Final Project/Fajrur Rahman Suprapto - Final Project.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 106,
   "source": [
    "# import library\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "import plotly.express as px\n",
    "import warnings\n",
    "\n",
    "warnings.filterwarnings('ignore')\n",
    "\n",
    "from sklearn.preprocessing import StandardScaler\n",
    "from sklearn.cluster import KMeans\n",
    "from sklearn.metrics import silhouette_score"
   ],
   "outputs": [],
   "metadata": {}
  },
  {
   "cell_type": "markdown",
   "source": [
    "# Reading and Understanding Data"
   ],
   "metadata": {}
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "source": [
    "# read the data\n",
    "df = pd.read_csv('Data_Negara_HELP.csv')\n",
    "df.head()"
   ],
   "outputs": [
    {
     "output_type": "execute_result",
     "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>Negara</th>\n",
       "      <th>Kematian_anak</th>\n",
       "      <th>Ekspor</th>\n",
       "      <th>Kesehatan</th>\n",
       "      <th>Impor</th>\n",
       "      <th>Pendapatan</th>\n",
       "      <th>Inflasi</th>\n",
       "      <th>Harapan_hidup</th>\n",
       "      <th>Jumlah_fertiliti</th>\n",
       "      <th>GDPperkapita</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Afghanistan</td>\n",
       "      <td>90.2</td>\n",
       "      <td>10.0</td>\n",
       "      <td>7.58</td>\n",
       "      <td>44.9</td>\n",
       "      <td>1610</td>\n",
       "      <td>9.44</td>\n",
       "      <td>56.2</td>\n",
       "      <td>5.82</td>\n",
       "      <td>553</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Albania</td>\n",
       "      <td>16.6</td>\n",
       "      <td>28.0</td>\n",
       "      <td>6.55</td>\n",
       "      <td>48.6</td>\n",
       "      <td>9930</td>\n",
       "      <td>4.49</td>\n",
       "      <td>76.3</td>\n",
       "      <td>1.65</td>\n",
       "      <td>4090</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Algeria</td>\n",
       "      <td>27.3</td>\n",
       "      <td>38.4</td>\n",
       "      <td>4.17</td>\n",
       "      <td>31.4</td>\n",
       "      <td>12900</td>\n",
       "      <td>16.10</td>\n",
       "      <td>76.5</td>\n",
       "      <td>2.89</td>\n",
       "      <td>4460</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Angola</td>\n",
       "      <td>119.0</td>\n",
       "      <td>62.3</td>\n",
       "      <td>2.85</td>\n",
       "      <td>42.9</td>\n",
       "      <td>5900</td>\n",
       "      <td>22.40</td>\n",
       "      <td>60.1</td>\n",
       "      <td>6.16</td>\n",
       "      <td>3530</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Antigua and Barbuda</td>\n",
       "      <td>10.3</td>\n",
       "      <td>45.5</td>\n",
       "      <td>6.03</td>\n",
       "      <td>58.9</td>\n",
       "      <td>19100</td>\n",
       "      <td>1.44</td>\n",
       "      <td>76.8</td>\n",
       "      <td>2.13</td>\n",
       "      <td>12200</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                Negara  Kematian_anak  Ekspor  Kesehatan  Impor  Pendapatan  \\\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",
       "   Inflasi  Harapan_hidup  Jumlah_fertiliti  GDPperkapita  \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  "
      ]
     },
     "metadata": {},
     "execution_count": 39
    }
   ],
   "metadata": {}
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "source": [
    "df.info()"
   ],
   "outputs": [
    {
     "output_type": "stream",
     "name": "stdout",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 167 entries, 0 to 166\n",
      "Data columns (total 10 columns):\n",
      " #   Column            Non-Null Count  Dtype  \n",
      "---  ------            --------------  -----  \n",
      " 0   Negara            167 non-null    object \n",
      " 1   Kematian_anak     167 non-null    float64\n",
      " 2   Ekspor            167 non-null    float64\n",
      " 3   Kesehatan         167 non-null    float64\n",
      " 4   Impor             167 non-null    float64\n",
      " 5   Pendapatan        167 non-null    int64  \n",
      " 6   Inflasi           167 non-null    float64\n",
      " 7   Harapan_hidup     167 non-null    float64\n",
      " 8   Jumlah_fertiliti  167 non-null    float64\n",
      " 9   GDPperkapita      167 non-null    int64  \n",
      "dtypes: float64(7), int64(2), object(1)\n",
      "memory usage: 13.2+ KB\n"
     ]
    }
   ],
   "metadata": {}
  },
  {
   "cell_type": "markdown",
   "source": [
    "# Exploratory Data Analysis"
   ],
   "metadata": {}
  },
  {
   "cell_type": "markdown",
   "source": [
    "## Data Cleaning"
   ],
   "metadata": {}
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "source": [
    "df.isnull().sum()"
   ],
   "outputs": [
    {
     "output_type": "execute_result",
     "data": {
      "text/plain": [
       "Negara              0\n",
       "Kematian_anak       0\n",
       "Ekspor              0\n",
       "Kesehatan           0\n",
       "Impor               0\n",
       "Pendapatan          0\n",
       "Inflasi             0\n",
       "Harapan_hidup       0\n",
       "Jumlah_fertiliti    0\n",
       "GDPperkapita        0\n",
       "dtype: int64"
      ]
     },
     "metadata": {},
     "execution_count": 5
    }
   ],
   "metadata": {}
  },
  {
   "cell_type": "markdown",
   "source": [
    "## Univariate Analysis"
   ],
   "metadata": {}
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "source": [
    "plt.figure(figsize=(16, 16))\n",
    "\n",
    "for i in enumerate(df.describe().columns):\n",
    "    plt.subplot(3,3, i[0]+1)\n",
    "    sns.distplot(df[i[1]])\n",
    "plt.show()"
   ],
   "outputs": [
    {
     "output_type": "display_data",
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 1152x1152 with 9 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     }
    }
   ],
   "metadata": {}
  },
  {
   "cell_type": "markdown",
   "source": [
    "## Bivariate Analysis"
   ],
   "metadata": {}
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "source": [
    "kematian = df.sort_values(by = ['Kematian_anak'], ascending = False)\n",
    "sns.barplot(data = kematian[:10], x = 'Kematian_anak', y = 'Negara')\n",
    "plt.title('Tingkat Kematian Anak')\n",
    "plt.xlabel('Jumlah Kematian Anak')\n",
    "plt.ylabel('Negara')\n",
    "plt.show()"
   ],
   "outputs": [
    {
     "output_type": "display_data",
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     }
    }
   ],
   "metadata": {}
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "source": [
    "fertiliti = df.sort_values(by = ['Jumlah_fertiliti'], ascending = False)\n",
    "sns.barplot(data = fertiliti[:10], x = 'Jumlah_fertiliti', y = 'Negara')\n",
    "plt.title('Tingkat Fertiliti')\n",
    "plt.xlabel('Jumlah Fertiliti')\n",
    "plt.ylabel('Negara')\n",
    "plt.show()"
   ],
   "outputs": [
    {
     "output_type": "display_data",
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     }
    }
   ],
   "metadata": {}
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "source": [
    "harapan_hidup = df.sort_values(by = ['Harapan_hidup'], ascending = True)\n",
    "sns.barplot(data = harapan_hidup[:10], x = 'Harapan_hidup', y = 'Negara')\n",
    "plt.title('Tingkat Harapan Hidup')\n",
    "plt.xlabel('Harapan Hidup')\n",
    "plt.ylabel('Negara')\n",
    "plt.show()"
   ],
   "outputs": [
    {
     "output_type": "display_data",
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     }
    }
   ],
   "metadata": {}
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "source": [
    "pendapatan = df.sort_values(by = ['Pendapatan'], ascending = True)\n",
    "sns.barplot(data = pendapatan[:10], x = 'Pendapatan', y = 'Negara')\n",
    "plt.title('Tingkat Pendapatan')\n",
    "plt.xlabel('Jumlah Pendapatan')\n",
    "plt.ylabel('Negara')\n",
    "plt.show()"
   ],
   "outputs": [
    {
     "output_type": "display_data",
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     }
    }
   ],
   "metadata": {}
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "source": [
    "gdp = df.sort_values(by = ['GDPperkapita'], ascending = True)\n",
    "sns.barplot(data = gdp[:10], x = 'GDPperkapita', y = 'Negara')\n",
    "plt.title('Tingkat GDP per Kapita')\n",
    "plt.xlabel('Jumlah GDP')\n",
    "plt.ylabel('Negara')\n",
    "plt.show()"
   ],
   "outputs": [
    {
     "output_type": "display_data",
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     }
    }
   ],
   "metadata": {}
  },
  {
   "cell_type": "markdown",
   "source": [
    "## Multivariate Analysis"
   ],
   "metadata": {}
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "source": [
    "plt.figure(figsize = (16, 12))\n",
    "sns.heatmap(df.corr(), annot = True)\n",
    "plt.show()"
   ],
   "outputs": [
    {
     "output_type": "display_data",
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 1152x864 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     }
    }
   ],
   "metadata": {}
  },
  {
   "cell_type": "markdown",
   "source": [
    "# Outliers Treatment"
   ],
   "metadata": {}
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "source": [
    "plt.figure(figsize = (15, 15))\n",
    "for index, column in enumerate(df.describe().columns):\n",
    "    plt.subplot(3, 3, index + 1)\n",
    "    sns.boxplot(x = column, data = df)\n",
    "plt.show()"
   ],
   "outputs": [
    {
     "output_type": "display_data",
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 1080x1080 with 9 Axes>"
      ]
     },
     "metadata": {}
    }
   ],
   "metadata": {}
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "source": [
    "numeric_columns = ['Kematian_anak', 'Ekspor', 'Kesehatan', 'Impor', 'Pendapatan', 'Inflasi', 'Harapan_hidup', 'Jumlah_fertiliti', 'GDPperkapita']"
   ],
   "outputs": [],
   "metadata": {}
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "source": [
    "Q1 = df[numeric_columns].quantile(0.25)\n",
    "Q3 = df[numeric_columns].quantile(0.75)\n",
    "IQR = Q3 - Q1\n",
    "lower = Q1 - IQR * 1.5\n",
    "upper = Q3 + IQR * 1.5"
   ],
   "outputs": [],
   "metadata": {}
  },
  {
   "cell_type": "code",
   "execution_count": 70,
   "source": [
    "df_non_outlier = df.copy()\n",
    "for x in numeric_columns:\n",
    "    filter_min = df_non_outlier[x] < lower[x]\n",
    "    filter_max = df_non_outlier[x] > upper[x]\n",
    "    df_non_outlier = df_non_outlier[~(filter_min | filter_max)]"
   ],
   "outputs": [],
   "metadata": {}
  },
  {
   "cell_type": "code",
   "execution_count": 71,
   "source": [
    "df_non_outlier"
   ],
   "outputs": [
    {
     "output_type": "execute_result",
     "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>Negara</th>\n",
       "      <th>Kematian_anak</th>\n",
       "      <th>Ekspor</th>\n",
       "      <th>Kesehatan</th>\n",
       "      <th>Impor</th>\n",
       "      <th>Pendapatan</th>\n",
       "      <th>Inflasi</th>\n",
       "      <th>Harapan_hidup</th>\n",
       "      <th>Jumlah_fertiliti</th>\n",
       "      <th>GDPperkapita</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Afghanistan</td>\n",
       "      <td>90.2</td>\n",
       "      <td>10.0</td>\n",
       "      <td>7.58</td>\n",
       "      <td>44.9</td>\n",
       "      <td>1610</td>\n",
       "      <td>9.44</td>\n",
       "      <td>56.2</td>\n",
       "      <td>5.82</td>\n",
       "      <td>553</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Albania</td>\n",
       "      <td>16.6</td>\n",
       "      <td>28.0</td>\n",
       "      <td>6.55</td>\n",
       "      <td>48.6</td>\n",
       "      <td>9930</td>\n",
       "      <td>4.49</td>\n",
       "      <td>76.3</td>\n",
       "      <td>1.65</td>\n",
       "      <td>4090</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Algeria</td>\n",
