PCA and Clustering.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### IMPORTING LIBRARIES"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "import sklearn\n",
    "from sklearn.preprocessing import StandardScaler\n",
    "from sklearn.cluster import KMeans\n",
    "from sklearn.metrics import silhouette_score\n",
    "from scipy.cluster import hierarchy\n",
    "from scipy.cluster.hierarchy import linkage\n",
    "from scipy.cluster.hierarchy import dendrogram\n",
    "from scipy.cluster.hierarchy import cut_tree\n",
    "from sklearn.decomposition import PCA\n",
    "from sklearn.neighbors import NearestNeighbors\n",
    "from random import sample\n",
    "from numpy.random import uniform\n",
    "from math import isnan\n",
    "import warnings\n",
    "warnings.filterwarnings('ignore')\n",
    "import os"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### READING THE DATASET"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "path=os.getcwd()\n",
    "df=pd.read_csv(path+r'\\Country-data.csv')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### GENERAL CHECKS ON DATA"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>country</th>\n",
       "      <th>child_mort</th>\n",
       "      <th>exports</th>\n",
       "      <th>health</th>\n",
       "      <th>imports</th>\n",
       "      <th>income</th>\n",
       "      <th>inflation</th>\n",
       "      <th>life_expec</th>\n",
       "      <th>total_fer</th>\n",
       "      <th>gdpp</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>0</td>\n",
       "      <td>Afghanistan</td>\n",
       "      <td>90.2</td>\n",
       "      <td>10.0</td>\n",
       "      <td>7.58</td>\n",
       "      <td>44.9</td>\n",
       "      <td>1610</td>\n",
       "      <td>9.44</td>\n",
       "      <td>56.2</td>\n",
       "      <td>5.82</td>\n",
       "      <td>553</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>1</td>\n",
       "      <td>Albania</td>\n",
       "      <td>16.6</td>\n",
       "      <td>28.0</td>\n",
       "      <td>6.55</td>\n",
       "      <td>48.6</td>\n",
       "      <td>9930</td>\n",
       "      <td>4.49</td>\n",
       "      <td>76.3</td>\n",
       "      <td>1.65</td>\n",
       "      <td>4090</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2</td>\n",
       "      <td>Algeria</td>\n",
       "      <td>27.3</td>\n",
       "      <td>38.4</td>\n",
       "      <td>4.17</td>\n",
       "      <td>31.4</td>\n",
       "      <td>12900</td>\n",
       "      <td>16.10</td>\n",
       "      <td>76.5</td>\n",
       "      <td>2.89</td>\n",
       "      <td>4460</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>3</td>\n",
       "      <td>Angola</td>\n",
       "      <td>119.0</td>\n",
       "      <td>62.3</td>\n",
       "      <td>2.85</td>\n",
       "      <td>42.9</td>\n",
       "      <td>5900</td>\n",
       "      <td>22.40</td>\n",
       "      <td>60.1</td>\n",
       "      <td>6.16</td>\n",
       "      <td>3530</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>4</td>\n",
       "      <td>Antigua and Barbuda</td>\n",
       "      <td>10.3</td>\n",
       "      <td>45.5</td>\n",
       "      <td>6.03</td>\n",
       "      <td>58.9</td>\n",
       "      <td>19100</td>\n",
       "      <td>1.44</td>\n",
       "      <td>76.8</td>\n",
       "      <td>2.13</td>\n",
       "      <td>12200</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "               country  child_mort  exports  health  imports  income  \\\n",
       "0          Afghanistan        90.2     10.0    7.58     44.9    1610   \n",
       "1              Albania        16.6     28.0    6.55     48.6    9930   \n",
       "2              Algeria        27.3     38.4    4.17     31.4   12900   \n",
       "3               Angola       119.0     62.3    2.85     42.9    5900   \n",
       "4  Antigua and Barbuda        10.3     45.5    6.03     58.9   19100   \n",
       "\n",
       "   inflation  life_expec  total_fer   gdpp  \n",
       "0       9.44        56.2       5.82    553  \n",
       "1       4.49        76.3       1.65   4090  \n",
       "2      16.10        76.5       2.89   4460  \n",
       "3      22.40        60.1       6.16   3530  \n",
       "4       1.44        76.8       2.13  12200  "
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head() #printing the top 5 rows in the dataframe"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(167, 10)"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.shape #shape of dataframe"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/html": [
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       "    .dataframe thead th {\n",
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       "</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",
       "      <td>count</td>\n",
       "      <td>167.000000</td>\n",
       "      <td>167.000000</td>\n",
       "      <td>167.000000</td>\n",
       "      <td>167.000000</td>\n",
       "      <td>167.000000</td>\n",
       "      <td>167.000000</td>\n",
       "      <td>167.000000</td>\n",
       "      <td>167.000000</td>\n",
       "      <td>167.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>mean</td>\n",
       "      <td>38.270060</td>\n",
       "      <td>41.108976</td>\n",
       "      <td>6.815689</td>\n",
       "      <td>46.890215</td>\n",
       "      <td>17144.688623</td>\n",
       "      <td>7.781832</td>\n",
       "      <td>70.555689</td>\n",
       "      <td>2.947964</td>\n",
       "      <td>12964.155689</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>std</td>\n",
       "      <td>40.328931</td>\n",
       "      <td>27.412010</td>\n",
       "      <td>2.746837</td>\n",
       "      <td>24.209589</td>\n",
       "      <td>19278.067698</td>\n",
       "      <td>10.570704</td>\n",
       "      <td>8.893172</td>\n",
       "      <td>1.513848</td>\n",
       "      <td>18328.704809</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>min</td>\n",
       "      <td>2.600000</td>\n",
       "      <td>0.109000</td>\n",
       "      <td>1.810000</td>\n",
       "      <td>0.065900</td>\n",
       "      <td>609.000000</td>\n",
       "      <td>-4.210000</td>\n",
       "      <td>32.100000</td>\n",
       "      <td>1.150000</td>\n",
       "      <td>231.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>25%</td>\n",
       "      <td>8.250000</td>\n",
       "      <td>23.800000</td>\n",
       "      <td>4.920000</td>\n",
       "      <td>30.200000</td>\n",
       "      <td>3355.000000</td>\n",
       "      <td>1.810000</td>\n",
       "      <td>65.300000</td>\n",
       "      <td>1.795000</td>\n",
       "      <td>1330.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>50%</td>\n",
       "      <td>19.300000</td>\n",
       "      <td>35.000000</td>\n",
       "      <td>6.320000</td>\n",
       "      <td>43.300000</td>\n",
       "      <td>9960.000000</td>\n",
       "      <td>5.390000</td>\n",
       "      <td>73.100000</td>\n",
       "      <td>2.410000</td>\n",
       "      <td>4660.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>75%</td>\n",
       "      <td>62.100000</td>\n",
       "      <td>51.350000</td>\n",
       "      <td>8.600000</td>\n",
       "      <td>58.750000</td>\n",
       "      <td>22800.000000</td>\n",
       "      <td>10.750000</td>\n",
       "      <td>76.800000</td>\n",
       "      <td>3.880000</td>\n",
       "      <td>14050.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>max</td>\n",
       "      <td>208.000000</td>\n",
       "      <td>200.000000</td>\n",
       "      <td>17.900000</td>\n",
       "      <td>174.000000</td>\n",
       "      <td>125000.000000</td>\n",
       "      <td>104.000000</td>\n",
       "      <td>82.800000</td>\n",
       "      <td>7.490000</td>\n",
       "      <td>105000.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</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": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['country', 'child_mort', 'exports', 'health', 'imports', 'income',\n",
       "       'inflation', 'life_expec', 'total_fer', 'gdpp'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "scrolled": true
   },
   "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: 13.2+ KB\n"
     ]
    }
   ],
   "source": [
    "df.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "country       0\n",
       "child_mort    0\n",
       "exports       0\n",
       "health        0\n",
       "imports       0\n",
       "income        0\n",
       "inflation     0\n",
       "life_expec    0\n",
       "total_fer     0\n",
       "gdpp          0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.isnull().sum() #calculating the null values in the dataframe"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_quant=df[['child_mort', 'exports', 'health', 'imports', 'income',\n",
    "       'inflation', 'life_expec', 'total_fer', 'gdpp']]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### CHECKING FOR OUTLIERS"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 2160x2880 with 9 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "cols=['child_mort', 'exports', 'health', 'imports', 'income',\n",
    "       'inflation', 'life_expec', 'total_fer', 'gdpp']\n",
    "plt.figure(figsize=(30,40))\n",
    "j=1\n",
    "for i in cols:\n",
    "    plt.subplot(3,3,j)\n",
    "    plt.boxplot(df[i])\n",
    "    plt.title(i)\n",
    "    j=j+1"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<p style=\"font-family: Arial; font-size:1.0em;color:blue;\">  We can observe some outliers in the above graphs, but as the data in the dataframe is very limited, we don't eliminate the  outliers. Infact, when the clustering is performed, there is a chance that all the ouliers may be clustered into one."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### STANDARDIZING THE VALUES"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<p style=\"font-family: Arial; font-size:1.0em;color:blue;\">Scaling is an important step in the data pre-processing of the model. It helps us in analysing the data more easily. Since, the range of features vary widely, scaling is used to limit or set the values in a certain range. This helps us in analysing the data better because all the features has the values in certain range<br>Standard Scaling  transforms the data in a way that the mean of the data will be 0 with standard deviation 1."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[ 1.29153238, -1.13827979,  0.27908825, ..., -1.61909203,\n",
       "         1.90288227, -0.67917961],\n",
       "       [-0.5389489 , -0.47965843, -0.09701618, ...,  0.64786643,\n",
       "        -0.85997281, -0.48562324],\n",
       "       [-0.27283273, -0.09912164, -0.96607302, ...,  0.67042323,\n",
       "        -0.0384044 , -0.46537561],\n",
       "       ...,\n",
       "       [-0.37231541,  1.13030491,  0.0088773 , ...,  0.28695762,\n",
       "        -0.66120626, -0.63775406],\n",
       "       [ 0.44841668, -0.40647827, -0.59727159, ..., -0.34463279,\n",
       "         1.14094382, -0.63775406],\n",
       "       [ 1.11495062, -0.15034774, -0.33801514, ..., -2.09278484,\n",
       "         1.6246091 , -0.62954556]])"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "scaler=StandardScaler() #standardization\n",
    "df_scaled=scaler.fit_transform(df[cols])\n",
    "df_scaled"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_scaled=pd.DataFrame(df_scaled)\n",
    "df_scaled.columns=cols"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "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>child_mort</th>\n",
       "      <th>exports</th>\n",
       "      <th>health</th>\n",
       "      <th>imports</th>\n",
       "      <th>income</th>\n",
       "      <th>inflation</th>\n",
       "      <th>life_expec</th>\n",
       "      <th>total_fer</th>\n",
       "      <th>gdpp</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>0</td>\n",
       "      <td>1.291532</td>\n",
       "      <td>-1.138280</td>\n",
       "      <td>0.279088</td>\n",
       "      <td>-0.082455</td>\n",
       "      <td>-0.808245</td>\n",
       "      <td>0.157336</td>\n",
       "      <td>-1.619092</td>\n",
       "      <td>1.902882</td>\n",
       "      <td>-0.679180</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>1</td>\n",
       "      <td>-0.538949</td>\n",
       "      <td>-0.479658</td>\n",
       "      <td>-0.097016</td>\n",
       "      <td>0.070837</td>\n",
       "      <td>-0.375369</td>\n",
       "      <td>-0.312347</td>\n",
       "      <td>0.647866</td>\n",
       "      <td>-0.859973</td>\n",
       "      <td>-0.485623</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2</td>\n",
       "      <td>-0.272833</td>\n",
       "      <td>-0.099122</td>\n",
       "      <td>-0.966073</td>\n",
       "      <td>-0.641762</td>\n",
       "      <td>-0.220844</td>\n",
       "      <td>0.789274</td>\n",
       "      <td>0.670423</td>\n",
       "      <td>-0.038404</td>\n",
       "      <td>-0.465376</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>3</td>\n",
       "      <td>2.007808</td>\n",
       "      <td>0.775381</td>\n",
       "      <td>-1.448071</td>\n",
       "      <td>-0.165315</td>\n",
       "      <td>-0.585043</td>\n",
       "      <td>1.387054</td>\n",
       "      <td>-1.179234</td>\n",
       "      <td>2.128151</td>\n",
       "      <td>-0.516268</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>4</td>\n",
       "      <td>-0.695634</td>\n",
       "      <td>0.160668</td>\n",
       "      <td>-0.286894</td>\n",
       "      <td>0.497568</td>\n",
       "      <td>0.101732</td>\n",
       "      <td>-0.601749</td>\n",
       "      <td>0.704258</td>\n",
       "      <td>-0.541946</td>\n",
       "      <td>-0.041817</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   child_mort   exports    health   imports    income  inflation  life_expec  \\\n",
       "0    1.291532 -1.138280  0.279088 -0.082455 -0.808245   0.157336   -1.619092   \n",
       "1   -0.538949 -0.479658 -0.097016  0.070837 -0.375369  -0.312347    0.647866   \n",
       "2   -0.272833 -0.099122 -0.966073 -0.641762 -0.220844   0.789274    0.670423   \n",
       "3    2.007808  0.775381 -1.448071 -0.165315 -0.585043   1.387054   -1.179234   \n",
       "4   -0.695634  0.160668 -0.286894  0.497568  0.101732  -0.601749    0.704258   \n",
       "\n",
       "   total_fer      gdpp  \n",
       "0   1.902882 -0.679180  \n",
       "1  -0.859973 -0.485623  \n",
       "2  -0.038404 -0.465376  \n",
       "3   2.128151 -0.516268  \n",
       "4  -0.541946 -0.041817  "
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_scaled.head() #printing the top 5 rows of the standardized dataframe"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### PRINCIPAL COMPONENT ANALYSIS (PCA)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<p style=\"font-family: Arial; font-size:1.0em;color:blue;\">Principal component analysis (PCA) is a method for reducing the dimensionality of large datasets for increasing interpretability of the model with minimal loss of information."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "PCA(copy=True, iterated_power='auto', n_components=None, random_state=42,\n",
       "    svd_solver='auto', tol=0.0, whiten=False)"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pca=PCA(random_state=42) #performing PCA\n",
