Data_Modelling.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "0b76fac6",
   "metadata": {},
   "source": [
    "## Task 1: Data Retrieving"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "cc4a082a",
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd # To retrieve the dataset `pandas` library has been imported as `pd`."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "ee318748",
   "metadata": {},
   "outputs": [],
   "source": [
    "pd.options.mode.chained_assignment = None  # default='warn'"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "21832db8",
   "metadata": {},
   "source": [
    "Here, `flag.data` file is loaded by generating 'my_file' object. The following approach `pd.read_csv` is used in order to convert a text file into `csv` file and also the correct arguments are applied including sep, decimal, header and names of the variables."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "5968c146",
   "metadata": {},
   "outputs": [
    {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>name</th>\n",
       "      <th>landmass</th>\n",
       "      <th>zone</th>\n",
       "      <th>area</th>\n",
       "      <th>population</th>\n",
       "      <th>language</th>\n",
       "      <th>religion</th>\n",
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       "  </thead>\n",
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       "      <th>0</th>\n",
       "      <td>Afghanistan</td>\n",
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       "      <td>white</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>American-Samoa</td>\n",
       "      <td>6</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
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       "      <th>4</th>\n",
       "      <td>Andorra</td>\n",
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       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>Angola</td>\n",
       "      <td>4</td>\n",
       "      <td>2</td>\n",
       "      <td>1247</td>\n",
       "      <td>7</td>\n",
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       "      <td>Anguilla</td>\n",
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       "      <th>7</th>\n",
       "      <td>Antigua-Barbuda</td>\n",
       "      <td>1</td>\n",
       "      <td>4</td>\n",
       "      <td>0</td>\n",
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       "      <td>black</td>\n",
       "      <td>red</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>Argentina</td>\n",
       "      <td>2</td>\n",
       "      <td>3</td>\n",
       "      <td>2777</td>\n",
       "      <td>28</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
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       "      <th>9</th>\n",
       "      <td>Argentine</td>\n",
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       "      <td>blue</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>Australia</td>\n",
       "      <td>6</td>\n",
       "      <td>2</td>\n",
       "      <td>7690</td>\n",
       "      <td>15</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
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       "      <td>blue</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>Austria</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>84</td>\n",
       "      <td>8</td>\n",
       "      <td>4</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>2</td>\n",
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       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
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       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>red</td>\n",
       "      <td>red</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>Bahamas</td>\n",
       "      <td>1</td>\n",
       "      <td>4</td>\n",
       "      <td>19</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
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       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>blue</td>\n",
       "      <td>blue</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>Bahrain</td>\n",
       "      <td>5</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>8</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
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       "      <td>red</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>Bangladesh</td>\n",
       "      <td>5</td>\n",
       "      <td>1</td>\n",
       "      <td>143</td>\n",
       "      <td>90</td>\n",
       "      <td>6</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
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       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>green</td>\n",
       "      <td>green</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>Barbados</td>\n",
       "      <td>1</td>\n",
       "      <td>4</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>blue</td>\n",
       "      <td>blue</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>Belgium</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>31</td>\n",
       "      <td>10</td>\n",
       "      <td>6</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
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       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>black</td>\n",
       "      <td>red</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>Belize</td>\n",
       "      <td>1</td>\n",
       "      <td>4</td>\n",
       "      <td>23</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>8</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>red</td>\n",
       "      <td>red</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>Benin</td>\n",
       "      <td>4</td>\n",
       "      <td>1</td>\n",
       "      <td>113</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>5</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>green</td>\n",
       "      <td>green</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>Bermuda</td>\n",
       "      <td>1</td>\n",
       "      <td>4</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>6</td>\n",
       "      <td>...</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>white</td>\n",
       "      <td>red</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>Bhutan</td>\n",
       "      <td>5</td>\n",
       "      <td>1</td>\n",
       "      <td>47</td>\n",
       "      <td>1</td>\n",
       "      <td>10</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>4</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>orange</td>\n",