       "      <td>27.3</td>\n",
       "      <td>38.4</td>\n",
       "      <td>4.17</td>\n",
       "      <td>31.4</td>\n",
       "      <td>12900</td>\n",
       "      <td>16.10</td>\n",
       "      <td>76.5</td>\n",
       "      <td>2.89</td>\n",
       "      <td>4460</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Angola</td>\n",
       "      <td>119.0</td>\n",
       "      <td>62.3</td>\n",
       "      <td>2.85</td>\n",
       "      <td>42.9</td>\n",
       "      <td>5900</td>\n",
       "      <td>22.40</td>\n",
       "      <td>60.1</td>\n",
       "      <td>6.16</td>\n",
       "      <td>3530</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Antigua and Barbuda</td>\n",
       "      <td>10.3</td>\n",
       "      <td>45.5</td>\n",
       "      <td>6.03</td>\n",
       "      <td>58.9</td>\n",
       "      <td>19100</td>\n",
       "      <td>1.44</td>\n",
       "      <td>76.8</td>\n",
       "      <td>2.13</td>\n",
       "      <td>12200</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>161</th>\n",
       "      <td>Uzbekistan</td>\n",
       "      <td>36.3</td>\n",
       "      <td>31.7</td>\n",
       "      <td>5.81</td>\n",
       "      <td>28.5</td>\n",
       "      <td>4240</td>\n",
       "      <td>16.50</td>\n",
       "      <td>68.8</td>\n",
       "      <td>2.34</td>\n",
       "      <td>1380</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>162</th>\n",
       "      <td>Vanuatu</td>\n",
       "      <td>29.2</td>\n",
       "      <td>46.6</td>\n",
       "      <td>5.25</td>\n",
       "      <td>52.7</td>\n",
       "      <td>2950</td>\n",
       "      <td>2.62</td>\n",
       "      <td>63.0</td>\n",
       "      <td>3.50</td>\n",
       "      <td>2970</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>164</th>\n",
       "      <td>Vietnam</td>\n",
       "      <td>23.3</td>\n",
       "      <td>72.0</td>\n",
       "      <td>6.84</td>\n",
       "      <td>80.2</td>\n",
       "      <td>4490</td>\n",
       "      <td>12.10</td>\n",
       "      <td>73.1</td>\n",
       "      <td>1.95</td>\n",
       "      <td>1310</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>165</th>\n",
       "      <td>Yemen</td>\n",
       "      <td>56.3</td>\n",
       "      <td>30.0</td>\n",
       "      <td>5.18</td>\n",
       "      <td>34.4</td>\n",
       "      <td>4480</td>\n",
       "      <td>23.60</td>\n",
       "      <td>67.5</td>\n",
       "      <td>4.67</td>\n",
       "      <td>1310</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>166</th>\n",
       "      <td>Zambia</td>\n",
       "      <td>83.1</td>\n",
       "      <td>37.0</td>\n",
       "      <td>5.89</td>\n",
       "      <td>30.9</td>\n",
       "      <td>3280</td>\n",
       "      <td>14.00</td>\n",
       "      <td>52.0</td>\n",
       "      <td>5.40</td>\n",
       "      <td>1460</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>128 rows × 10 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                  Negara  Kematian_anak  Ekspor  Kesehatan  Impor  Pendapatan  \\\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",
       "161           Uzbekistan           36.3    31.7       5.81   28.5        4240   \n",
       "162              Vanuatu           29.2    46.6       5.25   52.7        2950   \n",
       "164              Vietnam           23.3    72.0       6.84   80.2        4490   \n",
       "165                Yemen           56.3    30.0       5.18   34.4        4480   \n",
       "166               Zambia           83.1    37.0       5.89   30.9        3280   \n",
       "\n",
       "     Inflasi  Harapan_hidup  Jumlah_fertiliti  GDPperkapita  \n",
       "0       9.44           56.2              5.82           553  \n",
       "1       4.49           76.3              1.65          4090  \n",
       "2      16.10           76.5              2.89          4460  \n",
       "3      22.40           60.1              6.16          3530  \n",
       "4       1.44           76.8              2.13         12200  \n",
       "..       ...            ...               ...           ...  \n",
       "161    16.50           68.8              2.34          1380  \n",
       "162     2.62           63.0              3.50          2970  \n",
       "164    12.10           73.1              1.95          1310  \n",
       "165    23.60           67.5              4.67          1310  \n",
       "166    14.00           52.0              5.40          1460  \n",
       "\n",
       "[128 rows x 10 columns]"
      ]
     },
     "metadata": {},
     "execution_count": 71
    }
   ],
   "metadata": {}
  },
  {
   "cell_type": "markdown",
   "source": [
    "# Scaling Data"
   ],
   "metadata": {}
  },
  {
   "cell_type": "code",
   "execution_count": 93,
   "source": [
    "sc = StandardScaler()\n",
    "df_sc = sc.fit_transform(df_non_outlier[['GDPperkapita', 'Kesehatan']])"
   ],
   "outputs": [],
   "metadata": {}
  },
  {
   "cell_type": "markdown",
   "source": [
    "# Creating KMeans Clustering and Visualizing Clusters Formed"
   ],
   "metadata": {}
  },
  {
   "cell_type": "code",
   "execution_count": 94,
   "source": [
    "kmeans1 = KMeans(n_clusters = 2, random_state = 100).fit(df_sc)\n",
    "labels1 = kmeans1.labels_\n",
    "labels1"
   ],
   "outputs": [
    {
     "output_type": "execute_result",
     "data": {
      "text/plain": [
       "array([0, 0, 0, 0, 1, 1, 0, 0, 1, 1, 0, 1, 0, 0, 0, 0, 0, 1, 1, 1, 0, 0,\n",
       "       1, 0, 0, 0, 1, 0, 1, 0, 0, 0, 1, 0, 1, 1, 1, 0, 1, 0, 0, 0, 1, 0,\n",
       "       0, 0, 1, 0, 1, 0, 0, 0, 1, 0, 1, 0, 0, 0, 1, 1, 0, 1, 0, 0, 1, 0,\n",
       "       0, 1, 1, 1, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 0, 0, 0, 0, 0, 0,\n",
       "       0, 1, 0, 0, 0, 1, 1, 0, 0, 1, 0, 1, 0, 1, 1, 1, 1, 1, 1, 1, 0, 0,\n",
       "       0, 1, 0, 0, 0, 0, 0, 0, 1, 0, 1, 0, 1, 0, 0, 0, 0, 0], dtype=int32)"
      ]
     },
     "metadata": {},
     "execution_count": 94
    }
   ],
   "metadata": {}
  },
  {
   "cell_type": "code",
   "execution_count": 95,
   "source": [
    "df3 = pd.DataFrame(data = df_sc, columns = ['GDPperkapita', 'Kesehatan'])\n",
    "df3['Cluster'] = labels1\n",
    "df3"
   ],
   "outputs": [
    {
     "output_type": "execute_result",
     "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>GDPperkapita</th>\n",
       "      <th>Kesehatan</th>\n",
       "      <th>Cluster</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>-0.825561</td>\n",
       "      <td>0.560239</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>-0.328385</td>\n",
       "      <td>0.092995</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>-0.276376</td>\n",
       "      <td>-0.986655</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>-0.407101</td>\n",
       "      <td>-1.585453</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0.811593</td>\n",
       "      <td>-0.142895</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>123</th>\n",
       "      <td>-0.709315</td>\n",
       "      <td>-0.242695</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>124</th>\n",
       "      <td>-0.485817</td>\n",
       "      <td>-0.496730</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>125</th>\n",
       "      <td>-0.719154</td>\n",
       "      <td>0.224549</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>126</th>\n",
       "      <td>-0.719154</td>\n",
       "      <td>-0.528484</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>127</th>\n",
       "      <td>-0.698069</td>\n",
       "      <td>-0.206404</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>128 rows × 3 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     GDPperkapita  Kesehatan  Cluster\n",
       "0       -0.825561   0.560239        0\n",
       "1       -0.328385   0.092995        0\n",
       "2       -0.276376  -0.986655        0\n",
       "3       -0.407101  -1.585453        0\n",
       "4        0.811593  -0.142895        1\n",
       "..            ...        ...      ...\n",
       "123     -0.709315  -0.242695        0\n",
       "124     -0.485817  -0.496730        0\n",
       "125     -0.719154   0.224549        0\n",
       "126     -0.719154  -0.528484        0\n",
       "127     -0.698069  -0.206404        0\n",
       "\n",
       "[128 rows x 3 columns]"
      ]
     },
     "metadata": {},
     "execution_count": 95
    }
   ],
   "metadata": {}
  },
  {
   "cell_type": "code",
   "execution_count": 102,
   "source": [
    "print('n_cluster = 2')\n",
    "plt.figure(figsize = (12, 8))\n",
    "plt.scatter(df3['GDPperkapita'][df3.Cluster == 0], df3['Kesehatan'][df3.Cluster == 0], c = 'blue', s = 100)\n",
    "plt.scatter(df3['GDPperkapita'][df3.Cluster == 1], df3['Kesehatan'][df3.Cluster == 1], c = 'red', s = 100)\n",
    "plt.scatter(kmeans1.cluster_centers_[:, 0], kmeans1.cluster_centers_[:, 1], c = 'black', s = 300)\n",
    "plt.xlabel('GDP per Kapita')\n",
    "plt.ylabel('Kesehatan')\n",
    "plt.show()"
   ],
   "outputs": [
    {
     "output_type": "stream",
     "name": "stdout",
     "text": [
      "n_cluster = 2\n"
     ]
    },
    {
     "output_type": "display_data",
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAssAAAHeCAYAAABt+0ZuAAAAOXRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjQuMywgaHR0cHM6Ly9tYXRwbG90bGliLm9yZy/MnkTPAAAACXBIWXMAAAsTAAALEwEAmpwYAABMGElEQVR4nO3de3icdZ3//9c9mclxpq09WLUKZhDXtbsu4AHseohGK8slu7ombQUJNYrdbH+1ViEKKAJKDUJkoQuhq0aIwW2bfrlcLkAtRvNFqfLdCyweWBTTuWA5FUptm0kmyUzm/v0RWprDzNxzz8x9mHk+rotLm7kz80nyyeQ1n3l/3h/DNE1TAAAAAOYIuD0AAAAAwKsIywAAAEAGhGUAAAAgA8IyAAAAkAFhGQAAAMiAsAwAAABkEHR7ANmk02lNTVnvbFdVZeR1PSAxb5A/5gzsYN7ADuaNM0Khqoy3eTosT02ZOnx4zPL1ixbV53U9IDFvkD/mDOxg3sAO5o0zli2LZLyNMgwAAAAgA8IyAAAAkAFhGQAAAMiAsAwAAABkQFgGAAAAMiAsAwAAABkQlgEAAIAMCMsAAABABoRlAAAAIAPCMgAAAJABYRkAAADIgLAMAAAAZEBYBgAAADIgLM8SiO1XuHOLlkRXaOnyhVoSXaFw5xYFYvvdHhoAAAAcRlg+QfXgHi1uWqXa/j4F4iMyTFOB+Ihq+/u0uGmVqgf3uD1EAAAAOIiw/JJAbL8WtLfJSIzJSCVn3GakkjISY1rQ3sYKMwAAQAUhLL+kvmeblExmvyiZVP32m50ZEAAAAFxHWH5Jze5dc1aUZzNSSdUM7CjK41EbDQAA4H2E5ZcYo3Fr18WtXZcNtdEAAAD+QFh+idkQtnZd2Np1mVAbDQAA4B+E5ZdMtKyRGQxlvcYMhjTRuq6gx6E2GgAAwD8Iyy8ZP/cjkszsF4VCGtuwsaDHcbo2GgAAAPYRljVdQ7zoE2ul9Pxh2ayqkllXr6O9fUo3Rgt6LCdrowEAAFCYig/LM2qI01MZrzvcv1OTzasLfjynaqMBAABQuIoPy5ZqiI2Aau+5qyiP51RtNAAAAApX8WHZ6RrisY5NUih7WC5GbTQAAAAKV/Fh2eka4nRjVEd7+2TW1c9ZYTaDoaLVRgMAAKBwFR+W3aghnmxerUNDezXetl7pSESmYSgdiWi8bb0ODe0tSm00AAAAChd0ewBum2hZo9r+vqylGKWoIU43RhXv6la8q7uo9wsAAIDiqfiVZWqIAQAAkEnFh2VqiAEAAJBJxYdliRpiAAAAzM8wTTPHGc/uSSandPjwmOXrFy2qz+t6QGLeIH/MGdjBvIEdzBtnLFsWyXgbK8sOCsT2K9y5RUuiK7R0+UItia5QuHOLArH9bg8NAAAA8yAsO6R6cI8WN61SbX+fAvERGaapQHxEtf19Wty0StWDe9weIgAAAGYhLDsgENuvBe1tMhJjc1rUGamkjMSYFrS3scIMAADgMYRlB9T3bJOS2Y/UVjKp+u03OzMgAAAAWEJYdkDN7l1ZDz2RpleYawZ2ODQiAAAAWEFYdoAxGrd2XdzadQAAAHAGYdkBZkPY2nVha9cBAADAGYRlB0y0rJlzOuBsZjCkidZ1Do0IAAAAVhCWHTDWsUkKZQ/LCoU0tmGjMwNyGP2lAQCAXxGWHZBujOpob5/Muvo5K8xmMCSzrl5He/uUboy6NMLSob80AADwM8KyQyabV+vQ0F6Nt61XOhKRaRhKRyIab1uvQ0N7Ndm82u0hFh39pQEAgN8F3R5AJUk3RhXv6la8q9vtoTgin/7SlfI9AQAA/sLKMkqG/tIA4D3sIwHyQ1hGydBfGgC8hX0kQP4IyygZ+ksDgHewjwSwh7CMkqG/NAB4Rz77SAC8jLCMkqn0/tJWUT8IwAnsIwHsISyjZCq5v7RV1A8CcAr7SAB7CMsoqUrsL20V9YMAnMQ+EsAewjJK7lh/6ReHn9bBA0f04vDTind1V/SKskT9IABnsY8EsIewDLiE+kEATmIfCWAPYRlwCfWDAJzEPhLAHsIy4BLqBwE4jX0kQP6Cbg8AqFQTLWtU29+XtRSD+kEAxXZsH0m8q9vtoQC+4NjKcjKZ1CWXXKLzzjtPLS0tGhwcdOqhAU+ifhAAAO9zLCzfddddWrRokX7wgx/oO9/5jr72ta859dCAJ1E/CACA9zkWls8++2xt3rxZkmSapqqqqpx6aMCzqB8EAMDbDNM0TScfMB6Pq6OjQ2vWrNG5556b9dp0Oq2pKevDq6oKaGoqXegQUWGYN8gXcwZ2MG9gB/PGGaFQ5kVcRzf4Pfvss9q4caPOO++8nEFZkqamTB0+PGb5/hctqs/rekBi3iB/zBnYwbyBHcwbZyxbFsl4m2Nh+eDBg2pvb9cVV1yhd77znU49LAAAAGCbYzXLt956q44ePapbbrlFF1xwgS644AKNj4879fAAAABA3hyvWc5HMjlFGQZKjnmDfDFnYAfzBnYwb5yRrQyDE/wsCMT2K9y5RUuiK7R0+UItia5QuHOLArH9bg8NAAAAJURYzqF6cI8WN61SbX+fAvERGaapQHxEtf19Wty0StWDe9weIgAAAEqEsJxFILZfC9rbZCTG5hxJbKSSMhJjWtDexgozAABAmSIsZ1Hfs01KJrNflEyqfvvNzgwIAAAAjiIsZ1Gze9ecFeXZjFRSNQM7HBpRZaJmHAAAuIWwnIUxGrd2XdzadcgfNeMAAMBNhOUszIawtevC1q5DfqgZBwAAbiMsZzHRskZmMJT1GjMY0kTrOodGVFmoGQcAAG4jLGcx1rFJCmUPywqFNLZhozMDqjDUjAMA/IQ9NuWJsJxFujGqo719Muvq56wwm8GQzLp6He3tU7ox6tIIyxs14wAAv2CPTfkiLOcw2bxah4b2arxtvdKRiEzDUDoS0Xjbeh0a2qvJ5tVuD7FsUTMOAPAD9tiUN8KyBenGqOJd3Xpx+GkdPHBELw4/rXhXNyvKJUbNOADAD9hjU94Iy/AsasYBAH7AHpvyRliGZ1EzDgDwA/bYlDfCMjyNmnEAgNexx6a8Bd0eAJDLsZrxeFe320MBAGCOiZY1qu3vy1qKwR4b/2JluczR8xEAgNJij015IyyXMXo+AgBQeuyxKW+E5TJFz0cAAJzDHpvyRc1ymcqn5yO1wAAAFI49NuWJleUyRc9HAACAwhGWyxQ9HwEAAApHWC5T9HwEAAAoHGG5TE20rJmzI3c2ej4CAABkR1guU/R8BAAAKBxhuUzR8xEAAKBwhOUyRs9HAACAwhimaZpuDyKTZHJKhw+PWb5+0aL6vK4HJOYN8secgR3MG9jBvHHGsmWRjLexsgwAAABkQFgGAAAAMiAsAwAAABkQlgEAAIAMCMsAAABABoRlAAAAIAPCMgAAAJABYRkAAADIgLAMAAAAZEBYBgAAADIgLAMAAAAZEJYBAADguEBsv8KdW7QkukJLly/UkugKhTu3KBDb7/bQZiAsAwAAwFHVg3u0uGmVavv7FIiPyDBNBeIjqu3v0+KmVaoe3OP2EI8jLAMAAMAxgdh+LWhvk5EYk5FKzrjNSCVlJMa0oL3NMyvMhGUAAAA4pr5nm5RMZr8omVT99pudGVAOhGUAAAA4pmb3rjkryrMZqaRqBnY4NKLsCMsAACBvftmcBe8xRuPWrotbu67UCMsAACAvftqcBe8xG8LWrgtbu67UCMsAAMAyv23OgvdMtKyRGQxlvcYMhjTRus6hEWVHWAYAAJb5bXMWvGesY5MUyh6WFQppbMNGZwaUA2EZAABY5rfNWfCedGNUR3v7ZNbVz1lhNoMhmXX1Otrbp3Rj1KURzkRYBgAAlvltcxa8abJ5tQ4N7dV423qlIxGZhqF0JKLxtvU6NLRXk82r3R7icUG3BwAAAPzDbAjLiI/kvs4jm7PgXenGqOJd3Yp3dbs9lKxYWQYAAJb5bXMWUCjCMgAAsMxvm7OAQhGWAQCAZX7bnAUUirAMAADy4qfNWUChDNM0TbcHkUkyOaXDh8csX79oUX1e1wMS8wb5Y87ADuYN7GDeOGPZskjG21hZBl4SiO1XuHOLlkRXaOnyhVoSXaFw5xZOoQIAoIIRlgFJ1YN7tLhplWr7+xSIj8gwTQXiI6rt79PiplWqHtzj9hABAIALCMvA8LAWtLfJSIzNOZXKSCVlJMa0oL2NFWYAACoQYRkVL/BvN0jJ7Ee3KplU/fabnRkQAADwDMKyBdSylrfAD+6Ys6I8m5FKqmZgh0MjKm/8PgEA/IRuGDlUD+7RgvY2KZmcEajMYEgKhXS0t48WOT63dPlCGRZ+DUzD0MEDRxwYUfkql98ndqfDDuYN7GDeOINuGDYFYvupZa0E4bCly0yL12F+/D4BAPyIsJxFfc82alkrQPq88+ecQjWbGQxponXdjI9RTpAffp8AAH5EGUYWS6IrFIiP5LwuHYnoxeGni/a4cNaiF59V8IzTZSQyzx2zrl6HhvYeP761XMoJnFROv0+8LQo7mDewg3njDMowbDJG49aui1u7Dh51yik62tsns65+zgqzGQzJrKvX0d6+40GZcgJ7+H0CAPgRYTkLs4Fa1kox2bxah4b2arxtvdKRiEzDUDoS0Xjbeh0a2jtjlZhyAnv4fQIA+BFhOYuJljW2alnhT+nGqOJd3Xpx+GkdPHBELw4/rXhX9/EV5WNqdu+i1ZwN/D4BAPyIsJzFWMcmKZT9j7tCIY1t2OjMgOAJlBPYw+8TAMCPCMtZpBujedWyojJQTmAPv0/+Q8cXACAs55RPLSsqA+UE9vH75B/Vg3u0uGmVavv7FIiPyDBNBeIjqu3v0+KmVaoe3OP2EAHAEbSOQ8XLd94EYvu1uGlVXq3mUF7K/bmGOV4a5T5vUBrMG2fQOg4oIsoJUO7o+AIALyMsAzZQToByRscXAHhZ0O0BAH51rNVcvKvb7aEARUXHFwB4GSvLAIAZ6PgCAC8jLAMAZqDjCwC8jLAMAJiBA2QA4GXULJdQILZf9T3bpjfLjMZlNoQ10bJGYx2b6JQAZPDCCy9o58479Ic//EEjI0cUiSzUypUrtW7dJ7R06VK3h1cRjnV8WdDeJiWTMzb7mcGQFArR8QVAxaDPcolUD+7J+YeGjgne4KV5U8l+85uHdOON3RocvE+GYWh8fPz4bbW1tTJNU83Nq7V58+d1+ulvdXGklTNnArH9qt9+s2oGdsiIx2WGw5poXaexDRsJyjZUyrxBcTFvnJGtz7LjYfmRRx7R9ddfr+9///s5r/VCWLazOkxDf3/hich9t932HX31q5drfHxc2Z6SDMNQbW2trrrqGq1f/2kHRzgTcwZ2MG9gB/PGGZ45lOTb3/62vvzlL2tiYsLJh7XN7nGvNPQHrDsWlBOJRNagLEmmaSqRSOirX71ct932HYdGCACoZI6G5ZNOOknbtm1z8iFtC8T2a0F7m4zE2Jzm/EYqKSMxpgXtbQrE9s/5XBr6A9b85jcPHQ/K+TgWmPfte7hEIwMAYJqjG/w+9KEP6amnnrJ8fVWVoUWL6vO4PpDX9dkEem+VcgRepZJa9L3tSt808wWA5Yb+o6NFGy/sK+a8QX5uueXfZtQm52N8fFy33HKjdu0aKPKocmPOwA7mDexg3rjP090wpqZM12qWl9xxh4wcpRRGMinjjn4dvvramZ/bEJYRH8n5GGZDQ1nXIfmlGwj1YO544YUX9OMf/zhn6UUmpmnqRz+6V3/+85OOd8lgzsAO5g3sYN44wzM1y35SyHGvNPS3X++NyrFz5x0yDKOg+zAMQzt33lGkEQEAMBdhOYNCjnut9Ib+hdR7o3L84Q9/sF2Cccz4+Lge/X+/LtKIAACYy/Gw/NrXvla7du1y+mHzVsjq8LGG/mZd/Zz7MIMhmXX1Zd3Qn24gsGJk5EhR7ifxkx/xTgUAoGRYWc6g0NXhyebVOjS0V+Nt65WORGQahtKRiMbb1uvQ0N6yPpDEy91AArH9Cndu0ZLoCi1dvlBLoisU2PT/scrtgkhkYVHu5xXpNO9UAABKhrCcQTFWh9ONUcW7uvXi8NM6eOCIXhx+WvGu7rJdUT6mkHrvUspURx3o/S511C5YuXKlamtrC7qPOkl/K/FOBQCgZAjLWVTy6nAhCqn3LpWsddRJ6qjdsHbt+bY7YRxjSlov+pYDAEqHsJxDpa4OF8KL3UCoo/aeZcuWqbn5g7Y7YhiSzpG07Ni/HX6nAgBQGQjLKDovdgPxch11Jdu8+Qu2SzHqJF16wr+dfKcCAFA5CMsoOi92A/FqHXWlO/30t+qqq65RXV1dXp9XL+l6SW976d/l3rccAOAewjJKwmv13l6so8a09es/fTww5yrJMPRyUO448YYy7lsOAHAXYRkl46V6by/WUeNl69d/Wv/1Xz/SOed8WDU1NXNKM2qrq1Ur6SOGof+rl4NyJfQtBwC4yzAL3Y5eQsnkVF7noXN+OjIJxPZrcdMqGYnM88Osq9ehob2OhK5AbL/qe7ZN11KPxmU2hDXRskZjHZs8G/qcGvPBgwe1c+cdevTRP+jIkcNauHCR3vzmlfr4O/9eJ+36T9UM7JARj8sMhzXRuk5jGzY6/j3juQZ2MG9gB/PGGcuWRTLeRlhGxage3KMF7W1SMjljs58ZCknBkI729jlSHpJxHMGQFHJuHPnw45hLieca2MG8gR3MG2dkC8uUYcAR852cF+7c4mhf40x11OlPfdqxOuqs/Z5T3uz37McxAwBQLIRlZFSsgJvp5Lza/j7HT86br446fdM2x97G92O/Zz+OGQCAYqEMA/Mq1tvuXqsVno+T82ZJdIUC8ZGc16UjEb04/LQDI8rNj2MuNZ5rYAfzBnYwb5xBGQbyUsy33VmVnMmP/Z79OGYAAIqFsIw5ihlwOTlvJj/2e/bjmHPxQg29Gyr16waAQhCWMUcxAy6rkjP5sd+zH8ecjZdq6J1UqV83ABSKsDxLLGaos7NG0WhYy5eHFY2G1dlZo1gs+8li5aSYAbccVyULMdaxSQplD55eO43Oj2POpFI7e1Tq1w0AxUBYPsHgYJWamhrU3x9SPG7INA3F44b6+0NqamrQ4GCV20N0RDEDbrmtShYq3RjV0d4+mXX1c74vXj2Nzo9jzqRSa+gr9esuFGUr8ArmorvohvGSWMxQU1ODEonMK8h1daaGhkbV2OjZb1lRhDu3qLa/L2sphhkMabxtveJd3Vnvi24Y8wvE9qt++82eOI3OKj+OebZidfbw2+50OprkrxQH8fht3sAbXvHr/6uqtWs5FKrEOMHPgs7OGvX3h5RKZQ7LwaCptrakurombD2GXxQ74Hr99Df+gFWOpcsXyrDwlGcahg4eOJLxdr/NmWJ93ZWiVC/y/TZv4L5AbL8Wv2+VjDHvLjiVC1rHWbB7d/agLEmplKGBgRy1m2Wg2G+7Zzo5b7xtvWMn5wFS5dbQV+rXbRdlK/AK5qI3EJZfMjpq7boKadpQ9IA738l58a5uXgnDUZVaQ1+pX7ddtLyEV9Ts3iUjR1hmLpYeYfklDQ3WrqukhRcCLsqN3c4eszfXBJe8wleba8qpo4kTaHkJr2AuegNh+SUtLUkFg9lr+oJBU62tOd4OAeBZdkqM5utPbIz4qz9xOXU0cQJlK/AK5qI3EJZf0tExaWXhRRs2TDozoCKidzTwsnxKjMqpPzF7B6yjbAVeMdGyRmaOcMJcLD26YZxgcLBK7e11SiY1Y7NfMGgqFJJ6exNqbp6yff9uKMevqdjYoY5MitlGEf5BNwx4Bd0wnEM3DIuam6c0NDSqtrakIhFThmEqEpluFzc0NOq7UBmLGWpvr1MiYczp9JFKGUokpm9nhRmYHxu9KhNlK/CKdGNUUzt2MhddRliepbHRVFfXhIaH4/r1r0f1sY8ltWtXSGed1eC78oWenmorHWe0fXu1MwMCfIbNNZWLshV4hXn2P5TdXPTbiYSUYWRQDuUL0WhY8XjuYB+JmBoertw/9rw1ikw4+Q7FxHMN7Ci3eePVg8oow8iTlfKFCy6o0y9+4e1vH72jX+a3V7HwBjZ6AUDx+HXTtLfTnkuslC+kUtKaNfUaHKxyZlA20Dt62nytvwLxl1t/GT/+kdtDhEfRnxgAisevJxISludh5ehrydDUlLc3yNE72tqr2Kp1az33KhbewEYvACgev26aJizPw2r5guTtDXLl3DvaKr++ioV3zLfRy1ywwNebawDADX7dNM0Gv3lY3Rh3jJc3yJXDRsVCsEELpVBuG27gDOYN7CineePlv8ls8MuTlfKFE3nsBdAM5dY7Ol9+fRWL4mBjJwB4h183TbOyPI9YzFBTU4MSCWury15eWa50Xn4Vi9IqZXuiclrpgXOYN7CjnOZNqU7HLAZWlvPU2Giqtzfx0upyZW+Q8ztLr2JD3nsVi8L4tT0RAJQzv26aJixn0Nw8pZ07x1SVozNcuW+Q8ztaf1UmNnYCgDf58XRMyjByqPQNcuUg19vxUzt36i9nvdfFEaLYSl1+U05vi8I5zBvYwbxxBmUYBaj0DXLlINerWPPsf3B7iCiyfDd2shEQAJAJK8uoeMyb8pPPyvLIf3wv742AzBnYwbyBHcwbZxS8smyapn7729/qv//7v4//BwBeZbU90eTqc9gICADIKmjlok2bNunFF1/Uq1/9akmSYRh6+9vfXtKBAYBdYx2bVLvzP6Vsx6qGQpKZtrwRMN7VXdxBAgB8wVJYPnjwoHbs8NY53QCQybH2RLnKKyIXfXLOivJsRiqpmoEdhGUAqFCWyjAaGxt14MCBUo8FAIrGSnsiTngEAORiaWX54Ycf1vve9z4tXrz4+Md++ctflmxQAFAM6cao4l3dGVeFzYawDAsbAc1wuNhDAwD4hKWw/JOf/KTU4wAAx020rFFtf1/WUgwzyAmPAFDJLIXlffv26c4771TypY0wzz//vL773e+WdGAAUGpWNwJywiMAVC5LNctXXnml3vGOdygej+s1r3mNFi1aVOJhAUDpHdsIaNbVz2k1ZwZDMuvqdbS3T+nGqEsjBAC4zVJYfsUrXqEPf/jDCofD2rRpE5v9YFksZqizs0bRaFjLl4cVjYbV2VmjWMzI/cmAA6xsBAQAVC5LZRiBQECPP/64EomE9u/fryNHjpR6XCgDg4NVam+vUzIppVLT4Tgel/r7Q9q5M6Te3gTHhcMTcm0EBABULksry1/60pf0+OOP64ILLtDFF1+sj33sY6UeF3wuFjPU3l6nRMI4HpSPSaUMJRLTt7PCjHwEYvsV7tyiJdEVWrp8oZZEVyjcuYUT9gAAJWMpLO/bt0/nnHOO3vrWt+rOO+9UIGDp01DBenqqrRyMpu3bq50ZkA/kEwQrMTRWD+7R4qZVqu3vUyA+IsM0FYiPqLa/T4ubVql6cI/bQwQAlCHDNE0z04133323fvazn+nBBx/UWWedJUlKp9P605/+pHvuuafkg0smp3T48Jjl6xctqs/repRONBpWPJ571TgSMTU87O6BD16YN9WDe3KeNnesdjafa8tFILZfi5tWyUhk/jmZdfU6NLTXkc14Xpgz8B/mDexg3jhj2bJIxtuy1iy/+93v1rJly3T48GGtXbtW0nT98ute97rijhBlZ3TU2nUcjDYdBBe0t80bBI1UUkoltaC9TYeG9kqS5WvLqYNDfc82WXmron77zdQdAwCKKms9xcKFC3XmmWeqt7dXr3/96/Xa175Wr371q/XMM884NT74VEODtes4GC2/IJjPteWkZveurAeHSNMvFmoGdjg0IgBApbDUDeOyyy7Tvn37lEgklEgkdNJJJ2nXrl2lHhuKJBYz1NNTrd27QxodnQ6yLS1JdXRMqrExYxVOQVpakurvD83Z3HeiYNBUa2uO4FcB8gqCpixfW04rrMaotbcgDN6qABSI7Vd9z7bp55bRuMyGsCZa1misY1NZveMEOMXSTr3HHntM99xzj971rnfp3nvvVU1NTanHhSIZHKxSU1OD+vtDiscNmaaheNxQf39ITU0NGhysKsnjdnRMKhTKfk0oJG3YMFmSx/eTfIJgpYZGs8HaWxAmb1WgwrERFig+y4eSGIahsbExLV68uNRjQpG42b6tsdFUb29CdXWmgsGZq9fBoKm6uunbS7WyXSgnD1PJJwhWamicaFkz54S92cxgSBOt6xwaEeA9J+5/mP0OlJFKykiMaUF7W1l3zQFKwVJYXrlypb773e/qla98pbZs2aJEIlHqcZUNt06wi8UMtbXVKdePqpTt25qbpzQ0NKq2tqQiEVOGYSoSMdXWltTQ0KhnDyRxejU+nyBYqaFxrGOTrLxVMbZhozMDAjyoUvc0AKWWtXXciUZHR1VTU6P7779ff/d3f6clS5aUemy+bx033wl20vTKaiikkp1gd+xxp4OyP9q3uenEeROLGWpqalAikfn7VldnamhotGir4vm0RZPkqRZqTvJSyzyvPdfAH0o9b5ZEVygQH8l5XToS0YvDT5dsHCgunm+cka11nKWV5d/97nfq7u7WV77yFd133326/vrriza4cuVWCcSJj2slKEu0bzuRG4eppBujOtrbJ7Oufs6qsRkMyayr19HePqUbo3ldW24mm1fr0NBejbetVzoSkWkYSkciGm9br0NDe8uutzSQr0rd0wCUmqWwfOWVV+pv//Zvdc455xz/D9m5dYKdlcedzWvlrW6VrkjS7t3ZO3hI0y92BgZylATkKZ8gWMmhMd0YVbyrWy8OP62DB47oxeGnFe/qLssXB0C+KnVPA1BqlsowLrzwQt1+++1OjGcGr5Rh2Gm95tYJdlYf95hgcLqGuKtromhjKIQbpSsnzpvly8MyzdzfP8MwdeAAqzMnqqR2VbwtCjtKPW/CnVtU29+Xtb2kGQxpvG19WbWWLHc83zgjWxlG1rD8y1/+UpK0Y8cO/c3f/I1Wrlwpw5gOEu9617uKPMy5vBCW7YY3t0KX1cc9ptj1t4Vwo15Ymjlv/HRMt5d4qZ7YCfzxgh2lnjdeOxYexcHzjTNs1yzfc889uueeexSJRPTEE0/o3nvvPf6xSlBI3bFbJ9hZfVzJe+3b3CpdOVFLS3JOq7vZOExlJtpVAd5QyXsagFLKGpa/8Y1vHP/vM5/5jM4++2xt3rxZ11xzjVPjc1Uh4c2t0GXlcSVTb3pT2nPt29yqFz4Rh6nkz267qkBsv8KdW7QkukJLly/UkugKhTu3EKqBAlTyngagVCzVLPf39+u+++7TkSNH9NGPflRPPPGErrjiipIPzu0yjELeknerpMCtxy0Gt0pXZs8bt1r++ZWddlV+L9vgbVHYwbyBHcwbZxTcOu6ee+7R9773PUUiEV144YV65JFHijY4LxsdtXbdfF143DrBzs8n57lVujKbXw9TcUu+7aoo2wAA+ImlsGyapgzDOL65r7q6dDWjXlJXZ+26TOHNrdDl17DnpXrhxkZTXV0TGh6O68CBuIaH4+rqmvDkiwy35duuilPGAAB+YrkM495779UzzzyjU089VWeddZY+9alPlXxwbpZhDA5W6YIL6pRKSdkO9/Ba6zU/80I3DOQv33ZV5XDKGHMGdjBvYAfzxhm2W8edaHh4WH/6058UjUb1V3/1V0UbXDZuhWUroe0Yr9b/OsFO/+lc3O6zjPzl265q6fKFMiw87ZiGoYMHjhRzqEXDnIEdzBvYwbxxRsE1y4899piOHj2qV7/61dq6dat+9atfFW1wXmTtFDxTVVXerf8ttcHBKjU1Nai/P6R43JBpGorHDfX3h9TU1KDBwSpb9+vXEpJKlm+7Kk4ZAwD4ieXjrqurq3Xrrbdqy5Yt+vd///dSj8tVVlqYSYZqa1WR4a2Q/tNWUC/sP/m0q5poWTMnVM9mBkOaaF1X6mEDAJCTpbBcXV2tU089VclkUqeddpoCAUuf5ltWu2CMldG7IrGYoc7OGkWjYS1fHlY0GlZnZ828gdcLh4fAe9KNUcW7uvXi8NM6eOCIXhx+WvGu7jkHIIx1bJKVZtZjGzaWcLQAAFhjKfUahqHOzk695z3v0b333qtQrj90PueVFmZOybekwguHh8C/OGUMAOAnlsLyDTfcoI9+9KO68MILtXjxYn3rW98q9bhc5aUWZqVmp6SikP7TgMQpYwAA/7BchvHwww/r0ksv1dGjR3XkiDd3qBdLJR15bKekotJW3lEaVss2AABwk6WwfNlll+l1r3udnnjiCS1dulSXX355qcflKj+fgpcvOyUVlbTyDgAAKpulsHz48GG1tLQoGAzqjDPOUDqdLvW4XFcpLczslFRU0so7AACobJbbWgwPD0uSnnvuOVVV2euh6zfFbmGWT8cJp9gpqaiklfcTefHnBwAASitrWH7uueckSV/+8pd12WWX6dFHH9VnP/tZNTc323qwdDqtK664QmvXrtUFF1ygJ554wtb9+FGpDvEolN2SikpZeT/Gqz8/AABQWlmPuz733HN1++23a/HixZIk0zTV09OjXbt2aWhoKO8H27Nnj372s5+pq6tL+/bt0/bt29XT05PxereOuy42K8dnu3VstpfH5pRc84bvEWbz6nMNvI15AzuYN86wfdz1xo0bddFFFykej+svf/mLLrroIv3ud7/TnXfeaWsgDz30kN797ndLkk477TT9/ve/t3U/fuPlQzwqtaQiH17++QEAgNLKurIsSXfffbduv/12HT16VG1tbTr//PNtP9jll1+u1atX673vfa8kqampST/96U8VDAbnvT6dTmtqynpIq6oKaGrKe5sPlywJaGQkd13rggWmDh50Z/zDw9KNNxr6wQ8MxePTNcrnnWdq82ZTp5ziypDmjO/f/m3u+D73ucLHl2ve+OHnB2d59bkG3sa8gR3MG2eEQpnLKedPqSf48Ic/rKmpKQ0MDKi1tbWggYTDYY2e0H4hnU5nDMqSNDVllkUZRjxureHwyIhcG/+SJdLVV0//N9vhw5k/LxYz1NNTrd27Qxodnd4w2NKSVEfHZNFWowcHq9TeXqdkUsfb3I2MSN/9rtTXZ6i3N1FQjXSueeOHnx+c5dXnGngb8wZ2MG+cka0MI2tY/vznPy/DMGSapp588kmdd955OvnkkyVJ3d3deQ/kjDPO0M9//nOdc8452rdvn974xjfmfR9eYjUoNjRYO83Ob4d4zBdi43Gpvz+knTtDBYdYaeYJg7OlUoZSKam9va6k9cLl+vMDAAC5ZQ3L69atm/f/2/XBD35QDzzwgNatWyfTNLV169aC79Mt+QTFlpak+vuzH/7ht0M8nAqx+dQLd3VN2H6cbMrx5wcAAKzJWbPsJq92w8i3O0I5dlPo7KyxFCDb2pIFhdhoNKx4PHe9cCRianjYwvLvPOiGgXzxtijsYN7ADuaNM2x3w8D88u2OUI4dJ+wck22HnRMGi60cf34AAMAawrINdoJiuR3i4VSItXPCYCmU288PAABYQxmGDcuXh2WauUsDDMPUgQMlXPJ0kRPlEZIz5R68xYV8MWdgB/MGdjBvnEEZRpF5ZbXTTXaPyc5XR8ekQjkqOUIhacOGyYIeB/4UiO1XuHOLlkRXaOnyhVoSXaFw5xYFYvvdHpolfh8/AFQCwnKeYjFDK1akJZU+KHqZUyG2HOuFYzFDnZ01ikbDWr48rGg0rM7OGsViuVfq8bLqwT1a3LRKtf19CsRHZJimAvER1fb3aXHTKlUP7nF7iFn5ffwAUCkIy3kYHKzSe9/boD/9KSApe7Ap99VOJ0NsOdULDw5WqampQf39IcXjhkzTUDxuqL8/pKamBg0OZj5BCC8LxPZrQXubjMSYjNTMF6VGKikjMaYF7W2eXaH1+/gBoJi8/i4bYdmiWMzQ+vV1Gh83stYrV1X5c7XTDidDbGOjqa6uCQ0Px3XgQFzDw3F1dU346nt8Ym/q2TXYqZShRGL6dlaYc6vv2SYrLWnqt9/szIDy5PfxA0Cx+OFdNjb4WdTRUaP/839Cyr6ibOqUU9L6wQ/KPyiXE6c2TzjVm7oSLImuUCA+kvO6dCSiF4efLvrjFzpn3B4/3MFGLdhRzvMmENuvxU2rZCQyf31mXb0ODe1VujFa0rGwwa8I7rorV1CWJENPPhnIGJSpVa1sTvWmrgTGqLUOK0YpG3AXwO/jB4Bi8Mu7bIRli3L9LHNd57Va1XIL7n74erxwwEq5MBustZoxPdqSxu/jB4BiqNm9a86+jdmMVFI1AzscGtH8CMsO8FqtqteCe6Hc/HryCem0HCyeiZY1MoPZV+DNYEgTrescGlF+/D5+ACgGv7zLRli2KBi0dt187dTyPR67lLwW3Avl5teTb0h3qjd1JRjr2DT/L9uJQiGNbdjozIDy5PfxA0Ax+OVdNsKyRf/4j0nl6q0smfqnf5obdAYGvFOr6qXgXgxufT12QjoHrBRPujGqo719Muvq56zQmsGQzLp6He3tK/mGELv8Pn4AKAa/vMtGWLboi1+cVHWOvFVdLV1yycygMzhY5ala1XLbZObW12MnpJfjAStummxerUNDezXetl7pSESmYSgdiWi8bb0ODe3VZPNqt4eYld/HDwCF8su7bLSOy8PgYJXWr6/T5KRm9Fo2DFPV1dJttyVm9BaOxQw1NTUokbBWAhCJmBoeLm1iXr48nLVP9DGGYerAAe/vNCvG12Nn3kSjYcXjuR93vp9pLGZo+/ZqDQyEFI9P1yi3tia1YcMkQdknyrmVE0qHeQM7yn3eVA/u0YL2NimZnLHZzwyGpFBIR3v7HFk8oHVckTQ3T+n++0f1yU/OPITjk59M6v775x7CYWX18RinalXLbZOZW19PIe8WlMMBKwAAFIMf3mWzuG2tssVihnp6qrV7d0ijo9MBraUlqY6O7CuBVkoEjnGqVrWlJWnpYAy/bDJz6+tpaLBWNuOXFx0AALgl3RhVvKtb8a5ut4cyL1aWc8jU8eD220M688wGvfKVmduFWV19lJyrVS23TWZufT10tgAAoDIQlrPI1vFguk52+r9M4bm+3trjNDRoTglHqZTbJjO3vp5ye9EBAADmR1jOIp+a49nhub8/pPFxqaoq9+rj2rXOrj42N09paGhUbW0za6/b2pIaGppbe+11bnw95faiAwAAzI9uGFlY7XiQnanpED2/ujpTQ0OjhKois1pnHosZ6u2t1x13GHnVo5/4+XS2qDzlvjsdpcG8gR3MG2dk64ZBWM7CaluybAIBU4GX1u9PLOUIBk2FQlJvb8J3K7leNzhYpfb2OiWT2b/nx65LpaRk0r2fjd0NpHAPf7xgB/MGdjBvnEHrOJustiXLJp02VFOjsil58DqrJ+v94heB49edGJRnX1fqY7/zPTIbAAA4i7CchZWOB1aMjYm+ug6xerLe5ZfXun7st50jswEAgLMIy1lY6XhgBb12nWP1+OvHHgu4fuy3nSOzAQCAswjLWWTreGAVvXadZb23tTVWDh6xy2qwL2VgBwAA2RGWc5ivLZl07H9zo9eus4pRZ36iUr4rUMiR2QAAwBmEZQsaG80ZNccPPjiqT35yOjxPt4abG57ptesOqyfrvelNaddP4LMa7CnjAQDAPYRlG04Mz88/PzM80+3CXVZP1rvmmnHXT+DjyGwAALyPPssVopJ6+fqlz3IsZqipqUGJBIfW+A3PNbCDeQM7mDfOoM9yhau0Xr5Wj78+dt2nPmW68q4AR2YDAOB9rCyXOVYvc3N73nBktv+4PWfgT8wb2MG8cQYryy6IxQx1dtYoGg1r+fKwotGwOjtrHD9ggl6+3jd7AymH1gAA4B2E5RLwUtkDvXwBAADsIyxbkM8qsdeOMKaXLwAAgH1BtwfgdfN1VojHpf7+kHbuDM3pmGCl7GF8XLruumrdcstEKYcuSaqpmX687J5XKHSbOjoe1sjIEUUiC7Vy5UqtW/cJLV26tORjBAAA8Co2+GUQixm69tpq3XlnSJL1zXHRaFjxuJVVY1P/+Z/WWpPZaftmbfz/Lekbkn6kqippaurlVF1bWyvTNNXcvFqbN39ep5/+Vgtfkz9b1LF5AvlizsAO5g3sYN44I9sGP8LyPI6tJo+PS6aZPfgGg9Otxrq6pleJly8P5/ycY6x0obDaMzj/8fdIulhSQtOnEM7PMAzV1tbqqquu0fr1n8769dgZqxfwRIR8MWdgB/MGdjBvnEE3jDycWHNsJfTO3hxn9QhjSZqYyN6Fwk79s7XxHwvKY8oWlCXJNE0lEgl99auX67bbvlPUsQIAAHgdYXkWKzXHs524Oc7KEcbHpNOGdu7M3IXCTtu33J/z33o5KFt3LDDv2/dw0cYKAADgdYTlWay0WpstHH75/3d0TCqURxe2bN0q7LR9y/0539B06UX+xsfHdeON3fPeRos6AABQjgjLs1httXZMMGiqtfXlJdVjRxjnKm8o5lhOXNnO/jnPS/qR7I7NNE399Kd7dPDgwTm3WR3ryIjmbbvnlUNcYB8/QwBAOSIsz5JPzbEkhULShg2TMz5WrE1sVsdy4sp29s+5rYDRTDMMQzt33jHn49a/b3MPZ/HSIS6wh58hAKBcEZZnsVpzbBim6uqmV5FL1RLNylhmr2xn/5zfSsrZdDmr8fFxPfroH+Z8PJ9a7RM3/P3iFwE2BvocmzsBAOWMsDyLtZpjU//8z0kNDY2WtBWalbHMXtnO/jlHijKuI0cOz/lYvrXa0vSGv8svr2VjoM+xuRMAUM4Iy7McqzmuqzPnrJQGg6Zqa0194AMp/eQnIZ11VkPGusz6emuPl618IddY5lvZzvY5hrHQ2qByWLhwUV5jzSSVMvTYYwE2BvocmzuB7AKx/Qp3blFwySu0dPlCLYmuULhziwKx/W4PDYAFhOV5NDdPaWhoVG1tSUUipgzDVCRi6n3vS8k0paGhYM66zDVrkgoEsofGQMDU2rXZl+QyjaWtLfPKdqbPeetbV6q6utbeN+W4Or35zStzjrUYGxxPdOImRniLnY2oQKWoHtyjxU2rVNvfJ2NkRIZpKhAfUW1/nxY3rVL14B63hwggB07ws+gXvwho7dr6rCtoJ57IF4sZampqUCJh/ajsUnvhhRd0xhlv1sTERAH3UqtHH31US5cuzXpVPsd+ZztO/JhIxNTwcGnSFqcjFcbqz7qUP0OnMWdgRSC2X4ubVslIZJ4rZl29Dg3tVbox6uDI4Cc83ziDE/wKNDhYpTVr6pVKZb/uxLpMOyUUpbZs2TI1N39QhmF3o5Whk08+O2dQlqxvTnzTm9J5b2KEt9jZiApUgvqebbJS0F+//WZnBgTAFsJyDsd2+k9NGcq1Ajq7LtNOCUWpbd78BdXW2i3FqNNVV22xdKXVzYnXXDOe9yZGeIudjahAJajZvUtGKntYNlJJ1QzscGhEAOwgLOeQ7/HXs+syGxtNdXVNaHg4rgMH4hoejqura8LRFeUTnX76W3XVVdeorq4uz8+s11/91bX6678+w9LVVlfW3/3utOdW4JEfL76LAniBMWqt7MigoB/wNMJyDvkef33iASF2lfoktPXrP308MOcuyTAk1Uu6XsPDG/M6YMLqyroXV+CRH36GwFxmg7U/CGYx/nAAKBk2+OWwfHlYpmktpAaD0+Ggq8v+BrrBwSq1t9cpmdSMkB4MmgqFpN7eRNGCx759D+vGG7v105/ukWEYGh8/8cCSOk1vvjtH0qWS3vbyLTY3JsZihnp6qrV7d0ijo9Nt81pakuromHR11ZHNE8gXcwZWhDu3THfByFKKYQZDGm9br3hXt4Mjg5/wfOOMbBv8CMs5WO/qUHh3C7c6aBw8eFA7d96hRx/9g371q6N6+unFMs23SFovadmc6+28KHDyRUC+eCJCvpgzsIJuGCgGnm+cQTeMAlg7xnn6bWfTVNaDSnJx6yS0pUuXauPGzbr55v/Q4cN3yzS/L+kSzReUpfwPmOA4ZACVKN0Y1dHePpl19TKDM58zzWBIZl29jvb2EZQBjyMs52D1GOdAQBofz35QSS5eOAmtFAdMcBwygEo12bxah4b2arxtvcwFC2QahtKRiMbb1uvQ0F5NNq92e4gAcqAMw4JMJQRVVaampqRsLeXyKZuwWh9tGKYOHCjN7ulSHDDh9UMreIsL+WLOwA7mDexg3jiDMowCZdrpf+qpaQWD2T83nxXThgZr4ynlxulSHDDBccgAAMCvCMsWzdcv+amnAkUtm7AbVIvZaq4UB0x44UUAAACAHYTlAhR7xdROUB0crFJTU4P6+0OKxwurmZZKc8AExyEDAAC/IiwXoNgrpvkG1VJ1mSj2ARMchwwAAPyKsFyAUqyY5hNUS9llopjHdHMcMgAA8Cu6YRTArUNEjvF6l4nZYjFD27dXa2AgpHh8esW9tTWpDRs4wQ/+wpyBHcwb2MG8cQYn+JWQmyfTeaHVXDngiQj5Ys7ADuYN7GDeOCNbWM7R+AzzicUM9fRUa/fukEZHpbo66ZRT0nrqqYDGxpxbMW1osLZ5kC4TAAAA9lCznKf5uk+MjRkaHg4onZZuuGFcH/tYUrt2hQo6+toKukxYV8z2egAAoHJQhpEHKzXKkqlg0JmSDLdrpv0iV6nMzp1pnXUWb3HBOt4WhR3MG9jBvHEGJ/gViZXuE1Jx27hlQ5eJ3Ky011u3LsAKMwAAmBdhOQ+7d4dyntiXzeSkvTZu2djtiVwpZQmlbK8HAADKH2UYebDafSKbujpTTzzhbmcKNzt4OM1v7fXsmr3ptKFhuqa9o8PdtnzlirdFYQfzBnYwb5xBGUaRWD2xL5tEQq6u3pbq1D+vKvaR5F5U7CPPAQDAywjLebDSfcIKN9/yr7SyhGIfSe41lfbiBwAApxGW89DRMalQqNB7MTQwUPCd2Gal7jqVcneMxWTlBU4o5N/2epX24gcAAKcRlvOQrftEVZUpydqqs5tv+VdCWcKJrLzACYWkDRsmnRlQkVXaix+gGAKx/Qp3btGS6AotXb5QS6IrFO7cokBsv9tDA+BBhOU8Zeo+ceGFSdXUWLsPN9/yL/eyhNmstNfbsSPt201wlfbiByhU9eAeLW5apdr+PgXiIzJMU4H4iGr7+7S4aZWqB/e4PUQAHkNYtqGx0VRX14SGh+M6cCCu4eG4urom9PGPe/9EvUo89S9Xe72zz3Z7hPbV1xf3OqCcBWL7taC9TUZiTEZq5nOckUrKSIxpQXsbK8wAZiAsF5Ef3vL3wxhLIdMLHL+uKB/z2temlbv8x9TrXpd2YjiAp9X3bJOVIv/67Tc7MyAAvkBYLiI/nKjnhzHCuv/934CkXJ0ujJeuAypbze5dc1aUZzNSSdUM7HBoRAD8gL+gRWb3RL1yG2OlnBDotkTC2nVj9LMHZIxaK943KPIHcAJO8EPR+e2EQD/Pm0o5odBr/DxnKtmS6AoF4iM5r0tHInpx+OmiPz7zBnYwb5zBCX5FxqppZhyS4axK3LAJ2DXRskZmMPumDTMY0kTrOodGBMAPHA/L9913n77whS84/bBFU05HC5ci9HNIhrMqdcMmYMdYxyZZ+YUZ27DRmQEB8AVHw/LXv/51dXd3K5325858O6umXl2FLlXo55AMZ7FhE7Au3RjV0d4+mXX1c1aYzWBIZl29jvb2Kd0YdWmEALzI0bB8xhln6Morr3TyIYsq31VTr65Cl7JUgkMynOeHTaWAV0w2r9ahob0ab1uvdCQi0zCUjkQ03rZeh4b2arJ5tdtDBOAxJdngNzAwoNtvv33Gx7Zu3aq3vOUtevDBB7Vjxw7dcMMNOe8nnU5rasr68KqqApqaKt2q9ZIlAY2M5A6QCxaYevDBtN761oDGxjJfX19v6qGH0jrllGKOMrdNmwz19hpKJjOPLRQy9alPmbrppvymRz7fo4MHvfEOQ6nnDcoPcwZ2MG9gB/PGGaFQ5gXMYCkesLW1Va2trQXfz9SU6aluGPG4tTOgR0aka6+d0uRk9oX7yUnpuuum1NU1UYzhWXbHHeGsQVmSkklDd9whXX21xaXil3zsYzXq789eihEMmmppSerwYWe/7kzYaYx8MWdgB/MGdjBvnEE3jCJpaLB2XTjs7drdUpZKsOEMAACUE8JyHvJp0+Xl2t18Qn++2HAGAADKieNh+cwzz7RUr+xF+ayaljKQFqrUvXnZcAYAAMoFK8t5yGfV1MuHRThRKtHYaKqra0LDw3EdOBDX8HBcXV0TWVeUvdpmD97DXAEAOIWwnCerq6Zert31YqmEV9vswXuYKwAAJ5WkdVyxJJNTnuqGka/BwSq1t9cpmdSMzX7BoKlQSOrtTbhakhCLGdq+vVoDAyHF49MlIa2tSW3YMOloUI7FDDU1NSiRyLwqWFdnamhotCTj8tq8QWZuz5VjmDOwg3kDO5g3zqAbhku8Xrtrp1SiFDgiG1YxVwAATmNlGa6LRsOKx3PXmkYipoaHi98+hHnjH27PlWOYM7CDeQM7mDfOYGXZBjYQOcfLbfbgLcwVAIDTCMvzYAORs7zcZg/ewlwBADiNsDxLLGaovb1OiYQx5wS+VMpQIjF9OyvMxePlNnvwFuYKAMBphOVZ2EDkPC+32YO3MFcAAE4jLM+ye3dozorybKmUoYGBHH+xYZkX+z7Dm5grAACnEZZnYQORO7zeZg/ewVwBADiJ1nGzeKU11TGxmKGenmrt3h3S6Oj0BqeWlqQ6Opw9OKSc0ZYH+WLOwA7mDexg3jiD1nF58NIGIrpyAAAAuIuwPItXNhDRlQMAAMB9hOVZvLKBiK4cAAAA7iMszyPXBqJoNF3y0/3oyoH5cLIkAADOYoNfngYHq9TeXqdkUjPCbDBoKhSSensTRdmNv3x5WKaZOwAZhqkDB2jNUQi/bJ5wau4hN7/MGXgL8wZ2MG+cwQa/InGyjphjfXEiatgBAHAHYTkPTtYRe6krB9xHDTsAAO4gLOfByTpir3TlgDdQww4AgDsIy3lw8nQ/r3TlgDdwsiQAAO4gLOfB6TpijvXFMdSwAwDgDsJyHtyoI25sNNXVNaHh4bgOHIhreDiurq4JVpQrDDXsAAC4g7CcB+qI4RbmHgAA7iAs54E6YriFuQcAgDsIy3mijhhuYe4BAOA8TvBDxWPeIF/MGdjBvIEdzBtncIIfAAAAYANhGQAAAMiAsAwAAABkQFgGAAAAMiAsAwAAABkQlgEAAIAMCMsAAABABoRlAAAAIAPCMgAAAJABYRkAAADIgLAMAAAAZEBYLkOxmKHOzhpFo2EtXx5WNBpWZ2eNYjHD7aEBAAD4CmG5zAwOVqmpqUH9/SHF44ZM01A8bqi/P6SmpgYNDla5PUQAAADfICyXkVjMUHt7nRIJQ6nUzFXkVMpQIjF9OyvMAAAA1hCWy0hPT7WSyezXJJPS9u3VzgwIAADA5wjLZWT37tCcFeXZUilDAwOhjLdT7wwAAPAywnIZGR21dl08Pv/HqXcGAACYibBcRhoarF0XDs/9GPXOAAAAcxGWy0hLS1LBoJn1mmDQVGvr3MJm6p0BVArKzQDkg7BcRjo6JhXKXI4sSQqFpA0bJud8vBj1zgDgdZSbAcgXYbmMNDaa6u1NqK7OnLPCHAyaqqubvr2xce7qc6H1zgDgdZSbAbCDsFxmmpunNDQ0qra2pCIRU4ZhKhIx1daW1NDQqJqbp+b9vELqnQHADyg3A2AHYTkLv9a1NTaa6uqa0PBwXAcOxDU8HFdX18S8K8rHFFLvDAB+QLkZADsIyxlUWl1bIfXOAOAHlJsBsIOwPI9yrWvLtlJeSL0zAPgB5WYA7CAsz6Mc69qsrJTbrXcGAD+g3AyAHYZpmp5dKkwmp3T48Jjl6xctqs/r+kyi0bDi8dyrxpGIqeFh779fF4sZampqUCKR+WuqqzM1NDRakSvHxZo3qBzMGX9y+7mQeQM7mDfOWLYskvE2VpbnUW51beW4Ug4A+aLcDIAdhOV5lFtdGzvAAWAa5WYA8kVYnke51bW5sVLu17Z7AMqfnfaaACoXYXkefm6jNl9IDQatfW6xVsorre0eAAAoX4Tlefi1ri1TSE2lJMmZlfJybbsHAAAqE2E5A7/VtWULqaZpSMoeTou1Us5mQgAAUE5oHVcmOjtr1N+fayOfKcM4Fp6nBYOmQiGptzdRlBcAfmy7V8nzBvYwZ2AH8wZ2MG+cka11nMVqVnidlY4XkqFg0FRtral4fLpGubU1qQ0bJotWUlJubfcAAEBlIyyXCashNZVSSVd0GxqsBWG/tN0DAACVjZrlMuGV3tDl1nYPAABUNsJymfBKSPVz2z0AAIDZCMtlwish1a9t9wAAAOZDWC4TXgqpfmu7BwAAkAmt48pMLGZo+/ZqDQyEStbxotwwb5Av5gzsYN7ADuaNM7K1jiMsF0ksZqinp1q7d4c0Ojq94a6lJamODkKq1/FEhHwxZ2AH8wZ2MG+ckS0sU4ZRBJmOme7vD6mpqUGDg1VuDxEAAAA2EJYLlO2Y6VTKUCIxfXsslvtUOwAAAHgLYblAPT3VSuboxpZMStu3VzszIAAAABQNYblAVo6ZTqUMDQzk6OsGAAAAzyEsF8jqMdNWjoAGAACAtxCWC+SVY6YBAABQfITlAnnlmGkAAAAUH2G5QF45ZhoAAADFR1guUGOjqa1bx19aXXb3mGkAAAAUF2G5QIODVbrsslpNn4N4YlcMU6YpfeMb42punnJpdAAAACgEYbkAJx5IMjU1u33c9McuvbSWA0kAAAB8yrGwPDIyon/5l3/RJz7xCa1du1a/+c1vnHrokuFAEgAAgPLmWFj+3ve+p7POOkv9/f36xje+oauvvtqphy4ZDiQBAAAob0GnHmj9+vWqrp5eYZ2amlJNTY1TD10yHEgCAABQ3gzTNIvepmFgYEC33377jI9t3bpVb3nLW/TCCy/ooosu0mWXXaZ3vOMdWe8nnU5rasr68KqqApqaStsasx1LlgQ0MpK7HnnBAlMHDzo3LuTH6XkD/2POwA7mDexg3jgjFKrKeFtJwnImf/zjH/X5z39enZ2deu9735vz+mRySocPj1m+/0WL6vO6vlCdnTXq789eihEMmmprS6qra8KxcSE/Ts8b+B9zBnYwb2AH88YZy5ZFMt7mWM3yn//8Z23evFnd3d2WgrIfcCAJAABAeXOsZrm7u1uTk5O65pprJEnhcFg9PT1OPXxJNDZOHzjS3l6nZFIzVpiDQVOhkDiQBAAAwMccC8t+D8aZNDdPaWhoVNu3V2tgIKR4XAqHpdbWpDZsmCQoAwAA+JijNcv58nrNMsoD8wb5Ys7ADuYN7GDeOMMTNcsAAACA3xCWAQAAgAwIywAAAEAGhGUAAAAgA8IyAAAAkAFhGQAAAMiAsAwAAABkQFgGAAAAMiAsAwAAABkQlgEAAIAMCMsAAABABoRlAAAAIAPCMgAAAJCBYZqm6fYgAAAAAC9iZRkAAADIgLAMAAAAZEBYBgAAADIgLAMAAAAZEJYBAACADAjLAAAAQAZlGZbvu+8+feELX3B7GPCwdDqtK664QmvXrtUFF1ygJ554wu0hwSceeeQRXXDBBW4PAz6RTCZ1ySWX6LzzzlNLS4sGBwfdHhJ8YGpqSpdeeqnWrVunj3/84/rTn/7k9pAqWtDtARTb17/+df3yl7/UX//1X7s9FHjYT3/6U01OTmrnzp3at2+furq61NPT4/aw4HHf/va3ddddd6murs7tocAn7rrrLi1atEjXXXedDh8+rI985CNqbm52e1jwuJ///OeSpB07dujBBx/UDTfcwN8oF5XdyvIZZ5yhK6+80u1hwOMeeughvfvd75YknXbaafr973/v8ojgByeddJK2bdvm9jDgI2effbY2b94sSTJNU1VVVS6PCH7wgQ98QF/72tckSc8884wWLFjg8ogqm29XlgcGBnT77bfP+NjWrVt1zjnn6MEHH3RpVPCLeDyucDh8/N9VVVVKpVIKBn37KwEHfOhDH9JTTz3l9jDgIw0NDZKmn3M++9nP6nOf+5y7A4JvBINBffGLX9R9992nm266ye3hVDTfJoPW1la1tra6PQz4VDgc1ujo6PF/p9NpgjKAknj22We1ceNGnXfeeTr33HPdHg585Nprr9XFF1+sNWvW6J577lF9fb3bQ6pIZVeGAVhxxhln6P7775ck7du3T2984xtdHhGAcnTw4EG1t7frkksuUUtLi9vDgU/88Ic/1Pbt2yVJdXV1MgxDgQCRzS0spaEiffCDH9QDDzygdevWyTRNbd261e0hAShDt956q44ePapbbrlFt9xyi6TpjaK1tbUujwxetnr1al166aU6//zzlUqldNlllzFnXGSYpmm6PQgAAADAi1jTBwAAADIgLAMAAAAZEJYBAACADAjLAAAAQAaEZQAAACADWscBQJH87//+r6677jo999xzqq2tVW1trS655BKdeuqp2rZtm+6++2698pWv1NTUlGpra3XxxRfrzW9+s+68807ddNNNet3rXidJmpyc1IUXXqhzzjmn6GP8+7//ez3wwAOSpOHhYf3rv/6rrrzySr3zne+0fB/333+/nn32Wa1du1Y7d+7UP//zPysUChV9rADgBYRlACiCRCKhjo4Ofe1rX9Ppp58uSfrtb3+rq6++Wt///vclSevXr9fHP/5xSdNBdePGjfqv//ovSdKHP/xhXXzxxZKkw4cP6x//8R/1D//wDzIMoyTjffzxx7Vp0yZ1dXUdH69V73nPe47//+3bt+sjH/lIkUcHAN5BWAaAIvj5z3+us846a0bwfMtb3qK+vr55rz/llFO0cuVKPfTQQ3NuGxkZUW1t7Yyg/OCDD+rWW29VIBDQCy+8oLVr1+r888/XH//4R33961+XJC1atEhbt27Vo48+quuvv16hUEhr1qyZE2Yfe+wxbd68WTfeeKPe9KY3SZKee+45XXnllZqYmNALL7ygz33uc/rABz6gc845R29729v0+OOPa+HChfrWt76lH//4x9q/f79OPvlkvfDCC9qyZYu2bdumK664Qs8995yef/55vf/979eWLVsK/bYCgOsIywBQBE899ZROOumk4//u6OhQPB7X888/r9tvv33ez1myZIn+8pe/SJLuvvtuPfLIIzIMQ3V1dfrmN7855/oDBw7ohz/8odLptM4991ydffbZ+spXvqKtW7fqDW94gwYGBvSd73xHq1at0sTEhAYGBubcx+joqL70pS+pqqpKIyMjxz++f/9+ffKTn9SZZ56phx9+WNu2bdMHPvABjY+P69xzz9Xb3/52ffOb39TOnTu1cOFCSVJra6t6enp0ww036Nlnn9Vpp52m1tZWTUxM6D3veQ9hGUBZICwDQBG86lWv0u9///vj/+7p6ZEkrVmzRqlUat7PeeaZZ7R69Wo9+eSTM8owMjn99NNVXV0tSTr11FP15JNPanh4WFdddZUkKZlM6vWvf70kqbGxcd77MAxDN998sw4fPqxNmzZpYGBAS5Ys0bJly9TT06Pdu3fLMIzjYw4Gg3r7298uSTrjjDN0//3367TTTptzv4sWLdLvfvc7/frXv1Y4HNbk5GTWrwUA/IJuGABQBM3NzfrVr36lffv2Hf/YE088oeeee27euuPHH39cf/7zn+cNnpn8z//8j6amppRIJPTnP/9ZJ598shobG3Xttdfq+9//vi655BI1NTVJkgKB+Z/e6+vrtWLFCq1cuVLnn3++LrnkEqXTad144436p3/6J1133XU688wzZZqmJCmVSumxxx6TJD300EN6wxveMOP+DMNQOp3WnXfeqUgkou7ubrW3t2t8fPz4fQCAn7GyDABF0NDQoJ6eHnV3d+v6669XKpVSVVWVLr30Uq1YsUKSdNttt+nee+9VIBBQMBjUTTfdpGDQ+tNwKpXSRRddpMOHD6ujo0OLFy/WlVdeqS9+8YtKpVIyDEPXXHONnn/+eUv3197ergceeEC33HKLzj77bH3zm9/Uf/zHf+hVr3rV8fIQSfr2t7+tZ555Rq95zWu0ZcsW3X333cdve9vb3qbPfOYzuuKKK/SFL3xB+/btU3V1tU4++WQ9//zzWr58ueWvDwC8yDB56Q8Anvfggw9qx44duuGGGxx93Pe///360Y9+pJqaGkcfFwC8gjIMAAAAIANWlgEAAIAMWFkGAAAAMiAsAwAAABkQlgEAAIAMCMsAAABABoRlAAAAIAPCMgAAAJDB/w9X7S+ZsAQJPAAAAABJRU5ErkJggg==",
      "text/plain": [
       "<Figure size 864x576 with 1 Axes>"
      ]
     },
     "metadata": {}
    }
   ],
   "metadata": {}
  },
  {
   "cell_type": "code",
   "execution_count": 97,
   "source": [
    "plt.subplots(figsize = (12, 8))\n",
    "wcss = []\n",
    "for i in range(1, 11):\n",
    "    kmeans = KMeans(n_clusters=i, init='k-means++', random_state=100)\n",
    "    kmeans.fit(df3)\n",
    "    wcss.append(kmeans.inertia_)\n",