    "pca.fit(df_scaled)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[-0.41951945,  0.28389698,  0.15083782,  0.16148244,  0.39844111,\n",
       "        -0.19317293,  0.42583938, -0.40372896,  0.39264482],\n",
       "       [ 0.19288394,  0.61316349, -0.24308678,  0.67182064,  0.02253553,\n",
       "        -0.00840447, -0.22270674,  0.15523311, -0.0460224 ],\n",
       "       [-0.02954353,  0.14476069, -0.59663237, -0.29992674,  0.3015475 ,\n",
       "         0.64251951,  0.11391854,  0.01954925,  0.12297749],\n",
       "       [ 0.37065326,  0.00309102,  0.4618975 , -0.07190746,  0.39215904,\n",
       "         0.15044176, -0.20379723,  0.37830365,  0.53199457],\n",
       "       [-0.16896968,  0.05761584,  0.51800037,  0.25537642, -0.2471496 ,\n",
       "         0.7148691 ,  0.1082198 , -0.13526221, -0.18016662],\n",
       "       [ 0.20062815, -0.05933283,  0.00727646, -0.03003154,  0.16034699,\n",
       "         0.06628537, -0.60112652, -0.75068875,  0.01677876],\n",
       "       [-0.07948854, -0.70730269, -0.24983051,  0.59218953,  0.09556237,\n",
       "         0.10463252,  0.01848639,  0.02882643,  0.24299776],\n",
       "       [-0.68274306, -0.01419742,  0.07249683, -0.02894642,  0.35262369,\n",
       "        -0.01153775, -0.50466425,  0.29335267, -0.24969636],\n",
       "       [ 0.3275418 , -0.12308207,  0.11308797,  0.09903717,  0.61298247,\n",
       "        -0.02523614,  0.29403981, -0.02633585, -0.62564572]])"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pca.components_ # PCA components"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([0.4595174 , 0.17181626, 0.13004259, 0.11053162, 0.07340211,\n",
       "       0.02484235, 0.0126043 , 0.00981282, 0.00743056])"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pca.explained_variance_ratio_ #percentage of variance explained by each feature"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<BarContainer object of 9 artists>"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.bar(range(1,len(pca.explained_variance_ratio_)+1), pca.explained_variance_ratio_) #plotting the variance explained by each feature"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<p style=\"font-family: Arial; font-size:1.0em;color:blue;\"> For selecting the principal components,we find the variance explained by each feature and then find their cumulative sum . This helps us in selecting only the features which explain most of the variance rather than consiering even the features which are not important."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [],
   "source": [
    "cumsum=np.cumsum(pca.explained_variance_ratio_) #cumulative sum of variances"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x2412fc4a688>]"
      ]
     },
     "execution_count": 19,
     "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": [
    "plt.plot(range(1,len(cumsum)+1),cumsum) #plotting the cummulative variances to find the principal components"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([0.4595174 , 0.63133365, 0.76137624, 0.87190786, 0.94530998,\n",
       "       0.97015232, 0.98275663, 0.99256944, 1.        ])"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cumsum"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<p style=\"font-family: Arial; font-size:1.0em;color:blue;\"> From the above graph, we observed that 5 features explain more than 94.5 percent of data. So, we consider 5 features as Principal Components"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [],
   "source": [
    "pca=PCA(n_components=5,random_state=42) # performing PCA with 5 components\n",
    "df_pca=pca.fit_transform(df_scaled)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(167, 5)"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_pca.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_pca=pd.DataFrame(df_pca,columns=['feature-1','feature-2','feature-3','feature-4','feature-5'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "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-1</th>\n",
       "      <th>feature-2</th>\n",
       "      <th>feature-3</th>\n",
       "      <th>feature-4</th>\n",
       "      <th>feature-5</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>0</td>\n",
       "      <td>-2.913025</td>\n",
       "      <td>0.095621</td>\n",
       "      <td>-0.718118</td>\n",
       "      <td>1.005255</td>\n",
       "      <td>-0.158310</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>1</td>\n",
       "      <td>0.429911</td>\n",
       "      <td>-0.588156</td>\n",
       "      <td>-0.333486</td>\n",
       "      <td>-1.161059</td>\n",
       "      <td>0.174677</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2</td>\n",
       "      <td>-0.285225</td>\n",
       "      <td>-0.455174</td>\n",
       "      <td>1.221505</td>\n",
       "      <td>-0.868115</td>\n",
       "      <td>0.156475</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>3</td>\n",
       "      <td>-2.932423</td>\n",
       "      <td>1.695555</td>\n",
       "      <td>1.525044</td>\n",
       "      <td>0.839625</td>\n",
       "      <td>-0.273209</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>4</td>\n",
       "      <td>1.033576</td>\n",
       "      <td>0.136659</td>\n",
       "      <td>-0.225721</td>\n",
       "      <td>-0.847063</td>\n",
       "      <td>-0.193007</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   feature-1  feature-2  feature-3  feature-4  feature-5\n",
       "0  -2.913025   0.095621  -0.718118   1.005255  -0.158310\n",
       "1   0.429911  -0.588156  -0.333486  -1.161059   0.174677\n",
       "2  -0.285225  -0.455174   1.221505  -0.868115   0.156475\n",
       "3  -2.932423   1.695555   1.525044   0.839625  -0.273209\n",
       "4   1.033576   0.136659  -0.225721  -0.847063  -0.193007"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_pca.head() #top 5 rows  of dataframe"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<p style=\"font-family: Arial; font-size:1.0em;color:blue;\"> The above dataframe has the 5 principal components"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### HOPKINS SCORE"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<p style=\"font-family: Arial; font-size:1.0em;color:blue;\">Before evaluating the clustering performance, we have to make sure that the data we which will be clustered has clustering tendency and does not contain uniformly distributed points is very important. Hopkins statistic is a measure of clustering tendency. This tells us if the data is suitable for clustering or not. This score varies from 0 to 1. Hopkins statistic is a measure of clustering tendency. This tells us if the data is suitable for clustering or not. This score varies from 0 to 1.<br>If the score is close to 0, that means the data is not suitable for clustering.<br>If the score is close to 1, that means the data is suitable for clustering.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [],
   "source": [
    "def hopkins(X):\n",
    "    d = X.shape[1]\n",
    "    #d = len(vars) # columns\n",
    "    n = len(X) # rows\n",
    "    m = int(0.1 * n) \n",
    "    nbrs = NearestNeighbors(n_neighbors=1).fit(X.values)\n",
    " \n",
    "    rand_X = sample(range(0, n, 1), m)\n",
    " \n",
    "    ujd = []\n",
    "    wjd = []\n",
    "    for j in range(0, m):\n",
    "        u_dist, _ = nbrs.kneighbors(uniform(np.amin(X,axis=0),np.amax(X,axis=0),d).reshape(1, -1), 2, return_distance=True)\n",
    "        ujd.append(u_dist[0][1])\n",
    "        w_dist, _ = nbrs.kneighbors(X.iloc[rand_X[j]].values.reshape(1, -1), 2, return_distance=True)\n",
    "        wjd.append(w_dist[0][1])\n",
    " \n",
    "    H = sum(ujd) / (sum(ujd) + sum(wjd))\n",
    "    if isnan(H):\n",
    "        print(ujd, wjd)\n",
    "        H = 0\n",
    " \n",
    "    return H"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.7969941478198685"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "hopkins(df_pca) # hopkins score on the dataframe with principal components"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<p style=\"font-family: Arial; font-size:1.0em;color:blue;\"> As the score is 0.855, it indicates that the dataframe is good for clustering."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### CLUSTERING"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<p style=\"font-family: Arial; font-size:1.0em;color:blue;\"> Clustering is a unsupervised machine learning technique which is used to find the patterns in the data and helps in grouping the datapoints into various clusters. All the datapoints in the same cluster are said to be very similar when compared to the ones in other clusters.<br><br>\n",
    "</p>    \n",
    "<p style=\"font-family: Arial; font-size:1.0em;color:blue;\"> \n",
    "For this business proble, we will perform two types of clustering techniques.<br>\n",
    "1. K Means Clustering<br>\n",
    "2. Hierarchial Clustering<br>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### K-MEANS CLUSTERING"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<p style=\"font-family: Arial; font-size:1.0em;color:blue;\"> The k-means algorithm is the process of grouping the datapoints into k clusters such that the objects in a cluster are more similar to each other than to objects in different clusters. We will select the number of clusters k based on the sum of sqaure value or the silhouette score."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "For 2 clusters, the silhouette score is 0.3044199499231817\n",
      "For 3 clusters, the silhouette score is 0.3079769786519015\n",
      "For 4 clusters, the silhouette score is 0.3263683385174537\n",
      "For 5 clusters, the silhouette score is 0.3332587117271835\n",
      "For 6 clusters, the silhouette score is 0.2599661724540479\n",
      "For 7 clusters, the silhouette score is 0.27412281958499446\n",
      "For 8 clusters, the silhouette score is 0.24892086117288711\n",
      "For 9 clusters, the silhouette score is 0.24508705596048988\n",
      "For 10 clusters, the silhouette score is 0.2461721408753482\n",
      "For 11 clusters, the silhouette score is 0.23833881640908758\n",
      "For 12 clusters, the silhouette score is 0.236396122105971\n",
      "For 13 clusters, the silhouette score is 0.2296229139017313\n",
      "For 14 clusters, the silhouette score is 0.21852420442889842\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x24131b31bc8>]"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "ssd=[]\n",
    "silhouette=pd.DataFrame(columns=['no.of clusters','silhouette'])\n",
    "for i in range(2,15):\n",
    "    kmeans=KMeans(i,max_iter=100,random_state=42)     # performing kmeans clustering algorithm\n",
    "    kmeans.fit(df_pca)\n",
    "    ssd.append(kmeans.inertia_)\n",
    "    cluster_labels=kmeans.labels_     #appending the value of inertia in the ssd list\n",
    "    sil=silhouette_score(df_pca,cluster_labels)\n",
    "    silhouette=silhouette.append({'no.of clusters':i,'silhouette':sil} , ignore_index=True)\n",
    "    print('For',i,'clusters, the silhouette score is', sil)\n",
    "plt.plot(ssd)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<p style=\"font-family: Arial; font-size:1.0em;color:blue;\"> From the above Elbow curve, we can consider 4 or 5 as the number of clusters. We will also plot silhouette score for more understanding."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### SILHOUETTE SCORE"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<p style=\"font-family: Arial; font-size:1.0em;color:blue;\">The silhouette value is a measure of similarity of a data point in its own cluster (cohesion) compared to that of other clusters (separation).<br> The silhouette score ranges from −1 to +1.<br> When the silhouette score is high, that means the data point is similar to its own cluster and is poorly matched to other neighbouring clusters."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>no.of clusters</th>\n",
       "      <th>silhouette</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>0.304420</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>1</td>\n",
       "      <td>3.0</td>\n",
       "      <td>0.307977</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2</td>\n",
       "      <td>4.0</td>\n",
       "      <td>0.326368</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>3</td>\n",
       "      <td>5.0</td>\n",
       "      <td>0.333259</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>4</td>\n",
       "      <td>6.0</td>\n",
       "      <td>0.259966</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>5</td>\n",
       "      <td>7.0</td>\n",
       "      <td>0.274123</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>6</td>\n",
       "      <td>8.0</td>\n",
       "      <td>0.248921</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>7</td>\n",
       "      <td>9.0</td>\n",
       "      <td>0.245087</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>8</td>\n",
       "      <td>10.0</td>\n",
       "      <td>0.246172</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>9</td>\n",
       "      <td>11.0</td>\n",
       "      <td>0.238339</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>10</td>\n",
       "      <td>12.0</td>\n",
       "      <td>0.236396</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>11</td>\n",
       "      <td>13.0</td>\n",
       "      <td>0.229623</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>12</td>\n",
       "      <td>14.0</td>\n",
       "      <td>0.218524</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    no.of clusters  silhouette\n",
       "0              2.0    0.304420\n",
       "1              3.0    0.307977\n",
       "2              4.0    0.326368\n",
       "3              5.0    0.333259\n",
       "4              6.0    0.259966\n",
       "5              7.0    0.274123\n",
       "6              8.0    0.248921\n",
       "7              9.0    0.245087\n",
       "8             10.0    0.246172\n",
       "9             11.0    0.238339\n",
       "10            12.0    0.236396\n",
       "11            13.0    0.229623\n",
       "12            14.0    0.218524"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "silhouette "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x24130072d88>]"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.plot(silhouette['no.of clusters'],silhouette['silhouette']) # plotting the silhouette score for the corresponding number of clusters"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<p style=\"font-family: Arial; font-size:1.0em;color:blue;\"> From the above graph, we can determine that the silhouette score is highest when the the number of clusters is 5."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "KMeans(algorithm='auto', copy_x=True, init='k-means++', max_iter=100,\n",
       "       n_clusters=5, n_init=10, n_jobs=None, precompute_distances='auto',\n",
       "       random_state=42, tol=0.0001, verbose=0)"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "kmeans=KMeans(n_clusters=5,max_iter=100,random_state=42) #performing kmeans 5 clusters\n",
    "kmeans.fit(df_pca)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "539.4300513083451"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "kmeans.inertia_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [],
   "source": [
    "cluster_labels=kmeans.labels_ # assigning the clsuter id's "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{0, 1, 2, 3, 4}"
