UNSUPERVISED ON COUNTRY by MS.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "df = pd.read_csv(r\"C:\\Users\\dell\\Desktop\\Country-data.csv\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>country</th>\n",
       "      <th>child_mort</th>\n",
       "      <th>exports</th>\n",
       "      <th>health</th>\n",
       "      <th>imports</th>\n",
       "      <th>income</th>\n",
       "      <th>inflation</th>\n",
       "      <th>life_expec</th>\n",
       "      <th>total_fer</th>\n",
       "      <th>gdpp</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Afghanistan</td>\n",
       "      <td>90.2</td>\n",
       "      <td>10.0</td>\n",
       "      <td>7.58</td>\n",
       "      <td>44.9</td>\n",
       "      <td>1610</td>\n",
       "      <td>9.44</td>\n",
       "      <td>56.2</td>\n",
       "      <td>5.82</td>\n",
       "      <td>553</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Albania</td>\n",
       "      <td>16.6</td>\n",
       "      <td>28.0</td>\n",
       "      <td>6.55</td>\n",
       "      <td>48.6</td>\n",
       "      <td>9930</td>\n",
       "      <td>4.49</td>\n",
       "      <td>76.3</td>\n",
       "      <td>1.65</td>\n",
       "      <td>4090</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Algeria</td>\n",
       "      <td>27.3</td>\n",
       "      <td>38.4</td>\n",
       "      <td>4.17</td>\n",
       "      <td>31.4</td>\n",
       "      <td>12900</td>\n",
       "      <td>16.10</td>\n",
       "      <td>76.5</td>\n",
       "      <td>2.89</td>\n",
       "      <td>4460</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Angola</td>\n",
       "      <td>119.0</td>\n",
       "      <td>62.3</td>\n",
       "      <td>2.85</td>\n",
       "      <td>42.9</td>\n",
       "      <td>5900</td>\n",
       "      <td>22.40</td>\n",
       "      <td>60.1</td>\n",
       "      <td>6.16</td>\n",
       "      <td>3530</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Antigua and Barbuda</td>\n",
       "      <td>10.3</td>\n",
       "      <td>45.5</td>\n",
       "      <td>6.03</td>\n",
       "      <td>58.9</td>\n",
       "      <td>19100</td>\n",
       "      <td>1.44</td>\n",
       "      <td>76.8</td>\n",
       "      <td>2.13</td>\n",
       "      <td>12200</td>\n",
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       "      <td>...</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>163</th>\n",
       "      <td>Venezuela</td>\n",
       "      <td>17.1</td>\n",
       "      <td>28.5</td>\n",
       "      <td>4.91</td>\n",
       "      <td>17.6</td>\n",
       "      <td>16500</td>\n",
       "      <td>45.90</td>\n",
       "      <td>75.4</td>\n",
       "      <td>2.47</td>\n",
       "      <td>13500</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",
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       "<p>167 rows × 10 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                 country  child_mort  exports  health  imports  income  \\\n",
       "0            Afghanistan        90.2     10.0    7.58     44.9    1610   \n",
       "1                Albania        16.6     28.0    6.55     48.6    9930   \n",
       "2                Algeria        27.3     38.4    4.17     31.4   12900   \n",
       "3                 Angola       119.0     62.3    2.85     42.9    5900   \n",
       "4    Antigua and Barbuda        10.3     45.5    6.03     58.9   19100   \n",
       "..                   ...         ...      ...     ...      ...     ...   \n",
       "162              Vanuatu        29.2     46.6    5.25     52.7    2950   \n",
       "163            Venezuela        17.1     28.5    4.91     17.6   16500   \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",
       "     inflation  life_expec  total_fer   gdpp  \n",
       "0         9.44        56.2       5.82    553  \n",
       "1         4.49        76.3       1.65   4090  \n",
       "2        16.10        76.5       2.89   4460  \n",
       "3        22.40        60.1       6.16   3530  \n",
       "4         1.44        76.8       2.13  12200  \n",
       "..         ...         ...        ...    ...  \n",
       "162       2.62        63.0       3.50   2970  \n",
       "163      45.90        75.4       2.47  13500  \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",
       "[167 rows x 10 columns]"
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   "source": [
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "country        object\n",
       "child_mort    float64\n",
       "exports       float64\n",
       "health        float64\n",
       "imports       float64\n",
       "income          int64\n",
