       "162 -0.687187 -0.629440          1\n",
       "163  0.251829 -0.459211          1\n",
       "164 -0.622086 -0.767635          1\n",
       "165 -1.019460 -0.050605          1\n",
       "166 -1.375777  0.515909          1\n",
       "\n",
       "[167 rows x 3 columns]"
      ]
     },
     "execution_count": 218,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dat4=pcs_df2\n",
    "dat4.index = pd.RangeIndex(len(dat4.index))\n",
    "dat_km = pd.concat([dat4, pd.Series(model_clus2.labels_)], axis=1)\n",
    "dat_km.columns = ['PC1', 'PC2','ClusterID']\n",
    "dat_km"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 219,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1    129\n",
       "0     38\n",
       "Name: ClusterID, dtype: int64"
      ]
     },
     "execution_count": 219,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dat_km['ClusterID'].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 220,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x20b9d6674e0>"
      ]
     },
     "execution_count": 220,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.scatterplot(x='PC1',y='PC2',hue='ClusterID',legend='full',data=dat_km)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### We are ok to use two clusters, however, we can actually differentiate the countries into three clusters and would therefore like to see 3 clusters. Depending on th results we get from 3 clusters, we will decide which cluster to go ahead with"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Performing K means clustering using K = 3"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 221,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "2    91\n",
       "1    45\n",
       "0    31\n",
       "Name: ClusterID, dtype: int64"
      ]
     },
     "execution_count": 221,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model_clus3 = KMeans(n_clusters = 3, max_iter=50,random_state = 50)\n",
    "model_clus3.fit(dat3_1)\n",
    "\n",
    "dat4=pcs_df2\n",
    "dat4.index = pd.RangeIndex(len(dat4.index))\n",
    "dat_km = pd.concat([dat4, pd.Series(model_clus3.labels_)], axis=1)\n",
    "dat_km.columns = ['PC1', 'PC2','ClusterID']\n",
    "\n",
    "dat_km['ClusterID'].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 222,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x20b9d59ce48>"
      ]
     },
     "execution_count": 222,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.scatterplot(x='PC1',y='PC2',hue='ClusterID',legend='full',data=dat_km)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### We notice that even though cluster 2 was good from elbow curve and silhoutte perspective, cluster 3 gives us meaningful insights and is easier to explain to the client. This way we can dub the 3 clusters into either under- developed, developing and developed countries. These are some of the terms a normal layman would know and understand and hence would be benefiatial to use K = 3"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 223,
   "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>PC1</th>\n",
       "      <th>PC2</th>\n",
       "      <th>ClusterID</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",
       "      <td>-1.545850</td>\n",
       "      <td>0.624922</td>\n",
       "      <td>1</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",
       "      <td>-0.271605</td>\n",
       "      <td>-0.769327</td>\n",
       "      <td>2</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",
       "      <td>-0.291604</td>\n",
       "      <td>-0.481997</td>\n",
       "      <td>2</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",
       "      <td>-1.653162</td>\n",
       "      <td>1.380589</td>\n",
       "      <td>1</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",
       "      <td>0.374234</td>\n",
       "      <td>-0.592688</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     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       PC1       PC2  ClusterID  \n",
       "0       9.44        56.2       5.82    553 -1.545850  0.624922          1  \n",
       "1       4.49        76.3       1.65   4090 -0.271605 -0.769327          2  \n",
       "2      16.10        76.5       2.89   4460 -0.291604 -0.481997          2  \n",
       "3      22.40        60.1       6.16   3530 -1.653162  1.380589          1  \n",
       "4       1.44        76.8       2.13  12200  0.374234 -0.592688          2  "
      ]
     },
     "execution_count": 223,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dat5=pd.merge(country_df,dat_km, left_index=True,right_index=True)\n",
    "dat5.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 224,
   "metadata": {
    "scrolled": true
   },
   "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>PC1</th>\n",
       "      <th>PC2</th>\n",
       "      <th>ClusterID</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Afghanistan</td>\n",
       "      <td>90.2</td>\n",
       "      <td>10.00</td>\n",
       "      <td>7.58</td>\n",
       "      <td>44.9</td>\n",
       "      <td>1610</td>\n",
       "      <td>9.440</td>\n",
       "      <td>56.2</td>\n",
       "      <td>5.82</td>\n",
       "      <td>553</td>\n",
       "      <td>-1.545850</td>\n",
       "      <td>0.624922</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Albania</td>\n",
       "      <td>16.6</td>\n",
       "      <td>28.00</td>\n",
       "      <td>6.55</td>\n",
       "      <td>48.6</td>\n",
       "      <td>9930</td>\n",
       "      <td>4.490</td>\n",
       "      <td>76.3</td>\n",
       "      <td>1.65</td>\n",
       "      <td>4090</td>\n",
       "      <td>-0.271605</td>\n",
       "      <td>-0.769327</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Algeria</td>\n",
       "      <td>27.3</td>\n",
       "      <td>38.40</td>\n",
       "      <td>4.17</td>\n",
       "      <td>31.4</td>\n",
       "      <td>12900</td>\n",
       "      <td>16.100</td>\n",
       "      <td>76.5</td>\n",
       "      <td>2.89</td>\n",
       "      <td>4460</td>\n",
       "      <td>-0.291604</td>\n",
       "      <td>-0.481997</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Angola</td>\n",
       "      <td>119.0</td>\n",
       "      <td>62.30</td>\n",
       "      <td>2.85</td>\n",
       "      <td>42.9</td>\n",
       "      <td>5900</td>\n",
       "      <td>22.400</td>\n",
       "      <td>60.1</td>\n",
       "      <td>6.16</td>\n",
       "      <td>3530</td>\n",
       "      <td>-1.653162</td>\n",
       "      <td>1.380589</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Antigua and Barbuda</td>\n",
       "      <td>10.3</td>\n",
       "      <td>45.50</td>\n",
       "      <td>6.03</td>\n",
       "      <td>58.9</td>\n",
       "      <td>19100</td>\n",
       "      <td>1.440</td>\n",
       "      <td>76.8</td>\n",
       "      <td>2.13</td>\n",
       "      <td>12200</td>\n",
       "      <td>0.374234</td>\n",
       "      <td>-0.592688</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>Argentina</td>\n",
       "      <td>14.5</td>\n",
       "      <td>18.90</td>\n",
       "      <td>8.10</td>\n",
       "      <td>16.0</td>\n",
       "      <td>18700</td>\n",
       "      <td>20.900</td>\n",
       "      <td>75.8</td>\n",
       "      <td>2.37</td>\n",
       "      <td>10300</td>\n",
       "      <td>0.246840</td>\n",
       "      <td>-0.547132</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>Armenia</td>\n",
       "      <td>18.1</td>\n",
       "      <td>20.80</td>\n",
       "      <td>4.40</td>\n",
       "      <td>45.3</td>\n",
       "      <td>6700</td>\n",
       "      <td>7.770</td>\n",
       "      <td>73.3</td>\n",
       "      <td>1.69</td>\n",
       "      <td>3220</td>\n",
       "      <td>-0.423637</td>\n",
       "      <td>-0.806002</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>Australia</td>\n",
       "      <td>4.8</td>\n",
       "      <td>19.80</td>\n",
       "      <td>8.73</td>\n",
       "      <td>20.9</td>\n",
       "      <td>41400</td>\n",
       "      <td>1.160</td>\n",
       "      <td>82.0</td>\n",
       "      <td>1.93</td>\n",
       "      <td>51900</td>\n",
       "      <td>2.498433</td>\n",
       "      <td>0.464325</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>Austria</td>\n",
       "      <td>4.3</td>\n",
       "      <td>51.30</td>\n",
       "      <td>11.00</td>\n",
       "      <td>47.8</td>\n",
       "      <td>43200</td>\n",
       "      <td>0.873</td>\n",
       "      <td>80.5</td>\n",
       "      <td>1.44</td>\n",
       "      <td>46900</td>\n",
       "      <td>2.394680</td>\n",
       "      <td>0.378394</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>Azerbaijan</td>\n",
       "      <td>39.2</td>\n",
       "      <td>54.30</td>\n",
       "      <td>5.88</td>\n",
       "      <td>20.7</td>\n",
       "      <td>16000</td>\n",
       "      <td>13.800</td>\n",
       "      <td>69.1</td>\n",
       "      <td>1.92</td>\n",
       "      <td>5840</td>\n",
       "      <td>-0.287864</td>\n",
       "      <td>-0.145593</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>Bahamas</td>\n",
       "      <td>13.8</td>\n",
       "      <td>35.00</td>\n",
       "      <td>7.89</td>\n",
       "      <td>43.7</td>\n",
       "      <td>22900</td>\n",
       "      <td>-0.393</td>\n",
       "      <td>73.8</td>\n",
       "      <td>1.86</td>\n",
       "      <td>28000</td>\n",
       "      <td>0.986631</td>\n",
       "      <td>-0.128892</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>Bahrain</td>\n",
       "      <td>8.6</td>\n",
       "      <td>69.50</td>\n",