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "set(cluster_labels)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_kmeans=df_pca.copy()\n",
    "df_kmeans['kmeans_cluster_number']=cluster_labels"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [],
   "source": [
    "df['kmeans_cluster_number']=cluster_labels #adding kmeans cluster labels column in the original dataframe"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5, 0.95, 'Scatterplot of Principal components')"
      ]
     },
     "execution_count": 36,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x504 with 4 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(10,7))\n",
    "j=1\n",
    "kmeans_cols=['feature-2','feature-3','feature-4','feature-5']\n",
    "for i in kmeans_cols:\n",
    "    plt.subplot(2,2,j)\n",
    "    sns.scatterplot(df_kmeans['feature-1'],df_kmeans[i],hue=df_kmeans['kmeans_cluster_number'],palette=\"Set1\")\n",
    "    j+=1\n",
    "plt.suptitle(\"Scatterplot of Principal components\",y=0.95,fontsize=15)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "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",
       "      <th>kmeans_cluster_number</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>0</td>\n",
       "      <td>1.291532</td>\n",
       "      <td>-1.138280</td>\n",
       "      <td>0.279088</td>\n",
       "      <td>-0.082455</td>\n",
       "      <td>-0.808245</td>\n",
       "      <td>0.157336</td>\n",
       "      <td>-1.619092</td>\n",
       "      <td>1.902882</td>\n",
       "      <td>-0.679180</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>1</td>\n",
       "      <td>-0.538949</td>\n",
       "      <td>-0.479658</td>\n",
       "      <td>-0.097016</td>\n",
       "      <td>0.070837</td>\n",
       "      <td>-0.375369</td>\n",
       "      <td>-0.312347</td>\n",
       "      <td>0.647866</td>\n",
       "      <td>-0.859973</td>\n",
       "      <td>-0.485623</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2</td>\n",
       "      <td>-0.272833</td>\n",
       "      <td>-0.099122</td>\n",
       "      <td>-0.966073</td>\n",
       "      <td>-0.641762</td>\n",
       "      <td>-0.220844</td>\n",
       "      <td>0.789274</td>\n",
       "      <td>0.670423</td>\n",
       "      <td>-0.038404</td>\n",
       "      <td>-0.465376</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>3</td>\n",
       "      <td>2.007808</td>\n",
       "      <td>0.775381</td>\n",
       "      <td>-1.448071</td>\n",
       "      <td>-0.165315</td>\n",
       "      <td>-0.585043</td>\n",
       "      <td>1.387054</td>\n",
       "      <td>-1.179234</td>\n",
       "      <td>2.128151</td>\n",
       "      <td>-0.516268</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>4</td>\n",
       "      <td>-0.695634</td>\n",
       "      <td>0.160668</td>\n",
       "      <td>-0.286894</td>\n",
       "      <td>0.497568</td>\n",
       "      <td>0.101732</td>\n",
       "      <td>-0.601749</td>\n",
       "      <td>0.704258</td>\n",
       "      <td>-0.541946</td>\n",
       "      <td>-0.041817</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   child_mort   exports    health   imports    income  inflation  life_expec  \\\n",
       "0    1.291532 -1.138280  0.279088 -0.082455 -0.808245   0.157336   -1.619092   \n",
       "1   -0.538949 -0.479658 -0.097016  0.070837 -0.375369  -0.312347    0.647866   \n",
       "2   -0.272833 -0.099122 -0.966073 -0.641762 -0.220844   0.789274    0.670423   \n",
       "3    2.007808  0.775381 -1.448071 -0.165315 -0.585043   1.387054   -1.179234   \n",
       "4   -0.695634  0.160668 -0.286894  0.497568  0.101732  -0.601749    0.704258   \n",
       "\n",
       "   total_fer      gdpp  kmeans_cluster_number  \n",
       "0   1.902882 -0.679180                      1  \n",
       "1  -0.859973 -0.485623                      0  \n",
       "2  -0.038404 -0.465376                      0  \n",
       "3   2.128151 -0.516268                      1  \n",
       "4  -0.541946 -0.041817                      0  "
      ]
     },
     "execution_count": 37,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_scaled['kmeans_cluster_number']=cluster_labels\n",
    "df_scaled.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5, 0.95, 'Scatterplot of original features')"
      ]
     },
     "execution_count": 38,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1296x360 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "col=['child_mort','income']\n",
    "plt.figure(figsize=(18,5))\n",
    "j=1\n",
    "for i in col:\n",
    "    plt.subplot(1,2,j)\n",
    "    sns.scatterplot(df_scaled['gdpp'],df_scaled[i],hue=df_scaled['kmeans_cluster_number'],palette=\"Set1\")\n",
    "    j+=1\n",
    "plt.suptitle(\"Scatterplot of original features\",y=0.95,fontsize=15)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5, 0.95, 'Scatterplot of original features')"
      ]
     },
     "execution_count": 39,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1296x504 with 3 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "c=['inflation','life_expec', 'total_fer']\n",
    "plt.figure(figsize=(18,7))\n",
    "j=1\n",
    "for i in c:\n",
    "    plt.subplot(1,3,j)\n",
    "    sns.scatterplot(df_scaled['income'],df_scaled[i],hue=df_scaled['kmeans_cluster_number'],palette=\"Set1\")\n",
    "    j+=1\n",
    "plt.suptitle(\"Scatterplot of original features\",y=0.95,fontsize=15)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<p style=\"font-family: Arial; font-size:1.0em;color:blue;\"> The above plots depict that the clusters are well seperated and are very concise."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5, 0.95, 'GDP, Income and Child mortality plot for all the clusters')"
      ]
     },
     "execution_count": 40,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1008x216 with 3 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(14,3))\n",
    "j=1\n",
    "col=['child_mort','income','gdpp']\n",
    "for i in col:\n",
    "    plt.subplot(1,3,j)\n",
    "    sns.boxplot(y=df_scaled[i],x=df_scaled['kmeans_cluster_number'])\n",
    "    j=j+1\n",
    "plt.suptitle(\"GDP, Income and Child mortality plot for all the clusters\",y=0.95,fontsize=15)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<p style=\"font-family: Arial; font-size:1.0em;color:blue;\"> From the above boxplots, we can say that for the clusters 1 and 4, the child mortality is high, gdp is low and the net income is low which is the ideal conidtion for the countries that are in dire need for devolopment"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<p style=\"font-family: Arial; font-size:1.0em;color:blue;\"> Now, we determine the number of countries in both the  countries"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(47, 10)"
      ]
     },
     "execution_count": 41,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_scaled[df_scaled['kmeans_cluster_number']==1].shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(1, 10)"
      ]
     },
     "execution_count": 42,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_scaled[df_scaled['kmeans_cluster_number']==4].shape"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<p style=\"font-family: Arial; font-size:1.0em;color:blue;\"> We observed that the nummber of countries in cluster 1 are 47 and there is only one country in cluster 4.<br>So, apart from the country in cluster 4,we will  concentrate on the countries in cluster 1 to target the ccountries  which are in need of help!"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['child_mort', 'exports', 'health', 'imports', 'income', 'inflation',\n",
       "       'life_expec', 'total_fer', 'gdpp', 'kmeans_cluster_number'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 43,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_scaled.columns"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### HIERARCHIAL CLUSTERING"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<p style=\"font-family: Arial; font-size:1.0em;color:blue;\"> Hierarchical clustering is a clustering technique that successively combines the clusters based on similarity until only one group is remaining. Here we will use euclidean distance as the metric to combine the groups and will perform both SINGLE and COMPLETE linkages"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_hierarchy=df_pca.copy()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "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(df_hierarchy,method='single',metric='euclidean') #performing hierarchial clustering with single linkage\n",
    "dendrogram(mergings)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<p style=\"font-family: Arial; font-size:1.0em;color:blue;\"> As the dendrogram doesn't look stable, we now verify if complete Linkage menthod gives better results."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "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(df_hierarchy,method='complete',metric='euclidean') #performing hierarchial clustering with complete linkage\n",
    "dendrogram(mergings)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<p style=\"font-family: Arial; font-size:1.0em;color:blue;\"> From the above figure, we can say that the complete linkage provides better results and visualizations than the single linkage method. Thus, we consider this and go ahead with the cluster modelling."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {},
   "outputs": [],
   "source": [
    "cluster_labels = cut_tree(mergings, n_clusters=6).reshape(-1, )"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_hierarchy['hierarchial_cluster_number']=cluster_labels"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "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>feature-1</th>\n",
       "      <th>feature-2</th>\n",
       "      <th>feature-3</th>\n",
       "      <th>feature-4</th>\n",
       "      <th>feature-5</th>\n",
       "      <th>hierarchial_cluster_number</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>0</td>\n",
       "      <td>-2.913025</td>\n",
       "      <td>0.095621</td>\n",
       "      <td>-0.718118</td>\n",
       "      <td>1.005255</td>\n",
       "      <td>-0.158310</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>1</td>\n",
       "      <td>0.429911</td>\n",
       "      <td>-0.588156</td>\n",
       "      <td>-0.333486</td>\n",
       "      <td>-1.161059</td>\n",
       "      <td>0.174677</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2</td>\n",
       "      <td>-0.285225</td>\n",
       "      <td>-0.455174</td>\n",
       "      <td>1.221505</td>\n",
       "      <td>-0.868115</td>\n",
       "      <td>0.156475</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>3</td>\n",
       "      <td>-2.932423</td>\n",
       "      <td>1.695555</td>\n",
       "      <td>1.525044</td>\n",
       "      <td>0.839625</td>\n",
       "      <td>-0.273209</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>4</td>\n",
       "      <td>1.033576</td>\n",
       "      <td>0.136659</td>\n",
       "      <td>-0.225721</td>\n",
       "      <td>-0.847063</td>\n",
       "      <td>-0.193007</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   feature-1  feature-2  feature-3  feature-4  feature-5  \\\n",
       "0  -2.913025   0.095621  -0.718118   1.005255  -0.158310   \n",
       "1   0.429911  -0.588156  -0.333486  -1.161059   0.174677   \n",
       "2  -0.285225  -0.455174   1.221505  -0.868115   0.156475   \n",
       "3  -2.932423   1.695555   1.525044   0.839625  -0.273209   \n",
       "4   1.033576   0.136659  -0.225721  -0.847063  -0.193007   \n",
       "\n",
       "   hierarchial_cluster_number  \n",
       "0                           0  \n",
       "1                           1  \n",
       "2                           1  \n",
       "3                           0  \n",
       "4                           1  "
      ]
     },
     "execution_count": 49,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_hierarchy.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5, 0.95, 'Scatterplot of Principal components')"
      ]
     },
     "execution_count": 50,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x504 with 4 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(10,7))\n",
    "j=1\n",
    "hierarchy_cols=['feature-2','feature-3','feature-4','feature-5']\n",
    "for i in hierarchy_cols:\n",
    "    plt.subplot(2,2,j)\n",
    "    sns.scatterplot(df_hierarchy['feature-1'],df_hierarchy[i],hue=df_hierarchy['hierarchial_cluster_number'],palette=\"Set1\")\n",
    "    j+=1\n",
    "plt.suptitle(\"Scatterplot of Principal components\",y=0.95,fontsize=15)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {},
   "outputs": [],
   "source": [
    "df['hierarchial_cluster_number']=cluster_labels # assigning the hierarchial cluster id's to the dataframe"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_scaled['hierarchial_cluster_number']=cluster_labels"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "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>kmeans_cluster_number</th>\n",
       "      <th>hierarchial_cluster_number</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>0</td>\n",
       "      <td>Afghanistan</td>\n",
       "      <td>90.2</td>\n",
       "      <td>10.0</td>\n",
       "      <td>7.58</td>\n",
       "      <td>44.9</td>\n",
       "      <td>1610</td>\n",
       "      <td>9.44</td>\n",
       "      <td>56.2</td>\n",
       "      <td>5.82</td>\n",
       "      <td>553</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>1</td>\n",
       "      <td>Albania</td>\n",
       "      <td>16.6</td>\n",
       "      <td>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</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2</td>\n",
       "      <td>Algeria</td>\n",
       "      <td>27.3</td>\n",
       "      <td>38.4</td>\n",
       "      <td>4.17</td>\n",
       "      <td>31.4</td>\n",
       "      <td>12900</td>\n",
       "      <td>16.10</td>\n",
       "      <td>76.5</td>\n",
       "      <td>2.89</td>\n",
       "      <td>4460</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>3</td>\n",
       "      <td>Angola</td>\n",
       "      <td>119.0</td>\n",
       "      <td>62.3</td>\n",
       "      <td>2.85</td>\n",
       "      <td>42.9</td>\n",
       "      <td>5900</td>\n",
       "      <td>22.40</td>\n",
       "      <td>60.1</td>\n",
       "      <td>6.16</td>\n",
       "      <td>3530</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>4</td>\n",
       "      <td>Antigua and Barbuda</td>\n",
       "      <td>10.3</td>\n",
       "      <td>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</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  kmeans_cluster_number  \\\n",
       "0       9.44        56.2       5.82    553                      1   \n",
       "1       4.49        76.3       1.65   4090                      0   \n",
       "2      16.10        76.5       2.89   4460                      0   \n",
       "3      22.40        60.1       6.16   3530                      1   \n",
       "4       1.44        76.8       2.13  12200                      0   \n",
       "\n",
       "   hierarchial_cluster_number  \n",
       "0                           0  \n",
       "1                           1  \n",
       "2                           1  \n",