    "plt.plot(range(1,11), wcss)\n",
    "plt.title('Elbow Method')\n",
    "plt.xlabel('Clusters')\n",
    "plt.ylabel('WCSS')\n",
    "plt.show()"
   ],
   "outputs": [
    {
     "output_type": "display_data",
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 864x576 with 1 Axes>"
      ]
     },
     "metadata": {}
    }
   ],
   "metadata": {}
  },
  {
   "cell_type": "code",
   "execution_count": 98,
   "source": [
    "kmeans2 = KMeans(n_clusters = 3, random_state = 100).fit(df3)\n",
    "labels2 = kmeans2.labels_"
   ],
   "outputs": [],
   "metadata": {}
  },
  {
   "cell_type": "code",
   "execution_count": 109,
   "source": [
    "df4 = pd.DataFrame(data = df3, columns = ['GDPperkapita', 'Kesehatan'])\n",
    "df4['Cluster'] = labels2\n",
    "df4"
   ],
   "outputs": [
    {
     "output_type": "execute_result",
     "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>GDPperkapita</th>\n",
       "      <th>Kesehatan</th>\n",
       "      <th>Cluster</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>-0.825561</td>\n",
       "      <td>0.560239</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>-0.328385</td>\n",
       "      <td>0.092995</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>-0.276376</td>\n",
       "      <td>-0.986655</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>-0.407101</td>\n",
       "      <td>-1.585453</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0.811593</td>\n",
       "      <td>-0.142895</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>123</th>\n",
       "      <td>-0.709315</td>\n",
       "      <td>-0.242695</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>124</th>\n",
       "      <td>-0.485817</td>\n",
       "      <td>-0.496730</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>125</th>\n",
       "      <td>-0.719154</td>\n",
       "      <td>0.224549</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>126</th>\n",
       "      <td>-0.719154</td>\n",
       "      <td>-0.528484</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>127</th>\n",
       "      <td>-0.698069</td>\n",
       "      <td>-0.206404</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>128 rows × 3 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     GDPperkapita  Kesehatan  Cluster\n",
       "0       -0.825561   0.560239        1\n",
       "1       -0.328385   0.092995        1\n",
       "2       -0.276376  -0.986655        1\n",
       "3       -0.407101  -1.585453        1\n",
       "4        0.811593  -0.142895        0\n",
       "..            ...        ...      ...\n",
       "123     -0.709315  -0.242695        1\n",
       "124     -0.485817  -0.496730        1\n",
       "125     -0.719154   0.224549        1\n",
       "126     -0.719154  -0.528484        1\n",
       "127     -0.698069  -0.206404        1\n",
       "\n",
       "[128 rows x 3 columns]"
      ]
     },
     "metadata": {},
     "execution_count": 109
    }
   ],
   "metadata": {}
  },
  {
   "cell_type": "code",
   "execution_count": 110,
   "source": [
    "print('n_cluster = 3')\n",
    "plt.figure(figsize = (12, 8))\n",
    "plt.scatter(df4['GDPperkapita'][df4.Cluster == 0], df4['Kesehatan'][df4.Cluster == 0], c = 'blue', s = 100)\n",
    "plt.scatter(df4['GDPperkapita'][df4.Cluster == 1], df4['Kesehatan'][df4.Cluster == 1], c = 'red', s = 100)\n",
    "plt.scatter(df4['GDPperkapita'][df4.Cluster == 2], df4['Kesehatan'][df4.Cluster == 2], c = 'green', s = 100)\n",
    "plt.scatter(kmeans2.cluster_centers_[:, 0], kmeans2.cluster_centers_[:, 1], c = 'black', s = 300)\n",
    "plt.xlabel('GDP per Kapita')\n",
    "plt.ylabel('Kesehatan')\n",
    "plt.show()"
   ],
   "outputs": [
    {
     "output_type": "stream",
     "name": "stdout",
     "text": [
      "n_cluster = 3\n"
     ]
    },
    {
     "output_type": "display_data",
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 864x576 with 1 Axes>"
      ]
     },
     "metadata": {}
    }
   ],
   "metadata": {}
  },
  {
   "cell_type": "code",
   "execution_count": 111,
   "source": [
    "print('Skor n_cluster = 2 adalah', silhouette_score(df4, labels= labels1))\n",
    "print('Skor n_cluster = 3 adalah', silhouette_score(df4, labels= labels2))"
   ],
   "outputs": [
    {
     "output_type": "stream",
     "name": "stdout",
     "text": [
      "Skor n_cluster = 2 adalah 0.43244321492929155\n",
      "Skor n_cluster = 3 adalah 0.5419678597561084\n"
     ]
    }
   ],
   "metadata": {}
  },
  {
   "cell_type": "markdown",
   "source": [
    "# Report Countries"
   ],
   "metadata": {}
  },
  {
   "cell_type": "code",
   "execution_count": 138,
   "source": [
    "final_df = pd.concat([df, df4['Cluster']], axis = 1)\n",
    "final_df.head()"
   ],
   "outputs": [
    {
     "output_type": "execute_result",
     "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>Negara</th>\n",
       "      <th>Kematian_anak</th>\n",
       "      <th>Ekspor</th>\n",
       "      <th>Kesehatan</th>\n",
       "      <th>Impor</th>\n",
       "      <th>Pendapatan</th>\n",
       "      <th>Inflasi</th>\n",
       "      <th>Harapan_hidup</th>\n",
       "      <th>Jumlah_fertiliti</th>\n",
       "      <th>GDPperkapita</th>\n",
       "      <th>Cluster</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Afghanistan</td>\n",
       "      <td>90.2</td>\n",
       "      <td>10.0</td>\n",
       "      <td>7.58</td>\n",
       "      <td>44.9</td>\n",
       "      <td>1610</td>\n",
       "      <td>9.44</td>\n",
       "      <td>56.2</td>\n",
       "      <td>5.82</td>\n",
       "      <td>553</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Albania</td>\n",
       "      <td>16.6</td>\n",
       "      <td>28.0</td>\n",
       "      <td>6.55</td>\n",
       "      <td>48.6</td>\n",
       "      <td>9930</td>\n",
       "      <td>4.49</td>\n",
       "      <td>76.3</td>\n",
       "      <td>1.65</td>\n",
       "      <td>4090</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Algeria</td>\n",
       "      <td>27.3</td>\n",
       "      <td>38.4</td>\n",
       "      <td>4.17</td>\n",
       "      <td>31.4</td>\n",
       "      <td>12900</td>\n",
       "      <td>16.10</td>\n",
       "      <td>76.5</td>\n",
       "      <td>2.89</td>\n",
       "      <td>4460</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Angola</td>\n",
       "      <td>119.0</td>\n",
       "      <td>62.3</td>\n",
       "      <td>2.85</td>\n",
       "      <td>42.9</td>\n",
       "      <td>5900</td>\n",
       "      <td>22.40</td>\n",
       "      <td>60.1</td>\n",
       "      <td>6.16</td>\n",
       "      <td>3530</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Antigua and Barbuda</td>\n",
       "      <td>10.3</td>\n",
       "      <td>45.5</td>\n",
       "      <td>6.03</td>\n",
       "      <td>58.9</td>\n",
       "      <td>19100</td>\n",
       "      <td>1.44</td>\n",
       "      <td>76.8</td>\n",
       "      <td>2.13</td>\n",
       "      <td>12200</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                Negara  Kematian_anak  Ekspor  Kesehatan  Impor  Pendapatan  \\\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",
       "   Inflasi  Harapan_hidup  Jumlah_fertiliti  GDPperkapita  Cluster  \n",
       "0     9.44           56.2              5.82           553      1.0  \n",
       "1     4.49           76.3              1.65          4090      1.0  \n",
       "2    16.10           76.5              2.89          4460      1.0  \n",
       "3    22.40           60.1              6.16          3530      1.0  \n",
       "4     1.44           76.8              2.13         12200      0.0  "
      ]
     },
     "metadata": {},
     "execution_count": 138
    }
   ],
   "metadata": {}
  },
  {
   "cell_type": "code",
   "execution_count": 139,
   "source": [
    "cluster0 = final_df['Cluster'] == 0\n",
    "countries_cluster0 = final_df[(cluster0)]\n",
    "countries_cluster0"
   ],
   "outputs": [
    {
     "output_type": "execute_result",
     "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>Negara</th>\n",
       "      <th>Kematian_anak</th>\n",
       "      <th>Ekspor</th>\n",
       "      <th>Kesehatan</th>\n",
       "      <th>Impor</th>\n",
       "      <th>Pendapatan</th>\n",
       "      <th>Inflasi</th>\n",
       "      <th>Harapan_hidup</th>\n",
       "      <th>Jumlah_fertiliti</th>\n",
       "      <th>GDPperkapita</th>\n",
       "      <th>Cluster</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Antigua and Barbuda</td>\n",
       "      <td>10.3</td>\n",
       "      <td>45.500</td>\n",
       "      <td>6.03</td>\n",
       "      <td>58.9000</td>\n",
       "      <td>19100</td>\n",
       "      <td>1.440</td>\n",
       "      <td>76.8</td>\n",
       "      <td>2.13</td>\n",
       "      <td>12200</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>Austria</td>\n",
       "      <td>4.3</td>\n",
       "      <td>51.300</td>\n",
       "      <td>11.00</td>\n",
       "      <td>47.8000</td>\n",
       "      <td>43200</td>\n",
       "      <td>0.873</td>\n",
       "      <td>80.5</td>\n",
       "      <td>1.44</td>\n",
       "      <td>46900</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>Azerbaijan</td>\n",
       "      <td>39.2</td>\n",
       "      <td>54.300</td>\n",
       "      <td>5.88</td>\n",
       "      <td>20.7000</td>\n",
       "      <td>16000</td>\n",
       "      <td>13.800</td>\n",
       "      <td>69.1</td>\n",
       "      <td>1.92</td>\n",
       "      <td>5840</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>Bahrain</td>\n",
       "      <td>8.6</td>\n",
       "      <td>69.500</td>\n",
       "      <td>4.97</td>\n",
       "      <td>50.9000</td>\n",
       "      <td>41100</td>\n",
       "      <td>7.440</td>\n",
       "      <td>76.0</td>\n",
       "      <td>2.16</td>\n",
       "      <td>20700</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26</th>\n",
       "      <td>Burundi</td>\n",
       "      <td>93.6</td>\n",
       "      <td>8.920</td>\n",
       "      <td>11.60</td>\n",
       "      <td>39.2000</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>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>34</th>\n",
       "      <td>China</td>\n",
       "      <td>15.7</td>\n",
       "      <td>26.300</td>\n",
       "      <td>5.07</td>\n",
       "      <td>22.6000</td>\n",
       "      <td>9530</td>\n",
       "      <td>6.940</td>\n",
       "      <td>74.6</td>\n",
       "      <td>1.59</td>\n",
       "      <td>4560</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>35</th>\n",
       "      <td>Colombia</td>\n",
       "      <td>18.6</td>\n",
       "      <td>15.900</td>\n",
       "      <td>7.59</td>\n",
       "      <td>17.8000</td>\n",
       "      <td>10900</td>\n",
       "      <td>3.860</td>\n",
       "      <td>76.4</td>\n",
       "      <td>2.01</td>\n",
       "      <td>6250</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>36</th>\n",
       "      <td>Comoros</td>\n",
       "      <td>88.2</td>\n",
       "      <td>16.500</td>\n",
       "      <td>4.51</td>\n",
       "      <td>51.7000</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>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>42</th>\n",
       "      <td>Cyprus</td>\n",
       "      <td>3.6</td>\n",
       "      <td>50.200</td>\n",
       "      <td>5.97</td>\n",
       "      <td>57.5000</td>\n",
       "      <td>33900</td>\n",
       "      <td>2.010</td>\n",
       "      <td>79.9</td>\n",
       "      <td>1.42</td>\n",
       "      <td>30800</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>48</th>\n",
       "      <td>El Salvador</td>\n",
       "      <td>19.2</td>\n",
       "      <td>26.900</td>\n",
       "      <td>6.91</td>\n",
       "      <td>46.6000</td>\n",