       "      <td>4.97</td>\n",
       "      <td>50.9</td>\n",
       "      <td>41100</td>\n",
       "      <td>7.440</td>\n",
       "      <td>76.0</td>\n",
       "      <td>2.16</td>\n",
       "      <td>20700</td>\n",
       "      <td>1.393840</td>\n",
       "      <td>-0.104448</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>Bangladesh</td>\n",
       "      <td>49.4</td>\n",
       "      <td>16.00</td>\n",
       "      <td>3.52</td>\n",
       "      <td>21.8</td>\n",
       "      <td>2440</td>\n",
       "      <td>7.140</td>\n",
       "      <td>70.4</td>\n",
       "      <td>2.33</td>\n",
       "      <td>758</td>\n",
       "      <td>-1.021147</td>\n",
       "      <td>-0.244367</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>Barbados</td>\n",
       "      <td>14.2</td>\n",
       "      <td>39.50</td>\n",
       "      <td>7.97</td>\n",
       "      <td>48.7</td>\n",
       "      <td>15300</td>\n",
       "      <td>0.321</td>\n",
       "      <td>76.7</td>\n",
       "      <td>1.78</td>\n",
       "      <td>16000</td>\n",
       "      <td>0.331879</td>\n",
       "      <td>-0.489464</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>Belarus</td>\n",
       "      <td>5.5</td>\n",
       "      <td>51.40</td>\n",
       "      <td>5.61</td>\n",
       "      <td>64.5</td>\n",
       "      <td>16200</td>\n",
       "      <td>15.100</td>\n",
       "      <td>70.4</td>\n",
       "      <td>1.49</td>\n",
       "      <td>6030</td>\n",
       "      <td>0.130482</td>\n",
       "      <td>-0.870884</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>Belgium</td>\n",
       "      <td>4.5</td>\n",
       "      <td>76.40</td>\n",
       "      <td>10.70</td>\n",
       "      <td>74.7</td>\n",
       "      <td>41100</td>\n",
       "      <td>1.880</td>\n",
       "      <td>80.0</td>\n",
       "      <td>1.86</td>\n",
       "      <td>44400</td>\n",
       "      <td>2.240117</td>\n",
       "      <td>0.297614</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>Belize</td>\n",
       "      <td>18.8</td>\n",
       "      <td>58.20</td>\n",
       "      <td>5.20</td>\n",
       "      <td>57.5</td>\n",
       "      <td>7880</td>\n",
       "      <td>1.140</td>\n",
       "      <td>71.4</td>\n",
       "      <td>2.71</td>\n",
       "      <td>4340</td>\n",
       "      <td>-0.356138</td>\n",
       "      <td>-0.748857</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>Benin</td>\n",
       "      <td>111.0</td>\n",
       "      <td>23.80</td>\n",
       "      <td>4.10</td>\n",
       "      <td>37.2</td>\n",
       "      <td>1820</td>\n",
       "      <td>0.885</td>\n",
       "      <td>61.8</td>\n",
       "      <td>5.36</td>\n",
       "      <td>758</td>\n",
       "      <td>-1.782410</td>\n",
       "      <td>1.084548</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>Bhutan</td>\n",
       "      <td>42.7</td>\n",
       "      <td>42.50</td>\n",
       "      <td>5.20</td>\n",
       "      <td>70.7</td>\n",
       "      <td>6420</td>\n",
       "      <td>5.990</td>\n",
       "      <td>72.1</td>\n",
       "      <td>2.38</td>\n",
       "      <td>2180</td>\n",
       "      <td>-0.763670</td>\n",
       "      <td>-0.297366</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>Bolivia</td>\n",
       "      <td>46.6</td>\n",
       "      <td>41.20</td>\n",
       "      <td>4.84</td>\n",
       "      <td>34.3</td>\n",
       "      <td>5410</td>\n",
       "      <td>8.780</td>\n",
       "      <td>71.6</td>\n",
       "      <td>3.20</td>\n",
       "      <td>1980</td>\n",
       "      <td>-0.850067</td>\n",
       "      <td>-0.232776</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>Bosnia and Herzegovina</td>\n",
       "      <td>6.9</td>\n",
       "      <td>29.70</td>\n",
       "      <td>11.10</td>\n",
       "      <td>51.3</td>\n",
       "      <td>9720</td>\n",
       "      <td>1.400</td>\n",
       "      <td>76.8</td>\n",
       "      <td>1.31</td>\n",
       "      <td>4610</td>\n",
       "      <td>-0.144220</td>\n",
       "      <td>-0.972692</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>Botswana</td>\n",
       "      <td>52.5</td>\n",
       "      <td>43.60</td>\n",
       "      <td>8.30</td>\n",
       "      <td>51.3</td>\n",
       "      <td>13300</td>\n",
       "      <td>8.920</td>\n",
       "      <td>57.1</td>\n",
       "      <td>2.88</td>\n",
       "      <td>6350</td>\n",
       "      <td>-0.518283</td>\n",
       "      <td>0.111189</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>Brazil</td>\n",
       "      <td>19.8</td>\n",
       "      <td>10.70</td>\n",
       "      <td>9.01</td>\n",
       "      <td>11.8</td>\n",
       "      <td>14500</td>\n",
       "      <td>8.410</td>\n",
       "      <td>74.2</td>\n",
       "      <td>1.80</td>\n",
       "      <td>11200</td>\n",
       "      <td>0.077155</td>\n",
       "      <td>-0.479928</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>Brunei</td>\n",
       "      <td>10.5</td>\n",
       "      <td>67.40</td>\n",
       "      <td>2.84</td>\n",
       "      <td>28.0</td>\n",
       "      <td>80600</td>\n",
       "      <td>16.700</td>\n",
       "      <td>77.1</td>\n",
       "      <td>1.84</td>\n",
       "      <td>35300</td>\n",
       "      <td>3.142672</td>\n",
       "      <td>0.866088</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>Bulgaria</td>\n",
       "      <td>10.8</td>\n",
       "      <td>50.20</td>\n",
       "      <td>6.87</td>\n",
       "      <td>53.0</td>\n",
       "      <td>15300</td>\n",
       "      <td>1.110</td>\n",
       "      <td>73.9</td>\n",
       "      <td>1.57</td>\n",
       "      <td>6840</td>\n",
       "      <td>0.064769</td>\n",
       "      <td>-0.753191</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>Burkina Faso</td>\n",
       "      <td>116.0</td>\n",
       "      <td>19.20</td>\n",
       "      <td>6.74</td>\n",
       "      <td>29.6</td>\n",
       "      <td>1430</td>\n",
       "      <td>6.810</td>\n",
       "      <td>57.9</td>\n",
       "      <td>5.87</td>\n",
       "      <td>575</td>\n",
       "      <td>-1.861368</td>\n",
       "      <td>1.183233</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26</th>\n",
       "      <td>Burundi</td>\n",
       "      <td>93.6</td>\n",
       "      <td>8.92</td>\n",
       "      <td>11.60</td>\n",
       "      <td>39.2</td>\n",
       "      <td>764</td>\n",
       "      <td>12.300</td>\n",
       "      <td>57.7</td>\n",
       "      <td>6.26</td>\n",
       "      <td>231</td>\n",
       "      <td>-1.625017</td>\n",
       "      <td>0.678719</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27</th>\n",
       "      <td>Cambodia</td>\n",
       "      <td>44.4</td>\n",
       "      <td>54.10</td>\n",
       "      <td>5.68</td>\n",
       "      <td>59.5</td>\n",
       "      <td>2520</td>\n",
       "      <td>3.120</td>\n",
       "      <td>66.1</td>\n",
       "      <td>2.88</td>\n",
       "      <td>786</td>\n",
       "      <td>-0.957453</td>\n",
       "      <td>-0.351183</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28</th>\n",
       "      <td>Cameroon</td>\n",
       "      <td>108.0</td>\n",
       "      <td>22.20</td>\n",
       "      <td>5.13</td>\n",
       "      <td>27.0</td>\n",
       "      <td>2660</td>\n",
       "      <td>1.910</td>\n",
       "      <td>57.3</td>\n",
       "      <td>5.11</td>\n",
       "      <td>1310</td>\n",
       "      <td>-1.700516</td>\n",
       "      <td>1.044115</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29</th>\n",
       "      <td>Canada</td>\n",
       "      <td>5.6</td>\n",
       "      <td>29.10</td>\n",
       "      <td>11.30</td>\n",
       "      <td>31.0</td>\n",
       "      <td>40700</td>\n",
       "      <td>2.870</td>\n",
       "      <td>81.3</td>\n",
       "      <td>1.63</td>\n",
       "      <td>47400</td>\n",
       "      <td>2.314791</td>\n",
       "      <td>0.377347</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>137</th>\n",
       "      <td>South Africa</td>\n",
       "      <td>53.7</td>\n",
       "      <td>28.60</td>\n",
       "      <td>8.94</td>\n",
       "      <td>27.4</td>\n",
       "      <td>12000</td>\n",
       "      <td>6.350</td>\n",
       "      <td>54.3</td>\n",
       "      <td>2.59</td>\n",
       "      <td>7280</td>\n",
       "      <td>-0.543599</td>\n",
       "      <td>0.135918</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>138</th>\n",
       "      <td>South Korea</td>\n",
       "      <td>4.1</td>\n",
       "      <td>49.40</td>\n",
       "      <td>6.93</td>\n",
       "      <td>46.2</td>\n",
       "      <td>30400</td>\n",
       "      <td>3.160</td>\n",
       "      <td>80.1</td>\n",
       "      <td>1.23</td>\n",
       "      <td>22100</td>\n",
       "      <td>1.148123</td>\n",
       "      <td>-0.342988</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>139</th>\n",
       "      <td>Spain</td>\n",
       "      <td>3.8</td>\n",
       "      <td>25.50</td>\n",
       "      <td>9.54</td>\n",
       "      <td>26.8</td>\n",
       "      <td>32500</td>\n",
       "      <td>0.160</td>\n",
       "      <td>81.9</td>\n",
       "      <td>1.37</td>\n",
       "      <td>30700</td>\n",
       "      <td>1.509011</td>\n",
       "      <td>-0.137962</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>140</th>\n",
       "      <td>Sri Lanka</td>\n",
       "      <td>11.2</td>\n",
       "      <td>19.60</td>\n",
       "      <td>2.94</td>\n",