       "      <td>red</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>Bolivia</td>\n",
       "      <td>2</td>\n",
       "      <td>3</td>\n",
       "      <td>1099</td>\n",
       "      <td>6</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>red</td>\n",
       "      <td>green</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>Botswana</td>\n",
       "      <td>4</td>\n",
       "      <td>2</td>\n",
       "      <td>600</td>\n",
       "      <td>1</td>\n",
       "      <td>10</td>\n",
       "      <td>5</td>\n",
       "      <td>0</td>\n",
       "      <td>5</td>\n",
       "      <td>3</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>blue</td>\n",
       "      <td>blue</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>Brazil</td>\n",
       "      <td>2</td>\n",
       "      <td>3</td>\n",
       "      <td>8512</td>\n",
       "      <td>119</td>\n",
       "      <td>6</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>4</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>22</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>green</td>\n",
       "      <td>green</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>British-Virgin-Isles</td>\n",
       "      <td>1</td>\n",
       "      <td>4</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>6</td>\n",
       "      <td>...</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>white</td>\n",
       "      <td>blue</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>Brunei</td>\n",
       "      <td>5</td>\n",
       "      <td>1</td>\n",
       "      <td>6</td>\n",
       "      <td>0</td>\n",
       "      <td>10</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>4</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>white</td>\n",
       "      <td>gold</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26</th>\n",
       "      <td>Bulgaria</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>111</td>\n",
       "      <td>9</td>\n",
       "      <td>5</td>\n",
       "      <td>6</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>5</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>white</td>\n",
       "      <td>red</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27</th>\n",
       "      <td>Burkina</td>\n",
       "      <td>4</td>\n",
       "      <td>4</td>\n",
       "      <td>274</td>\n",
       "      <td>7</td>\n",
       "      <td>3</td>\n",
       "      <td>5</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>3</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>red</td>\n",
       "      <td>green</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28</th>\n",
       "      <td>Burma</td>\n",
       "      <td>5</td>\n",
       "      <td>1</td>\n",
       "      <td>678</td>\n",
       "      <td>35</td>\n",
       "      <td>10</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>14</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>blue</td>\n",
       "      <td>red</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29</th>\n",
       "      <td>Burundi</td>\n",
       "      <td>4</td>\n",
       "      <td>2</td>\n",
       "      <td>28</td>\n",
       "      <td>4</td>\n",
       "      <td>10</td>\n",
       "      <td>5</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>...</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>white</td>\n",
       "      <td>white</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>30</th>\n",
       "      <td>Cameroon</td>\n",
       "      <td>4</td>\n",
       "      <td>1</td>\n",
       "      <td>474</td>\n",
       "      <td>8</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>green</td>\n",
       "      <td>gold</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>31</th>\n",
       "      <td>Canada</td>\n",
       "      <td>1</td>\n",
       "      <td>4</td>\n",
       "      <td>9976</td>\n",
       "      <td>24</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>red</td>\n",
       "      <td>red</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>32</th>\n",
       "      <td>Cape-Verde-Islands</td>\n",
       "      <td>4</td>\n",
       "      <td>4</td>\n",
       "      <td>4</td>\n",
       "      <td>0</td>\n",
       "      <td>6</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>5</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>red</td>\n",
       "      <td>green</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>33</th>\n",
       "      <td>Cayman-Islands</td>\n",
       "      <td>1</td>\n",
       "      <td>4</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>6</td>\n",
       "      <td>...</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>4</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>white</td>\n",
       "      <td>blue</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>34</th>\n",
       "      <td>Central-African-Republic</td>\n",
       "      <td>4</td>\n",
       "      <td>1</td>\n",
       "      <td>623</td>\n",
       "      <td>2</td>\n",
       "      <td>10</td>\n",
       "      <td>5</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>5</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>blue</td>\n",
       "      <td>gold</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>35</th>\n",
       "      <td>Chad</td>\n",
       "      <td>4</td>\n",
       "      <td>1</td>\n",
       "      <td>1284</td>\n",
       "      <td>4</td>\n",
       "      <td>3</td>\n",
       "      <td>5</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>blue</td>\n",
       "      <td>red</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>36</th>\n",
       "      <td>Chile</td>\n",
       "      <td>2</td>\n",
       "      <td>3</td>\n",
       "      <td>757</td>\n",
       "      <td>11</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>3</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>blue</td>\n",
       "      <td>red</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>37</th>\n",
       "      <td>China</td>\n",
       "      <td>5</td>\n",
       "      <td>1</td>\n",
       "      <td>9561</td>\n",
       "      <td>1008</td>\n",
       "      <td>7</td>\n",
       "      <td>6</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>5</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>red</td>\n",
       "      <td>red</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>38</th>\n",
       "      <td>Colombia</td>\n",
       "      <td>2</td>\n",
       "      <td>4</td>\n",