       "3                           0  \n",
       "4                           1  "
      ]
     },
     "execution_count": 53,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
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       "        vertical-align: middle;\n",
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       "\n",
       "    .dataframe tbody tr th {\n",
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       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>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>kmeans_cluster_number</th>\n",
       "      <th>hierarchial_cluster_number</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>0</td>\n",
       "      <td>1.291532</td>\n",
       "      <td>-1.138280</td>\n",
       "      <td>0.279088</td>\n",
       "      <td>-0.082455</td>\n",
       "      <td>-0.808245</td>\n",
       "      <td>0.157336</td>\n",
       "      <td>-1.619092</td>\n",
       "      <td>1.902882</td>\n",
       "      <td>-0.679180</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>1</td>\n",
       "      <td>-0.538949</td>\n",
       "      <td>-0.479658</td>\n",
       "      <td>-0.097016</td>\n",
       "      <td>0.070837</td>\n",
       "      <td>-0.375369</td>\n",
       "      <td>-0.312347</td>\n",
       "      <td>0.647866</td>\n",
       "      <td>-0.859973</td>\n",
       "      <td>-0.485623</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2</td>\n",
       "      <td>-0.272833</td>\n",
       "      <td>-0.099122</td>\n",
       "      <td>-0.966073</td>\n",
       "      <td>-0.641762</td>\n",
       "      <td>-0.220844</td>\n",
       "      <td>0.789274</td>\n",
       "      <td>0.670423</td>\n",
       "      <td>-0.038404</td>\n",
       "      <td>-0.465376</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>3</td>\n",
       "      <td>2.007808</td>\n",
       "      <td>0.775381</td>\n",
       "      <td>-1.448071</td>\n",
       "      <td>-0.165315</td>\n",
       "      <td>-0.585043</td>\n",
       "      <td>1.387054</td>\n",
       "      <td>-1.179234</td>\n",
       "      <td>2.128151</td>\n",
       "      <td>-0.516268</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>4</td>\n",
       "      <td>-0.695634</td>\n",
       "      <td>0.160668</td>\n",
       "      <td>-0.286894</td>\n",
       "      <td>0.497568</td>\n",
       "      <td>0.101732</td>\n",
       "      <td>-0.601749</td>\n",
       "      <td>0.704258</td>\n",
       "      <td>-0.541946</td>\n",
       "      <td>-0.041817</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   child_mort   exports    health   imports    income  inflation  life_expec  \\\n",
       "0    1.291532 -1.138280  0.279088 -0.082455 -0.808245   0.157336   -1.619092   \n",
       "1   -0.538949 -0.479658 -0.097016  0.070837 -0.375369  -0.312347    0.647866   \n",
       "2   -0.272833 -0.099122 -0.966073 -0.641762 -0.220844   0.789274    0.670423   \n",
       "3    2.007808  0.775381 -1.448071 -0.165315 -0.585043   1.387054   -1.179234   \n",
       "4   -0.695634  0.160668 -0.286894  0.497568  0.101732  -0.601749    0.704258   \n",
       "\n",
       "   total_fer      gdpp  kmeans_cluster_number  hierarchial_cluster_number  \n",
       "0   1.902882 -0.679180                      1                           0  \n",
       "1  -0.859973 -0.485623                      0                           1  \n",
       "2  -0.038404 -0.465376                      0                           1  \n",
       "3   2.128151 -0.516268                      1                           0  \n",
       "4  -0.541946 -0.041817                      0                           1  "
      ]
     },
     "execution_count": 54,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_scaled.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_pca[['kmeans_cluster_number','hierarchial_cluster_number']]=df[['kmeans_cluster_number','hierarchial_cluster_number']]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "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-1</th>\n",
       "      <th>feature-2</th>\n",
       "      <th>feature-3</th>\n",
       "      <th>feature-4</th>\n",
       "      <th>feature-5</th>\n",
       "      <th>kmeans_cluster_number</th>\n",
       "      <th>hierarchial_cluster_number</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>0</td>\n",
       "      <td>-2.913025</td>\n",
       "      <td>0.095621</td>\n",
       "      <td>-0.718118</td>\n",
       "      <td>1.005255</td>\n",
       "      <td>-0.158310</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>1</td>\n",
       "      <td>0.429911</td>\n",
       "      <td>-0.588156</td>\n",
       "      <td>-0.333486</td>\n",
       "      <td>-1.161059</td>\n",
       "      <td>0.174677</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2</td>\n",
       "      <td>-0.285225</td>\n",
       "      <td>-0.455174</td>\n",
       "      <td>1.221505</td>\n",
       "      <td>-0.868115</td>\n",
       "      <td>0.156475</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>3</td>\n",
       "      <td>-2.932423</td>\n",
       "      <td>1.695555</td>\n",
       "      <td>1.525044</td>\n",
       "      <td>0.839625</td>\n",
       "      <td>-0.273209</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>4</td>\n",
       "      <td>1.033576</td>\n",
       "      <td>0.136659</td>\n",
       "      <td>-0.225721</td>\n",
       "      <td>-0.847063</td>\n",
       "      <td>-0.193007</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   feature-1  feature-2  feature-3  feature-4  feature-5  \\\n",
       "0  -2.913025   0.095621  -0.718118   1.005255  -0.158310   \n",
       "1   0.429911  -0.588156  -0.333486  -1.161059   0.174677   \n",
       "2  -0.285225  -0.455174   1.221505  -0.868115   0.156475   \n",
       "3  -2.932423   1.695555   1.525044   0.839625  -0.273209   \n",
       "4   1.033576   0.136659  -0.225721  -0.847063  -0.193007   \n",
       "\n",
       "   kmeans_cluster_number  hierarchial_cluster_number  \n",
       "0                      1                           0  \n",
       "1                      0                           1  \n",
       "2                      0                           1  \n",
       "3                      1                           0  \n",
       "4                      0                           1  "
      ]
     },
     "execution_count": 56,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_pca.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5, 0.95, 'Scatterplot of original features')"
      ]
     },
     "execution_count": 57,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1296x360 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "col=['child_mort','income']\n",
    "plt.figure(figsize=(18,5))\n",
    "j=1\n",
    "for i in col:\n",
    "    plt.subplot(1,2,j)\n",
    "    sns.scatterplot(df_scaled['gdpp'],df_scaled[i],hue=df_scaled['hierarchial_cluster_number'],palette=\"Set1\")\n",
    "    j+=1\n",
    "plt.suptitle(\"Scatterplot of original features\",y=0.95,fontsize=15)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5, 0.95, 'Scatterplot of original features')"
      ]
     },
     "execution_count": 58,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1296x504 with 3 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "c=['inflation','life_expec', 'total_fer']\n",
    "plt.figure(figsize=(18,7))\n",
    "j=1\n",
    "for i in c:\n",
    "    plt.subplot(1,3,j)\n",
    "    sns.scatterplot(df_scaled['income'],df_scaled[i],hue=df_scaled['hierarchial_cluster_number'],palette=\"Set1\")\n",
    "    j+=1\n",
    "plt.suptitle(\"Scatterplot of original features\",y=0.95,fontsize=15)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "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>kmeans_cluster_number</th>\n",
       "      <th>hierarchial_cluster_number</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>0</td>\n",
       "      <td>Afghanistan</td>\n",
       "      <td>90.2</td>\n",
       "      <td>10.0</td>\n",
       "      <td>7.58</td>\n",
       "      <td>44.9</td>\n",
       "      <td>1610</td>\n",
       "      <td>9.44</td>\n",
       "      <td>56.2</td>\n",
       "      <td>5.82</td>\n",
       "      <td>553</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>1</td>\n",
       "      <td>Albania</td>\n",
       "      <td>16.6</td>\n",
       "      <td>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</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2</td>\n",
       "      <td>Algeria</td>\n",
       "      <td>27.3</td>\n",
       "      <td>38.4</td>\n",
       "      <td>4.17</td>\n",
       "      <td>31.4</td>\n",
       "      <td>12900</td>\n",
       "      <td>16.10</td>\n",
       "      <td>76.5</td>\n",
       "      <td>2.89</td>\n",
       "      <td>4460</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>3</td>\n",
       "      <td>Angola</td>\n",
       "      <td>119.0</td>\n",
       "      <td>62.3</td>\n",
       "      <td>2.85</td>\n",
       "      <td>42.9</td>\n",
       "      <td>5900</td>\n",
       "      <td>22.40</td>\n",
       "      <td>60.1</td>\n",
       "      <td>6.16</td>\n",
       "      <td>3530</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>4</td>\n",
       "      <td>Antigua and Barbuda</td>\n",
       "      <td>10.3</td>\n",
       "      <td>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</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  kmeans_cluster_number  \\\n",
       "0       9.44        56.2       5.82    553                      1   \n",
       "1       4.49        76.3       1.65   4090                      0   \n",
       "2      16.10        76.5       2.89   4460                      0   \n",
       "3      22.40        60.1       6.16   3530                      1   \n",
       "4       1.44        76.8       2.13  12200                      0   \n",
       "\n",
       "   hierarchial_cluster_number  \n",
       "0                           0  \n",
       "1                           1  \n",
       "2                           1  \n",
       "3                           0  \n",
       "4                           1  "
      ]
     },
     "execution_count": 59,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "metadata": {},
   "outputs": [
    {
     "data": {
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Us32gqr4yO9sRvjjJdpJ7W2v/8HrCMv+OHTuWBx54IFtbW1lcXMzx48d7R7omm5ubeeQDjyS3dAyxvXP3yO8/0i/D4/2WBthvVfVrSdpzvd5ae+M+xgEAmCd/Lclt2fk71MurKq21n+0bCQCYJ1daZv1ikt+qqv979/gNSX6pql6U5IPP8ZlPJ/mB1tp7qurmJA9X1UZr7bnezw1gNBplMpkkSRYWFjIajTonug63JNuv2u6doquFh650p1KAA+H/6h0AAGDeVNXPJfkrSR5N8pndp1t2fgANAHBFrqjMaq39g6p6IMkrszMS/rdba2d2X/6bz/GZjyb56O7jJ6rqsSQvyXOXX9wAlpeXs7q6mvvvvz+rq6tZWlrqHQkArkhr7f/tnQEAYA4dTfJ17fIFtAEArsGVTmaltfZwdq6PddWq6rYkL0/y7md57c4kdybJrbfeei1fz5x5wxvekHe84x154xvtxgTA/Kiqf9la+++r6n15lu0GW2u3d4gFADB078/OJSg+2jsIADC/rrjMulZV9SVJfjXJ97fW/viZr7fW7k1yb5IcPXrUr3RuAL/2a7+WJ598Mvfff39OnjzZOw4AXKk37d6/vmsKAID5spzkg1X120k+e9111xsFAK7GTC94U1WL2SmyfqG19tZZrsV8mE6nmUwmaa1lMpnk0qVLvSMBwBXZ3UI5rbWPPNutdz4AgIF6c5L/Nsn/nuTHn3aDPTedTnPixAl/bwI4gGZWZlVVJbkvyWOttZ+Y1TrMl/F4nMvbZG9vb2c8HndOBABXp6q+rarOVdUfVdUfV9UTVfV50+cAAOxcd/TZbr1zcTCNx+OcPXvW35sADqBZTmbdkeS7krymqh7dvX3LDNdjDmxsbGRraytJsrW1ldOnT3dOBABX7ceSvLG19l+01r60tXZza+1Le4cCABiSqnrX7v0Tuz8A+mM/BGKW7AYEcLDNrMxqrb2rtVattdtbay/bvT0wq/WYD8eOHcvi4mKSZHFxMcePH++cCACu2n9qrT3WOwQAwJC11l65e3/z7g+AvtQPgZgluwEBHGwzvWYWPNNoNMrODpTJwsJCRqNR50QAcGV2txf8tiRnqupfVNV3XH5u99grDAEAACAASURBVHkAAKATuwEBHGzKLPbV8vJyVldXU1VZXV3N0tJS70gAcKXesHu7OcmTSY4/7bnXd8wFAAA3PLsBARxsyiz23Wg0yu23324qC4C50lr7ntba9yR5QZK/87Tjk52jAQDADc9uQAAHmzKLfbe8vJxTp06ZygJgXt3eWnv88kFr7RNJXt4xDwAA3PDsBgRwsCmzAACuzkJV/fnLB1X1ZUkOdcwDAADEbkB7ZTqd5sSJE7l06VLvKACfpcwCALg6P57k31XVP6iqH03y75L8WOdMAAA3lKr6yqp6sKoeq6oPVNWbemeiP7sB7Y3xeJyzZ89mPB73jgLwWX5FDHRz4cKFPJHkvrTeUbr6aJJPXrjQOwZwhVprP1tVZ5K8Jkkl+bbW2gc7xwIAuNF8OskPtNbeU1U3J3m4qjacl8H1mU6nmUwmaa1lMplkNBopB4FBMJkFAHCVWmsfbK39o9baKX8wYZZs8QIAz6619tHW2nt2Hz+R5LEkL+mbCubfeDxOazs/Ot7e3jadBQyGySygm5WVlTw+neZ7U72jdHVfWm5ZWekdA4ABevoWLydPnuwdBwAGqapuS/LyJO9+xvN3JrkzSW699dZ9zwXzaGNjI1tbW0mSra2tnD592nkoMAgmswAAYICeucWL6SwA+HxV9SVJfjXJ97fW/vjpr7XW7m2tHW2tHT18+HCfgDBnjh07lsXFxSTJ4uJijh8/3jkRwA5lFgAADJAtXgDg+VXVYnaKrF9orb21dx44CEajUap2dtBZWFjIaDTqnAhghzILAAAG6Nm2eAEAdtTOX9vvS/JYa+0neueBg2J5eTmrq6upqqyurmZpaal3JIAkyiwAABgkW7wAwPO6I8l3JXlNVT26e/uW3qHgIBiNRrn99ttNZQGDcqh3AAAA4PONRqNMJpMktngBgGdqrb0rSfXOAQfR8vJyTp061TsGwOcwmcW+m06nOXHihIuYAwA8D1u8AAAAwA5lFvtuPB7n7NmzLmIOAPAF2OIFAAAAbDPIPptOp5lMJmmtZTKZZDQazeWvjC9cuJD8UbLw0A3eBz+eXGgXeqcAgAPLFi8AAABgMot9Nh6P01pLkmxvb5vOAgAAAAAAnpfJLPbVxsZGtra2kiRbW1s5ffp0Tp482TnV1VtZWcnFupjtV233jtLVwkMLWXnJSu8YAAAAAAAcYCaz2FfHjh3L4uJikmRxcTHHjx/vnAgAAAAAABgyZRb7ajQapaqSJAsLCy5mDgAAAAAAPC9lFvtqeXk5q6urqaqsrq5maWmpdyQAAAAAAGDAXDOLfTcajXL+/HlTWQAAAAAAwBekzGLfLS8v59SpU71jAAAAAAAAc8A2gwAAAAAAAAyWMgsAAAAAAIDBUmYBAAAAAAAwWMosAAAAAAAAButQ7wAAAAAAALDXPpbkvrQua1/avV/qsvqOjyW5peP6sJeUWQAAAAAAHChHjhzpuv7Fc+eSJLd81Vd1y3BL+v9zgL2izAIAAAAA4EBZW1sbxPrr6+tdc8BBocwCAAAAADgg1tfXs7m5ec2fv3DhQpJkZWXlunIcOXKke6EEHBzKLACAA+jChQtZePKP8sIPvr13lG4WnryUCxc+3TsGAADMlaeeeqp3BIDPo8wCAAAAADggrncayvZ4wBApswAADqCVlZX8p08dyp9+3et7R+nmhR98e1ZWXtw7BgAAAHCdFnoHAAAAAAAAgOeizAIAAAAAAGCwlFkAAAAAAAAMljILAAAAAACAwVJmAQAAAAAAMFjKLAAAAAAAAAZLmQUAMABV9U+r6uNV9f7eWQAAAACGRJnFvptOpzlx4kQuXbrUOwoADMk/T/K63iEAAAAAhkaZxb4bj8c5e/ZsxuNx7ygAMBittXcm+cPeOQAAYF75ATXAwaXMYl9Np9NMJpO01jKZTJxcAMBVqKo7q+pMVZ25ePFi7zgAADAo6+vree9735v19fXeUQDYY8os9tV4PE5rLUmyvb1tOgsArkJr7d7W2tHW2tHDhw/3jgMAAIMxnU7z0EMPJUkefPBBP6AGOGCUWeyrjY2NbG1tJUm2trZy+vTpzokAAAAAmHfPnMYynQVwsCiz2FfHjh3L4uJikmRxcTHHjx/vnAgAAACAeXd5KuuyBx98sE8QAGZCmcW+Go1GqaokycLCQkajUedEADAMVfVLSf6/JF9TVReq6nt7ZwIAAAAYAmUW+2p5eTmrq6upqqyurmZpaal3JAAYhNbad7TWvqK1tthaW2mt3dc7EwAAAMAQHOodgBvPaDTK+fPn538q6/Fk4aGOffAnd++/pF+EPJ7kJR3XBwAAAEjyqle96nO2Gnz1q1/dLwwAe06Zxb5bXl7OqVOnese4LkeOHOkdIefOnUuSfNVLvqpfiJcM458FAAAAcGNbW1v7nDJrbW2tXxgA9pwyC67BEE6ILmdYX1/vnAQAAACgr+Xl5c9OZ7361a92aQuAA0aZBQAAAADMvbW1tXziE58YxI+QAdhbyiwAAAAAYO4dhEtbAPDsFnoHAAAAAAAAgOeizAIAAAAAAGCwbDPIvptOp7n77rvz5je/2cU4AQAAAOBp1tfXs7m52W39c+fOJUn3a48dOXKkewZgOJRZ7Lt77rkn733ve3PPPffkh3/4h3vHAQAAAIDB2NzczCMfeCS5pVOA7Z27R37/kU4Bkjzeb2lgmJRZ7KvpdJqNjY0kyenTp3PXXXeZzgIAAACAp7sl2X7Vdu8U3Sw85Oo4wOfy/wrsq3vuuSfb2zv/It7e3s4999zTOREAAAAAADBkyiz21Tve8Y7POb48pQUAAAAA1+PDH/5wVldXu15vCoDZUGaxr6rqeY8BAAAA4Fq85S1vyZ/8yZ/kR3/0R3tHAWCPKbPYV9/8zd/8Ocevfe1rOyUBAAAA4KD48Ic/nPPnzydJzp8/bzoL4IBRZrGv7rrrriws7PzPbmFhIXfddVfnRAAAAADMu7e85S2fc2w6C+BgUWaxr5aXl3Ps2LEkyfHjx7O0tNQ5EQAAAADz7vJU1nMdAzDflFnsu7vuuisvfelLTWUBAAAAsCduu+225z0GYL4d6h2AG8/y8nJOnTrVOwYD8bEk96V1W//S7n3PGcGPJbml4/oAAAD8/+3dfZBdaX0f+O+vpQYEY5sd9Swv07BDoRm7WBebFy1Zx8sYbFpFZ2Gws3EtZF9udmEhTiyR1WZTLKFckMJbm0rs7PbYmwyLKV9XUiGQ2IXAak/3sODBrqzDAIN4M1YHC2gLw7RYMOMR5mr62T+6Z9AMkqalvq1zbvfnU6VSn9v3nPPV0y3p1+d3nvMw6d7ylrfkda973aPbP/dzP9dhGgDGTTML6MyhQ4e6jpAHTp9Okjz91ls7y/D09GMsAAAAYFLddtttueWWW3LmzJnccsstfs4G2GU0s4DOHDt2rOsIj2ZYWFjoOAkAAACwHW95y1vyxje+ceJnZa2uribfTKY+vIdXiPlGstpWu04B9IhmFgAAAAAw8W677bYsLi52HQOAHaCZBQAAAADQE7Ozs3mgHsj6S9a7jtKZqQ9PZfbm2a5jAD2yh+eqAgAAAAAA0HeaWQAA0FNra2s5evRozp0713UUAAAA6IxmFgAA9NRwOMypU6cyHA67jgIAAACd0cwCAIAeWltby+LiYlprWVxcNDsLAACAPUszCwAAemg4HKa1liRZX183OwsAAIA9a8eaWVX1rqr6WlV9eqfOAQAAu9Xy8nJGo1GSZDQaZWlpqeNEAAAA0I2dnJn1q0levoPHZ0JZyBwA4InNzc1leno6STI9PZ0jR450nAgAAAC6sWPNrNbavUm+vlPHZ3Lddddd+eQnP5m77rqr6ygAAL01GAwe/biqHrMNAAAAe0nna2ZV1eur6r6quu+BBx7oOg47bG1tLcvLy0mSpaUls7MAAC5jZmYmN998c5Lk2c9+dg4ePNhxIgAAAOjG/q4DtNbekeQdSXL48OHWcRx22F133ZX19