       "      <td>26.8</td>\n",
       "      <td>8560</td>\n",
       "      <td>22.800</td>\n",
       "      <td>74.4</td>\n",
       "      <td>2.20</td>\n",
       "      <td>2810</td>\n",
       "      <td>-0.294096</td>\n",
       "      <td>-0.934947</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>141</th>\n",
       "      <td>St. Vincent and the Grenadines</td>\n",
       "      <td>20.7</td>\n",
       "      <td>26.90</td>\n",
       "      <td>4.47</td>\n",
       "      <td>57.1</td>\n",
       "      <td>9920</td>\n",
       "      <td>4.440</td>\n",
       "      <td>71.6</td>\n",
       "      <td>2.07</td>\n",
       "      <td>6230</td>\n",
       "      <td>-0.249299</td>\n",
       "      <td>-0.636030</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>142</th>\n",
       "      <td>Sudan</td>\n",
       "      <td>76.7</td>\n",
       "      <td>19.70</td>\n",
       "      <td>6.32</td>\n",
       "      <td>17.2</td>\n",
       "      <td>3370</td>\n",
       "      <td>19.600</td>\n",
       "      <td>66.3</td>\n",
       "      <td>4.88</td>\n",
       "      <td>1480</td>\n",
       "      <td>-1.295183</td>\n",
       "      <td>0.378660</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>143</th>\n",
       "      <td>Suriname</td>\n",
       "      <td>24.1</td>\n",
       "      <td>52.50</td>\n",
       "      <td>7.01</td>\n",
       "      <td>38.4</td>\n",
       "      <td>14200</td>\n",
       "      <td>7.200</td>\n",
       "      <td>70.3</td>\n",
       "      <td>2.52</td>\n",
       "      <td>8300</td>\n",
       "      <td>-0.081826</td>\n",
       "      <td>-0.451337</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>144</th>\n",
       "      <td>Sweden</td>\n",
       "      <td>3.0</td>\n",
       "      <td>46.20</td>\n",
       "      <td>9.63</td>\n",
       "      <td>40.7</td>\n",
       "      <td>42900</td>\n",
       "      <td>0.991</td>\n",
       "      <td>81.5</td>\n",
       "      <td>1.98</td>\n",
       "      <td>52100</td>\n",
       "      <td>2.575451</td>\n",
       "      <td>0.453148</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>145</th>\n",
       "      <td>Switzerland</td>\n",
       "      <td>4.5</td>\n",
       "      <td>64.00</td>\n",
       "      <td>11.50</td>\n",
       "      <td>53.3</td>\n",
       "      <td>55500</td>\n",
       "      <td>0.317</td>\n",
       "      <td>82.2</td>\n",
       "      <td>1.52</td>\n",
       "      <td>74600</td>\n",
       "      <td>3.722540</td>\n",
       "      <td>1.151944</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>146</th>\n",
       "      <td>Tajikistan</td>\n",
       "      <td>52.4</td>\n",
       "      <td>14.90</td>\n",
       "      <td>5.98</td>\n",
       "      <td>58.6</td>\n",
       "      <td>2110</td>\n",
       "      <td>12.500</td>\n",
       "      <td>69.6</td>\n",
       "      <td>3.51</td>\n",
       "      <td>738</td>\n",
       "      <td>-1.068615</td>\n",
       "      <td>-0.184818</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>147</th>\n",
       "      <td>Tanzania</td>\n",
       "      <td>71.9</td>\n",
       "      <td>18.70</td>\n",
       "      <td>6.01</td>\n",
       "      <td>29.1</td>\n",
       "      <td>2090</td>\n",
       "      <td>9.250</td>\n",
       "      <td>59.3</td>\n",
       "      <td>5.43</td>\n",
       "      <td>702</td>\n",
       "      <td>-1.305094</td>\n",
       "      <td>0.237911</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>148</th>\n",
       "      <td>Thailand</td>\n",
       "      <td>14.9</td>\n",
       "      <td>66.50</td>\n",
       "      <td>3.88</td>\n",
       "      <td>60.8</td>\n",
       "      <td>13500</td>\n",
       "      <td>4.080</td>\n",
       "      <td>76.6</td>\n",
       "      <td>1.55</td>\n",
       "      <td>5080</td>\n",
       "      <td>-0.102107</td>\n",
       "      <td>-0.729118</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>149</th>\n",
       "      <td>Timor-Leste</td>\n",
       "      <td>62.6</td>\n",
       "      <td>2.20</td>\n",
       "      <td>9.12</td>\n",
       "      <td>27.8</td>\n",
       "      <td>1850</td>\n",
       "      <td>26.500</td>\n",
       "      <td>71.1</td>\n",
       "      <td>6.23</td>\n",
       "      <td>3600</td>\n",
       "      <td>-1.103531</td>\n",
       "      <td>0.092046</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>150</th>\n",
       "      <td>Togo</td>\n",
       "      <td>90.3</td>\n",
       "      <td>40.20</td>\n",
       "      <td>7.65</td>\n",
       "      <td>57.3</td>\n",
       "      <td>1210</td>\n",
       "      <td>1.180</td>\n",
       "      <td>58.7</td>\n",
       "      <td>4.87</td>\n",
       "      <td>488</td>\n",
       "      <td>-1.562209</td>\n",
       "      <td>0.619402</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>151</th>\n",
       "      <td>Tonga</td>\n",
       "      <td>17.4</td>\n",
       "      <td>12.40</td>\n",
       "      <td>5.07</td>\n",
       "      <td>60.3</td>\n",
       "      <td>4980</td>\n",
       "      <td>3.680</td>\n",
       "      <td>69.9</td>\n",
       "      <td>3.91</td>\n",
       "      <td>3550</td>\n",
       "      <td>-0.459887</td>\n",
       "      <td>-0.841663</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>152</th>\n",
       "      <td>Tunisia</td>\n",
       "      <td>17.4</td>\n",
       "      <td>50.50</td>\n",
       "      <td>6.21</td>\n",
       "      <td>55.3</td>\n",
       "      <td>10400</td>\n",
       "      <td>3.820</td>\n",
       "      <td>76.9</td>\n",
       "      <td>2.14</td>\n",
       "      <td>4140</td>\n",
       "      <td>-0.264310</td>\n",
       "      <td>-0.743448</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>153</th>\n",
       "      <td>Turkey</td>\n",
       "      <td>19.1</td>\n",
       "      <td>20.40</td>\n",
       "      <td>6.74</td>\n",
       "      <td>25.5</td>\n",
       "      <td>18000</td>\n",
       "      <td>7.010</td>\n",
       "      <td>78.2</td>\n",
       "      <td>2.15</td>\n",
       "      <td>10700</td>\n",
       "      <td>0.182248</td>\n",
       "      <td>-0.449976</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>154</th>\n",
       "      <td>Turkmenistan</td>\n",
       "      <td>62.0</td>\n",
       "      <td>76.30</td>\n",
       "      <td>2.50</td>\n",
       "      <td>44.5</td>\n",
       "      <td>9940</td>\n",
       "      <td>2.310</td>\n",
       "      <td>67.9</td>\n",
       "      <td>2.83</td>\n",
       "      <td>4440</td>\n",
       "      <td>-0.805756</td>\n",
       "      <td>0.224763</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>155</th>\n",
       "      <td>Uganda</td>\n",
       "      <td>81.0</td>\n",
       "      <td>17.10</td>\n",
       "      <td>9.01</td>\n",
       "      <td>28.6</td>\n",
       "      <td>1540</td>\n",
       "      <td>10.600</td>\n",
       "      <td>56.8</td>\n",
       "      <td>6.15</td>\n",
       "      <td>595</td>\n",
       "      <td>-1.436015</td>\n",
       "      <td>0.424738</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>156</th>\n",
       "      <td>Ukraine</td>\n",
       "      <td>11.7</td>\n",
       "      <td>47.10</td>\n",
       "      <td>7.72</td>\n",
       "      <td>51.1</td>\n",
       "      <td>7820</td>\n",
       "      <td>13.400</td>\n",
       "      <td>70.4</td>\n",
       "      <td>1.44</td>\n",
       "      <td>2970</td>\n",
       "      <td>-0.318725</td>\n",
       "      <td>-0.932505</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>157</th>\n",
       "      <td>United Arab Emirates</td>\n",
       "      <td>8.6</td>\n",
       "      <td>77.70</td>\n",
       "      <td>3.66</td>\n",
       "      <td>63.6</td>\n",
       "      <td>57600</td>\n",
       "      <td>12.500</td>\n",
       "      <td>76.5</td>\n",
       "      <td>1.87</td>\n",
       "      <td>35000</td>\n",
       "      <td>2.409693</td>\n",
       "      <td>0.453683</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>158</th>\n",
       "      <td>United Kingdom</td>\n",
       "      <td>5.2</td>\n",
       "      <td>28.20</td>\n",
       "      <td>9.64</td>\n",
       "      <td>30.8</td>\n",
       "      <td>36200</td>\n",
       "      <td>1.570</td>\n",
       "      <td>80.3</td>\n",
       "      <td>1.92</td>\n",
       "      <td>38900</td>\n",
       "      <td>1.887872</td>\n",
       "      <td>0.121104</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>159</th>\n",
       "      <td>United States</td>\n",
       "      <td>7.3</td>\n",
       "      <td>12.40</td>\n",
       "      <td>17.90</td>\n",
       "      <td>15.8</td>\n",
       "      <td>49400</td>\n",
       "      <td>1.220</td>\n",
       "      <td>78.7</td>\n",
       "      <td>1.93</td>\n",
       "      <td>48400</td>\n",
       "      <td>2.610052</td>\n",
       "      <td>0.573043</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>160</th>\n",
       "      <td>Uruguay</td>\n",
       "      <td>10.6</td>\n",
       "      <td>26.30</td>\n",
       "      <td>8.35</td>\n",
       "      <td>25.4</td>\n",
       "      <td>17100</td>\n",
       "      <td>4.910</td>\n",
       "      <td>76.4</td>\n",
       "      <td>2.08</td>\n",
       "      <td>11900</td>\n",
       "      <td>0.295688</td>\n",
       "      <td>-0.624116</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>161</th>\n",
       "      <td>Uzbekistan</td>\n",
       "      <td>36.3</td>\n",
       "      <td>31.70</td>\n",
       "      <td>5.81</td>\n",
       "      <td>28.5</td>\n",
       "      <td>4240</td>\n",