       "      <td>1139</td>\n",
       "      <td>28</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>gold</td>\n",
       "      <td>red</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>39</th>\n",
       "      <td>Comorro-Islands</td>\n",
       "      <td>4</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>4</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>green</td>\n",
       "      <td>green</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>40</th>\n",
       "      <td>Congo</td>\n",
       "      <td>4</td>\n",
       "      <td>2</td>\n",
       "      <td>342</td>\n",
       "      <td>2</td>\n",
       "      <td>10</td>\n",
       "      <td>5</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>red</td>\n",
       "      <td>red</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>41</th>\n",
       "      <td>Cook-Islands</td>\n",
       "      <td>6</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>4</td>\n",
       "      <td>...</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>15</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>white</td>\n",
       "      <td>blue</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>42</th>\n",
       "      <td>Costa-Rica</td>\n",
       "      <td>1</td>\n",
       "      <td>4</td>\n",
       "      <td>51</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>5</td>\n",
       "      <td>3</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>blue</td>\n",
       "      <td>blue</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>43</th>\n",
       "      <td>Cuba</td>\n",
       "      <td>1</td>\n",
       "      <td>4</td>\n",
       "      <td>115</td>\n",
       "      <td>10</td>\n",
       "      <td>2</td>\n",
       "      <td>6</td>\n",
       "      <td>0</td>\n",
       "      <td>5</td>\n",
       "      <td>3</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>blue</td>\n",
       "      <td>blue</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>44</th>\n",
       "      <td>Cyprus</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>9</td>\n",
       "      <td>1</td>\n",
       "      <td>6</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>white</td>\n",
       "      <td>white</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>45</th>\n",
       "      <td>Czechoslovakia</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>128</td>\n",
       "      <td>15</td>\n",
       "      <td>5</td>\n",
       "      <td>6</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>white</td>\n",
       "      <td>red</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>46</th>\n",
       "      <td>Denmark</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>43</td>\n",
       "      <td>5</td>\n",
       "      <td>6</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>red</td>\n",
       "      <td>red</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>47</th>\n",
       "      <td>Djibouti</td>\n",
       "      <td>4</td>\n",
       "      <td>1</td>\n",
       "      <td>22</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>4</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>white</td>\n",
       "      <td>green</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>48</th>\n",
       "      <td>Dominica</td>\n",
       "      <td>1</td>\n",
       "      <td>4</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>6</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>10</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>green</td>\n",
       "      <td>green</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>49</th>\n",
       "      <td>Dominican-Republic</td>\n",
       "      <td>1</td>\n",
       "      <td>4</td>\n",
       "      <td>49</td>\n",
       "      <td>6</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>blue</td>\n",
       "      <td>blue</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50</th>\n",
       "      <td>Ecuador</td>\n",
       "      <td>2</td>\n",
       "      <td>3</td>\n",
       "      <td>284</td>\n",
       "      <td>8</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>gold</td>\n",
       "      <td>red</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>51</th>\n",
       "      <td>Egypt</td>\n",
       "      <td>4</td>\n",
       "      <td>1</td>\n",
       "      <td>1001</td>\n",
       "      <td>47</td>\n",
       "      <td>8</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>4</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>red</td>\n",
       "      <td>black</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>52</th>\n",
       "      <td>El-Salvador</td>\n",
       "      <td>1</td>\n",
       "      <td>4</td>\n",
       "      <td>21</td>\n",
       "      <td>5</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>2</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>blue</td>\n",
       "      <td>blue</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>53</th>\n",
       "      <td>Equatorial-Guinea</td>\n",
       "      <td>4</td>\n",
       "      <td>1</td>\n",
       "      <td>28</td>\n",
       "      <td>0</td>\n",
       "      <td>10</td>\n",
       "      <td>5</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>4</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>green</td>\n",
       "      <td>red</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>54</th>\n",
       "      <td>Ethiopia</td>\n",
       "      <td>4</td>\n",
       "      <td>1</td>\n",
       "      <td>1222</td>\n",
       "      <td>31</td>\n",
       "      <td>10</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>green</td>\n",
       "      <td>red</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>55</th>\n",
       "      <td>Faeroes</td>\n",
       "      <td>3</td>\n",
       "      <td>4</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>6</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>white</td>\n",
       "      <td>white</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>56</th>\n",
       "      <td>Falklands-Malvinas</td>\n",
       "      <td>2</td>\n",
       "      <td>3</td>\n",
       "      <td>12</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>6</td>\n",
       "      <td>...</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>white</td>\n",
       "      <td>blue</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>57</th>\n",
       "      <td>Fiji</td>\n",
       "      <td>6</td>\n",
       "      <td>2</td>\n",