       "      <td>7300</td>\n",
       "      <td>2.650</td>\n",
       "      <td>74.1</td>\n",
       "      <td>2.27</td>\n",
       "      <td>2990</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>54</th>\n",
       "      <td>France</td>\n",
       "      <td>4.2</td>\n",
       "      <td>26.800</td>\n",
       "      <td>11.90</td>\n",
       "      <td>28.1000</td>\n",
       "      <td>36900</td>\n",
       "      <td>1.050</td>\n",
       "      <td>81.4</td>\n",
       "      <td>2.03</td>\n",
       "      <td>40600</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>59</th>\n",
       "      <td>Ghana</td>\n",
       "      <td>74.7</td>\n",
       "      <td>29.500</td>\n",
       "      <td>5.22</td>\n",
       "      <td>45.9000</td>\n",
       "      <td>3060</td>\n",
       "      <td>16.600</td>\n",
       "      <td>62.2</td>\n",
       "      <td>4.27</td>\n",
       "      <td>1310</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>67</th>\n",
       "      <td>Hungary</td>\n",
       "      <td>6.0</td>\n",
       "      <td>81.800</td>\n",
       "      <td>7.33</td>\n",
       "      <td>76.5000</td>\n",
       "      <td>22300</td>\n",
       "      <td>2.330</td>\n",
       "      <td>74.5</td>\n",
       "      <td>1.25</td>\n",
       "      <td>13100</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>71</th>\n",
       "      <td>Iran</td>\n",
       "      <td>19.3</td>\n",
       "      <td>24.400</td>\n",
       "      <td>5.60</td>\n",
       "      <td>19.4000</td>\n",
       "      <td>17400</td>\n",
       "      <td>15.900</td>\n",
       "      <td>74.5</td>\n",
       "      <td>1.76</td>\n",
       "      <td>6530</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>87</th>\n",
       "      <td>Lesotho</td>\n",
       "      <td>99.7</td>\n",
       "      <td>39.400</td>\n",
       "      <td>11.10</td>\n",
       "      <td>101.0000</td>\n",
       "      <td>2380</td>\n",
       "      <td>4.150</td>\n",
       "      <td>46.5</td>\n",
       "      <td>3.30</td>\n",
       "      <td>1170</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>93</th>\n",
       "      <td>Madagascar</td>\n",
       "      <td>62.2</td>\n",
       "      <td>25.000</td>\n",
       "      <td>3.77</td>\n",
       "      <td>43.0000</td>\n",
       "      <td>1390</td>\n",
       "      <td>8.790</td>\n",
       "      <td>60.8</td>\n",
       "      <td>4.60</td>\n",
       "      <td>413</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>94</th>\n",
       "      <td>Malawi</td>\n",
       "      <td>90.5</td>\n",
       "      <td>22.800</td>\n",
       "      <td>6.59</td>\n",
       "      <td>34.9000</td>\n",
       "      <td>1030</td>\n",
       "      <td>12.100</td>\n",
       "      <td>53.1</td>\n",
       "      <td>5.31</td>\n",
       "      <td>459</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>99</th>\n",
       "      <td>Mauritania</td>\n",
       "      <td>97.4</td>\n",
       "      <td>50.700</td>\n",
       "      <td>4.41</td>\n",
       "      <td>61.2000</td>\n",
       "      <td>3320</td>\n",
       "      <td>18.900</td>\n",
       "      <td>68.2</td>\n",
       "      <td>4.98</td>\n",
       "      <td>1200</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>102</th>\n",
       "      <td>Moldova</td>\n",
       "      <td>17.2</td>\n",
       "      <td>39.200</td>\n",
       "      <td>11.70</td>\n",
       "      <td>78.5000</td>\n",
       "      <td>3910</td>\n",
       "      <td>11.100</td>\n",
       "      <td>69.7</td>\n",
       "      <td>1.27</td>\n",
       "      <td>1630</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>103</th>\n",
       "      <td>Mongolia</td>\n",
       "      <td>26.1</td>\n",
       "      <td>46.700</td>\n",
       "      <td>5.44</td>\n",
       "      <td>56.7000</td>\n",
       "      <td>7710</td>\n",
       "      <td>39.200</td>\n",
       "      <td>66.2</td>\n",
       "      <td>2.64</td>\n",
       "      <td>2650</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>106</th>\n",
       "      <td>Mozambique</td>\n",
       "      <td>101.0</td>\n",
       "      <td>31.500</td>\n",
       "      <td>5.21</td>\n",
       "      <td>46.2000</td>\n",
       "      <td>918</td>\n",
       "      <td>7.640</td>\n",
       "      <td>54.5</td>\n",
       "      <td>5.56</td>\n",
       "      <td>419</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>107</th>\n",
       "      <td>Myanmar</td>\n",
       "      <td>64.4</td>\n",
       "      <td>0.109</td>\n",
       "      <td>1.97</td>\n",
       "      <td>0.0659</td>\n",
       "      <td>3720</td>\n",
       "      <td>7.040</td>\n",
       "      <td>66.8</td>\n",
       "      <td>2.41</td>\n",
       "      <td>988</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>118</th>\n",
       "      <td>Paraguay</td>\n",
       "      <td>24.1</td>\n",
       "      <td>55.100</td>\n",
       "      <td>5.87</td>\n",
       "      <td>51.5000</td>\n",
       "      <td>7290</td>\n",
       "      <td>6.100</td>\n",
       "      <td>74.1</td>\n",
       "      <td>2.73</td>\n",
       "      <td>3230</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                  Negara  Kematian_anak  Ekspor  Kesehatan     Impor  \\\n",
       "4    Antigua and Barbuda           10.3  45.500       6.03   58.9000   \n",
       "8                Austria            4.3  51.300      11.00   47.8000   \n",
       "9             Azerbaijan           39.2  54.300       5.88   20.7000   \n",
       "11               Bahrain            8.6  69.500       4.97   50.9000   \n",
       "26               Burundi           93.6   8.920      11.60   39.2000   \n",
       "34                 China           15.7  26.300       5.07   22.6000   \n",
       "35              Colombia           18.6  15.900       7.59   17.8000   \n",
       "36               Comoros           88.2  16.500       4.51   51.7000   \n",
       "42                Cyprus            3.6  50.200       5.97   57.5000   \n",
       "48           El Salvador           19.2  26.900       6.91   46.6000   \n",
       "54                France            4.2  26.800      11.90   28.1000   \n",
       "59                 Ghana           74.7  29.500       5.22   45.9000   \n",
       "67               Hungary            6.0  81.800       7.33   76.5000   \n",
       "71                  Iran           19.3  24.400       5.60   19.4000   \n",
       "87               Lesotho           99.7  39.400      11.10  101.0000   \n",
       "93            Madagascar           62.2  25.000       3.77   43.0000   \n",
       "94                Malawi           90.5  22.800       6.59   34.9000   \n",
       "99            Mauritania           97.4  50.700       4.41   61.2000   \n",
       "102              Moldova           17.2  39.200      11.70   78.5000   \n",
       "103             Mongolia           26.1  46.700       5.44   56.7000   \n",
       "106           Mozambique          101.0  31.500       5.21   46.2000   \n",
       "107              Myanmar           64.4   0.109       1.97    0.0659   \n",
       "118             Paraguay           24.1  55.100       5.87   51.5000   \n",
       "\n",
       "     Pendapatan  Inflasi  Harapan_hidup  Jumlah_fertiliti  GDPperkapita  \\\n",
       "4         19100    1.440           76.8              2.13         12200   \n",
       "8         43200    0.873           80.5              1.44         46900   \n",
       "9         16000   13.800           69.1              1.92          5840   \n",
       "11        41100    7.440           76.0              2.16         20700   \n",
       "26          764   12.300           57.7              6.26           231   \n",
       "34         9530    6.940           74.6              1.59          4560   \n",
       "35        10900    3.860           76.4              2.01          6250   \n",
       "36         1410    3.870           65.9              4.75           769   \n",
       "42        33900    2.010           79.9              1.42         30800   \n",
       "48         7300    2.650           74.1              2.27          2990   \n",
       "54        36900    1.050           81.4              2.03         40600   \n",
       "59         3060   16.600           62.2              4.27          1310   \n",
       "67        22300    2.330           74.5              1.25         13100   \n",
       "71        17400   15.900           74.5              1.76          6530   \n",
       "87         2380    4.150           46.5              3.30          1170   \n",
       "93         1390    8.790           60.8              4.60           413   \n",
       "94         1030   12.100           53.1              5.31           459   \n",
       "99         3320   18.900           68.2              4.98          1200   \n",
       "102        3910   11.100           69.7              1.27          1630   \n",
       "103        7710   39.200           66.2              2.64          2650   \n",
       "106         918    7.640           54.5              5.56           419   \n",
       "107        3720    7.040           66.8              2.41           988   \n",
       "118        7290    6.100           74.1              2.73          3230   \n",
       "\n",
       "     Cluster  \n",
       "4        0.0  \n",
       "8        0.0  \n",
       "9        0.0  \n",
       "11       0.0  \n",
       "26       0.0  \n",
       "34       0.0  \n",
       "35       0.0  \n",
       "36       0.0  \n",
       "42       0.0  \n",
       "48       0.0  \n",
       "54       0.0  \n",
       "59       0.0  \n",
       "67       0.0  \n",
       "71       0.0  \n",
       "87       0.0  \n",
       "93       0.0  \n",
       "94       0.0  \n",
       "99       0.0  \n",
       "102      0.0  \n",
       "103      0.0  \n",
       "106      0.0  \n",
       "107      0.0  \n",
       "118      0.0  "
      ]
     },
     "metadata": {},
     "execution_count": 139
    }
   ],
   "metadata": {}
  },
  {
   "cell_type": "code",
   "execution_count": 140,
   "source": [
    "cluster1 = final_df['Cluster'] == 1\n",
    "countries_cluster1 = final_df[(cluster1)]\n",
    "countries_cluster1"
   ],
   "outputs": [
    {
     "output_type": "execute_result",
     "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>Negara</th>\n",
       "      <th>Kematian_anak</th>\n",
       "      <th>Ekspor</th>\n",
       "      <th>Kesehatan</th>\n",
       "      <th>Impor</th>\n",
       "      <th>Pendapatan</th>\n",
       "      <th>Inflasi</th>\n",
       "      <th>Harapan_hidup</th>\n",
       "      <th>Jumlah_fertiliti</th>\n",
       "      <th>GDPperkapita</th>\n",
       "      <th>Cluster</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Afghanistan</td>\n",
       "      <td>90.2</td>\n",
       "      <td>10.0</td>\n",
       "      <td>7.58</td>\n",
       "      <td>44.9</td>\n",
       "      <td>1610</td>\n",
       "      <td>9.44</td>\n",
       "      <td>56.2</td>\n",
       "      <td>5.82</td>\n",
       "      <td>553</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Albania</td>\n",
       "      <td>16.6</td>\n",
       "      <td>28.0</td>\n",
       "      <td>6.55</td>\n",
       "      <td>48.6</td>\n",
       "      <td>9930</td>\n",
       "      <td>4.49</td>\n",
       "      <td>76.3</td>\n",
       "      <td>1.65</td>\n",
       "      <td>4090</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Algeria</td>\n",
       "      <td>27.3</td>\n",
       "      <td>38.4</td>\n",
       "      <td>4.17</td>\n",
       "      <td>31.4</td>\n",
       "      <td>12900</td>\n",
       "      <td>16.10</td>\n",
       "      <td>76.5</td>\n",
       "      <td>2.89</td>\n",
       "      <td>4460</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Angola</td>\n",
       "      <td>119.0</td>\n",