       "inflation     float64\n",
       "life_expec    float64\n",
       "total_fer     float64\n",
       "gdpp            int64\n",
       "dtype: object"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.dtypes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['country', 'child_mort', 'exports', 'health', 'imports', 'income',\n",
       "       'inflation', 'life_expec', 'total_fer', 'gdpp'],\n",
       "      dtype='object')"
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     "execution_count": 5,
     "metadata": {},
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   "source": [
    "df.columns"
   ]
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  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
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       "      <th>child_mort</th>\n",
       "      <th>exports</th>\n",
       "      <th>health</th>\n",
       "      <th>imports</th>\n",
       "      <th>income</th>\n",
       "      <th>inflation</th>\n",
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       "  </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",
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       "    <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",
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       "      <td>1.65</td>\n",
       "      <td>4090</td>\n",
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       "    <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",
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       "      <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",
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      "text/plain": [
       "               country  child_mort  exports  health  imports  income  \\\n",
       "0          Afghanistan        90.2     10.0    7.58     44.9    1610   \n",
       "1              Albania        16.6     28.0    6.55     48.6    9930   \n",
       "2              Algeria        27.3     38.4    4.17     31.4   12900   \n",
       "3               Angola       119.0     62.3    2.85     42.9    5900   \n",
       "4  Antigua and Barbuda        10.3     45.5    6.03     58.9   19100   \n",
       "\n",
       "   inflation  life_expec  total_fer   gdpp  \n",
       "0       9.44        56.2       5.82    553  \n",
       "1       4.49        76.3       1.65   4090  \n",
       "2      16.10        76.5       2.89   4460  \n",
       "3      22.40        60.1       6.16   3530  \n",
       "4       1.44        76.8       2.13  12200  "
      ]
     },
     "execution_count": 6,
     "metadata": {},
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    }
   ],
   "source": [
    "df.head()"
   ]
  },
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   "execution_count": 7,
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       "      <th></th>\n",
       "      <th>country</th>\n",
       "      <th>child_mort</th>\n",
       "      <th>exports</th>\n",
       "      <th>health</th>\n",
       "      <th>imports</th>\n",
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       "  <tbody>\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>163</th>\n",
       "      <td>Venezuela</td>\n",
       "      <td>17.1</td>\n",
       "      <td>28.5</td>\n",
       "      <td>4.91</td>\n",
       "      <td>17.6</td>\n",
       "      <td>16500</td>\n",
       "      <td>45.90</td>\n",
       "      <td>75.4</td>\n",
       "      <td>2.47</td>\n",
       "      <td>13500</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",
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      ],
      "text/plain": [
       "       country  child_mort  exports  health  imports  income  inflation  \\\n",
       "162    Vanuatu        29.2     46.6    5.25     52.7    2950       2.62   \n",
       "163  Venezuela        17.1     28.5    4.91     17.6   16500      45.90   \n",
       "164    Vietnam        23.3     72.0    6.84     80.2    4490      12.10   \n",
       "165      Yemen        56.3     30.0    5.18     34.4    4480      23.60   \n",
       "166     Zambia        83.1     37.0    5.89     30.9    3280      14.00   \n",
       "\n",
       "     life_expec  total_fer   gdpp  \n",
       "162        63.0       3.50   2970  \n",
       "163        75.4       2.47  13500  \n",
       "164        73.1       1.95   1310  \n",
       "165        67.5       4.67   1310  \n",
       "166        52.0       5.40   1460  "
      ]
     },
     "execution_count": 7,
     "metadata": {},
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   ],
   "source": [
    "df.tail()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "df = df.drop('country',axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>child_mort</th>\n",