       "      <td>18</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>7</td>\n",
       "      <td>...</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>white</td>\n",
       "      <td>blue</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>58</th>\n",
       "      <td>Finland</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>337</td>\n",
       "      <td>5</td>\n",
       "      <td>9</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>white</td>\n",
       "      <td>white</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>59</th>\n",
       "      <td>France</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>547</td>\n",
       "      <td>54</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>blue</td>\n",
       "      <td>red</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>60 rows × 30 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                        name  landmass  zone  area  population  language  \\\n",
       "0                Afghanistan         5     1   648          16        10   \n",
       "1                    Albania         3     1    29           3         6   \n",
       "2                    Algeria         4     1  2388          20         8   \n",
       "3             American-Samoa         6     3     0           0         1   \n",
       "4                    Andorra         3     1     0           0         6   \n",
       "5                     Angola         4     2  1247           7        10   \n",
       "6                   Anguilla         1     4     0           0         1   \n",
       "7            Antigua-Barbuda         1     4     0           0         1   \n",
       "8                  Argentina         2     3  2777          28         2   \n",
       "9                  Argentine         2     3  2777          28         2   \n",
       "10                 Australia         6     2  7690          15         1   \n",
       "11                   Austria         3     1    84           8         4   \n",
       "12                   Bahamas         1     4    19           0         1   \n",
       "13                   Bahrain         5     1     1           0         8   \n",
       "14                Bangladesh         5     1   143          90         6   \n",
       "15                  Barbados         1     4     0           0         1   \n",
       "16                   Belgium         3     1    31          10         6   \n",
       "17                    Belize         1     4    23           0         1   \n",
       "18                     Benin         4     1   113           3         3   \n",
       "19                   Bermuda         1     4     0           0         1   \n",
       "20                    Bhutan         5     1    47           1        10   \n",
       "21                   Bolivia         2     3  1099           6         2   \n",
       "22                  Botswana         4     2   600           1        10   \n",
       "23                    Brazil         2     3  8512         119         6   \n",
       "24      British-Virgin-Isles         1     4     0           0         1   \n",
       "25                    Brunei         5     1     6           0        10   \n",
       "26                  Bulgaria         3     1   111           9         5   \n",
       "27                   Burkina         4     4   274           7         3   \n",
       "28                     Burma         5     1   678          35        10   \n",
       "29                   Burundi         4     2    28           4        10   \n",
       "30                  Cameroon         4     1   474           8         3   \n",
       "31                    Canada         1     4  9976          24         1   \n",
       "32        Cape-Verde-Islands         4     4     4           0         6   \n",
       "33            Cayman-Islands         1     4     0           0         1   \n",
       "34  Central-African-Republic         4     1   623           2        10   \n",
       "35                      Chad         4     1  1284           4         3   \n",
       "36                     Chile         2     3   757          11         2   \n",
       "37                     China         5     1  9561        1008         7   \n",
       "38                  Colombia         2     4  1139          28         2   \n",
       "39           Comorro-Islands         4     2     2           0         3   \n",
       "40                     Congo         4     2   342           2        10   \n",
       "41              Cook-Islands         6     3     0           0         1   \n",
       "42                Costa-Rica         1     4    51           2         2   \n",
       "43                      Cuba         1     4   115          10         2   \n",
       "44                    Cyprus         3     1     9           1         6   \n",
       "45            Czechoslovakia         3     1   128          15         5   \n",
       "46                   Denmark         3     1    43           5         6   \n",
       "47                  Djibouti         4     1    22           0         3   \n",
       "48                  Dominica         1     4     0           0         1   \n",
       "49        Dominican-Republic         1     4    49           6         2   \n",
       "50                   Ecuador         2     3   284           8         2   \n",
       "51                     Egypt         4     1  1001          47         8   \n",
       "52               El-Salvador         1     4    21           5         2   \n",
       "53         Equatorial-Guinea         4     1    28           0        10   \n",
       "54                  Ethiopia         4     1  1222          31        10   \n",
       "55                   Faeroes         3     4     1           0         6   \n",
       "56        Falklands-Malvinas         2     3    12           0         1   \n",
       "57                      Fiji         6     2    18           1         1   \n",
       "58                   Finland         3     1   337           5         9   \n",
       "59                    France         3     1   547          54         3   \n",
       "\n",
       "    religion  bars  stripes  colours  ...  saltires  quarters  sunstars  \\\n",
       "0          2     0        3        5  ...         0         0         1   \n",
       "1          6     0        0        3  ...         0         0         1   \n",
       "2          2     2        0        3  ...         0         0         1   \n",