       "      <td>62.3</td>\n",
       "      <td>2.85</td>\n",
       "      <td>42.9</td>\n",
       "      <td>5900</td>\n",
       "      <td>22.40</td>\n",
       "      <td>60.1</td>\n",
       "      <td>6.16</td>\n",
       "      <td>3530</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>Armenia</td>\n",
       "      <td>18.1</td>\n",
       "      <td>20.8</td>\n",
       "      <td>4.40</td>\n",
       "      <td>45.3</td>\n",
       "      <td>6700</td>\n",
       "      <td>7.77</td>\n",
       "      <td>73.3</td>\n",
       "      <td>1.69</td>\n",
       "      <td>3220</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>123</th>\n",
       "      <td>Qatar</td>\n",
       "      <td>9.0</td>\n",
       "      <td>62.3</td>\n",
       "      <td>1.81</td>\n",
       "      <td>23.8</td>\n",
       "      <td>125000</td>\n",
       "      <td>6.98</td>\n",
       "      <td>79.5</td>\n",
       "      <td>2.07</td>\n",
       "      <td>70300</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>124</th>\n",
       "      <td>Romania</td>\n",
       "      <td>11.5</td>\n",
       "      <td>32.6</td>\n",
       "      <td>5.58</td>\n",
       "      <td>38.8</td>\n",
       "      <td>17800</td>\n",
       "      <td>3.53</td>\n",
       "      <td>73.7</td>\n",
       "      <td>1.59</td>\n",
       "      <td>8230</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>125</th>\n",
       "      <td>Russia</td>\n",
       "      <td>10.0</td>\n",
       "      <td>29.2</td>\n",
       "      <td>5.08</td>\n",
       "      <td>21.1</td>\n",
       "      <td>23100</td>\n",
       "      <td>14.20</td>\n",
       "      <td>69.2</td>\n",
       "      <td>1.57</td>\n",
       "      <td>10700</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>126</th>\n",
       "      <td>Rwanda</td>\n",
       "      <td>63.6</td>\n",
       "      <td>12.0</td>\n",
       "      <td>10.50</td>\n",
       "      <td>30.0</td>\n",
       "      <td>1350</td>\n",
       "      <td>2.61</td>\n",
       "      <td>64.6</td>\n",
       "      <td>4.51</td>\n",
       "      <td>563</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>127</th>\n",
       "      <td>Samoa</td>\n",
       "      <td>18.9</td>\n",
       "      <td>29.2</td>\n",
       "      <td>6.47</td>\n",
       "      <td>53.1</td>\n",
       "      <td>5400</td>\n",
       "      <td>1.72</td>\n",
       "      <td>71.5</td>\n",
       "      <td>4.34</td>\n",
       "      <td>3450</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>80 rows × 11 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "          Negara  Kematian_anak  Ekspor  Kesehatan  Impor  Pendapatan  \\\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",
       "6        Armenia           18.1    20.8       4.40   45.3        6700   \n",
       "..           ...            ...     ...        ...    ...         ...   \n",
       "123        Qatar            9.0    62.3       1.81   23.8      125000   \n",
       "124      Romania           11.5    32.6       5.58   38.8       17800   \n",
       "125       Russia           10.0    29.2       5.08   21.1       23100   \n",
       "126       Rwanda           63.6    12.0      10.50   30.0        1350   \n",
       "127        Samoa           18.9    29.2       6.47   53.1        5400   \n",
       "\n",
       "     Inflasi  Harapan_hidup  Jumlah_fertiliti  GDPperkapita  Cluster  \n",
       "0       9.44           56.2              5.82           553      1.0  \n",
       "1       4.49           76.3              1.65          4090      1.0  \n",
       "2      16.10           76.5              2.89          4460      1.0  \n",
       "3      22.40           60.1              6.16          3530      1.0  \n",
       "6       7.77           73.3              1.69          3220      1.0  \n",
       "..       ...            ...               ...           ...      ...  \n",
       "123     6.98           79.5              2.07         70300      1.0  \n",
       "124     3.53           73.7              1.59          8230      1.0  \n",
       "125    14.20           69.2              1.57         10700      1.0  \n",
       "126     2.61           64.6              4.51           563      1.0  \n",
       "127     1.72           71.5              4.34          3450      1.0  \n",
       "\n",
       "[80 rows x 11 columns]"
      ]
     },
     "metadata": {},
     "execution_count": 140
    }
   ],
   "metadata": {}
  },
  {
   "cell_type": "code",
   "execution_count": 141,
   "source": [
    "cluster2 = final_df['Cluster'] == 2\n",
    "countries_cluster2 = final_df[(cluster2)]\n",
    "countries_cluster2"
   ],
   "outputs": [
    {
     "output_type": "execute_result",
     "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>Negara</th>\n",
       "      <th>Kematian_anak</th>\n",
       "      <th>Ekspor</th>\n",
       "      <th>Kesehatan</th>\n",
       "      <th>Impor</th>\n",
       "      <th>Pendapatan</th>\n",
       "      <th>Inflasi</th>\n",
       "      <th>Harapan_hidup</th>\n",
       "      <th>Jumlah_fertiliti</th>\n",
       "      <th>GDPperkapita</th>\n",
       "      <th>Cluster</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>Argentina</td>\n",
       "      <td>14.5</td>\n",
       "      <td>18.9</td>\n",
       "      <td>8.10</td>\n",
       "      <td>16.0</td>\n",
       "      <td>18700</td>\n",
       "      <td>20.900</td>\n",
       "      <td>75.8</td>\n",
       "      <td>2.37</td>\n",
       "      <td>10300</td>\n",
       "      <td>2.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>Benin</td>\n",
       "      <td>111.0</td>\n",
       "      <td>23.8</td>\n",
       "      <td>4.10</td>\n",
       "      <td>37.2</td>\n",
       "      <td>1820</td>\n",
       "      <td>0.885</td>\n",
       "      <td>61.8</td>\n",
       "      <td>5.36</td>\n",
       "      <td>758</td>\n",
       "      <td>2.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>Bhutan</td>\n",
       "      <td>42.7</td>\n",
       "      <td>42.5</td>\n",
       "      <td>5.20</td>\n",
       "      <td>70.7</td>\n",
       "      <td>6420</td>\n",
       "      <td>5.990</td>\n",
       "      <td>72.1</td>\n",
       "      <td>2.38</td>\n",
       "      <td>2180</td>\n",
       "      <td>2.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>Bolivia</td>\n",
       "      <td>46.6</td>\n",
       "      <td>41.2</td>\n",
       "      <td>4.84</td>\n",
       "      <td>34.3</td>\n",
       "      <td>5410</td>\n",
       "      <td>8.780</td>\n",
       "      <td>71.6</td>\n",
       "      <td>3.20</td>\n",
       "      <td>1980</td>\n",
       "      <td>2.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>Brazil</td>\n",
       "      <td>19.8</td>\n",
       "      <td>10.7</td>\n",
       "      <td>9.01</td>\n",
       "      <td>11.8</td>\n",
       "      <td>14500</td>\n",
       "      <td>8.410</td>\n",
       "      <td>74.2</td>\n",
       "      <td>1.80</td>\n",
       "      <td>11200</td>\n",
       "      <td>2.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28</th>\n",
       "      <td>Cameroon</td>\n",
       "      <td>108.0</td>\n",
       "      <td>22.2</td>\n",
       "      <td>5.13</td>\n",
       "      <td>27.0</td>\n",
       "      <td>2660</td>\n",
       "      <td>1.910</td>\n",
       "      <td>57.3</td>\n",
       "      <td>5.11</td>\n",
       "      <td>1310</td>\n",
       "      <td>2.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>32</th>\n",
       "      <td>Chad</td>\n",
       "      <td>150.0</td>\n",
       "      <td>36.8</td>\n",
       "      <td>4.53</td>\n",
       "      <td>43.5</td>\n",
       "      <td>1930</td>\n",
       "      <td>6.390</td>\n",
       "      <td>56.5</td>\n",
       "      <td>6.59</td>\n",
       "      <td>897</td>\n",
       "      <td>2.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>38</th>\n",
       "      <td>Congo, Rep.</td>\n",
       "      <td>63.9</td>\n",
       "      <td>85.1</td>\n",
       "      <td>2.46</td>\n",
       "      <td>54.7</td>\n",
       "      <td>5190</td>\n",
       "      <td>20.700</td>\n",
       "      <td>60.4</td>\n",
       "      <td>4.95</td>\n",
       "      <td>2740</td>\n",
       "      <td>2.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>46</th>\n",
       "      <td>Ecuador</td>\n",
       "      <td>25.1</td>\n",
       "      <td>27.9</td>\n",
       "      <td>8.06</td>\n",
       "      <td>32.4</td>\n",
       "      <td>9350</td>\n",
       "      <td>7.470</td>\n",
       "      <td>76.7</td>\n",
       "      <td>2.66</td>\n",
       "      <td>4660</td>\n",
       "      <td>2.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>52</th>\n",
       "      <td>Fiji</td>\n",
       "      <td>24.1</td>\n",
       "      <td>57.8</td>\n",
       "      <td>4.86</td>\n",
       "      <td>63.9</td>\n",
       "      <td>7350</td>\n",
       "      <td>4.230</td>\n",
       "      <td>65.3</td>\n",
       "      <td>2.67</td>\n",
       "      <td>3650</td>\n",
       "      <td>2.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>58</th>\n",
       "      <td>Germany</td>\n",
       "      <td>4.2</td>\n",
       "      <td>42.3</td>\n",
       "      <td>11.60</td>\n",
       "      <td>37.1</td>\n",
       "      <td>40400</td>\n",
       "      <td>0.758</td>\n",
       "      <td>80.1</td>\n",
       "      <td>1.39</td>\n",
       "      <td>41800</td>\n",
       "      <td>2.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>61</th>\n",
       "      <td>Grenada</td>\n",
       "      <td>14.6</td>\n",
       "      <td>23.8</td>\n",
       "      <td>5.86</td>\n",
       "      <td>49.2</td>\n",
       "      <td>11200</td>\n",
       "      <td>0.480</td>\n",
       "      <td>71.3</td>\n",
       "      <td>2.24</td>\n",
       "      <td>7370</td>\n",
       "      <td>2.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>64</th>\n",
       "      <td>Guinea-Bissau</td>\n",
       "      <td>114.0</td>\n",
       "      <td>14.9</td>\n",
       "      <td>8.50</td>\n",
       "      <td>35.2</td>\n",
       "      <td>1390</td>\n",
       "      <td>2.970</td>\n",
       "      <td>55.6</td>\n",
       "      <td>5.05</td>\n",
       "      <td>547</td>\n",
       "      <td>2.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>68</th>\n",
       "      <td>Iceland</td>\n",
       "      <td>2.6</td>\n",
       "      <td>53.4</td>\n",
       "      <td>9.40</td>\n",
       "      <td>43.3</td>\n",
       "      <td>38800</td>\n",
       "      <td>5.470</td>\n",
       "      <td>82.0</td>\n",
       "      <td>2.20</td>\n",
       "      <td>41900</td>\n",
       "      <td>2.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>69</th>\n",
       "      <td>India</td>\n",
       "      <td>58.8</td>\n",
       "      <td>22.6</td>\n",
       "      <td>4.05</td>\n",
       "      <td>27.1</td>\n",
       "      <td>4410</td>\n",
       "      <td>8.980</td>\n",
       "      <td>66.2</td>\n",
       "      <td>2.60</td>\n",
       "      <td>1350</td>\n",
       "      <td>2.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>80</th>\n",
       "      <td>Kenya</td>\n",
       "      <td>62.2</td>\n",
       "      <td>20.7</td>\n",
       "      <td>4.75</td>\n",
       "      <td>33.6</td>\n",
       "      <td>2480</td>\n",
       "      <td>2.090</td>\n",
       "      <td>62.8</td>\n",
       "      <td>4.37</td>\n",
       "      <td>967</td>\n",
       "      <td>2.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>81</th>\n",
       "      <td>Kiribati</td>\n",
       "      <td>62.7</td>\n",
       "      <td>13.3</td>\n",
       "      <td>11.30</td>\n",
       "      <td>79.9</td>\n",
       "      <td>1730</td>\n",
       "      <td>1.520</td>\n",
       "      <td>60.7</td>\n",