       "      <th>exports</th>\n",
       "      <th>health</th>\n",
       "      <th>imports</th>\n",
       "      <th>income</th>\n",
       "      <th>inflation</th>\n",
       "      <th>life_expec</th>\n",
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       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\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>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>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>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>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",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>162</th>\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>163</th>\n",
       "      <td>17.1</td>\n",
       "      <td>28.5</td>\n",
       "      <td>4.91</td>\n",
       "      <td>17.6</td>\n",
       "      <td>16500</td>\n",
       "      <td>45.90</td>\n",
       "      <td>75.4</td>\n",
       "      <td>2.47</td>\n",
       "      <td>13500</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>164</th>\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>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>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>167 rows × 9 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     child_mort  exports  health  imports  income  inflation  life_expec  \\\n",
       "0          90.2     10.0    7.58     44.9    1610       9.44        56.2   \n",
       "1          16.6     28.0    6.55     48.6    9930       4.49        76.3   \n",
       "2          27.3     38.4    4.17     31.4   12900      16.10        76.5   \n",
       "3         119.0     62.3    2.85     42.9    5900      22.40        60.1   \n",
       "4          10.3     45.5    6.03     58.9   19100       1.44        76.8   \n",
       "..          ...      ...     ...      ...     ...        ...         ...   \n",
       "162        29.2     46.6    5.25     52.7    2950       2.62        63.0   \n",
       "163        17.1     28.5    4.91     17.6   16500      45.90        75.4   \n",
       "164        23.3     72.0    6.84     80.2    4490      12.10        73.1   \n",
       "165        56.3     30.0    5.18     34.4    4480      23.60        67.5   \n",
       "166        83.1     37.0    5.89     30.9    3280      14.00        52.0   \n",
       "\n",
       "     total_fer   gdpp  \n",
       "0         5.82    553  \n",
       "1         1.65   4090  \n",
       "2         2.89   4460  \n",
       "3         6.16   3530  \n",
       "4         2.13  12200  \n",
       "..         ...    ...  \n",
       "162       3.50   2970  \n",
       "163       2.47  13500  \n",
       "164       1.95   1310  \n",
       "165       4.67   1310  \n",
       "166       5.40   1460  \n",
       "\n",
       "[167 rows x 9 columns]"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>child_mort</th>\n",
       "      <th>exports</th>\n",
       "      <th>health</th>\n",
       "      <th>imports</th>\n",
       "      <th>income</th>\n",
       "      <th>inflation</th>\n",
       "      <th>life_expec</th>\n",
       "      <th>total_fer</th>\n",
       "      <th>gdpp</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>child_mort</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>-0.318093</td>\n",
       "      <td>-0.200402</td>\n",
       "      <td>-0.127211</td>\n",
       "      <td>-0.524315</td>\n",
       "      <td>0.288276</td>\n",
       "      <td>-0.886676</td>\n",
       "      <td>0.848478</td>\n",
       "      <td>-0.483032</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>exports</th>\n",
       "      <td>-0.318093</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>-0.114408</td>\n",
       "      <td>0.737381</td>\n",
       "      <td>0.516784</td>\n",
       "      <td>-0.107294</td>\n",
       "      <td>0.316313</td>\n",
       "      <td>-0.320011</td>\n",
       "      <td>0.418725</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>health</th>\n",
       "      <td>-0.200402</td>\n",
       "      <td>-0.114408</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.095717</td>\n",
       "      <td>0.129579</td>\n",
       "      <td>-0.255376</td>\n",
       "      <td>0.210692</td>\n",
       "      <td>-0.196674</td>\n",
       "      <td>0.345966</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>imports</th>\n",
       "      <td>-0.127211</td>\n",
       "      <td>0.737381</td>\n",
       "      <td>0.095717</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.122406</td>\n",
       "      <td>-0.246994</td>\n",