fUkGwuZ33XXXXnzm9/ccSoA2J2mHvp6nvLZD3Ry7vr2nyRJ2lO+v5PzJxt//uSZnZ1/u9bW1nL27NkkydmzZ3Pu3DkNLQDYVFXvSvKKJF9rrf1w13kAgJ3VeTOLveWee+55zPby8rJmFgDsgEOHDnV6/tOnv5UkufX5XTaTntn5OGzHcDhMaxv3erXWMhwOc/z48Y5TAUBv/GqSX0ryax3nAACuA80srququuI2ADAex44d68X5FxYWOs0xyZaXlzMajZIko9EoS0tLmlkAsKm1dm9V3dJ1DgDg+tixNbOq6l8m+bdJfrCqVqvqtTt1LibHT/zETzxm+2Uve1lHSQAA+m1ubi7T09NJkunp6Rw5cqTjRAAwWazTDgC7x47NzGqtvWanjs3kesMb3pDl5eWsr69namoqb3jDG7qOBADQS4PBIIuLi0mSqampDAaDjhMBwGSxTjsT7RvJ1Id3bB7ClT24+fsN3Zw+SfKNJDd3eH6gdzxmkOtqZmYmc3Nzufvuu3PkyBGLmAMAXMbMzEzm5+dz4sSJzM/Pq5sAAPaIrtd9PX36dJLk1ptv7S7Ezd2PA9Avmllcd294wxvyx3/8x2ZlAQA8gVe+8pW55557cscdd3QdBQCA68T6twDfq6O5quxlMzMzufPOO91dDADwBN7//vfnoYceyokTJ7qOAgC9Yq12ANhbNLMAAKCH1tbWsri4mNZaFhcXc+7cua4jAUBvtNZe01p7VmtturU221r7la4zAQA7RzMLAAB6aDgcprWNterX19czHA47TgQAAADd0MziultbW8vRo0fdXQwAcAXLy8sZjUZJktFolKWlpY4TAQA7xbUSALgyzSyuu+FwmFOnTrm7GADgCubm5jI9PZ0kmZ6ezpEjRzpOBADsFNdKAODK9ncdgL3l8Ws/DAaDHDx4sOtYAAC9MxgMsri4mCSZmprKYDDoOBEAsBNcK/muhYWFrKysXPP+q6urSZLZ2dlt5Th06FCOHTu2rWMAMF5mZnFdWfsBAGBrZmZmMj8/n6rK/Pz8nr2oBQC7nWsl43P+/PmcP3++6xgA7AAzs7iuLrX2w/HjxztOBQDQT4PBIGfOnDErCwB2MddKvmu7s6Ee2X9hYWEccQDoETOzuK6s/QAAAADwXa6VAMAT08ziuhoMBqmqJNZ+AAB4IhaDB4Ddz7USAHhimllcV9Z+AADYmscvBn/u3LmuIwEAO8C1EgB4YppZXHeDwSAvfOEL3WkEAHAFFoMHgL3DtRIAuDLNLK67mZmZ3Hnnne40AgC4gkstBg8A7E6ulQDAlWlmAQBAD1kMHgAAADZoZgEAQA9ZDB4AAAA2aGYBAEAPWQweAAAANuzvOgAAAHBpg8EgZ86cMSsLAACAPU0zCwAAeuqRxeABAABgL/OYQQAAAAAAAHpLMwsAAAAAAIDe0swCAAAAAACgtzSzAAAAAAAA6C3NLAAA6Km1tbUcPXo0586d6zoKAAAAdEYzCwAAemo4HObUqVMZDoddRwEAAIDOaGYBAEAPra2tZXFxMa21LC4ump0FAADAnqWZBQAAPTQcDtNaS5Ksr6+bnQUAAMCepZkFAAA9tLy8nNFolCQZjUZZWlrqOBEAAAB0Y3/XAQAAgO81NzeXkydPZjQaZXp6OkeOHOk6EgAA7BkLCwtZWVm55v1Pnz6dJDl27Ni2chw6dGjbx4DdQDOLq7Ldf8STZHV1NUkyOzt7zcfwjzgAsNsNBoOcPHkySVJVGQwGHScCAAC26sCBA11HgF1FM4vr7vz5811HAADovZmZmdx88805c+ZMnv3sZ+fgwYNdRwIAgD3DjfTQL5pZXJVx/CP+yDEWFha2fSwAgN1qbW0tZ8+eTZKcPXs2586d09ACAABgT5rqOgAAAPC9hsNhWmtJktZahsNhx4kAAACgG2ZmQQfGsfbYOBaRtPYYAPTX8vJyRqNRkmQ0GmVpaSnHjx/vOBUAAH233etO47jmlLjuBIyXmVkwoQ4cOGAhSQDYxebm5jI9PZ0kmZ6ezpEjRzpOBADAXuCaE9BHZmZBB9yVAgA8kcFgkMXFxSTJ1NRUBoNBx4kAAJgErjsBu5GZWQAA0EMzMzOZn59PVWV+fj4HDx7sOhIAAAB0wswsAADoqcFgkDNnzpiVBQAAwJ6mmQUAADtku4tvr66uJkne9ra3bSuHxbcBAACYZJpZAADQU+fPn+86AgAAAHROMwuYWNu92z1JTp8+nWR7i6O62x2Ay9nu/w+P7L+wsDCOOAAAADCRNLOAPe3AgQNdRwAAAAAA4Ao0s4CJZTYUAAAAAMDuN9V1AAAAAAAAALgczSwAAAAAAAB6SzMLAAAAAACA3tLMAgAAAAAAoLc0swAAAAAAAOgtzSwAAAAAAAB6SzMLAAAAAACA3tLMAgDoiap6eVV9vqpWqupNXecBAAAA6APNLACAHqiqfUl+Ocl8khckeU1VvaDbVAAAAADd08wCAOiHFyVZaa19obX2nSTvTvKqjjMBAAAAdE4zCwCgH25O8uWLtlc3X3tUVb2+qu6rqvseeOCB6xoOAAAAoCuaWQAA/VCXeK09ZqO1d7TWDrfWDt90003XKRYAAABAtzSzAAD6YTXJcy7ank1ytqMsAAAAAL2hmXUV1tbWcvTo0Zw7d67rKADA7vPRJLdW1fOq6klJXp3kRMeZAAAAADqnmXUVhsNhTp06leFw2HUUAGCXaa1dSPKzSe5O8rkk72mtfabbVAAAAADd08zaorW1tSwuLqa1lsXFRbOzAICxa62dbK3d1lp7fmvt57vOAwAAANAHmllbNBwO09rGGuzr6+tmZwEAAAAAAFwH+7sOMCmWl5czGo2SJKPRKEtLSzl+/HjHqQAAAACgewsLC1lZWek0w+nTp5Mkx44d6zTHoUOHOs8AsNtoZm3R3NxcTp48mdFolOnp6Rw5cqTrSAAAAADQCysrK/mDT388z73h4c4yPGm08RCqb5/5aGcZvvTgvs7ODbCbaWZt0WAwyOLiYpJkamoqg8Gg40QAAAAA0B/PveHhvOXwg13H6NTb77uh6wgAu5I1s7ZoZmYm8/PzqarMz8/n4MGDXUcCAAAAAADY9czMugqDwSBnzpwxKwsAAAAAAOA60cy6CjMzM7nzzju7jgEAAAAAALBneMwgAAAAAAAAvaWZBQAAAAAAQG9pZgEAAAAAANBbmlkAAAAAAAD0lmYWAAAAABOnql5eVZ+vqpWqelPXeQCAnaOZBQAAAMBEqap9SX45yXySFyR5TVW9oNtUAMBO0cwCAAAAYNK8KMlKa+0LrbXvJHl3kld1nAkA2CGaWQAAAABMmpuTfPmi7dXN1x5VVa+vqvuq6r4HHnjguoYDAMZLMwsAAACASVOXeK09ZqO1d7TWDrfWDt90003XKRYAsBM0swAAAACYNKtJnnPR9mySsx1lAQB22I42s6rq5VX1+apaqao37eS5AAAAANgzPprk1qp6XlU9Kcmrk5zoONM1W1tby9GjR3Pu3LmuowBAL+3fqQNX1b4kv5xkLht3y3y0qk601j67U+cEAGA8FhYWsrKycs37nz59Okly7NixbeU4dOjQto8BAOw+rbULVfWzSe5Osi/Ju1prn+k41jUbDoc5depUhsNhjh8/3nUcAOidnZyZ9aIkK621L7TWvpPk3UletYPnAwCgJw4cOJADBw50HQMA2MVaaydba7e11p7fWvv5rvNcq7W1tSwuLqa1lsXFRbOzAOASdmxmVpKbk3z5ou3VJH/p8W+qqtcneX2SPPe5z93BOAAAbJXZUAAA18dwOExrLUmyvr5udhYAXMJOzsyqS7zWvueF1t7RWjvcWjt800037WAcAAAAAOiX5eXljEajJMloNMrS0lLHiQCgf3aymbWa5DkXbc8mObuD5wMAAACAiTI3N5fp6ekkyfT0dI4cOdJxIgDon518zOBHk9xaVc9L8kdJXp3kr+/g+diC7S7mPg7jWhB+OywmDwAAAPTBYDDI4uJikmRqaiqDwaDjRADQPzvWzGqtXaiqn01yd5J9Sd7VWvvMTp2PrVlZWckffPrjee4ND3eW4UmjjQmB3z7z0U7O/6UH93VyXgAAAIDHm5mZyfz8fE6cOJH5+fkcPHiw60gA0Ds7OTMrrbWTSU7u5Dm4es+94eG85fCDXcfozNvvu6HrCAAAAACPGgwGOXPmjFlZAHAZO7lmFgAAAAAAAGzLjs7MAgCASdb1eqN9WGs0sd4oAOy04XCYU6dOZTgc5vjx413HAYDe0cwCAIDL6Hq90a7XGk2sNwoAO21tbS0nT55May0nT57MYDCwbhYAPI5mFgAAXIH1Rq03CgA7aTgc5sKFC0mS0WhkdhYAXIJmFgAAAAB0ZGlpKa21JElrLXffffdENrNWV1fzp9/at+dvhPnit/blaaurXccA2HWmug4AAAAAAHvVM57xjCtuAwBmZgEAAABAZ7761a9ecXtSzM7O5tsXvrKnH8+cbDyi+Smzs13HANh1zMwCAAAAgI7cfvvtj9n+sR/7sY6SAEB/aWYBAAAAAADQW5pZAAAAANCRj3zkI4/ZvvfeeztKAgD9pZl1FdbW1nL06NGcO3eu6ygAAAAA7AJzc3PZv39jWfv9+/fnyJEjHScCgP7RzLoKw+Ewp06dynA47DoKAAAAALvAYDDI1NTGJbp9+/ZlMBh0nAgA+kcza4vW1tayuLiY1loWFxfNzgIAAABg22ZmZjI/P5+qyvz8fA4ePNh1JADoHc2sLRoOh2mtJUnW19fNzgIAAABgLAaDQV74whealQUAl6GZtUXLy8sZjUZJktFolKWlpY4TAQAAALAbzMzM5M477zQrCwAuQzNri+bm5jI9PZ0kmZ6ethgnAAAAAADAdaCZtUWDwSBVlSSZmpoy7RsAAAAAAOA60MzaopmZmbz0pS9Nkrz0pS817RsAAAAAAOA60MwCAAAAAACgtzSztmhtbS0f+tCHkiQf+tCHcu7cuY4TAQAAALAbrK2t5ejRo643AcBl7O86wKQYDodprSVJ1tfXMxwOc/z48Y5TAQCwk1ZXV/On39qXt993Q9dROvPFb+3L01ZXu44BALvacDjMqVOnXG8CgMswM2uLlpeXMxqNkiSj0ShLS0sdJwIAAABg0q2trWVxcTGttSwuLpqdBQCXYGbWFs3NzeXEiRNpraWqcuTIka4jAQCww2ZnZ/PtC1/JWw4/2HWUzrz9vhvylNnZrmMAwK7laUAA8MTMzNqiV77ylY8WFq213HHHHR0nAgAAAGDSeRoQADwxzawtev/735+qSpJUVU6cONFxIgAAAAAm3dzcXKanp5Mk09PTngYEAJfgMYNbtLy8/JiZWUtLSxM55dsi5hYxBwAAAPpjMBhkcXExSTI1NZXBYNBxIgDoH82sLZqbm8vJkyczGo3cJQMAAADAWMzMzGR+fj4nTpzI/Px8Dh482HWka/alB7u9gfqrD208hOoZT13vLMOXHtyX2zo7O8DupZm1RYPBICdPnkyy8ZjBSb1LxiLmFjEHAAAA+mUwGOTMmTMTe70pSQ4dOtR1hHzn9OkkyVNuubWzDLelH2MBsNtoZm3RzMxMbr755pw5cybPfvazJ/ouGQAAAAD6Y2ZmJnfeeWfXMbbl2LFjXUd4NMPCwkLHSQAYt6muA0yKtbW1nD17Nkly9uzZnDt3ruNEAAAAAAAAu59m1hYNh8Osr288b3d9fT3D4bDjRAAAAAAAALufZtYWLS8v58KFC0mSCxcuZGlpqeNEAAAAAAAAu581s7boxS9+ce6+++5Ht2+//fYO0wAAAADQFwsLC1lZWbnm/VdXV5Mks7Oz28px6NChXqxdBQDjppkFAAAAAB06f/581xEAoNc0s7boIx/5yGO277333rz5zW/uKA0AAAAAfbHd2VCP7L+wsDCOOACw62hmbdHc3Fze9773Pbp95MiRDtMAAHC9fOnBfXn7fTd0cu6vPrSxxO0znrreyfmTjT//bZ2dHQAA6NLCwkIWFxevef+HHnoorbUxJro2VZWnPvWp17z//Py8x7h2TDNri175ylc+ppl1xx13dJgGAIDr4dChQ52e/zunTydJnnLLrZ1luC3djwMAAAB7m2bWFr33ve99zPZ73vMejxkEANjlur7zziOHAACALh07dqzzn4sg0czasg9+8IOP2b7nnns0swAAANiTPHLouzx2CABg52lmbdHji+w+FN3Xqst1H5Lu136w7gMAwN6ztraWt73tbXnrW9+agwcPdh0HAACAq6CZtUUvfvGL8+EPf/jR7dtvv727MNvQh/UOul77wboPAAB7z3A4zKlTpzIcDnP8+PGu48DE88ghAACupz3TzFpYWMjKyso17//FL37xMdv333//NRXuhw4d6rTg78MPG9Z+AADgelpbW8vi4mJaa1lcXMxgMDA7CwAAYILsmWbWdn3zm9+84jYAANBPw+Hw0ceEr6+vm50FwGNs9wbocTi9+RSbrm9C7vombAC4nD3TzNruf8S/8Au/kPe9731Jkv379+cVr3iFH4CBXrD49ndZfBuAS1leXs5oNEqSjEajLC0t7clafhwXa1dXV5Mks7Oz13wMF0qBvllZWcknPvXZrD/1xs4y1Hc2fib72L//484yTD309c7ODQBPZM80s7ZrMBjkxIkTaa1l3759GQwGXUcCAAC2YG5uLidPnsxoNMr09HSOHDnSdaSJdf78+a4jAOyI9afemG+/4BVdx+jUUz77ga4jAMBlaWZt0czMTG688cacO3cu8/PznrEP9IbFtwHY7bY7o2g0Gj06M+vChQs5ffr0RK5/u13jyG79WwAAoAtTXQeYJM985jPztKc9zawsAACYINPT09m/f+M+vhtvvDHT09MdJwIAAOBqTMzMrD4sxnnmzJkkydve9rbOMkz63aAAAHC1xlH//szP/EzOnDmTd77znZ6yAAAA8w1G5QAADndJREFUMGEmppllMU4LcQIAMJncmLZhuzem9WEcT58+nWQ8DcZr5QY/YNxWV1cz9dA39/yaUVMPncvq6oWuYwDAJU1MMyuxGOdeL6oAAJhMKysr+cRnPpE8vcMQ6xu/feKPPtHN+b+x/UOsrKzk9++/P8/c/qGu2SPPqf/G/fd3cv5ubisEAAC6NjHNLHfJuEMGAIDJtLq62nWE5IauA4xnHJ6Z5LWp7YeZUL+S1nUEYBeanZ3NA//fn3Saob69cf72lO/vMkVmZ2c7PP/2ZyGPawaxWcAA/TMxzSwAAGCCXchYZidds4c3f9/X0fndkwbQW4cOHeo6Qk6f/laS5Nbndzn/9pm9GIvtOHDgQNcRANghE9PMcpdMshvukEnGc5eMO2QAgEng7uINL3nJS3qz1tOtt97aWYZJv0AIsFv14frCIxkWFhY6TtKtPnwtAOiniWlmbfcHv9XV1Zw/f35bxzj/8LeTJAfWr/2xHgcOHNhGQ2ry75BJ3CUDALBVu6Vu2u6FqXHcUDUOXTcFV1dXcy7J26/xUXsX8ujSYZ2ayrX/IPqdJA/24bGVAI/jBhYA2FkT08zqww/Ajzzjfjuzoya9qJjk7AAA15vaqT92Q2Pw6U9/+rZu0Hv4z/4std59O6umprL/yU++pn33Z2McAHab3fD/FADspGqtPwvoHj58uN13331dxwAArqCqPtZaO9x1jr1O3QQAk0Ht1A9qJwCYDJernaa6CAMAAAAAAABboZkFAAAAAABAb2lmAQAAAAAA0FuaWQAAAAAAAPSWZhYAAAAAAAC9pZkFAAAAAABAb2lmAQB0rKp+uqo+U1XrVXW46zwAAH2mdgKAvUczCwCge59O8leT3Nt1EACACaB2AoA9Zn/XAQAA9rrW2ueSpKq6jgIA0HtqJwDYe8zMAgCYEFX1+qq6r6rue+CBB7qOAwDQa2onANg9zMwCALgOquqeJM+8xKf+fmvtfVs5RmvtHUnekSSHDx9uY4wHANAraicA4GKaWQAA10Fr7WVdZwAAmBRqJwDgYh4zCAAAAAAAQG9pZgEAdKyqfqqqVpP8SJLfrKq7u84EANBXaicA2Hs8ZhAAoGOttd9I8htd5wAAmARqJwDYe8zMAgAAAAAAoLc0swAAAAAAAOgtzSwAAAAAAAB6SzMLAAAAAACA3tLMAgAAAAAAoLc0swAAAAAAAOgtzSwAAAAAAAB6SzMLAAAAAACA3qrWWtcZHlVVDyT5Ytc5nsBMkrWuQ+wCxnE8jON4GMfxMI7jMQnj+B+11m7qOsRep27aU4zj+BjL8TCO42Ecx2MSxlHt1ANqpz3FOI6HcRwP4zgexnF8JmEsL1k79aqZNQmq6r7W2uGuc0w64zgexnE8jON4GMfxMI7sJr6fx8M4jo+xHA/jOB7GcTyMI7uJ7+fxMI7jYRzHwziOh3Ecn0keS48ZBAAAAAAAoLc0swAAAAAAAOgtzayr946uA+wSxnE8jON4GMfxMI7jYRzZTXw/j4dxHB9jOR7GcTyM43gYR3YT38/jYRzHwziOh3EcD+M4PhM7ltbMAgAAAAAAoLfMzAIAAAAAAKC3NLMAAAAAAADoLc2sLaqql1fV56tqpare1HWeSVVV76qqr1XVp7vOMqmq6jlV9aGq+lxVfaaq3th1pklUVU+pqn9XVZ/cHMe3dZ1pklXVvqr6RFV9oOssk6yqzlTVp6rq/qq6r+s8sB1qp+1TN42H2mk81E7jpXbaPnUTu43aafvUTuOhdhoPtdN4qZ22bzfUTtbM2oKq2pfkD5LMJVlN8tEkr2mtfbbTYBOoqm5P8mCSX2ut/XDXeSZRVT0rybNaax+vqu9L8rEkP+n78epUVSV5WmvtwaqaTvI7Sd7YWvt/O442karqeJLDSb6/tfaKrvNMqqo6k+Rwa22t6yywHWqn8VA3jYfaaTzUTuOldto+dRO7idppPNRO46F2Gg+103ipnbZvN9ROZmZtzYuSrLTWvtBa+06Sdyd5VceZJlJr7d4kX+86xyRrrX2ltfbxzY+/leRzSW7uNtXkaRse3Nyc3vylu38Nqmo2yX+R5J1dZwF6Q+00Buqm8VA7jYfaaXzUTsAlqJ3GQO00Hmqn8VA7jY/aiUdoZm3NzUm+fNH2avwjTg9U1S1J/nyS3+s2yWTanKJ8f5KvJVlurRnHa/N/JPl7Sda7DrILtCRLVfWxqnp912FgG9RO9JLaaXvUTmOjdhoPdRO7idqJXlI7bY/aaWzUTuMx8bWTZtbW1CVe00mnU1V1Q5J/k+TvtNb+pOs8k6i19nBr7c8lmU3yoqryGIKrVFWvSPK11trHus6yS/xoa+0vJJlP8rc3H5MBk0jtRO+onbZP7bR9aqexUjexm6id6B210/apnbZP7TRWE187aWZtzWqS51y0PZvkbEdZIJvP2v03Sf5Fa+3Xu84z6Vpr30jy4SQv7zjKJPrRJHdsPnf33Ul+vKr+ebeRJldr7ezm719L8hvZeNwITCK1E72idhovtdO2qJ3GRN3ELqN2olfUTuOldtoWtdOY7IbaSTNraz6a5Naqel5VPSnJq5Oc6DgTe9TmApK/kuRzrbVf7DrPpKqqm6rq6ZsfH0jysiS/322qydNa+19ba7OttVuy8W/j/9Na+286jjWRquppm4vrpqqeluRIkk93mwqumdqJ3lA7jYfaaTzUTuOhbmIXUjvRG2qn8VA7jYfaaTx2S+2kmbUFrbULSX42yd3ZWPTwPa21z3SbajJV1b9M8m+T/GBVrVbVa7vONIF+NMl/m407Ee7f/PVXug41gZ6V5ENVdSobPzgst9Y+0HEm9rZnJPmdqvpkkn+X5Ddba7/VcSa4Jmqn8VA3jY3aaTzUTvSJuoldRe00HmqnsVE7jYfaiT7ZFbVTteYRvAAAAAAAAPSTmVkAAAAAAAD0lmYWAAAAAAAAvaWZBQAAAAAAQG9pZgEAAAAAANBbmlkAAAAAAAD0lmYWAAAAAAAAvaWZBWNWVbdU1acv8fo/qKqXXecsZ6pq5hKv31FVb3qCff9GVf3SNZzzwavdZ3O/n6yqF1zLvtfL5b62AMC1UzupnQCArVM7qZ1gr9rfdQDYK1prP3c176+q/a21C+N63+OynEhy4mr2uQ5+MskHknx2qztcy5+9S5OWFwC6pHZ6QmonAOBRaqcnpHaCCWdmFuyMfVX1f1fVZ6pqqaoOVNWvVtVfS5Kq+otV9dtV9bGquruqnrX5+oer6n+rqt9O8saqemVV/V5VfaKq7qmqZ2y+761V9Y6qWkrya1W1r6r+cVV9qqpOVdXRi7IcraqPb37uhzb3f/Tul8ud44lU1TOq6jeq6pObv/7y4z7/kqr6wEXbv1RVf2Pz4/+9qj67mfUfb+57R5J/VFX3V9XzN3/91uYYfeSi7L9aVb9YVR9K8g8vk+2tVfWuzfH8QlUd23z9MXe4VNXfraq3XjT2/6Sq7q2qz1XVf1pVv15Vp6vq7Rcdfn9VDTez/+uqeurVfE23MrYAsAepndROaicA2Dq1k9pJ7cSeY2YW7Ixbk7ymtfY/VtV7kvyXj3yiqqaT3JnkVa21B6rqv0ry80n+h823PL219mOb7/0PkvxnrbVWVa9L8veS/M+b7/uLSf7z1tr5qvqZJM9L8udbaxeq6saLsqy11v5CVf2tJH83yesel/V3rnCOK1lI8tuttZ+qqn1JbtjKwGxm+6kkP7R5zqe31r5RVSeSfKC19q833/fBJH+ztXa6qv5Skv8ryY9vHua2JC9rrT18hVP9UJKXJvm+JJ+vqn+6hXjfaa3dXlVvTPK+bIzx15P8+6r6J5vv+cEkr22t/W5VvSvJ36qq/zNb/JoCAJekdroMtRMAcAlqp8tQO8HupZkFO+MPW2v3b378sSS3XPS5H0zyw0mWqypJ9iX5ykWf/1cXfTyb5F9t3m3xpCR/eNHnTrTWzm9+/LIk/+yRqcStta9f9L5fvyjHX71E1iud40p+PMl/t3m+h5N8c4v7/UmSbyd5Z1X9ZjameD9GVd2Q5C8nee/mGCXJky96y3ufoKBIkt9srf1Zkj+rqq8l2cqdP49Mgf9Uks+01r6ymecLSZ6T5BtJvtxa+93N9/3zJMeS/Fa2/jUFAL6X2uny1E4AwOOpnS5P7QS7lGYW7Iw/u+jjh5McuGi7svEf1o9cZt8/vejjO5P8YmvtRFW9JMlbL/O+StKeIMvDufTf+SudYzsu5LGPMn1KkmzewfOiJD+R5NVJfjbfvfPlEVNJvtFa+3OXOfafXub1iz3+a7D/cpkusc/64/Zfz3fH7vHj3HJ1X1MA4HupndROV5sXAPYytZPa6WrzwsSzZhZcf59PclNV/UiyMf27qv7jy7z3B5L80ebHgysccynJ36yq/ZvHvPEK773WczzeB5P8zOb59lXV9z/u819M8oKqenJV/UA2iohH7n75gdbaySR/J8kjhcO3sjE1O621P0nyh1X105v7VFX9J1eR7XK+muQ/rKqDVfXkJK+4hmM895GvXZLXZGO6/NV8TQGAq6N2UjsBAFundlI7wa6kmQXXWWvtO0n+WpJ/WFWfTHJ/NqY2X8pbszHl+SNJ1q5w2Hcm+VKSU5vH/OtXEWmr53i8NyZ5aVV9KhtTyR/zn2hr7ctJ3pPkVJJ/keQTm5/6viQfqKpTSX47yf+0+fq7k/wvtbEg6POT/NdJXrv55/lMklddRbZLaq2NkvyDJL+XjWnmv38Nh/lcksFm/huT/NOr/JoCAFdB7aR2AgC2Tu2kdoLdqlq73AxRAAAAAAAA6JaZWQAAAAAAAPTWpRblA3hUVf39JD/9uJff21r7+S7yXKyq/vtsTDu/2O+21v52F3kAANROAABbp3YCtspjBgEAAAAAAOgtjxkEAAAAAACgtzSzAAAAAAAA6C3NLAAAAAAAAHpLMwsAAAAAAIDe+v8B/Jx4+0vpu4IAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 2160x720 with 3 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "col=['gdpp','child_mort','income']\n",
    "plt.figure(figsize=(30,10))\n",
    "j=1\n",
    "for i in col:\n",
    "    plt.subplot(1,3,j)\n",
    "    sns.boxplot(y=df_scaled[i],x=df_scaled['hierarchial_cluster_number'])\n",
    "    j=j+1"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<p style=\"font-family: Arial; font-size:1.0em;color:blue;\"> From the above boxplots, we can say that for the clusters 0 and 4, the child mortality is high, gdp is low and the net income is low which is the ideal conidtion for the countries that are in dire need for devolopment"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "49"
      ]
     },
     "execution_count": 61,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(list(df['country'][(df['hierarchial_cluster_number']==0)])) #number of countries where the hierarchial cluster number is 0"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1"
      ]
     },
     "execution_count": 62,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(list(df['country'][(df['hierarchial_cluster_number']==4)])) #number of countries where the hierarchial cluster number is 4"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<p style=\"font-family: Arial; font-size:1.0em;color:blue;\"> We observed that the nummber of countries in cluster 0 are 49 and there is only one country in cluster 4.<br>So, apart from the country in cluster 4,we will  concentrate on the countries in cluster 0 to target the ccountries  which are in need of help!"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### FINAL ANALYSIS"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "47"
      ]
     },
     "execution_count": 63,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(list(df['country'][(df['kmeans_cluster_number']==1)])) # number of countries in cluster 1 based on k means clustering"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1"
      ]
     },
     "execution_count": 64,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(list(df['country'][(df['kmeans_cluster_number']==4)])) # number of countries in cluster 4 based on k means clustering"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_kmeans_cluster1=df[(df['kmeans_cluster_number']==1)]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_hierarchial_cluster0=df[(df['hierarchial_cluster_number']==0)]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "49"
      ]
     },
     "execution_count": 67,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(list(df['country'][(df['hierarchial_cluster_number']==0)])) # number of countries in cluster 0 based on hierarchial cluster number"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1"
      ]
     },
     "execution_count": 68,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(list(df['country'][(df['hierarchial_cluster_number']==4)])) # number of countries in cluster 4 based on hierarchial cluster number"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "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>kmeans_cluster_number</th>\n",
       "      <th>hierarchial_cluster_number</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>0</td>\n",
       "      <td>Afghanistan</td>\n",
       "      <td>90.2</td>\n",