       "      <td>3.84</td>\n",
       "      <td>1490</td>\n",
       "      <td>2.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>89</th>\n",
       "      <td>Libya</td>\n",
       "      <td>16.6</td>\n",
       "      <td>65.6</td>\n",
       "      <td>3.88</td>\n",
       "      <td>42.1</td>\n",
       "      <td>29600</td>\n",
       "      <td>14.200</td>\n",
       "      <td>76.1</td>\n",
       "      <td>2.41</td>\n",
       "      <td>12100</td>\n",
       "      <td>2.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>97</th>\n",
       "      <td>Mali</td>\n",
       "      <td>137.0</td>\n",
       "      <td>22.8</td>\n",
       "      <td>4.98</td>\n",
       "      <td>35.1</td>\n",
       "      <td>1870</td>\n",
       "      <td>4.370</td>\n",
       "      <td>59.5</td>\n",
       "      <td>6.55</td>\n",
       "      <td>708</td>\n",
       "      <td>2.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>101</th>\n",
       "      <td>Micronesia, Fed. Sts.</td>\n",
       "      <td>40.0</td>\n",
       "      <td>23.5</td>\n",
       "      <td>14.20</td>\n",
       "      <td>81.0</td>\n",
       "      <td>3340</td>\n",
       "      <td>3.800</td>\n",
       "      <td>65.4</td>\n",
       "      <td>3.46</td>\n",
       "      <td>2860</td>\n",
       "      <td>2.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>104</th>\n",
       "      <td>Montenegro</td>\n",
       "      <td>6.8</td>\n",
       "      <td>37.0</td>\n",
       "      <td>9.11</td>\n",
       "      <td>62.7</td>\n",
       "      <td>14000</td>\n",
       "      <td>1.600</td>\n",
       "      <td>76.4</td>\n",
       "      <td>1.77</td>\n",
       "      <td>6680</td>\n",
       "      <td>2.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>105</th>\n",
       "      <td>Morocco</td>\n",
       "      <td>33.5</td>\n",
       "      <td>32.2</td>\n",
       "      <td>5.20</td>\n",
       "      <td>43.0</td>\n",
       "      <td>6440</td>\n",
       "      <td>0.976</td>\n",
       "      <td>73.5</td>\n",
       "      <td>2.58</td>\n",
       "      <td>2830</td>\n",
       "      <td>2.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>111</th>\n",
       "      <td>New Zealand</td>\n",
       "      <td>6.2</td>\n",
       "      <td>30.3</td>\n",
       "      <td>10.10</td>\n",
       "      <td>28.0</td>\n",
       "      <td>32300</td>\n",
       "      <td>3.730</td>\n",
       "      <td>80.9</td>\n",
       "      <td>2.17</td>\n",
       "      <td>33700</td>\n",
       "      <td>2.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>120</th>\n",
       "      <td>Philippines</td>\n",
       "      <td>31.9</td>\n",
       "      <td>34.8</td>\n",
       "      <td>3.61</td>\n",
       "      <td>36.6</td>\n",
       "      <td>5600</td>\n",
       "      <td>4.220</td>\n",
       "      <td>69.0</td>\n",
       "      <td>3.16</td>\n",
       "      <td>2130</td>\n",
       "      <td>2.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>122</th>\n",
       "      <td>Portugal</td>\n",
       "      <td>3.9</td>\n",
       "      <td>29.9</td>\n",
       "      <td>11.00</td>\n",
       "      <td>37.4</td>\n",
       "      <td>27200</td>\n",
       "      <td>0.643</td>\n",
       "      <td>79.8</td>\n",
       "      <td>1.39</td>\n",
       "      <td>22500</td>\n",
       "      <td>2.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                    Negara  Kematian_anak  Ekspor  Kesehatan  Impor  \\\n",
       "5                Argentina           14.5    18.9       8.10   16.0   \n",
       "17                   Benin          111.0    23.8       4.10   37.2   \n",
       "18                  Bhutan           42.7    42.5       5.20   70.7   \n",
       "19                 Bolivia           46.6    41.2       4.84   34.3   \n",
       "22                  Brazil           19.8    10.7       9.01   11.8   \n",
       "28                Cameroon          108.0    22.2       5.13   27.0   \n",
       "32                    Chad          150.0    36.8       4.53   43.5   \n",
       "38             Congo, Rep.           63.9    85.1       2.46   54.7   \n",
       "46                 Ecuador           25.1    27.9       8.06   32.4   \n",
       "52                    Fiji           24.1    57.8       4.86   63.9   \n",
       "58                 Germany            4.2    42.3      11.60   37.1   \n",
       "61                 Grenada           14.6    23.8       5.86   49.2   \n",
       "64           Guinea-Bissau          114.0    14.9       8.50   35.2   \n",
       "68                 Iceland            2.6    53.4       9.40   43.3   \n",
       "69                   India           58.8    22.6       4.05   27.1   \n",
       "80                   Kenya           62.2    20.7       4.75   33.6   \n",
       "81                Kiribati           62.7    13.3      11.30   79.9   \n",
       "89                   Libya           16.6    65.6       3.88   42.1   \n",
       "97                    Mali          137.0    22.8       4.98   35.1   \n",
       "101  Micronesia, Fed. Sts.           40.0    23.5      14.20   81.0   \n",
       "104             Montenegro            6.8    37.0       9.11   62.7   \n",
       "105                Morocco           33.5    32.2       5.20   43.0   \n",
       "111            New Zealand            6.2    30.3      10.10   28.0   \n",
       "120            Philippines           31.9    34.8       3.61   36.6   \n",
       "122               Portugal            3.9    29.9      11.00   37.4   \n",
       "\n",
       "     Pendapatan  Inflasi  Harapan_hidup  Jumlah_fertiliti  GDPperkapita  \\\n",
       "5         18700   20.900           75.8              2.37         10300   \n",
       "17         1820    0.885           61.8              5.36           758   \n",
       "18         6420    5.990           72.1              2.38          2180   \n",
       "19         5410    8.780           71.6              3.20          1980   \n",
       "22        14500    8.410           74.2              1.80         11200   \n",
       "28         2660    1.910           57.3              5.11          1310   \n",
       "32         1930    6.390           56.5              6.59           897   \n",
       "38         5190   20.700           60.4              4.95          2740   \n",
       "46         9350    7.470           76.7              2.66          4660   \n",
       "52         7350    4.230           65.3              2.67          3650   \n",
       "58        40400    0.758           80.1              1.39         41800   \n",
       "61        11200    0.480           71.3              2.24          7370   \n",
       "64         1390    2.970           55.6              5.05           547   \n",
       "68        38800    5.470           82.0              2.20         41900   \n",
       "69         4410    8.980           66.2              2.60          1350   \n",
       "80         2480    2.090           62.8              4.37           967   \n",
       "81         1730    1.520           60.7              3.84          1490   \n",
       "89        29600   14.200           76.1              2.41         12100   \n",
       "97         1870    4.370           59.5              6.55           708   \n",
       "101        3340    3.800           65.4              3.46          2860   \n",
       "104       14000    1.600           76.4              1.77          6680   \n",
       "105        6440    0.976           73.5              2.58          2830   \n",
       "111       32300    3.730           80.9              2.17         33700   \n",
       "120        5600    4.220           69.0              3.16          2130   \n",
       "122       27200    0.643           79.8              1.39         22500   \n",
       "\n",
       "     Cluster  \n",
       "5        2.0  \n",
       "17       2.0  \n",
       "18       2.0  \n",
       "19       2.0  \n",
       "22       2.0  \n",
       "28       2.0  \n",
       "32       2.0  \n",
       "38       2.0  \n",
       "46       2.0  \n",
       "52       2.0  \n",
       "58       2.0  \n",
       "61       2.0  \n",
       "64       2.0  \n",
       "68       2.0  \n",
       "69       2.0  \n",
       "80       2.0  \n",
       "81       2.0  \n",
       "89       2.0  \n",
       "97       2.0  \n",
       "101      2.0  \n",
       "104      2.0  \n",
       "105      2.0  \n",
       "111      2.0  \n",
       "120      2.0  \n",
       "122      2.0  "
      ]
     },
     "metadata": {},
     "execution_count": 141
    }
   ],
   "metadata": {}
  },
  {
   "cell_type": "code",
   "execution_count": 145,
   "source": [
    "countries_cluster1[['Negara', 'GDPperkapita', 'Kesehatan', 'Cluster']].sort_values(by = ['GDPperkapita', 'Kesehatan'], ascending = True).head(10).reset_index(drop = True)"
   ],
   "outputs": [
    {
     "output_type": "execute_result",
     "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>Negara</th>\n",
       "      <th>GDPperkapita</th>\n",
       "      <th>Kesehatan</th>\n",
       "      <th>Cluster</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Liberia</td>\n",
       "      <td>327</td>\n",
       "      <td>11.80</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Congo, Dem. Rep.</td>\n",
       "      <td>334</td>\n",
       "      <td>7.91</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Niger</td>\n",
       "      <td>348</td>\n",
       "      <td>5.16</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Central African Republic</td>\n",
       "      <td>446</td>\n",
       "      <td>3.98</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Eritrea</td>\n",
       "      <td>482</td>\n",
       "      <td>2.66</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>Afghanistan</td>\n",
       "      <td>553</td>\n",
       "      <td>7.58</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>Gambia</td>\n",
       "      <td>562</td>\n",
       "      <td>5.69</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>Rwanda</td>\n",
       "      <td>563</td>\n",
       "      <td>10.50</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>Burkina Faso</td>\n",
       "      <td>575</td>\n",
       "      <td>6.74</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>Nepal</td>\n",
       "      <td>592</td>\n",
       "      <td>5.25</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                     Negara  GDPperkapita  Kesehatan  Cluster\n",
       "0                   Liberia           327      11.80      1.0\n",
       "1          Congo, Dem. Rep.           334       7.91      1.0\n",
       "2                     Niger           348       5.16      1.0\n",
       "3  Central African Republic           446       3.98      1.0\n",
       "4                   Eritrea           482       2.66      1.0\n",
       "5               Afghanistan           553       7.58      1.0\n",
       "6                    Gambia           562       5.69      1.0\n",
       "7                    Rwanda           563      10.50      1.0\n",
       "8              Burkina Faso           575       6.74      1.0\n",
       "9                     Nepal           592       5.25      1.0"
      ]
     },
     "metadata": {},
     "execution_count": 145
    }
   ],
   "metadata": {}
  }
 ],
 "metadata": {
  "orig_nbformat": 4,
  "language_info": {
   "name": "python",
   "version": "3.9.5",
   "mimetype": "text/x-python",
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "pygments_lexer": "ipython3",
   "nbconvert_exporter": "python",
   "file_extension": ".py"
  },
  "kernelspec": {
   "name": "python3",
   "display_name": "Python 3.9.5 64-bit"
  },
  "interpreter": {
   "hash": "aee8b7b246df8f9039afb4144a1f6fd8d2ca17a180786b69acc140d282b71a49"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}