       "      <td>0.054391</td>\n",
       "      <td>-0.159048</td>\n",
       "      <td>0.115498</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>income</th>\n",
       "      <td>-0.524315</td>\n",
       "      <td>0.516784</td>\n",
       "      <td>0.129579</td>\n",
       "      <td>0.122406</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>-0.147756</td>\n",
       "      <td>0.611962</td>\n",
       "      <td>-0.501840</td>\n",
       "      <td>0.895571</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>inflation</th>\n",
       "      <td>0.288276</td>\n",
       "      <td>-0.107294</td>\n",
       "      <td>-0.255376</td>\n",
       "      <td>-0.246994</td>\n",
       "      <td>-0.147756</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>-0.239705</td>\n",
       "      <td>0.316921</td>\n",
       "      <td>-0.221631</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>life_expec</th>\n",
       "      <td>-0.886676</td>\n",
       "      <td>0.316313</td>\n",
       "      <td>0.210692</td>\n",
       "      <td>0.054391</td>\n",
       "      <td>0.611962</td>\n",
       "      <td>-0.239705</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>-0.760875</td>\n",
       "      <td>0.600089</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>total_fer</th>\n",
       "      <td>0.848478</td>\n",
       "      <td>-0.320011</td>\n",
       "      <td>-0.196674</td>\n",
       "      <td>-0.159048</td>\n",
       "      <td>-0.501840</td>\n",
       "      <td>0.316921</td>\n",
       "      <td>-0.760875</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>-0.454910</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>gdpp</th>\n",
       "      <td>-0.483032</td>\n",
       "      <td>0.418725</td>\n",
       "      <td>0.345966</td>\n",
       "      <td>0.115498</td>\n",
       "      <td>0.895571</td>\n",
       "      <td>-0.221631</td>\n",
       "      <td>0.600089</td>\n",
       "      <td>-0.454910</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "            child_mort   exports    health   imports    income  inflation  \\\n",
       "child_mort    1.000000 -0.318093 -0.200402 -0.127211 -0.524315   0.288276   \n",
       "exports      -0.318093  1.000000 -0.114408  0.737381  0.516784  -0.107294   \n",
       "health       -0.200402 -0.114408  1.000000  0.095717  0.129579  -0.255376   \n",
       "imports      -0.127211  0.737381  0.095717  1.000000  0.122406  -0.246994   \n",
       "income       -0.524315  0.516784  0.129579  0.122406  1.000000  -0.147756   \n",
       "inflation     0.288276 -0.107294 -0.255376 -0.246994 -0.147756   1.000000   \n",
       "life_expec   -0.886676  0.316313  0.210692  0.054391  0.611962  -0.239705   \n",
       "total_fer     0.848478 -0.320011 -0.196674 -0.159048 -0.501840   0.316921   \n",
       "gdpp         -0.483032  0.418725  0.345966  0.115498  0.895571  -0.221631   \n",
       "\n",
       "            life_expec  total_fer      gdpp  \n",
       "child_mort   -0.886676   0.848478 -0.483032  \n",
       "exports       0.316313  -0.320011  0.418725  \n",
       "health        0.210692  -0.196674  0.345966  \n",
       "imports       0.054391  -0.159048  0.115498  \n",
       "income        0.611962  -0.501840  0.895571  \n",
       "inflation    -0.239705   0.316921 -0.221631  \n",
       "life_expec    1.000000  -0.760875  0.600089  \n",
       "total_fer    -0.760875   1.000000 -0.454910  \n",
       "gdpp          0.600089  -0.454910  1.000000  "
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.corr()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Axes: >"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
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",
      "text/plain": [
       "<Figure size 640x480 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.heatmap(df.corr(),linewidths=3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "child_mort    0\n",
       "exports       0\n",
       "health        0\n",
       "imports       0\n",
       "income        0\n",
       "inflation     0\n",
       "life_expec    0\n",
       "total_fer     0\n",
       "gdpp          0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.isna().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Axes: >"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "df.boxplot()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>child_mort</th>\n",
       "      <th>exports</th>\n",
       "      <th>health</th>\n",
       "      <th>imports</th>\n",
       "      <th>income</th>\n",
       "      <th>inflation</th>\n",
       "      <th>life_expec</th>\n",
       "      <th>total_fer</th>\n",