       "      <td>10.00</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</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>3</td>\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</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>17</td>\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</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>21</td>\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>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>25</td>\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</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>26</td>\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</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>28</td>\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</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>31</td>\n",
       "      <td>Central African Republic</td>\n",
       "      <td>149.0</td>\n",
       "      <td>11.80</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",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>32</td>\n",
       "      <td>Chad</td>\n",
       "      <td>150.0</td>\n",
       "      <td>36.80</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",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>36</td>\n",
       "      <td>Comoros</td>\n",
       "      <td>88.2</td>\n",
       "      <td>16.50</td>\n",
       "      <td>4.51</td>\n",
       "      <td>51.7</td>\n",
       "      <td>1410</td>\n",
       "      <td>3.870</td>\n",
       "      <td>65.9</td>\n",
       "      <td>4.75</td>\n",
       "      <td>769</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>37</td>\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>20.800</td>\n",
       "      <td>57.5</td>\n",
       "      <td>6.54</td>\n",
       "      <td>334</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>38</td>\n",
       "      <td>Congo, Rep.</td>\n",
       "      <td>63.9</td>\n",
       "      <td>85.10</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",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>40</td>\n",
       "      <td>Cote d'Ivoire</td>\n",
       "      <td>111.0</td>\n",
       "      <td>50.60</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",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>49</td>\n",
       "      <td>Equatorial Guinea</td>\n",
       "      <td>111.0</td>\n",
       "      <td>85.80</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",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>50</td>\n",
       "      <td>Eritrea</td>\n",
       "      <td>55.2</td>\n",
       "      <td>4.79</td>\n",
       "      <td>2.66</td>\n",
       "      <td>23.3</td>\n",
       "      <td>1420</td>\n",
       "      <td>11.600</td>\n",
       "      <td>61.7</td>\n",
       "      <td>4.61</td>\n",
       "      <td>482</td>\n",
       "      <td>1</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    10.00    7.58     44.9    1610   \n",
       "3                     Angola       119.0    62.30    2.85     42.9    5900   \n",
       "17                     Benin       111.0    23.80    4.10     37.2    1820   \n",
       "21                  Botswana        52.5    43.60    8.30     51.3   13300   \n",
       "25              Burkina Faso       116.0    19.20    6.74     29.6    1430   \n",
       "26                   Burundi        93.6     8.92   11.60     39.2     764   \n",
       "28                  Cameroon       108.0    22.20    5.13     27.0    2660   \n",
       "31  Central African Republic       149.0    11.80    3.98     26.5     888   \n",
       "32                      Chad       150.0    36.80    4.53     43.5    1930   \n",
       "36                   Comoros        88.2    16.50    4.51     51.7    1410   \n",
       "37          Congo, Dem. Rep.       116.0    41.10    7.91     49.6     609   \n",
       "38               Congo, Rep.        63.9    85.10    2.46     54.7    5190   \n",
       "40             Cote d'Ivoire       111.0    50.60    5.30     43.3    2690   \n",
       "49         Equatorial Guinea       111.0    85.80    4.48     58.9   33700   \n",
       "50                   Eritrea        55.2     4.79    2.66     23.3    1420   \n",
       "\n",
       "    inflation  life_expec  total_fer   gdpp  kmeans_cluster_number  \\\n",
       "0       9.440        56.2       5.82    553                      1   \n",
       "3      22.400        60.1       6.16   3530                      1   \n",
       "17      0.885        61.8       5.36    758                      1   \n",
       "21      8.920        57.1       2.88   6350                      1   \n",
       "25      6.810        57.9       5.87    575                      1   \n",
       "26     12.300        57.7       6.26    231                      1   \n",
       "28      1.910        57.3       5.11   1310                      1   \n",
       "31      2.010        47.5       5.21    446                      1   \n",
       "32      6.390        56.5       6.59    897                      1   \n",
       "36      3.870        65.9       4.75    769                      1   \n",
       "37     20.800        57.5       6.54    334                      1   \n",
       "38     20.700        60.4       4.95   2740                      1   \n",
       "40      5.390        56.3       5.27   1220                      1   \n",
       "49     24.900        60.9       5.21  17100                      1   \n",
       "50     11.600        61.7       4.61    482                      1   \n",
       "\n",
       "    hierarchial_cluster_number  \n",
       "0                            0  \n",
       "3                            0  \n",
       "17                           0  \n",
       "21                           0  \n",
       "25                           0  \n",
       "26                           0  \n",
       "28                           0  \n",
       "31                           0  \n",
       "32                           0  \n",
       "36                           0  \n",
       "37                           0  \n",
       "38                           0  \n",
       "40                           0  \n",
       "49                           0  \n",
       "50                           1  "
      ]
     },
     "execution_count": 69,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_kmeans_cluster1.head(15)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 70,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>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>kmeans_cluster_number</th>\n",
       "      <th>hierarchial_cluster_number</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>count</td>\n",
       "      <td>47.000000</td>\n",
       "      <td>47.000000</td>\n",
       "      <td>47.000000</td>\n",
       "      <td>47.000000</td>\n",
       "      <td>47.000000</td>\n",
       "      <td>47.000000</td>\n",
       "      <td>47.000000</td>\n",
       "      <td>47.000000</td>\n",
       "      <td>47.000000</td>\n",
       "      <td>47.0</td>\n",
       "      <td>47.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>mean</td>\n",
       "      <td>91.565957</td>\n",
       "      <td>28.615298</td>\n",
       "      <td>6.322553</td>\n",
       "      <td>41.954594</td>\n",
       "      <td>3911.978723</td>\n",
       "      <td>9.956702</td>\n",
       "      <td>59.321277</td>\n",
       "      <td>4.935106</td>\n",
       "      <td>1893.829787</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.063830</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>std</td>\n",
       "      <td>33.163641</td>\n",
       "      <td>18.642083</td>\n",
       "      <td>2.732857</td>\n",
       "      <td>18.428966</td>\n",
       "      <td>5638.991578</td>\n",
       "      <td>7.268299</td>\n",
       "      <td>6.536285</td>\n",
       "      <td>1.100332</td>\n",
       "      <td>2958.561345</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.247092</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>min</td>\n",
       "      <td>36.900000</td>\n",
       "      <td>0.109000</td>\n",
       "      <td>1.970000</td>\n",
       "      <td>0.065900</td>\n",
       "      <td>609.000000</td>\n",
       "      <td>0.885000</td>\n",
       "      <td>32.100000</td>\n",
       "      <td>2.410000</td>\n",
       "      <td>231.000000</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>25%</td>\n",
       "      <td>63.800000</td>\n",
       "      <td>16.650000</td>\n",
       "      <td>4.495000</td>\n",
       "      <td>29.800000</td>\n",
       "      <td>1390.000000</td>\n",
       "      <td>4.010000</td>\n",
       "      <td>56.650000</td>\n",
       "      <td>4.440000</td>\n",
       "      <td>550.000000</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>50%</td>\n",
       "      <td>89.300000</td>\n",
       "      <td>23.800000</td>\n",
       "      <td>5.660000</td>\n",
       "      <td>40.300000</td>\n",
       "      <td>1870.000000</td>\n",
       "      <td>8.790000</td>\n",
       "      <td>59.500000</td>\n",
       "      <td>5.050000</td>\n",
       "      <td>897.000000</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>75%</td>\n",
       "      <td>111.000000</td>\n",
       "      <td>38.200000</td>\n",
       "      <td>8.105000</td>\n",
       "      <td>49.450000</td>\n",
       "      <td>3545.000000</td>\n",
       "      <td>16.350000</td>\n",
       "      <td>63.350000</td>\n",
       "      <td>5.495000</td>\n",
       "      <td>1385.000000</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>max</td>\n",
       "      <td>208.000000</td>\n",
       "      <td>85.800000</td>\n",
       "      <td>13.100000</td>\n",
       "      <td>101.000000</td>\n",
       "      <td>33700.000000</td>\n",
       "      <td>26.500000</td>\n",
       "      <td>71.100000</td>\n",
       "      <td>7.490000</td>\n",
       "      <td>17100.000000</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       child_mort    exports     health     imports        income  inflation  \\\n",
       "count   47.000000  47.000000  47.000000   47.000000     47.000000  47.000000   \n",
       "mean    91.565957  28.615298   6.322553   41.954594   3911.978723   9.956702   \n",
       "std     33.163641  18.642083   2.732857   18.428966   5638.991578   7.268299   \n",
       "min     36.900000   0.109000   1.970000    0.065900    609.000000   0.885000   \n",
       "25%     63.800000  16.650000   4.495000   29.800000   1390.000000   4.010000   \n",
       "50%     89.300000  23.800000   5.660000   40.300000   1870.000000   8.790000   \n",
       "75%    111.000000  38.200000   8.105000   49.450000   3545.000000  16.350000   \n",
       "max    208.000000  85.800000  13.100000  101.000000  33700.000000  26.500000   \n",
       "\n",
       "       life_expec  total_fer          gdpp  kmeans_cluster_number  \\\n",
       "count   47.000000  47.000000     47.000000                   47.0   \n",
       "mean    59.321277   4.935106   1893.829787                    1.0   \n",
       "std      6.536285   1.100332   2958.561345                    0.0   \n",
       "min     32.100000   2.410000    231.000000                    1.0   \n",
       "25%     56.650000   4.440000    550.000000                    1.0   \n",
       "50%     59.500000   5.050000    897.000000                    1.0   \n",
       "75%     63.350000   5.495000   1385.000000                    1.0   \n",
       "max     71.100000   7.490000  17100.000000                    1.0   \n",
       "\n",
       "       hierarchial_cluster_number  \n",
       "count                   47.000000  \n",
       "mean                     0.063830  \n",
       "std                      0.247092  \n",
       "min                      0.000000  \n",
       "25%                      0.000000  \n",
       "50%                      0.000000  \n",
       "75%                      0.000000  \n",
       "max                      1.000000  "
      ]
     },
     "execution_count": 70,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_kmeans_cluster1.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 71,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>gdpp</th>\n",
       "      <th>income</th>\n",
       "      <th>child_mort</th>\n",
       "      <th>country</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>0</td>\n",
       "      <td>553</td>\n",
       "      <td>1610</td>\n",
       "      <td>90.2</td>\n",
       "      <td>Afghanistan</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>3</td>\n",
       "      <td>3530</td>\n",
       "      <td>5900</td>\n",
       "      <td>119.0</td>\n",
       "      <td>Angola</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>17</td>\n",
       "      <td>758</td>\n",
       "      <td>1820</td>\n",
       "      <td>111.0</td>\n",
       "      <td>Benin</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>21</td>\n",
       "      <td>6350</td>\n",
       "      <td>13300</td>\n",
       "      <td>52.5</td>\n",
       "      <td>Botswana</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>25</td>\n",
       "      <td>575</td>\n",
       "      <td>1430</td>\n",
       "      <td>116.0</td>\n",
       "      <td>Burkina Faso</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>26</td>\n",
       "      <td>231</td>\n",
       "      <td>764</td>\n",
       "      <td>93.6</td>\n",
       "      <td>Burundi</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>28</td>\n",
       "      <td>1310</td>\n",
       "      <td>2660</td>\n",
       "      <td>108.0</td>\n",
       "      <td>Cameroon</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>31</td>\n",
       "      <td>446</td>\n",
       "      <td>888</td>\n",
       "      <td>149.0</td>\n",
       "      <td>Central African Republic</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>32</td>\n",
       "      <td>897</td>\n",
       "      <td>1930</td>\n",
       "      <td>150.0</td>\n",
       "      <td>Chad</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>36</td>\n",
       "      <td>769</td>\n",
       "      <td>1410</td>\n",
       "      <td>88.2</td>\n",
       "      <td>Comoros</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>37</td>\n",
       "      <td>334</td>\n",
       "      <td>609</td>\n",
       "      <td>116.0</td>\n",
       "      <td>Congo, Dem. Rep.</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>38</td>\n",
       "      <td>2740</td>\n",
       "      <td>5190</td>\n",
       "      <td>63.9</td>\n",
       "      <td>Congo, Rep.</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>40</td>\n",
       "      <td>1220</td>\n",
       "      <td>2690</td>\n",
       "      <td>111.0</td>\n",
       "      <td>Cote d'Ivoire</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>49</td>\n",
       "      <td>17100</td>\n",
       "      <td>33700</td>\n",
       "      <td>111.0</td>\n",
       "      <td>Equatorial Guinea</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>50</td>\n",
       "      <td>482</td>\n",
       "      <td>1420</td>\n",
       "      <td>55.2</td>\n",
       "      <td>Eritrea</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>55</td>\n",
       "      <td>8750</td>\n",
       "      <td>15400</td>\n",
       "      <td>63.7</td>\n",
       "      <td>Gabon</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>56</td>\n",
       "      <td>562</td>\n",
       "      <td>1660</td>\n",
       "      <td>80.3</td>\n",
       "      <td>Gambia</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>59</td>\n",
       "      <td>1310</td>\n",
       "      <td>3060</td>\n",
       "      <td>74.7</td>\n",
       "      <td>Ghana</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>63</td>\n",