       "3          1     0        0        5  ...         0         0         0   \n",
       "4          0     3        0        3  ...         0         0         0   \n",
       "5          5     0        2        3  ...         0         0         1   \n",
       "6          1     0        1        3  ...         0         0         0   \n",
       "7          1     0        1        5  ...         0         0         1   \n",
       "8          0     0        3        2  ...         0         0         0   \n",
       "9          0     0        3        3  ...         0         0         1   \n",
       "10         1     0        0        3  ...         1         1         6   \n",
       "11         0     0        3        2  ...         0         0         0   \n",
       "12         1     0        3        3  ...         0         0         0   \n",
       "13         2     0        0        2  ...         0         0         0   \n",
       "14         2     0        0        2  ...         0         0         0   \n",
       "15         1     3        0        3  ...         0         0         0   \n",
       "16         0     3        0        3  ...         0         0         0   \n",
       "17         1     0        2        8  ...         0         0         0   \n",
       "18         5     0        0        2  ...         0         0         1   \n",
       "19         1     0        0        6  ...         1         1         0   \n",
       "20         3     0        0        4  ...         0         0         0   \n",
       "21         0     0        3        3  ...         0         0         0   \n",
       "22         5     0        5        3  ...         0         0         0   \n",
       "23         0     0        0        4  ...         0         0        22   \n",
       "24         1     0        0        6  ...         1         1         0   \n",
       "25         2     0        0        4  ...         0         0         0   \n",
       "26         6     0        3        5  ...         0         0         1   \n",
       "27         5     0        2        3  ...         0         0         1   \n",
       "28         3     0        0        3  ...         0         1        14   \n",
       "29         5     0        0        3  ...         1         0         3   \n",
       "30         1     3        0        3  ...         0         0         1   \n",
       "31         1     2        0        2  ...         0         0         0   \n",
       "32         0     1        2        5  ...         0         0         1   \n",
       "33         1     0        0        6  ...         1         1         4   \n",
       "34         5     1        0        5  ...         0         0         1   \n",
       "35         5     3        0        3  ...         0         0         0   \n",
       "36         0     0        2        3  ...         0         1         1   \n",
       "37         6     0        0        2  ...         0         0         5   \n",
       "38         0     0        3        3  ...         0         0         0   \n",
       "39         2     0        0        2  ...         0         0         4   \n",
       "40         5     0        0        3  ...         0         0         1   \n",
       "41         1     0        0        4  ...         1         1        15   \n",
       "42         0     0        5        3  ...         0         0         0   \n",
       "43         6     0        5        3  ...         0         0         1   \n",
       "44         1     0        0        3  ...         0         0         0   \n",
       "45         6     0        0        3  ...         0         0         0   \n",
       "46         1     0        0        2  ...         0         0         0   \n",
       "47         2     0        0        4  ...         0         0         1   \n",
       "48         1     0        0        6  ...         0         0        10   \n",
       "49         0     0        0        3  ...         0         0         0   \n",
       "50         0     0        3        3  ...         0         0         0   \n",
       "51         2     0        3        4  ...         0         0         0   \n",
       "52         0     0        3        2  ...         0         0         0   \n",
       "53         5     0        3        4  ...         0         0         0   \n",
       "54         1     0        3        3  ...         0         0         0   \n",
       "55         1     0        0        3  ...         0         0         0   \n",
       "56         1     0        0        6  ...         1         1         0   \n",
       "57         1     0        0        7  ...         1         1         0   \n",
       "58         1     0        0        2  ...         0         0         0   \n",
       "59         0     3        0        3  ...         0         0         0   \n",
       "\n",
       "    crescent  triangle  icon  animate text  topleft  botright  \n",
       "0          0         0     1        0    0    black     green  \n",
       "1          0         0     0        1    0      red       red  \n",
       "2          1         0     0        0    0    green     white  \n",
       "3          0         1     1        1    0     blue       red  \n",
       "4          0         0     0        0    0     blue       red  \n",
       "5          0         0     1        0    0      red     black  \n",
       "6          0         0     0        1    0    white      blue  \n",
       "7          0         1     0        0    0    black       red  \n",
       "8          0         0     0        0    0     blue      blue  \n",
       "9          0         0     0        0    0     blue      blue  \n",
       "10         0         0     0        0    0    white      blue  \n",
       "11         0         0     0        0    0      red       red  \n",
       "12         0         1     0        0    0     blue      blue  \n",
       "13         0         0     0        0    0    white       red  \n",
       "14         0         0     0        0    0    green     green  \n",
       "15         0         0     1        0    0     blue      blue  \n",
       "16         0         0     0        0    0    black       red  \n",
       "17         0         0     1        1    1      red       red  \n",
       "18         0         0     0        0    0    green     green  \n",
       "19         0         0     1        1    0    white       red  \n",