       "      <th>gdpp</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\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>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>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>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>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": [
       "   child_mort  exports  health  imports  income  inflation  life_expec  \\\n",
       "0        90.2     10.0    7.58     44.9    1610       9.44        56.2   \n",
       "1        16.6     28.0    6.55     48.6    9930       4.49        76.3   \n",
       "2        27.3     38.4    4.17     31.4   12900      16.10        76.5   \n",
       "3       119.0     62.3    2.85     42.9    5900      22.40        60.1   \n",
       "4        10.3     45.5    6.03     58.9   19100       1.44        76.8   \n",
       "\n",
       "   total_fer   gdpp  \n",
       "0       5.82    553  \n",
       "1       1.65   4090  \n",
       "2       2.89   4460  \n",
       "3       6.16   3530  \n",
       "4       2.13  12200  "
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.preprocessing import MinMaxScaler\n",
    "minmax = MinMaxScaler()\n",
    "\n",
    "new = minmax.fit_transform(df)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "numpy.ndarray"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "type(new)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [],
   "source": [
    "df = pd.DataFrame(new,columns=['child_mort', 'exports', 'health', 'imports', 'income',\n",
    "       'inflation', 'life_expec', 'total_fer', 'gdpp'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>child_mort</th>\n",
       "      <th>exports</th>\n",
       "      <th>health</th>\n",
       "      <th>imports</th>\n",
       "      <th>income</th>\n",
       "      <th>inflation</th>\n",
       "      <th>life_expec</th>\n",
       "      <th>total_fer</th>\n",
       "      <th>gdpp</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0.426485</td>\n",
       "      <td>0.049482</td>\n",
       "      <td>0.358608</td>\n",
       "      <td>0.257765</td>\n",
       "      <td>0.008047</td>\n",
       "      <td>0.126144</td>\n",
       "      <td>0.475345</td>\n",
       "      <td>0.736593</td>\n",
       "      <td>0.003073</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0.068160</td>\n",
       "      <td>0.139531</td>\n",
       "      <td>0.294593</td>\n",
       "      <td>0.279037</td>\n",
       "      <td>0.074933</td>\n",
       "      <td>0.080399</td>\n",
       "      <td>0.871795</td>\n",
       "      <td>0.078864</td>\n",
       "      <td>0.036833</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0.120253</td>\n",
       "      <td>0.191559</td>\n",
       "      <td>0.146675</td>\n",
       "      <td>0.180149</td>\n",
       "      <td>0.098809</td>\n",
       "      <td>0.187691</td>\n",
       "      <td>0.875740</td>\n",
       "      <td>0.274448</td>\n",
       "      <td>0.040365</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0.566699</td>\n",
       "      <td>0.311125</td>\n",
       "      <td>0.064636</td>\n",
       "      <td>0.246266</td>\n",
       "      <td>0.042535</td>\n",
       "      <td>0.245911</td>\n",
       "      <td>0.552268</td>\n",
       "      <td>0.790221</td>\n",
       "      <td>0.031488</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0.037488</td>\n",
       "      <td>0.227079</td>\n",
       "      <td>0.262275</td>\n",
       "      <td>0.338255</td>\n",
       "      <td>0.148652</td>\n",
       "      <td>0.052213</td>\n",
       "      <td>0.881657</td>\n",
       "      <td>0.154574</td>\n",
       "      <td>0.114242</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   child_mort   exports    health   imports    income  inflation  life_expec  \\\n",
       "0    0.426485  0.049482  0.358608  0.257765  0.008047   0.126144    0.475345   \n",
       "1    0.068160  0.139531  0.294593  0.279037  0.074933   0.080399    0.871795   \n",
       "2    0.120253  0.191559  0.146675  0.180149  0.098809   0.187691    0.875740   \n",
       "3    0.566699  0.311125  0.064636  0.246266  0.042535   0.245911    0.552268   \n",
       "4    0.037488  0.227079  0.262275  0.338255  0.148652   0.052213    0.881657   \n",
       "\n",
       "   total_fer      gdpp  \n",
       "0   0.736593  0.003073  \n",
       "1   0.078864  0.036833  \n",
       "2   0.274448  0.040365  \n",
       "3   0.790221  0.031488  \n",
       "4   0.154574  0.114242  "
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "\n",
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       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>child_mort</th>\n",