       "      <td>16.500</td>\n",
       "      <td>68.8</td>\n",
       "      <td>2.34</td>\n",
       "      <td>1380</td>\n",
       "      <td>-0.784252</td>\n",
       "      <td>-0.487622</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>162</th>\n",
       "      <td>Vanuatu</td>\n",
       "      <td>29.2</td>\n",
       "      <td>46.60</td>\n",
       "      <td>5.25</td>\n",
       "      <td>52.7</td>\n",
       "      <td>2950</td>\n",
       "      <td>2.620</td>\n",
       "      <td>63.0</td>\n",
       "      <td>3.50</td>\n",
       "      <td>2970</td>\n",
       "      <td>-0.687187</td>\n",
       "      <td>-0.629440</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>163</th>\n",
       "      <td>Venezuela</td>\n",
       "      <td>17.1</td>\n",
       "      <td>28.50</td>\n",
       "      <td>4.91</td>\n",
       "      <td>17.6</td>\n",
       "      <td>16500</td>\n",
       "      <td>45.900</td>\n",
       "      <td>75.4</td>\n",
       "      <td>2.47</td>\n",
       "      <td>13500</td>\n",
       "      <td>0.251829</td>\n",
       "      <td>-0.459211</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>164</th>\n",
       "      <td>Vietnam</td>\n",
       "      <td>23.3</td>\n",
       "      <td>72.00</td>\n",
       "      <td>6.84</td>\n",
       "      <td>80.2</td>\n",
       "      <td>4490</td>\n",
       "      <td>12.100</td>\n",
       "      <td>73.1</td>\n",
       "      <td>1.95</td>\n",
       "      <td>1310</td>\n",
       "      <td>-0.622086</td>\n",
       "      <td>-0.767635</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>165</th>\n",
       "      <td>Yemen</td>\n",
       "      <td>56.3</td>\n",
       "      <td>30.00</td>\n",
       "      <td>5.18</td>\n",
       "      <td>34.4</td>\n",
       "      <td>4480</td>\n",
       "      <td>23.600</td>\n",
       "      <td>67.5</td>\n",
       "      <td>4.67</td>\n",
       "      <td>1310</td>\n",
       "      <td>-1.019460</td>\n",
       "      <td>-0.050605</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>166</th>\n",
       "      <td>Zambia</td>\n",
       "      <td>83.1</td>\n",
       "      <td>37.00</td>\n",
       "      <td>5.89</td>\n",
       "      <td>30.9</td>\n",
       "      <td>3280</td>\n",
       "      <td>14.000</td>\n",
       "      <td>52.0</td>\n",
       "      <td>5.40</td>\n",
       "      <td>1460</td>\n",
       "      <td>-1.375777</td>\n",
       "      <td>0.515909</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>167 rows × 13 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                            country  child_mort  exports  health  imports  \\\n",
       "0                       Afghanistan        90.2    10.00    7.58     44.9   \n",
       "1                           Albania        16.6    28.00    6.55     48.6   \n",
       "2                           Algeria        27.3    38.40    4.17     31.4   \n",
       "3                            Angola       119.0    62.30    2.85     42.9   \n",
       "4               Antigua and Barbuda        10.3    45.50    6.03     58.9   \n",
       "5                         Argentina        14.5    18.90    8.10     16.0   \n",
       "6                           Armenia        18.1    20.80    4.40     45.3   \n",
       "7                         Australia         4.8    19.80    8.73     20.9   \n",
       "8                           Austria         4.3    51.30   11.00     47.8   \n",
       "9                        Azerbaijan        39.2    54.30    5.88     20.7   \n",
       "10                          Bahamas        13.8    35.00    7.89     43.7   \n",
       "11                          Bahrain         8.6    69.50    4.97     50.9   \n",
       "12                       Bangladesh        49.4    16.00    3.52     21.8   \n",
       "13                         Barbados        14.2    39.50    7.97     48.7   \n",
       "14                          Belarus         5.5    51.40    5.61     64.5   \n",
       "15                          Belgium         4.5    76.40   10.70     74.7   \n",
       "16                           Belize        18.8    58.20    5.20     57.5   \n",
       "17                            Benin       111.0    23.80    4.10     37.2   \n",
       "18                           Bhutan        42.7    42.50    5.20     70.7   \n",
       "19                          Bolivia        46.6    41.20    4.84     34.3   \n",
       "20           Bosnia and Herzegovina         6.9    29.70   11.10     51.3   \n",
       "21                         Botswana        52.5    43.60    8.30     51.3   \n",
       "22                           Brazil        19.8    10.70    9.01     11.8   \n",
       "23                           Brunei        10.5    67.40    2.84     28.0   \n",
       "24                         Bulgaria        10.8    50.20    6.87     53.0   \n",
       "25                     Burkina Faso       116.0    19.20    6.74     29.6   \n",
       "26                          Burundi        93.6     8.92   11.60     39.2   \n",
       "27                         Cambodia        44.4    54.10    5.68     59.5   \n",
       "28                         Cameroon       108.0    22.20    5.13     27.0   \n",
       "29                           Canada         5.6    29.10   11.30     31.0   \n",
       "..                              ...         ...      ...     ...      ...   \n",
       "137                    South Africa        53.7    28.60    8.94     27.4   \n",
       "138                     South Korea         4.1    49.40    6.93     46.2   \n",
       "139                           Spain         3.8    25.50    9.54     26.8   \n",
       "140                       Sri Lanka        11.2    19.60    2.94     26.8   \n",
       "141  St. Vincent and the Grenadines        20.7    26.90    4.47     57.1   \n",
       "142                           Sudan        76.7    19.70    6.32     17.2   \n",
       "143                        Suriname        24.1    52.50    7.01     38.4   \n",
       "144                          Sweden         3.0    46.20    9.63     40.7   \n",
       "145                     Switzerland         4.5    64.00   11.50     53.3   \n",
       "146                      Tajikistan        52.4    14.90    5.98     58.6   \n",
       "147                        Tanzania        71.9    18.70    6.01     29.1   \n",
       "148                        Thailand        14.9    66.50    3.88     60.8   \n",
       "149                     Timor-Leste        62.6     2.20    9.12     27.8   \n",
       "150                            Togo        90.3    40.20    7.65     57.3   \n",
       "151                           Tonga        17.4    12.40    5.07     60.3   \n",
       "152                         Tunisia        17.4    50.50    6.21     55.3   \n",
       "153                          Turkey        19.1    20.40    6.74     25.5   \n",
       "154                    Turkmenistan        62.0    76.30    2.50     44.5   \n",
       "155                          Uganda        81.0    17.10    9.01     28.6   \n",
       "156                         Ukraine        11.7    47.10    7.72     51.1   \n",
       "157            United Arab Emirates         8.6    77.70    3.66     63.6   \n",
       "158                  United Kingdom         5.2    28.20    9.64     30.8   \n",
       "159                   United States         7.3    12.40   17.90     15.8   \n",
       "160                         Uruguay        10.6    26.30    8.35     25.4   \n",
       "161                      Uzbekistan        36.3    31.70    5.81     28.5   \n",
       "162                         Vanuatu        29.2    46.60    5.25     52.7   \n",
       "163                       Venezuela        17.1    28.50    4.91     17.6   \n",
       "164                         Vietnam        23.3    72.00    6.84     80.2   \n",
       "165                           Yemen        56.3    30.00    5.18     34.4   \n",
       "166                          Zambia        83.1    37.00    5.89     30.9   \n",
       "\n",
       "     income  inflation  life_expec  total_fer   gdpp       PC1       PC2  \\\n",
       "0      1610      9.440        56.2       5.82    553 -1.545850  0.624922   \n",
       "1      9930      4.490        76.3       1.65   4090 -0.271605 -0.769327   \n",
       "2     12900     16.100        76.5       2.89   4460 -0.291604 -0.481997   \n",
       "3      5900     22.400        60.1       6.16   3530 -1.653162  1.380589   \n",
       "4     19100      1.440        76.8       2.13  12200  0.374234 -0.592688   \n",
       "5     18700     20.900        75.8       2.37  10300  0.246840 -0.547132   \n",
       "6      6700      7.770        73.3       1.69   3220 -0.423637 -0.806002   \n",
       "7     41400      1.160        82.0       1.93  51900  2.498433  0.464325   \n",
       "8     43200      0.873        80.5       1.44  46900  2.394680  0.378394   \n",
       "9     16000     13.800        69.1       1.92   5840 -0.287864 -0.145593   \n",
       "10    22900     -0.393        73.8       1.86  28000  0.986631 -0.128892   \n",
       "11    41100      7.440        76.0       2.16  20700  1.393840 -0.104448   \n",
       "12     2440      7.140        70.4       2.33    758 -1.021147 -0.244367   \n",
       "13    15300      0.321        76.7       1.78  16000  0.331879 -0.489464   \n",
       "14    16200     15.100        70.4       1.49   6030  0.130482 -0.870884   \n",
       "15    41100      1.880        80.0       1.86  44400  2.240117  0.297614   \n",
       "16     7880      1.140        71.4       2.71   4340 -0.356138 -0.748857   \n",