       "20         0         0     0        1    0   orange       red  \n",
       "21         0         0     0        0    0      red     green  \n",
       "22         0         0     0        0    0     blue      blue  \n",
       "23         0         0     0        0    1    green     green  \n",
       "24         0         0     1        1    1    white      blue  \n",
       "25         0         1     1        1    1    white      gold  \n",
       "26         0         0     1        1    0    white       red  \n",
       "27         0         0     0        0    0      red     green  \n",
       "28         0         0     1        1    0     blue       red  \n",
       "29         0         0     0        0    0    white     white  \n",
       "30         0         0     0        0    0    green      gold  \n",
       "31         0         0     0        1    0      red       red  \n",
       "32         0         0     0        1    0      red     green  \n",
       "33         0         0     1        1    1    white      blue  \n",
       "34         0         0     0        0    0     blue      gold  \n",
       "35         0         0     0        0    0     blue       red  \n",
       "36         0         0     0        0    0     blue       red  \n",
       "37         0         0     0        0    0      red       red  \n",
       "38         0         0     0        0    0     gold       red  \n",
       "39         1         0     0        0    0    green     green  \n",
       "40         0         0     1        1    0      red       red  \n",
       "41         0         0     0        0    0    white      blue  \n",
       "42         0         0     0        0    0     blue      blue  \n",
       "43         0         1     0        0    0     blue      blue  \n",
       "44         0         0     1        1    0    white     white  \n",
       "45         0         1     0        0    0    white       red  \n",
       "46         0         0     0        0    0      red       red  \n",
       "47         0         1     0        0    0    white     green  \n",
       "48         0         0     0        1    0    green     green  \n",
       "49         0         0     0        0    0     blue      blue  \n",
       "50         0         0     0        0    0     gold       red  \n",
       "51         0         0     0        1    1      red     black  \n",
       "52         0         0     0        0    0     blue      blue  \n",
       "53         0         1     0        0    0    green       red  \n",
       "54         0         0     0        0    0    green       red  \n",
       "55         0         0     0        0    0    white     white  \n",
       "56         0         0     1        1    1    white      blue  \n",
       "57         0         0     1        1    0    white      blue  \n",
       "58         0         0     0        0    0    white     white  \n",
       "59         0         0     0        0    0     blue       red  \n",
       "\n",
       "[60 rows x 30 columns]"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "my_file=\"flag.data\"\n",
    "\n",
    "flag=pd.read_csv(my_file, sep=',', decimal='.', header = None, \\\n",
    "                      names=['name', 'landmass', 'zone', 'area', 'population', 'language', 'religion',\n",
    "                             'bars', 'stripes', 'colours', 'red','green', 'blue', 'gold', 'white',\n",
    "                             'black', 'orange', 'mainhue', 'circles', 'crosses', 'saltires', 'quarters',\n",
    "                             'sunstars', 'crescent', 'triangle', 'icon', 'animate', 'text', 'topleft', 'botright'])\n",
    "flag.head(60)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "3ab59176",
   "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>name</th>\n",
       "      <th>landmass</th>\n",
       "      <th>zone</th>\n",
       "      <th>area</th>\n",
       "      <th>population</th>\n",
       "      <th>language</th>\n",
       "      <th>religion</th>\n",
       "      <th>bars</th>\n",
       "      <th>stripes</th>\n",
       "      <th>colours</th>\n",
       "      <th>...</th>\n",
       "      <th>saltires</th>\n",
       "      <th>quarters</th>\n",
       "      <th>sunstars</th>\n",
       "      <th>crescent</th>\n",
       "      <th>triangle</th>\n",
       "      <th>icon</th>\n",
       "      <th>animate</th>\n",
       "      <th>text</th>\n",
       "      <th>topleft</th>\n",
       "      <th>botright</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>134</th>\n",
       "      <td>Papua-New-Guinea</td>\n",
       "      <td>6</td>\n",
       "      <td>2</td>\n",
       "      <td>463</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>5</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>4</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>5</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>red</td>\n",
       "      <td>black</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>135</th>\n",
       "      <td>Parguay</td>\n",
       "      <td>2</td>\n",
       "      <td>3</td>\n",
       "      <td>407</td>\n",
       "      <td>3</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>6</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>red</td>\n",
       "      <td>blue</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>136</th>\n",
       "      <td>Peru</td>\n",
       "      <td>2</td>\n",
       "      <td>3</td>\n",
       "      <td>1285</td>\n",
       "      <td>14</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>red</td>\n",
       "      <td>red</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>137</th>\n",
       "      <td>Philippines</td>\n",
       "      <td>6</td>\n",
       "      <td>1</td>\n",
       "      <td>300</td>\n",
       "      <td>48</td>\n",
       "      <td>10</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>4</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>4</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>blue</td>\n",
       "      <td>red</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>138</th>\n",
       "      <td>Poland</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>313</td>\n",
       "      <td>36</td>\n",
       "      <td>5</td>\n",
       "      <td>6</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>white</td>\n",
       "      <td>red</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>139</th>\n",
       "      <td>Portugal</td>\n",
       "      <td>3</td>\n",
       "      <td>4</td>\n",