       "      <th>exports</th>\n",
       "      <th>health</th>\n",
       "      <th>imports</th>\n",
       "      <th>income</th>\n",
       "      <th>inflation</th>\n",
       "      <th>life_expec</th>\n",
       "      <th>total_fer</th>\n",
       "      <th>gdpp</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>child_mort</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>-0.318093</td>\n",
       "      <td>-0.200402</td>\n",
       "      <td>-0.127211</td>\n",
       "      <td>-0.524315</td>\n",
       "      <td>0.288276</td>\n",
       "      <td>-0.886676</td>\n",
       "      <td>0.848478</td>\n",
       "      <td>-0.483032</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>exports</th>\n",
       "      <td>-0.318093</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>-0.114408</td>\n",
       "      <td>0.737381</td>\n",
       "      <td>0.516784</td>\n",
       "      <td>-0.107294</td>\n",
       "      <td>0.316313</td>\n",
       "      <td>-0.320011</td>\n",
       "      <td>0.418725</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>health</th>\n",
       "      <td>-0.200402</td>\n",
       "      <td>-0.114408</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.095717</td>\n",
       "      <td>0.129579</td>\n",
       "      <td>-0.255376</td>\n",
       "      <td>0.210692</td>\n",
       "      <td>-0.196674</td>\n",
       "      <td>0.345966</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>imports</th>\n",
       "      <td>-0.127211</td>\n",
       "      <td>0.737381</td>\n",
       "      <td>0.095717</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.122406</td>\n",
       "      <td>-0.246994</td>\n",
       "      <td>0.054391</td>\n",
       "      <td>-0.159048</td>\n",
       "      <td>0.115498</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>income</th>\n",
       "      <td>-0.524315</td>\n",
       "      <td>0.516784</td>\n",
       "      <td>0.129579</td>\n",
       "      <td>0.122406</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>-0.147756</td>\n",
       "      <td>0.611962</td>\n",
       "      <td>-0.501840</td>\n",
       "      <td>0.895571</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>inflation</th>\n",
       "      <td>0.288276</td>\n",
       "      <td>-0.107294</td>\n",
       "      <td>-0.255376</td>\n",
       "      <td>-0.246994</td>\n",
       "      <td>-0.147756</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>-0.239705</td>\n",
       "      <td>0.316921</td>\n",
       "      <td>-0.221631</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>life_expec</th>\n",
       "      <td>-0.886676</td>\n",
       "      <td>0.316313</td>\n",
       "      <td>0.210692</td>\n",
       "      <td>0.054391</td>\n",
       "      <td>0.611962</td>\n",
       "      <td>-0.239705</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>-0.760875</td>\n",
       "      <td>0.600089</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>total_fer</th>\n",
       "      <td>0.848478</td>\n",
       "      <td>-0.320011</td>\n",
       "      <td>-0.196674</td>\n",
       "      <td>-0.159048</td>\n",
       "      <td>-0.501840</td>\n",
       "      <td>0.316921</td>\n",
       "      <td>-0.760875</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>-0.454910</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>gdpp</th>\n",
       "      <td>-0.483032</td>\n",
       "      <td>0.418725</td>\n",
       "      <td>0.345966</td>\n",
       "      <td>0.115498</td>\n",
       "      <td>0.895571</td>\n",
       "      <td>-0.221631</td>\n",
       "      <td>0.600089</td>\n",
       "      <td>-0.454910</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "            child_mort   exports    health   imports    income  inflation  \\\n",
       "child_mort    1.000000 -0.318093 -0.200402 -0.127211 -0.524315   0.288276   \n",
       "exports      -0.318093  1.000000 -0.114408  0.737381  0.516784  -0.107294   \n",
       "health       -0.200402 -0.114408  1.000000  0.095717  0.129579  -0.255376   \n",
       "imports      -0.127211  0.737381  0.095717  1.000000  0.122406  -0.246994   \n",
       "income       -0.524315  0.516784  0.129579  0.122406  1.000000  -0.147756   \n",
       "inflation     0.288276 -0.107294 -0.255376 -0.246994 -0.147756   1.000000   \n",
       "life_expec   -0.886676  0.316313  0.210692  0.054391  0.611962  -0.239705   \n",
       "total_fer     0.848478 -0.320011 -0.196674 -0.159048 -0.501840   0.316921   \n",
       "gdpp         -0.483032  0.418725  0.345966  0.115498  0.895571  -0.221631   \n",
       "\n",
       "            life_expec  total_fer      gdpp  \n",