       "17     1820      0.885        61.8       5.36    758 -1.782410  1.084548   \n",
       "18     6420      5.990        72.1       2.38   2180 -0.763670 -0.297366   \n",
       "19     5410      8.780        71.6       3.20   1980 -0.850067 -0.232776   \n",
       "20     9720      1.400        76.8       1.31   4610 -0.144220 -0.972692   \n",
       "21    13300      8.920        57.1       2.88   6350 -0.518283  0.111189   \n",
       "22    14500      8.410        74.2       1.80  11200  0.077155 -0.479928   \n",
       "23    80600     16.700        77.1       1.84  35300  3.142672  0.866088   \n",
       "24    15300      1.110        73.9       1.57   6840  0.064769 -0.753191   \n",
       "25     1430      6.810        57.9       5.87    575 -1.861368  1.183233   \n",
       "26      764     12.300        57.7       6.26    231 -1.625017  0.678719   \n",
       "27     2520      3.120        66.1       2.88    786 -0.957453 -0.351183   \n",
       "28     2660      1.910        57.3       5.11   1310 -1.700516  1.044115   \n",
       "29    40700      2.870        81.3       1.63  47400  2.314791  0.377347   \n",
       "..      ...        ...         ...        ...    ...       ...       ...   \n",
       "137   12000      6.350        54.3       2.59   7280 -0.543599  0.135918   \n",
       "138   30400      3.160        80.1       1.23  22100  1.148123 -0.342988   \n",
       "139   32500      0.160        81.9       1.37  30700  1.509011 -0.137962   \n",
       "140    8560     22.800        74.4       2.20   2810 -0.294096 -0.934947   \n",
       "141    9920      4.440        71.6       2.07   6230 -0.249299 -0.636030   \n",
       "142    3370     19.600        66.3       4.88   1480 -1.295183  0.378660   \n",
       "143   14200      7.200        70.3       2.52   8300 -0.081826 -0.451337   \n",
       "144   42900      0.991        81.5       1.98  52100  2.575451  0.453148   \n",
       "145   55500      0.317        82.2       1.52  74600  3.722540  1.151944   \n",
       "146    2110     12.500        69.6       3.51    738 -1.068615 -0.184818   \n",
       "147    2090      9.250        59.3       5.43    702 -1.305094  0.237911   \n",
       "148   13500      4.080        76.6       1.55   5080 -0.102107 -0.729118   \n",
       "149    1850     26.500        71.1       6.23   3600 -1.103531  0.092046   \n",
       "150    1210      1.180        58.7       4.87    488 -1.562209  0.619402   \n",
       "151    4980      3.680        69.9       3.91   3550 -0.459887 -0.841663   \n",
       "152   10400      3.820        76.9       2.14   4140 -0.264310 -0.743448   \n",
       "153   18000      7.010        78.2       2.15  10700  0.182248 -0.449976   \n",
       "154    9940      2.310        67.9       2.83   4440 -0.805756  0.224763   \n",
       "155    1540     10.600        56.8       6.15    595 -1.436015  0.424738   \n",
       "156    7820     13.400        70.4       1.44   2970 -0.318725 -0.932505   \n",
       "157   57600     12.500        76.5       1.87  35000  2.409693  0.453683   \n",
       "158   36200      1.570        80.3       1.92  38900  1.887872  0.121104   \n",
       "159   49400      1.220        78.7       1.93  48400  2.610052  0.573043   \n",
       "160   17100      4.910        76.4       2.08  11900  0.295688 -0.624116   \n",
       "161    4240     16.500        68.8       2.34   1380 -0.784252 -0.487622   \n",
       "162    2950      2.620        63.0       3.50   2970 -0.687187 -0.629440   \n",
       "163   16500     45.900        75.4       2.47  13500  0.251829 -0.459211   \n",
       "164    4490     12.100        73.1       1.95   1310 -0.622086 -0.767635   \n",
       "165    4480     23.600        67.5       4.67   1310 -1.019460 -0.050605   \n",
       "166    3280     14.000        52.0       5.40   1460 -1.375777  0.515909   \n",
       "\n",
       "     ClusterID  \n",
       "0            1  \n",
       "1            2  \n",
       "2            2  \n",
       "3            1  \n",
       "4            2  \n",
       "5            2  \n",
       "6            2  \n",
       "7            0  \n",
       "8            0  \n",
       "9            2  \n",
       "10           2  \n",
       "11           0  \n",
       "12           2  \n",
       "13           2  \n",
       "14           2  \n",
       "15           0  \n",
       "16           2  \n",
       "17           1  \n",
       "18           2  \n",
       "19           2  \n",
       "20           2  \n",
       "21           2  \n",
       "22           2  \n",
       "23           0  \n",
       "24           2  \n",
       "25           1  \n",
       "26           1  \n",
       "27           2  \n",
       "28           1  \n",
       "29           0  \n",
       "..         ...  \n",
       "137          2  \n",
       "138          2  \n",
       "139          0  \n",
       "140          2  \n",
       "141          2  \n",
       "142          1  \n",
       "143          2  \n",
       "144          0  \n",
       "145          0  \n",
       "146          2  \n",
       "147          1  \n",
       "148          2  \n",
       "149          1  \n",
       "150          1  \n",
       "151          2  \n",
       "152          2  \n",
       "153          2  \n",
       "154          1  \n",
       "155          1  \n",
       "156          2  \n",
       "157          0  \n",
       "158          0  \n",
       "159          0  \n",
       "160          2  \n",
       "161          2  \n",
       "162          2  \n",
       "163          2  \n",
       "164          2  \n",
       "165          1  \n",
       "166          1  \n",
       "\n",
       "[167 rows x 13 columns]"
      ]
     },
     "execution_count": 224,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dat6 = dat5[['country','child_mort','gdpp','income','ClusterID']]\n",
    "\n",
    "dat5"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Performing visualisations on the dataframe as per the instructions"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 225,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x20ba0de7e10>"
      ]
     },
     "execution_count": 225,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.scatterplot(x='PC1',y='PC2',hue='ClusterID',legend='full',data=dat5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 226,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x20ba0a20c50>"
      ]
     },
     "execution_count": 226,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.scatterplot(x='gdpp',y='income',hue='ClusterID',legend='full',data=dat5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 227,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x20ba0e6eef0>"
      ]
     },
     "execution_count": 227,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.scatterplot(x='gdpp',y='child_mort',hue='ClusterID',legend='full',data=dat5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 228,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x20ba0fe07f0>"
      ]
     },
     "execution_count": 228,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.scatterplot(x='income',y='life_expec',hue='ClusterID',legend='full',data=dat5)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Finding the mean of each variable based on Cluster ID"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 229,
   "metadata": {},
   "outputs": [],
   "source": [
    "child_mort_mean = pd.DataFrame(dat5.groupby(['ClusterID']).child_mort.mean())\n",
    "\n",
    "gdpp_mean = pd.DataFrame(dat5.groupby(['ClusterID']).gdpp.mean())\n",
    "\n",
    "income_mean = pd.DataFrame(dat5.groupby(['ClusterID']).income.mean())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 230,
   "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>child_mort</th>\n",
       "      <th>gdpp</th>\n",
       "      <th>income</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ClusterID</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>5.648387</td>\n",
       "      <td>45677.419355</td>\n",
       "      <td>49616.129032</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>95.368889</td>\n",
       "      <td>1445.777778</td>\n",
       "      <td>3144.511111</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>21.147253</td>\n",
       "      <td>7515.978022</td>\n",
       "      <td>13006.153846</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "           child_mort          gdpp        income\n",
       "ClusterID                                        \n",
       "0            5.648387  45677.419355  49616.129032\n",
       "1           95.368889   1445.777778   3144.511111\n",
       "2           21.147253   7515.978022  13006.153846"
      ]
     },
     "execution_count": 230,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df1 = pd.merge(child_mort_mean,gdpp_mean,on='ClusterID',how = 'left')\n",
    "\n",
    "df2 = pd.merge(df1,income_mean,on='ClusterID',how = 'left')\n",
    "\n",
    "df2"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### We can observe the following from the data above -\n",
    "\n",
    "    - Cluster 0 - Has low child mortality, high gddp and high income (Developed)\n",
    "    - Cluster 1 - Has high child mortality, low gddp and low income (under developed)\n",
    "    - Cluster 2 - Has medium child mortality, medium gddp and medium income (Developing)\n",
    "    "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Now to identify the top 5 countries which require the most help, we can select the countries which are less than the lowest mean amongst the three clusters"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 231,