       "      <td>648</td>\n",
       "      <td>1190</td>\n",
       "      <td>109.0</td>\n",
       "      <td>Guinea</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>64</td>\n",
       "      <td>547</td>\n",
       "      <td>1390</td>\n",
       "      <td>114.0</td>\n",
       "      <td>Guinea-Bissau</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>66</td>\n",
       "      <td>662</td>\n",
       "      <td>1500</td>\n",
       "      <td>208.0</td>\n",
       "      <td>Haiti</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>72</td>\n",
       "      <td>4500</td>\n",
       "      <td>12700</td>\n",
       "      <td>36.9</td>\n",
       "      <td>Iraq</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>80</td>\n",
       "      <td>967</td>\n",
       "      <td>2480</td>\n",
       "      <td>62.2</td>\n",
       "      <td>Kenya</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>81</td>\n",
       "      <td>1490</td>\n",
       "      <td>1730</td>\n",
       "      <td>62.7</td>\n",
       "      <td>Kiribati</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>84</td>\n",
       "      <td>1140</td>\n",
       "      <td>3980</td>\n",
       "      <td>78.9</td>\n",
       "      <td>Lao</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>87</td>\n",
       "      <td>1170</td>\n",
       "      <td>2380</td>\n",
       "      <td>99.7</td>\n",
       "      <td>Lesotho</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>88</td>\n",
       "      <td>327</td>\n",
       "      <td>700</td>\n",
       "      <td>89.3</td>\n",
       "      <td>Liberia</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>93</td>\n",
       "      <td>413</td>\n",
       "      <td>1390</td>\n",
       "      <td>62.2</td>\n",
       "      <td>Madagascar</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>94</td>\n",
       "      <td>459</td>\n",
       "      <td>1030</td>\n",
       "      <td>90.5</td>\n",
       "      <td>Malawi</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>97</td>\n",
       "      <td>708</td>\n",
       "      <td>1870</td>\n",
       "      <td>137.0</td>\n",
       "      <td>Mali</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>99</td>\n",
       "      <td>1200</td>\n",
       "      <td>3320</td>\n",
       "      <td>97.4</td>\n",
       "      <td>Mauritania</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>106</td>\n",
       "      <td>419</td>\n",
       "      <td>918</td>\n",
       "      <td>101.0</td>\n",
       "      <td>Mozambique</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>107</td>\n",
       "      <td>988</td>\n",
       "      <td>3720</td>\n",
       "      <td>64.4</td>\n",
       "      <td>Myanmar</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>108</td>\n",
       "      <td>5190</td>\n",
       "      <td>8460</td>\n",
       "      <td>56.0</td>\n",
       "      <td>Namibia</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>112</td>\n",
       "      <td>348</td>\n",
       "      <td>814</td>\n",
       "      <td>123.0</td>\n",
       "      <td>Niger</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>116</td>\n",
       "      <td>1040</td>\n",
       "      <td>4280</td>\n",
       "      <td>92.1</td>\n",
       "      <td>Pakistan</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>126</td>\n",
       "      <td>563</td>\n",
       "      <td>1350</td>\n",
       "      <td>63.6</td>\n",
       "      <td>Rwanda</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>129</td>\n",
       "      <td>1000</td>\n",
       "      <td>2180</td>\n",
       "      <td>66.8</td>\n",
       "      <td>Senegal</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>132</td>\n",
       "      <td>399</td>\n",
       "      <td>1220</td>\n",
       "      <td>160.0</td>\n",
       "      <td>Sierra Leone</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>137</td>\n",
       "      <td>7280</td>\n",
       "      <td>12000</td>\n",
       "      <td>53.7</td>\n",
       "      <td>South Africa</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>142</td>\n",
       "      <td>1480</td>\n",
       "      <td>3370</td>\n",
       "      <td>76.7</td>\n",
       "      <td>Sudan</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>147</td>\n",
       "      <td>702</td>\n",
       "      <td>2090</td>\n",
       "      <td>71.9</td>\n",
       "      <td>Tanzania</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>149</td>\n",
       "      <td>3600</td>\n",
       "      <td>1850</td>\n",
       "      <td>62.6</td>\n",
       "      <td>Timor-Leste</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>150</td>\n",
       "      <td>488</td>\n",
       "      <td>1210</td>\n",
       "      <td>90.3</td>\n",
       "      <td>Togo</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>155</td>\n",
       "      <td>595</td>\n",
       "      <td>1540</td>\n",
       "      <td>81.0</td>\n",
       "      <td>Uganda</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>165</td>\n",
       "      <td>1310</td>\n",
       "      <td>4480</td>\n",
       "      <td>56.3</td>\n",
       "      <td>Yemen</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>166</td>\n",
       "      <td>1460</td>\n",
       "      <td>3280</td>\n",
       "      <td>83.1</td>\n",
       "      <td>Zambia</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      gdpp  income  child_mort                   country\n",
       "0      553    1610        90.2               Afghanistan\n",
       "3     3530    5900       119.0                    Angola\n",
       "17     758    1820       111.0                     Benin\n",
       "21    6350   13300        52.5                  Botswana\n",
       "25     575    1430       116.0              Burkina Faso\n",
       "26     231     764        93.6                   Burundi\n",
       "28    1310    2660       108.0                  Cameroon\n",
       "31     446     888       149.0  Central African Republic\n",
       "32     897    1930       150.0                      Chad\n",
       "36     769    1410        88.2                   Comoros\n",
       "37     334     609       116.0          Congo, Dem. Rep.\n",
       "38    2740    5190        63.9               Congo, Rep.\n",
       "40    1220    2690       111.0             Cote d'Ivoire\n",
       "49   17100   33700       111.0         Equatorial Guinea\n",
       "50     482    1420        55.2                   Eritrea\n",
       "55    8750   15400        63.7                     Gabon\n",
       "56     562    1660        80.3                    Gambia\n",
       "59    1310    3060        74.7                     Ghana\n",
       "63     648    1190       109.0                    Guinea\n",
       "64     547    1390       114.0             Guinea-Bissau\n",
       "66     662    1500       208.0                     Haiti\n",
       "72    4500   12700        36.9                      Iraq\n",
       "80     967    2480        62.2                     Kenya\n",
       "81    1490    1730        62.7                  Kiribati\n",
       "84    1140    3980        78.9                       Lao\n",
       "87    1170    2380        99.7                   Lesotho\n",
       "88     327     700        89.3                   Liberia\n",
       "93     413    1390        62.2                Madagascar\n",
       "94     459    1030        90.5                    Malawi\n",
       "97     708    1870       137.0                      Mali\n",
       "99    1200    3320        97.4                Mauritania\n",
       "106    419     918       101.0                Mozambique\n",
       "107    988    3720        64.4                   Myanmar\n",
       "108   5190    8460        56.0                   Namibia\n",
       "112    348     814       123.0                     Niger\n",
       "116   1040    4280        92.1                  Pakistan\n",
       "126    563    1350        63.6                    Rwanda\n",
       "129   1000    2180        66.8                   Senegal\n",
       "132    399    1220       160.0              Sierra Leone\n",
       "137   7280   12000        53.7              South Africa\n",
       "142   1480    3370        76.7                     Sudan\n",
       "147    702    2090        71.9                  Tanzania\n",
       "149   3600    1850        62.6               Timor-Leste\n",
       "150    488    1210        90.3                      Togo\n",
       "155    595    1540        81.0                    Uganda\n",
       "165   1310    4480        56.3                     Yemen\n",
       "166   1460    3280        83.1                    Zambia"
      ]
     },
     "execution_count": 71,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df[['gdpp','income','child_mort','country']][df['kmeans_cluster_number']==1] #list of countries with kmeans cluster 1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 72,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>gdpp</th>\n",
       "      <th>income</th>\n",
       "      <th>child_mort</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>0</td>\n",
       "      <td>-0.679180</td>\n",
       "      <td>-0.808245</td>\n",
       "      <td>1.291532</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>1</td>\n",
       "      <td>-0.485623</td>\n",
       "      <td>-0.375369</td>\n",
       "      <td>-0.538949</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2</td>\n",
       "      <td>-0.465376</td>\n",
       "      <td>-0.220844</td>\n",
       "      <td>-0.272833</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>3</td>\n",
       "      <td>-0.516268</td>\n",
       "      <td>-0.585043</td>\n",
       "      <td>2.007808</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>4</td>\n",
       "      <td>-0.041817</td>\n",
       "      <td>0.101732</td>\n",
       "      <td>-0.695634</td>\n",
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       "    <tr>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>162</td>\n",
       "      <td>-0.546913</td>\n",
       "      <td>-0.738527</td>\n",
       "      <td>-0.225578</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>163</td>\n",
       "      <td>0.029323</td>\n",
       "      <td>-0.033542</td>\n",
       "      <td>-0.526514</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>164</td>\n",
       "      <td>-0.637754</td>\n",
       "      <td>-0.658404</td>\n",
       "      <td>-0.372315</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>165</td>\n",
       "      <td>-0.637754</td>\n",
       "      <td>-0.658924</td>\n",
       "      <td>0.448417</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>166</td>\n",
       "      <td>-0.629546</td>\n",
       "      <td>-0.721358</td>\n",
       "      <td>1.114951</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>167 rows × 3 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "         gdpp    income  child_mort\n",
       "0   -0.679180 -0.808245    1.291532\n",
       "1   -0.485623 -0.375369   -0.538949\n",
       "2   -0.465376 -0.220844   -0.272833\n",
       "3   -0.516268 -0.585043    2.007808\n",
       "4   -0.041817  0.101732   -0.695634\n",
       "..        ...       ...         ...\n",
       "162 -0.546913 -0.738527   -0.225578\n",
       "163  0.029323 -0.033542   -0.526514\n",
       "164 -0.637754 -0.658404   -0.372315\n",
       "165 -0.637754 -0.658924    0.448417\n",
       "166 -0.629546 -0.721358    1.114951\n",
       "\n",
       "[167 rows x 3 columns]"
      ]
     },
     "execution_count": 72,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_scaled[['gdpp','income','child_mort']]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<p style=\"font-family: Arial; font-size:1.0em;color:blue;\"> Now, we concentrate on the countries where the  GDP is low, income is low and the child mortality is high. So, we find the countries which are GDP is less than the 30th percentile,  Income is less than the 30th percentile  and the child mortality Rate is greater than the 75th percentile."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 73,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>country</th>\n",
       "      <th>child_mort</th>\n",
       "      <th>exports</th>\n",
       "      <th>health</th>\n",
       "      <th>imports</th>\n",
       "      <th>income</th>\n",
       "      <th>inflation</th>\n",
       "      <th>life_expec</th>\n",
       "      <th>total_fer</th>\n",
       "      <th>gdpp</th>\n",
       "      <th>kmeans_cluster_number</th>\n",
       "      <th>hierarchial_cluster_number</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>31</td>\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",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>37</td>\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>20.80</td>\n",
       "      <td>57.5</td>\n",
       "      <td>6.54</td>\n",
       "      <td>334</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>64</td>\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>2.97</td>\n",
       "      <td>55.6</td>\n",
       "      <td>5.05</td>\n",
       "      <td>547</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>112</td>\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>2.55</td>\n",
       "      <td>58.8</td>\n",
       "      <td>7.49</td>\n",
       "      <td>348</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>132</td>\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",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                      country  child_mort  exports  health  imports  income  \\\n",
       "31   Central African Republic       149.0     11.8    3.98     26.5     888   \n",
       "37           Congo, Dem. Rep.       116.0     41.1    7.91     49.6     609   \n",
       "64              Guinea-Bissau       114.0     14.9    8.50     35.2    1390   \n",
       "112                     Niger       123.0     22.2    5.16     49.1     814   \n",
       "132              Sierra Leone       160.0     16.8   13.10     34.5    1220   \n",
       "\n",
       "     inflation  life_expec  total_fer  gdpp  kmeans_cluster_number  \\\n",
       "31        2.01        47.5       5.21   446                      1   \n",
       "37       20.80        57.5       6.54   334                      1   \n",
       "64        2.97        55.6       5.05   547                      1   \n",
       "112       2.55        58.8       7.49   348                      1   \n",
       "132      17.20        55.0       5.20   399                      1   \n",
       "\n",
       "     hierarchial_cluster_number  \n",
       "31                            0  \n",
       "37                            0  \n",
       "64                            0  \n",
       "112                           0  \n",
       "132                           0  "
      ]
     },