       "      <td>92</td>\n",
       "      <td>10</td>\n",
       "      <td>6</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>5</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>green</td>\n",
       "      <td>red</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>140</th>\n",
       "      <td>Puerto-Rico</td>\n",
       "      <td>1</td>\n",
       "      <td>4</td>\n",
       "      <td>9</td>\n",
       "      <td>3</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>5</td>\n",
       "      <td>3</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>red</td>\n",
       "      <td>red</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>141</th>\n",
       "      <td>Qatar</td>\n",
       "      <td>5</td>\n",
       "      <td>1</td>\n",
       "      <td>11</td>\n",
       "      <td>0</td>\n",
       "      <td>8</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>white</td>\n",
       "      <td>brown</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>142</th>\n",
       "      <td>Romania</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>237</td>\n",
       "      <td>22</td>\n",
       "      <td>6</td>\n",
       "      <td>6</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>7</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>blue</td>\n",
       "      <td>red</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>143</th>\n",
       "      <td>Rwanda</td>\n",
       "      <td>4</td>\n",
       "      <td>2</td>\n",
       "      <td>26</td>\n",
       "      <td>5</td>\n",
       "      <td>10</td>\n",
       "      <td>5</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>4</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>red</td>\n",
       "      <td>green</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>144</th>\n",
       "      <td>San-Marino</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>6</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>white</td>\n",
       "      <td>blue</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>145</th>\n",
       "      <td>Sao-Tome</td>\n",
       "      <td>4</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>6</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>4</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>green</td>\n",
       "      <td>green</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>146</th>\n",
       "      <td>Saudi-Arabia</td>\n",
       "      <td>5</td>\n",
       "      <td>1</td>\n",
       "      <td>2150</td>\n",
       "      <td>9</td>\n",
       "      <td>8</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>green</td>\n",
       "      <td>green</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>147</th>\n",
       "      <td>Senegal</td>\n",
       "      <td>4</td>\n",
       "      <td>4</td>\n",
       "      <td>196</td>\n",
       "      <td>6</td>\n",
       "      <td>3</td>\n",
       "      <td>2</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>green</td>\n",
       "      <td>red</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>148</th>\n",
       "      <td>Seychelles</td>\n",
       "      <td>4</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>red</td>\n",
       "      <td>green</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>149</th>\n",
       "      <td>Sierra-Leone</td>\n",
       "      <td>4</td>\n",
       "      <td>4</td>\n",
       "      <td>72</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>5</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>green</td>\n",
       "      <td>blue</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>150</th>\n",
       "      <td>Singapore</td>\n",
       "      <td>5</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>7</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>5</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>red</td>\n",
       "      <td>white</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>151</th>\n",
       "      <td>Soloman-Islands</td>\n",
       "      <td>6</td>\n",
       "      <td>2</td>\n",
       "      <td>30</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>4</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>5</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>blue</td>\n",
       "      <td>green</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>152</th>\n",
       "      <td>Somalia</td>\n",
       "      <td>4</td>\n",
       "      <td>1</td>\n",
       "      <td>637</td>\n",
       "      <td>5</td>\n",
       "      <td>10</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>blue</td>\n",
       "      <td>blue</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>153</th>\n",
       "      <td>South-Africa</td>\n",
       "      <td>4</td>\n",
       "      <td>2</td>\n",
       "      <td>1221</td>\n",
       "      <td>29</td>\n",
       "      <td>6</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>5</td>\n",
       "      <td>...</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>orange</td>\n",
       "      <td>blue</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>154</th>\n",
       "      <td>South-Korea</td>\n",
       "      <td>5</td>\n",
       "      <td>1</td>\n",
       "      <td>99</td>\n",
       "      <td>39</td>\n",
       "      <td>10</td>\n",
       "      <td>7</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>4</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>white</td>\n",
       "      <td>white</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>155</th>\n",
       "      <td>South-Yemen</td>\n",
       "      <td>5</td>\n",
       "      <td>1</td>\n",
       "      <td>288</td>\n",
       "      <td>2</td>\n",
       "      <td>8</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>4</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>red</td>\n",
       "      <td>black</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>156</th>\n",
       "      <td>Spain</td>\n",
       "      <td>3</td>\n",
       "      <td>4</td>\n",
       "      <td>505</td>\n",
       "      <td>38</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>2</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>red</td>\n",
       "      <td>red</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>157</th>\n",
       "      <td>Sri-Lanka</td>\n",
       "      <td>5</td>\n",
       "      <td>1</td>\n",
       "      <td>66</td>\n",
       "      <td>15</td>\n",
       "      <td>10</td>\n",