   "metadata": {},
   "outputs": [],
   "source": [
    "fin1 = country_df[country_df['child_mort']>96]\n",
    "fin2 = country_df[country_df['gdpp']<=1445]\n",
    "fin3 = country_df[country_df['income']<=3144]\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 232,
   "metadata": {},
   "outputs": [],
   "source": [
    "fin4 =fin1.sort_values(by = [\"child_mort\"],axis = 0,ascending = False)\n",
    "fin5 =fin2.sort_values(by = [\"gdpp\"],axis = 0,ascending = True)\n",
    "fin6 =fin3.sort_values(by = [\"income\"],axis = 0,ascending = True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Now that we have identified the countries with the greatest need and have sorted them"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 233,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>country</th>\n",
       "      <th>child_mort</th>\n",
       "      <th>exports</th>\n",
       "      <th>health</th>\n",
       "      <th>imports</th>\n",
       "      <th>income</th>\n",
       "      <th>inflation</th>\n",
       "      <th>life_expec</th>\n",
       "      <th>total_fer</th>\n",
       "      <th>gdpp</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>66</th>\n",
       "      <td>Haiti</td>\n",
       "      <td>208.0</td>\n",
       "      <td>15.3</td>\n",
       "      <td>6.91</td>\n",
       "      <td>64.7</td>\n",
       "      <td>1500</td>\n",
       "      <td>5.45</td>\n",
       "      <td>32.1</td>\n",
       "      <td>3.33</td>\n",
       "      <td>662</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>132</th>\n",
       "      <td>Sierra Leone</td>\n",
       "      <td>160.0</td>\n",
       "      <td>16.8</td>\n",
       "      <td>13.10</td>\n",
       "      <td>34.5</td>\n",
       "      <td>1220</td>\n",
       "      <td>17.20</td>\n",
       "      <td>55.0</td>\n",
       "      <td>5.20</td>\n",
       "      <td>399</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>32</th>\n",
       "      <td>Chad</td>\n",
       "      <td>150.0</td>\n",
       "      <td>36.8</td>\n",
       "      <td>4.53</td>\n",
       "      <td>43.5</td>\n",
       "      <td>1930</td>\n",
       "      <td>6.39</td>\n",
       "      <td>56.5</td>\n",
       "      <td>6.59</td>\n",
       "      <td>897</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>31</th>\n",
       "      <td>Central African Republic</td>\n",
       "      <td>149.0</td>\n",
       "      <td>11.8</td>\n",
       "      <td>3.98</td>\n",
       "      <td>26.5</td>\n",
       "      <td>888</td>\n",
       "      <td>2.01</td>\n",
       "      <td>47.5</td>\n",
       "      <td>5.21</td>\n",
       "      <td>446</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>97</th>\n",
       "      <td>Mali</td>\n",
       "      <td>137.0</td>\n",
       "      <td>22.8</td>\n",
       "      <td>4.98</td>\n",
       "      <td>35.1</td>\n",
       "      <td>1870</td>\n",
       "      <td>4.37</td>\n",
       "      <td>59.5</td>\n",
       "      <td>6.55</td>\n",
       "      <td>708</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                      country  child_mort  exports  health  imports  income  \\\n",
       "66                      Haiti       208.0     15.3    6.91     64.7    1500   \n",
       "132              Sierra Leone       160.0     16.8   13.10     34.5    1220   \n",
       "32                       Chad       150.0     36.8    4.53     43.5    1930   \n",
       "31   Central African Republic       149.0     11.8    3.98     26.5     888   \n",
       "97                       Mali       137.0     22.8    4.98     35.1    1870   \n",
       "\n",
       "     inflation  life_expec  total_fer  gdpp  \n",
       "66        5.45        32.1       3.33   662  \n",
       "132      17.20        55.0       5.20   399  \n",
       "32        6.39        56.5       6.59   897  \n",
       "31        2.01        47.5       5.21   446  \n",
       "97        4.37        59.5       6.55   708  "
      ]
     },
     "execution_count": 233,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "fin4.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 234,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>country</th>\n",
       "      <th>child_mort</th>\n",
       "      <th>exports</th>\n",
       "      <th>health</th>\n",
       "      <th>imports</th>\n",
       "      <th>income</th>\n",
       "      <th>inflation</th>\n",
       "      <th>life_expec</th>\n",
       "      <th>total_fer</th>\n",
       "      <th>gdpp</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>26</th>\n",
       "      <td>Burundi</td>\n",
       "      <td>93.6</td>\n",
       "      <td>8.92</td>\n",
       "      <td>11.60</td>\n",
       "      <td>39.2</td>\n",
       "      <td>764</td>\n",
       "      <td>12.30</td>\n",
       "      <td>57.7</td>\n",
       "      <td>6.26</td>\n",
       "      <td>231</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>88</th>\n",
       "      <td>Liberia</td>\n",
       "      <td>89.3</td>\n",
       "      <td>19.10</td>\n",
       "      <td>11.80</td>\n",
       "      <td>92.6</td>\n",
       "      <td>700</td>\n",
       "      <td>5.47</td>\n",
       "      <td>60.8</td>\n",
       "      <td>5.02</td>\n",
       "      <td>327</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>37</th>\n",
       "      <td>Congo, Dem. Rep.</td>\n",
       "      <td>116.0</td>\n",
       "      <td>41.10</td>\n",
       "      <td>7.91</td>\n",
       "      <td>49.6</td>\n",
       "      <td>609</td>\n",
       "      <td>20.80</td>\n",
       "      <td>57.5</td>\n",
       "      <td>6.54</td>\n",
       "      <td>334</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>112</th>\n",
       "      <td>Niger</td>\n",
       "      <td>123.0</td>\n",
       "      <td>22.20</td>\n",
       "      <td>5.16</td>\n",
       "      <td>49.1</td>\n",
       "      <td>814</td>\n",
       "      <td>2.55</td>\n",
       "      <td>58.8</td>\n",
       "      <td>7.49</td>\n",
       "      <td>348</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>132</th>\n",
       "      <td>Sierra Leone</td>\n",
       "      <td>160.0</td>\n",
       "      <td>16.80</td>\n",
       "      <td>13.10</td>\n",
       "      <td>34.5</td>\n",
       "      <td>1220</td>\n",
       "      <td>17.20</td>\n",
       "      <td>55.0</td>\n",
       "      <td>5.20</td>\n",
       "      <td>399</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "              country  child_mort  exports  health  imports  income  \\\n",
       "26            Burundi        93.6     8.92   11.60     39.2     764   \n",
       "88            Liberia        89.3    19.10   11.80     92.6     700   \n",
       "37   Congo, Dem. Rep.       116.0    41.10    7.91     49.6     609   \n",
       "112             Niger       123.0    22.20    5.16     49.1     814   \n",
       "132      Sierra Leone       160.0    16.80   13.10     34.5    1220   \n",
       "\n",
       "     inflation  life_expec  total_fer  gdpp  \n",
       "26       12.30        57.7       6.26   231  \n",
       "88        5.47        60.8       5.02   327  \n",
       "37       20.80        57.5       6.54   334  \n",
       "112       2.55        58.8       7.49   348  \n",
       "132      17.20        55.0       5.20   399  "
      ]
     },
     "execution_count": 234,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "fin5.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 235,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>country</th>\n",
       "      <th>child_mort</th>\n",
       "      <th>exports</th>\n",
       "      <th>health</th>\n",
       "      <th>imports</th>\n",
       "      <th>income</th>\n",
       "      <th>inflation</th>\n",
       "      <th>life_expec</th>\n",
       "      <th>total_fer</th>\n",
       "      <th>gdpp</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>37</th>\n",
       "      <td>Congo, Dem. Rep.</td>\n",
       "      <td>116.0</td>\n",
       "      <td>41.10</td>\n",
       "      <td>7.91</td>\n",
       "      <td>49.6</td>\n",
       "      <td>609</td>\n",
       "      <td>20.80</td>\n",
       "      <td>57.5</td>\n",
       "      <td>6.54</td>\n",
       "      <td>334</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>88</th>\n",
       "      <td>Liberia</td>\n",
       "      <td>89.3</td>\n",
       "      <td>19.10</td>\n",
       "      <td>11.80</td>\n",
       "      <td>92.6</td>\n",
       "      <td>700</td>\n",
       "      <td>5.47</td>\n",
       "      <td>60.8</td>\n",
       "      <td>5.02</td>\n",
       "      <td>327</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26</th>\n",
       "      <td>Burundi</td>\n",
       "      <td>93.6</td>\n",
       "      <td>8.92</td>\n",
       "      <td>11.60</td>\n",
       "      <td>39.2</td>\n",
       "      <td>764</td>\n",
       "      <td>12.30</td>\n",
       "      <td>57.7</td>\n",
       "      <td>6.26</td>\n",
       "      <td>231</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>112</th>\n",
       "      <td>Niger</td>\n",
       "      <td>123.0</td>\n",
       "      <td>22.20</td>\n",
       "      <td>5.16</td>\n",
       "      <td>49.1</td>\n",
       "      <td>814</td>\n",
       "      <td>2.55</td>\n",
       "      <td>58.8</td>\n",
       "      <td>7.49</td>\n",
       "      <td>348</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>31</th>\n",
       "      <td>Central African Republic</td>\n",
       "      <td>149.0</td>\n",
       "      <td>11.80</td>\n",
       "      <td>3.98</td>\n",
       "      <td>26.5</td>\n",
       "      <td>888</td>\n",