     "execution_count": 73,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_kmeans_cluster1[(df_kmeans_cluster1['child_mort']>df_kmeans_cluster1['child_mort'].quantile(0.75))&(df_kmeans_cluster1['income']<df_kmeans_cluster1['income'].quantile(0.30))&(df_kmeans_cluster1['gdpp']<df_kmeans_cluster1['gdpp'].quantile(0.30))]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<p style=\"font-family: Arial; font-size:1.0em;color:blue;\"> By performing the above operation, we got the Central African Republic, Congo, niger and Sierra Leone countries.<br>Apart from these 5 countries, from Cluster 4, we also got Nigeria as the country which is in dire need of help. "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<p style=\"font-family: Arial; font-size:1.0em;color:blue;\">As a Data Analyst, I suggest the HELP Organization to provide the necessary help for the below countries:<br>1.Central African Republic<br> 2. Congo, Dem. Rep.<br>3. Guinea-Bissau<br>4. Niger\t<br>5. Sierra Leone\t<br>6. Nigeria"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 74,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>country</th>\n",
       "      <th>child_mort</th>\n",
       "      <th>exports</th>\n",
       "      <th>health</th>\n",
       "      <th>imports</th>\n",
       "      <th>income</th>\n",
       "      <th>inflation</th>\n",
       "      <th>life_expec</th>\n",
       "      <th>total_fer</th>\n",
       "      <th>gdpp</th>\n",
       "      <th>kmeans_cluster_number</th>\n",
       "      <th>hierarchial_cluster_number</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>0</td>\n",
       "      <td>Afghanistan</td>\n",
       "      <td>90.2</td>\n",
       "      <td>10.0</td>\n",
       "      <td>7.58</td>\n",
       "      <td>44.9</td>\n",
       "      <td>1610</td>\n",
       "      <td>9.440</td>\n",
       "      <td>56.2</td>\n",
       "      <td>5.82</td>\n",
       "      <td>553</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>3</td>\n",
       "      <td>Angola</td>\n",
       "      <td>119.0</td>\n",
       "      <td>62.3</td>\n",
       "      <td>2.85</td>\n",
       "      <td>42.9</td>\n",
       "      <td>5900</td>\n",
       "      <td>22.400</td>\n",
       "      <td>60.1</td>\n",
       "      <td>6.16</td>\n",
       "      <td>3530</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>17</td>\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",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>21</td>\n",
       "      <td>Botswana</td>\n",
       "      <td>52.5</td>\n",
       "      <td>43.6</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>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>25</td>\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",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "         country  child_mort  exports  health  imports  income  inflation  \\\n",
       "0    Afghanistan        90.2     10.0    7.58     44.9    1610      9.440   \n",
       "3         Angola       119.0     62.3    2.85     42.9    5900     22.400   \n",
       "17         Benin       111.0     23.8    4.10     37.2    1820      0.885   \n",
       "21      Botswana        52.5     43.6    8.30     51.3   13300      8.920   \n",
       "25  Burkina Faso       116.0     19.2    6.74     29.6    1430      6.810   \n",
       "\n",
       "    life_expec  total_fer  gdpp  kmeans_cluster_number  \\\n",
       "0         56.2       5.82   553                      1   \n",
       "3         60.1       6.16  3530                      1   \n",
       "17        61.8       5.36   758                      1   \n",
       "21        57.1       2.88  6350                      1   \n",
       "25        57.9       5.87   575                      1   \n",
       "\n",
       "    hierarchial_cluster_number  \n",
       "0                            0  \n",
       "3                            0  \n",
       "17                           0  \n",
       "21                           0  \n",
       "25                           0  "
      ]
     },
     "execution_count": 74,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_hierarchial_cluster0.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 75,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "<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",
       "      <th>kmeans_cluster_number</th>\n",
       "      <th>hierarchial_cluster_number</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>count</td>\n",
       "      <td>49.000000</td>\n",
       "      <td>49.000000</td>\n",
       "      <td>49.000000</td>\n",
       "      <td>49.000000</td>\n",
       "      <td>49.000000</td>\n",
       "      <td>49.000000</td>\n",
       "      <td>49.000000</td>\n",
       "      <td>49.000000</td>\n",
       "      <td>49.000000</td>\n",
       "      <td>49.000000</td>\n",
       "      <td>49.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>mean</td>\n",
       "      <td>87.667347</td>\n",
       "      <td>29.704082</td>\n",
       "      <td>6.737143</td>\n",
       "      <td>45.200000</td>\n",
       "      <td>3834.755102</td>\n",
       "      <td>10.063980</td>\n",
       "      <td>59.757143</td>\n",
       "      <td>4.841633</td>\n",
       "      <td>1905.306122</td>\n",
       "      <td>0.897959</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>std</td>\n",
       "      <td>35.662751</td>\n",
       "      <td>17.873066</td>\n",
       "      <td>2.716925</td>\n",
       "      <td>18.285388</td>\n",
       "      <td>5530.154370</td>\n",
       "      <td>7.275785</td>\n",
       "      <td>6.735076</td>\n",
       "      <td>1.158635</td>\n",
       "      <td>2900.019649</td>\n",
       "      <td>0.305839</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>min</td>\n",
       "      <td>28.100000</td>\n",
       "      <td>2.200000</td>\n",
       "      <td>2.460000</td>\n",
       "      <td>17.200000</td>\n",
       "      <td>609.000000</td>\n",
       "      <td>0.885000</td>\n",
       "      <td>32.100000</td>\n",
       "      <td>2.340000</td>\n",
       "      <td>231.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>25%</td>\n",
       "      <td>62.600000</td>\n",
       "      <td>17.100000</td>\n",
       "      <td>4.930000</td>\n",
       "      <td>33.600000</td>\n",
       "      <td>1390.000000</td>\n",
       "      <td>3.870000</td>\n",
       "      <td>56.800000</td>\n",
       "      <td>4.240000</td>\n",
       "      <td>562.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>50%</td>\n",
       "      <td>83.100000</td>\n",
       "      <td>23.800000</td>\n",
       "      <td>5.890000</td>\n",
       "      <td>42.900000</td>\n",
       "      <td>1930.000000</td>\n",
       "      <td>8.790000</td>\n",
       "      <td>60.100000</td>\n",
       "      <td>5.020000</td>\n",
       "      <td>897.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>75%</td>\n",
       "      <td>111.000000</td>\n",
       "      <td>39.400000</td>\n",
       "      <td>8.410000</td>\n",
       "      <td>51.700000</td>\n",
       "      <td>3340.000000</td>\n",
       "      <td>16.500000</td>\n",
       "      <td>64.000000</td>\n",
       "      <td>5.430000</td>\n",
       "      <td>1460.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>max</td>\n",
       "      <td>208.000000</td>\n",
       "      <td>85.800000</td>\n",
       "      <td>14.200000</td>\n",
       "      <td>101.000000</td>\n",
       "      <td>33700.000000</td>\n",
       "      <td>26.500000</td>\n",
       "      <td>71.100000</td>\n",
       "      <td>7.490000</td>\n",
       "      <td>17100.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
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      ],
      "text/plain": [
       "       child_mort    exports     health     imports        income  inflation  \\\n",
       "count   49.000000  49.000000  49.000000   49.000000     49.000000  49.000000   \n",
       "mean    87.667347  29.704082   6.737143   45.200000   3834.755102  10.063980   \n",
       "std     35.662751  17.873066   2.716925   18.285388   5530.154370   7.275785   \n",
       "min     28.100000   2.200000   2.460000   17.200000    609.000000   0.885000   \n",
       "25%     62.600000  17.100000   4.930000   33.600000   1390.000000   3.870000   \n",
       "50%     83.100000  23.800000   5.890000   42.900000   1930.000000   8.790000   \n",
       "75%    111.000000  39.400000   8.410000   51.700000   3340.000000  16.500000   \n",
       "max    208.000000  85.800000  14.200000  101.000000  33700.000000  26.500000   \n",
       "\n",
       "       life_expec  total_fer          gdpp  kmeans_cluster_number  \\\n",
       "count   49.000000  49.000000     49.000000              49.000000   \n",
       "mean    59.757143   4.841633   1905.306122               0.897959   \n",
       "std      6.735076   1.158635   2900.019649               0.305839   \n",
       "min     32.100000   2.340000    231.000000               0.000000   \n",
       "25%     56.800000   4.240000    562.000000               1.000000   \n",
       "50%     60.100000   5.020000    897.000000               1.000000   \n",
       "75%     64.000000   5.430000   1460.000000               1.000000   \n",
       "max     71.100000   7.490000  17100.000000               1.000000   \n",
       "\n",
       "       hierarchial_cluster_number  \n",
       "count                        49.0  \n",
       "mean                          0.0  \n",
       "std                           0.0  \n",
       "min                           0.0  \n",
       "25%                           0.0  \n",
       "50%                           0.0  \n",
       "75%                           0.0  \n",
       "max                           0.0  "
      ]
     },
     "execution_count": 75,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_hierarchial_cluster0.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 76,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "Int64Index: 49 entries, 0 to 166\n",
      "Data columns (total 12 columns):\n",
      "country                       49 non-null object\n",
      "child_mort                    49 non-null float64\n",
      "exports                       49 non-null float64\n",
      "health                        49 non-null float64\n",
      "imports                       49 non-null float64\n",
      "income                        49 non-null int64\n",
      "inflation                     49 non-null float64\n",
      "life_expec                    49 non-null float64\n",
      "total_fer                     49 non-null float64\n",
      "gdpp                          49 non-null int64\n",
      "kmeans_cluster_number         49 non-null int32\n",
      "hierarchial_cluster_number    49 non-null int32\n",
      "dtypes: float64(7), int32(2), int64(2), object(1)\n",
      "memory usage: 4.6+ KB\n"
     ]
    }
   ],
   "source": [
    "df_hierarchial_cluster0.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 77,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "    <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",
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       "  <tbody>\n",
       "    <tr>\n",
       "      <td>25</td>\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>6.81</td>\n",
       "      <td>57.9</td>\n",
       "      <td>5.87</td>\n",
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       "    <tr>\n",
       "      <td>31</td>\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",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>37</td>\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>20.80</td>\n",
       "      <td>57.5</td>\n",
       "      <td>6.54</td>\n",
       "      <td>334</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>64</td>\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>2.97</td>\n",
       "      <td>55.6</td>\n",
       "      <td>5.05</td>\n",
       "      <td>547</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>112</td>\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>2.55</td>\n",
       "      <td>58.8</td>\n",
       "      <td>7.49</td>\n",
       "      <td>348</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>132</td>\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",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                      country  child_mort  exports  health  imports  income  \\\n",
       "25               Burkina Faso       116.0     19.2    6.74     29.6    1430   \n",
       "31   Central African Republic       149.0     11.8    3.98     26.5     888   \n",
       "37           Congo, Dem. Rep.       116.0     41.1    7.91     49.6     609   \n",
       "64              Guinea-Bissau       114.0     14.9    8.50     35.2    1390   \n",
       "112                     Niger       123.0     22.2    5.16     49.1     814   \n",
       "132              Sierra Leone       160.0     16.8   13.10     34.5    1220   \n",
       "\n",
       "     inflation  life_expec  total_fer  gdpp  kmeans_cluster_number  \\\n",
       "25        6.81        57.9       5.87   575                      1   \n",
       "31        2.01        47.5       5.21   446                      1   \n",
       "37       20.80        57.5       6.54   334                      1   \n",
       "64        2.97        55.6       5.05   547                      1   \n",
       "112       2.55        58.8       7.49   348                      1   \n",
       "132      17.20        55.0       5.20   399                      1   \n",
       "\n",
       "     hierarchial_cluster_number  \n",
       "25                            0  \n",
       "31                            0  \n",
       "37                            0  \n",
       "64                            0  \n",
       "112                           0  \n",
       "132                           0  "
      ]
     },
     "execution_count": 77,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_hierarchial_cluster0[(df_hierarchial_cluster0['child_mort']>df_hierarchial_cluster0['child_mort'].quantile(0.75))&(df_hierarchial_cluster0['income']<df_hierarchial_cluster0['income'].quantile(0.30))&(df_hierarchial_cluster0['gdpp']<df_hierarchial_cluster0['gdpp'].quantile(0.30))]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<p style=\"font-family: Arial; font-size:1.0em;color:blue;\"> By performing the above operation, we got the Central African Republic, Congo, niger, Guinea-Bissau, Burkina Faso and Sierra Leone countries.<br>Apart from these 4 countries, from Cluster 6, we also got Nigeria as the country which is in dire need of help."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<p style=\"font-family: Arial; font-size:1.0em;color:blue;\">As a Data Analyst, I suggest the HELP Organization to provide the necessary help for the below countries:<br>1. Burkina Faso<br>2. Central African Republic<br> 3. Congo, Dem. Rep.<br>4. Guinea-Bissau<br>5. Niger\t<br>6. Sierra Leone\t<br>7. Nigeria"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<p style=\"font-family: Arial; font-size:1.0em;color:blue;\">From the above modelling, we  can consider any one model and provide the solutions. Generally, the results might not be the same for the types of clustering. But as the visualizations are better and the clusters are well seperated in Kmeans clustering rather than the hierarcchial clustering, as a Data Analyst I will suggest the HELP organization with the reulsts I've received in K means Clustering. However countries are also predicted by the Hierarchial clustering. This makes the result even more stronger."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<p style=\"font-family: Arial; font-size:1.0em;color:blue;\">1. Nigeria<br>2. Central African Republic<br> 3. Congo, Dem. Rep.<br>4. Guinea-Bissau<br>5. Niger\t<br>6. Sierra Leone"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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