       "child_mort   -0.886676   0.848478 -0.483032  \n",
       "exports       0.316313  -0.320011  0.418725  \n",
       "health        0.210692  -0.196674  0.345966  \n",
       "imports       0.054391  -0.159048  0.115498  \n",
       "income        0.611962  -0.501840  0.895571  \n",
       "inflation    -0.239705   0.316921 -0.221631  \n",
       "life_expec    1.000000  -0.760875  0.600089  \n",
       "total_fer    -0.760875   1.000000 -0.454910  \n",
       "gdpp          0.600089  -0.454910  1.000000  "
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.corr()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [],
   "source": [
    "x = df.values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[0.42648491 0.04948197 0.35860783 ... 0.47534517 0.73659306 0.00307343]\n",
      " [0.06815969 0.13953104 0.29459291 ... 0.87179487 0.07886435 0.03683341]\n",
      " [0.12025316 0.1915594  0.14667495 ... 0.87573964 0.27444795 0.04036499]\n",
      " ...\n",
      " [0.10077897 0.35965101 0.31261653 ... 0.8086785  0.12618297 0.01029885]\n",
      " [0.26144109 0.1495365  0.20944686 ... 0.69822485 0.55520505 0.01029885]\n",
      " [0.39191821 0.18455558 0.25357365 ... 0.39250493 0.670347   0.01173057]]\n"
     ]
    }
   ],
   "source": [
    "print(x)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "c:\\Users\\dell\\anaconda3\\Lib\\site-packages\\sklearn\\cluster\\_kmeans.py:1412: FutureWarning: The default value of `n_init` will change from 10 to 'auto' in 1.4. Set the value of `n_init` explicitly to suppress the warning\n",
      "  super()._check_params_vs_input(X, default_n_init=10)\n",
      "c:\\Users\\dell\\anaconda3\\Lib\\site-packages\\sklearn\\cluster\\_kmeans.py:1436: UserWarning: KMeans is known to have a memory leak on Windows with MKL, when there are less chunks than available threads. You can avoid it by setting the environment variable OMP_NUM_THREADS=1.\n",
      "  warnings.warn(\n",
      "c:\\Users\\dell\\anaconda3\\Lib\\site-packages\\sklearn\\cluster\\_kmeans.py:1412: FutureWarning: The default value of `n_init` will change from 10 to 'auto' in 1.4. Set the value of `n_init` explicitly to suppress the warning\n",
      "  super()._check_params_vs_input(X, default_n_init=10)\n",
      "c:\\Users\\dell\\anaconda3\\Lib\\site-packages\\sklearn\\cluster\\_kmeans.py:1436: UserWarning: KMeans is known to have a memory leak on Windows with MKL, when there are less chunks than available threads. You can avoid it by setting the environment variable OMP_NUM_THREADS=1.\n",
      "  warnings.warn(\n",
      "c:\\Users\\dell\\anaconda3\\Lib\\site-packages\\sklearn\\cluster\\_kmeans.py:1412: FutureWarning: The default value of `n_init` will change from 10 to 'auto' in 1.4. Set the value of `n_init` explicitly to suppress the warning\n",
      "  super()._check_params_vs_input(X, default_n_init=10)\n",
      "c:\\Users\\dell\\anaconda3\\Lib\\site-packages\\sklearn\\cluster\\_kmeans.py:1436: UserWarning: KMeans is known to have a memory leak on Windows with MKL, when there are less chunks than available threads. You can avoid it by setting the environment variable OMP_NUM_THREADS=1.\n",
      "  warnings.warn(\n",
      "c:\\Users\\dell\\anaconda3\\Lib\\site-packages\\sklearn\\cluster\\_kmeans.py:1412: FutureWarning: The default value of `n_init` will change from 10 to 'auto' in 1.4. Set the value of `n_init` explicitly to suppress the warning\n",
      "  super()._check_params_vs_input(X, default_n_init=10)\n",
      "c:\\Users\\dell\\anaconda3\\Lib\\site-packages\\sklearn\\cluster\\_kmeans.py:1436: UserWarning: KMeans is known to have a memory leak on Windows with MKL, when there are less chunks than available threads. You can avoid it by setting the environment variable OMP_NUM_THREADS=1.\n",
      "  warnings.warn(\n",
      "c:\\Users\\dell\\anaconda3\\Lib\\site-packages\\sklearn\\cluster\\_kmeans.py:1412: FutureWarning: The default value of `n_init` will change from 10 to 'auto' in 1.4. Set the value of `n_init` explicitly to suppress the warning\n",
      "  super()._check_params_vs_input(X, default_n_init=10)\n",
      "c:\\Users\\dell\\anaconda3\\Lib\\site-packages\\sklearn\\cluster\\_kmeans.py:1436: UserWarning: KMeans is known to have a memory leak on Windows with MKL, when there are less chunks than available threads. You can avoid it by setting the environment variable OMP_NUM_THREADS=1.\n",
      "  warnings.warn(\n",
      "c:\\Users\\dell\\anaconda3\\Lib\\site-packages\\sklearn\\cluster\\_kmeans.py:1412: FutureWarning: The default value of `n_init` will change from 10 to 'auto' in 1.4. Set the value of `n_init` explicitly to suppress the warning\n",