       "      <td>2.01</td>\n",
       "      <td>47.5</td>\n",
       "      <td>5.21</td>\n",
       "      <td>446</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                      country  child_mort  exports  health  imports  income  \\\n",
       "37           Congo, Dem. Rep.       116.0    41.10    7.91     49.6     609   \n",
       "88                    Liberia        89.3    19.10   11.80     92.6     700   \n",
       "26                    Burundi        93.6     8.92   11.60     39.2     764   \n",
       "112                     Niger       123.0    22.20    5.16     49.1     814   \n",
       "31   Central African Republic       149.0    11.80    3.98     26.5     888   \n",
       "\n",
       "     inflation  life_expec  total_fer  gdpp  \n",
       "37       20.80        57.5       6.54   334  \n",
       "88        5.47        60.8       5.02   327  \n",
       "26       12.30        57.7       6.26   231  \n",
       "112       2.55        58.8       7.49   348  \n",
       "31        2.01        47.5       5.21   446  "
      ]
     },
     "execution_count": 235,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "fin6.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 5. Perform Hierarchical clustering"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 236,
   "metadata": {},
   "outputs": [],
   "source": [
    "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": [
    "### 5.1 Single Linkage"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 237,
   "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": [
    "mergings = linkage(pcs_df2,method = \"single\",metric = \"euclidean\")\n",
    "dendrogram(mergings)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 5.2 Complete Linkage"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 238,
   "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": [
    "mergings = linkage(pcs_df2,method = \"complete\",metric = \"euclidean\")\n",
    "dendrogram(mergings)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 239,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(167,)"
      ]
     },
     "execution_count": 239,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# taking out 3 clusters\n",
    "\n",
    "cluster_labels = cut_tree(mergings, n_clusters=3).reshape(-1,)\n",
    "cluster_labels.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 240,
   "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>PC1</th>\n",
       "      <th>PC2</th>\n",
       "      <th>ClusterID</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>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",
       "      <td>-1.545850</td>\n",
       "      <td>0.624922</td>\n",
       "      <td>1</td>\n",
       "      <td>0</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",
       "      <td>-0.271605</td>\n",
       "      <td>-0.769327</td>\n",
       "      <td>2</td>\n",
       "      <td>1</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",
       "      <td>-0.291604</td>\n",
       "      <td>-0.481997</td>\n",
       "      <td>2</td>\n",
       "      <td>1</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",
       "      <td>-1.653162</td>\n",
       "      <td>1.380589</td>\n",
       "      <td>1</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>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",
       "      <td>0.374234</td>\n",
       "      <td>-0.592688</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "               country  child_mort  exports  health  imports  income  \\\n",
       "0          Afghanistan        90.2     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       PC1       PC2  ClusterID  \\\n",
       "0       9.44        56.2       5.82    553 -1.545850  0.624922          1   \n",
       "1       4.49        76.3       1.65   4090 -0.271605 -0.769327          2   \n",
       "2      16.10        76.5       2.89   4460 -0.291604 -0.481997          2   \n",
       "3      22.40        60.1       6.16   3530 -1.653162  1.380589          1   \n",
       "4       1.44        76.8       2.13  12200  0.374234 -0.592688          2   \n",
       "\n",
       "   Cluster_labels  \n",
       "0               0  \n",
       "1               1  \n",
       "2               1  \n",
       "3               0  \n",
       "4               1  "
      ]
     },
     "execution_count": 240,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dat5['Cluster_labels'] = cluster_labels\n",
    "\n",
    "dat5.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 241,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1    127\n",
       "0     33\n",
       "2      7\n",
       "Name: Cluster_labels, dtype: int64"
      ]
     },
     "execution_count": 241,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dat5['Cluster_labels'].value_counts()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 5.3 Visualising data based on complete linkage hierarichal clustering"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 242,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x20ba05029e8>"
      ]
     },
     "execution_count": 242,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.boxplot(x='Cluster_labels',y='gdpp',data = dat5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 243,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x20ba0702c50>"
      ]
     },
     "execution_count": 243,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.boxplot(x='Cluster_labels',y='child_mort',data = dat5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 244,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x20ba073c7f0>"
      ]
     },
     "execution_count": 244,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.boxplot(x='Cluster_labels',y='income',data = dat5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 245,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x20ba0978e80>"
      ]
     },
     "execution_count": 245,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.boxplot(x='Cluster_labels',y='life_expec',data = dat5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 246,
   "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>child_mort</th>\n",
       "      <th>gdpp</th>\n",
       "      <th>income</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Cluster_labels</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>107.603030</td>\n",
       "      <td>1385.363636</td>\n",
       "      <td>3071.606061</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>22.020472</td>\n",
       "      <td>13080.291339</td>\n",
       "      <td>17318.110236</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>6.228571</td>\n",
       "      <td>65442.857143</td>\n",
       "      <td>80342.857143</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                child_mort          gdpp        income\n",
       "Cluster_labels                                        \n",
       "0               107.603030   1385.363636   3071.606061\n",
       "1                22.020472  13080.291339  17318.110236\n",
       "2                 6.228571  65442.857143  80342.857143"
      ]
     },
     "execution_count": 246,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "child_mort1_mean = pd.DataFrame(dat5.groupby(['Cluster_labels']).child_mort.mean())\n",
    "\n",
    "gdpp1_mean = pd.DataFrame(dat5.groupby(['Cluster_labels']).gdpp.mean())\n",
    "\n",
    "income1_mean = pd.DataFrame(dat5.groupby(['Cluster_labels']).income.mean())\n",
    "\n",
    "df7 = pd.merge(child_mort1_mean,gdpp1_mean,on='Cluster_labels',how = 'left')\n",
    "\n",
    "df8 = pd.merge(df7,income1_mean,on='Cluster_labels',how = 'left')\n",
    "\n",
    "df8"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### We can observe the following from the data above -\n",
    "\n",
    "    - Cluster 0 - Has low child mortality, high gddp and high income (Developed)\n",
    "    - Cluster 1 - Has medium child mortality, medium gddp and medium income (Developing)\n",
    "    - Cluster 2 - Has high child mortality, low gddp and low income (under developed)\n",
    "    "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 247,
   "metadata": {},
   "outputs": [],
   "source": [
    "fin7 = country_df[country_df['child_mort']>108]\n",
    "fin8 = country_df[country_df['gdpp']<=1385]\n",
    "fin9 = country_df[country_df['income']<=3070]\n",
    "\n",
    "fin10 =fin7.sort_values(by = [\"child_mort\"],axis = 0,ascending = False)\n",
    "fin11 =fin8.sort_values(by = [\"gdpp\"],axis = 0,ascending = True)\n",
    "fin12 =fin9.sort_values(by = [\"income\"],axis = 0,ascending = True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 248,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>country</th>\n",
       "      <th>child_mort</th>\n",
       "      <th>exports</th>\n",
       "      <th>health</th>\n",
       "      <th>imports</th>\n",
       "      <th>income</th>\n",
       "      <th>inflation</th>\n",
       "      <th>life_expec</th>\n",
       "      <th>total_fer</th>\n",
       "      <th>gdpp</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>66</th>\n",
       "      <td>Haiti</td>\n",
       "      <td>208.0</td>\n",
       "      <td>15.3</td>\n",
       "      <td>6.91</td>\n",
       "      <td>64.7</td>\n",
       "      <td>1500</td>\n",
       "      <td>5.45</td>\n",
       "      <td>32.1</td>\n",
       "      <td>3.33</td>\n",
       "      <td>662</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>132</th>\n",
       "      <td>Sierra Leone</td>\n",
       "      <td>160.0</td>\n",
       "      <td>16.8</td>\n",
       "      <td>13.10</td>\n",
       "      <td>34.5</td>\n",