      "  super()._check_params_vs_input(X, default_n_init=10)\n",
      "c:\\Users\\dell\\anaconda3\\Lib\\site-packages\\sklearn\\cluster\\_kmeans.py:1436: UserWarning: KMeans is known to have a memory leak on Windows with MKL, when there are less chunks than available threads. You can avoid it by setting the environment variable OMP_NUM_THREADS=1.\n",
      "  warnings.warn(\n",
      "c:\\Users\\dell\\anaconda3\\Lib\\site-packages\\sklearn\\cluster\\_kmeans.py:1412: FutureWarning: The default value of `n_init` will change from 10 to 'auto' in 1.4. Set the value of `n_init` explicitly to suppress the warning\n",
      "  super()._check_params_vs_input(X, default_n_init=10)\n",
      "c:\\Users\\dell\\anaconda3\\Lib\\site-packages\\sklearn\\cluster\\_kmeans.py:1436: UserWarning: KMeans is known to have a memory leak on Windows with MKL, when there are less chunks than available threads. You can avoid it by setting the environment variable OMP_NUM_THREADS=1.\n",
      "  warnings.warn(\n",
      "c:\\Users\\dell\\anaconda3\\Lib\\site-packages\\sklearn\\cluster\\_kmeans.py:1412: FutureWarning: The default value of `n_init` will change from 10 to 'auto' in 1.4. Set the value of `n_init` explicitly to suppress the warning\n",
      "  super()._check_params_vs_input(X, default_n_init=10)\n",
      "c:\\Users\\dell\\anaconda3\\Lib\\site-packages\\sklearn\\cluster\\_kmeans.py:1436: UserWarning: KMeans is known to have a memory leak on Windows with MKL, when there are less chunks than available threads. You can avoid it by setting the environment variable OMP_NUM_THREADS=1.\n",
      "  warnings.warn(\n",
      "c:\\Users\\dell\\anaconda3\\Lib\\site-packages\\sklearn\\cluster\\_kmeans.py:1412: FutureWarning: The default value of `n_init` will change from 10 to 'auto' in 1.4. Set the value of `n_init` explicitly to suppress the warning\n",
      "  super()._check_params_vs_input(X, default_n_init=10)\n",
      "c:\\Users\\dell\\anaconda3\\Lib\\site-packages\\sklearn\\cluster\\_kmeans.py:1436: UserWarning: KMeans is known to have a memory leak on Windows with MKL, when there are less chunks than available threads. You can avoid it by setting the environment variable OMP_NUM_THREADS=1.\n",
      "  warnings.warn(\n",
      "c:\\Users\\dell\\anaconda3\\Lib\\site-packages\\sklearn\\cluster\\_kmeans.py:1412: FutureWarning: The default value of `n_init` will change from 10 to 'auto' in 1.4. Set the value of `n_init` explicitly to suppress the warning\n",
      "  super()._check_params_vs_input(X, default_n_init=10)\n",
      "c:\\Users\\dell\\anaconda3\\Lib\\site-packages\\sklearn\\cluster\\_kmeans.py:1436: UserWarning: KMeans is known to have a memory leak on Windows with MKL, when there are less chunks than available threads. You can avoid it by setting the environment variable OMP_NUM_THREADS=1.\n",
      "  warnings.warn(\n"
     ]
    }
   ],
   "source": [
    "from sklearn.cluster import KMeans\n",
    "wcss = []\n",
    "\n",
    "for i in range(1,11):\n",
    "    kmeans = KMeans(n_clusters=i,init='k-means++',random_state=42)\n",
    "    kmeans.fit(x)\n",
    "    wcss.append(kmeans.inertia_)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0, 0.5, 'wcss')"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.plot(range(1,11),wcss)\n",
    "plt.title('elbow method')\n",
    "plt.grid()\n",
    "plt.xlabel('range')\n",
    "plt.ylabel('wcss')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "c:\\Users\\dell\\anaconda3\\Lib\\site-packages\\sklearn\\cluster\\_kmeans.py:1412: FutureWarning: The default value of `n_init` will change from 10 to 'auto' in 1.4. Set the value of `n_init` explicitly to suppress the warning\n",
      "  super()._check_params_vs_input(X, default_n_init=10)\n",
      "c:\\Users\\dell\\anaconda3\\Lib\\site-packages\\sklearn\\cluster\\_kmeans.py:1436: UserWarning: KMeans is known to have a memory leak on Windows with MKL, when there are less chunks than available threads. You can avoid it by setting the environment variable OMP_NUM_THREADS=1.\n",
      "  warnings.warn(\n"
     ]
    }
   ],
   "source": [
    "# we are taking 3 as k because it have the highest bending point\n",
    "\n",
    "kmean = KMeans(n_clusters=3,init='k-means++',random_state=42)\n",
    "y1_predkm = kmean.fit_predict(x)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "the silhouette score is: 0.34265474105126204\n"
     ]
    }
   ],
   "source": [
    "from sklearn.metrics import silhouette_score\n",
    "print(\"the silhouette score is:\", silhouette_score(x,y1_predkm))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "base",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.11.5"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}