       "      <td>1220</td>\n",
       "      <td>17.20</td>\n",
       "      <td>55.0</td>\n",
       "      <td>5.20</td>\n",
       "      <td>399</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>32</th>\n",
       "      <td>Chad</td>\n",
       "      <td>150.0</td>\n",
       "      <td>36.8</td>\n",
       "      <td>4.53</td>\n",
       "      <td>43.5</td>\n",
       "      <td>1930</td>\n",
       "      <td>6.39</td>\n",
       "      <td>56.5</td>\n",
       "      <td>6.59</td>\n",
       "      <td>897</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>31</th>\n",
       "      <td>Central African Republic</td>\n",
       "      <td>149.0</td>\n",
       "      <td>11.8</td>\n",
       "      <td>3.98</td>\n",
       "      <td>26.5</td>\n",
       "      <td>888</td>\n",
       "      <td>2.01</td>\n",
       "      <td>47.5</td>\n",
       "      <td>5.21</td>\n",
       "      <td>446</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>97</th>\n",
       "      <td>Mali</td>\n",
       "      <td>137.0</td>\n",
       "      <td>22.8</td>\n",
       "      <td>4.98</td>\n",
       "      <td>35.1</td>\n",
       "      <td>1870</td>\n",
       "      <td>4.37</td>\n",
       "      <td>59.5</td>\n",
       "      <td>6.55</td>\n",
       "      <td>708</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                      country  child_mort  exports  health  imports  income  \\\n",
       "66                      Haiti       208.0     15.3    6.91     64.7    1500   \n",
       "132              Sierra Leone       160.0     16.8   13.10     34.5    1220   \n",
       "32                       Chad       150.0     36.8    4.53     43.5    1930   \n",
       "31   Central African Republic       149.0     11.8    3.98     26.5     888   \n",
       "97                       Mali       137.0     22.8    4.98     35.1    1870   \n",
       "\n",
       "     inflation  life_expec  total_fer  gdpp  \n",
       "66        5.45        32.1       3.33   662  \n",
       "132      17.20        55.0       5.20   399  \n",
       "32        6.39        56.5       6.59   897  \n",
       "31        2.01        47.5       5.21   446  \n",
       "97        4.37        59.5       6.55   708  "
      ]
     },
     "execution_count": 248,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "fin10.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 249,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>country</th>\n",
       "      <th>child_mort</th>\n",
       "      <th>exports</th>\n",
       "      <th>health</th>\n",
       "      <th>imports</th>\n",
       "      <th>income</th>\n",
       "      <th>inflation</th>\n",
       "      <th>life_expec</th>\n",
       "      <th>total_fer</th>\n",
       "      <th>gdpp</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>26</th>\n",
       "      <td>Burundi</td>\n",
       "      <td>93.6</td>\n",
       "      <td>8.92</td>\n",
       "      <td>11.60</td>\n",
       "      <td>39.2</td>\n",
       "      <td>764</td>\n",
       "      <td>12.30</td>\n",
       "      <td>57.7</td>\n",
       "      <td>6.26</td>\n",
       "      <td>231</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>88</th>\n",
       "      <td>Liberia</td>\n",
       "      <td>89.3</td>\n",
       "      <td>19.10</td>\n",
       "      <td>11.80</td>\n",
       "      <td>92.6</td>\n",
       "      <td>700</td>\n",
       "      <td>5.47</td>\n",
       "      <td>60.8</td>\n",
       "      <td>5.02</td>\n",
       "      <td>327</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>37</th>\n",
       "      <td>Congo, Dem. Rep.</td>\n",
       "      <td>116.0</td>\n",
       "      <td>41.10</td>\n",
       "      <td>7.91</td>\n",
       "      <td>49.6</td>\n",
       "      <td>609</td>\n",
       "      <td>20.80</td>\n",
       "      <td>57.5</td>\n",
       "      <td>6.54</td>\n",
       "      <td>334</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>112</th>\n",
       "      <td>Niger</td>\n",
       "      <td>123.0</td>\n",
       "      <td>22.20</td>\n",
       "      <td>5.16</td>\n",
       "      <td>49.1</td>\n",
       "      <td>814</td>\n",
       "      <td>2.55</td>\n",
       "      <td>58.8</td>\n",
       "      <td>7.49</td>\n",
       "      <td>348</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>132</th>\n",
       "      <td>Sierra Leone</td>\n",
       "      <td>160.0</td>\n",
       "      <td>16.80</td>\n",
       "      <td>13.10</td>\n",
       "      <td>34.5</td>\n",
       "      <td>1220</td>\n",
       "      <td>17.20</td>\n",
       "      <td>55.0</td>\n",
       "      <td>5.20</td>\n",
       "      <td>399</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "              country  child_mort  exports  health  imports  income  \\\n",
       "26            Burundi        93.6     8.92   11.60     39.2     764   \n",
       "88            Liberia        89.3    19.10   11.80     92.6     700   \n",
       "37   Congo, Dem. Rep.       116.0    41.10    7.91     49.6     609   \n",
       "112             Niger       123.0    22.20    5.16     49.1     814   \n",
       "132      Sierra Leone       160.0    16.80   13.10     34.5    1220   \n",
       "\n",
       "     inflation  life_expec  total_fer  gdpp  \n",
       "26       12.30        57.7       6.26   231  \n",
       "88        5.47        60.8       5.02   327  \n",
       "37       20.80        57.5       6.54   334  \n",
       "112       2.55        58.8       7.49   348  \n",
       "132      17.20        55.0       5.20   399  "
      ]
     },
     "execution_count": 249,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "fin11.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 250,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>country</th>\n",
       "      <th>child_mort</th>\n",
       "      <th>exports</th>\n",
       "      <th>health</th>\n",
       "      <th>imports</th>\n",
       "      <th>income</th>\n",
       "      <th>inflation</th>\n",
       "      <th>life_expec</th>\n",
       "      <th>total_fer</th>\n",
       "      <th>gdpp</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>37</th>\n",
       "      <td>Congo, Dem. Rep.</td>\n",
       "      <td>116.0</td>\n",
       "      <td>41.10</td>\n",
       "      <td>7.91</td>\n",
       "      <td>49.6</td>\n",
       "      <td>609</td>\n",
       "      <td>20.80</td>\n",
       "      <td>57.5</td>\n",
       "      <td>6.54</td>\n",
       "      <td>334</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>88</th>\n",
       "      <td>Liberia</td>\n",
       "      <td>89.3</td>\n",
       "      <td>19.10</td>\n",
       "      <td>11.80</td>\n",
       "      <td>92.6</td>\n",
       "      <td>700</td>\n",
       "      <td>5.47</td>\n",
       "      <td>60.8</td>\n",
       "      <td>5.02</td>\n",
       "      <td>327</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26</th>\n",
       "      <td>Burundi</td>\n",
       "      <td>93.6</td>\n",
       "      <td>8.92</td>\n",
       "      <td>11.60</td>\n",
       "      <td>39.2</td>\n",
       "      <td>764</td>\n",
       "      <td>12.30</td>\n",
       "      <td>57.7</td>\n",
       "      <td>6.26</td>\n",
       "      <td>231</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>112</th>\n",
       "      <td>Niger</td>\n",
       "      <td>123.0</td>\n",
       "      <td>22.20</td>\n",
       "      <td>5.16</td>\n",
       "      <td>49.1</td>\n",
       "      <td>814</td>\n",
       "      <td>2.55</td>\n",
       "      <td>58.8</td>\n",
       "      <td>7.49</td>\n",
       "      <td>348</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>31</th>\n",
       "      <td>Central African Republic</td>\n",
       "      <td>149.0</td>\n",
       "      <td>11.80</td>\n",
       "      <td>3.98</td>\n",
       "      <td>26.5</td>\n",
       "      <td>888</td>\n",
       "      <td>2.01</td>\n",
       "      <td>47.5</td>\n",
       "      <td>5.21</td>\n",
       "      <td>446</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                      country  child_mort  exports  health  imports  income  \\\n",
       "37           Congo, Dem. Rep.       116.0    41.10    7.91     49.6     609   \n",
       "88                    Liberia        89.3    19.10   11.80     92.6     700   \n",
       "26                    Burundi        93.6     8.92   11.60     39.2     764   \n",
       "112                     Niger       123.0    22.20    5.16     49.1     814   \n",
       "31   Central African Republic       149.0    11.80    3.98     26.5     888   \n",
       "\n",
       "     inflation  life_expec  total_fer  gdpp  \n",
       "37       20.80        57.5       6.54   334  \n",
       "88        5.47        60.8       5.02   327  \n",
       "26       12.30        57.7       6.26   231  \n",
       "112       2.55        58.8       7.49   348  \n",
       "31        2.01        47.5       5.21   446  "
      ]
     },
     "execution_count": 250,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "fin12.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 6. Final list of companies to be considered for aid"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### The following companies should be considered for aid after considering both K means clustering and Hierarchical clustering\n",
    "\n",
    "    - Sierra Leone\n",
    "    - Congo, Dem. Rep.\n",
    "    - Liberia\n",
    "    - Burundi\n",
    "    - Central African Repubic"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.7.3"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}