       "      <td>3</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>4</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>gold</td>\n",
       "      <td>gold</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>158</th>\n",
       "      <td>St-Helena</td>\n",
       "      <td>4</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>7</td>\n",
       "      <td>...</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>white</td>\n",
       "      <td>blue</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>159</th>\n",
       "      <td>St-Kitts-Nevis</td>\n",
       "      <td>1</td>\n",
       "      <td>4</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>5</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>green</td>\n",
       "      <td>red</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>160</th>\n",
       "      <td>St-Lucia</td>\n",
       "      <td>1</td>\n",
       "      <td>4</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>4</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>blue</td>\n",
       "      <td>blue</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>161</th>\n",
       "      <td>St-Vincent</td>\n",
       "      <td>1</td>\n",
       "      <td>4</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>5</td>\n",
       "      <td>0</td>\n",
       "      <td>4</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>blue</td>\n",
       "      <td>green</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>162</th>\n",
       "      <td>Sudan</td>\n",
       "      <td>4</td>\n",
       "      <td>1</td>\n",
       "      <td>2506</td>\n",
       "      <td>20</td>\n",
       "      <td>8</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>4</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>red</td>\n",
       "      <td>black</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>163</th>\n",
       "      <td>Surinam</td>\n",
       "      <td>2</td>\n",
       "      <td>4</td>\n",
       "      <td>63</td>\n",
       "      <td>0</td>\n",
       "      <td>6</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>5</td>\n",
       "      <td>4</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>green</td>\n",
       "      <td>green</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>164</th>\n",
       "      <td>Swaziland</td>\n",
       "      <td>4</td>\n",
       "      <td>2</td>\n",
       "      <td>17</td>\n",
       "      <td>1</td>\n",
       "      <td>10</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>5</td>\n",
       "      <td>7</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>blue</td>\n",
       "      <td>blue</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>165</th>\n",
       "      <td>Sweden</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>450</td>\n",
       "      <td>8</td>\n",
       "      <td>6</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>blue</td>\n",
       "      <td>blue</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>166</th>\n",
       "      <td>Switzerland</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>41</td>\n",
       "      <td>6</td>\n",
       "      <td>4</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>red</td>\n",
       "      <td>red</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>167</th>\n",
       "      <td>Syria</td>\n",
       "      <td>5</td>\n",
       "      <td>1</td>\n",
       "      <td>185</td>\n",
       "      <td>10</td>\n",
       "      <td>8</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>4</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>red</td>\n",
       "      <td>black</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>168</th>\n",
       "      <td>Taiwan</td>\n",
       "      <td>5</td>\n",
       "      <td>1</td>\n",
       "      <td>36</td>\n",
       "      <td>18</td>\n",
       "      <td>7</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>blue</td>\n",
       "      <td>red</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>169</th>\n",
       "      <td>Tanzania</td>\n",
       "      <td>4</td>\n",
       "      <td>2</td>\n",
       "      <td>945</td>\n",
       "      <td>18</td>\n",
       "      <td>10</td>\n",
       "      <td>5</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>4</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>green</td>\n",
       "      <td>blue</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>170</th>\n",
       "      <td>Thailand</td>\n",
       "      <td>5</td>\n",
       "      <td>1</td>\n",
       "      <td>514</td>\n",
       "      <td>49</td>\n",
       "      <td>10</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>5</td>\n",
       "      <td>3</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>red</td>\n",
       "      <td>red</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>171</th>\n",
       "      <td>Togo</td>\n",
       "      <td>4</td>\n",
       "      <td>1</td>\n",
       "      <td>57</td>\n",
       "      <td>2</td>\n",
       "      <td>3</td>\n",
       "      <td>7</td>\n",
       "      <td>0</td>\n",
       "      <td>5</td>\n",
       "      <td>4</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>red</td>\n",
       "      <td>green</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>172</th>\n",
       "      <td>Tonga</td>\n",
       "      <td>6</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>10</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>white</td>\n",
       "      <td>red</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>173</th>\n",
       "      <td>Trinidad-Tobago</td>\n",
       "      <td>2</td>\n",
       "      <td>4</td>\n",
       "      <td>5</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>white</td>\n",
       "      <td>white</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>174</th>\n",
       "      <td>Tunisia</td>\n",
       "      <td>4</td>\n",
       "      <td>1</td>\n",
       "      <td>164</td>\n",
       "      <td>7</td>\n",
       "      <td>8</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>red</td>\n",
       "      <td>red</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>175</th>\n",
       "      <td>Turkey</td>\n",
       "      <td>5</td>\n",
       "      <td>1</td>\n",
       "      <td>781</td>\n",
       "      <td>45</td>\n",
       "      <td>9</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>red</td>\n",
       "      <td>red</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>176</th>\n",
       "      <td>Turks-Cocos-Islands</td>\n",
       "      <td>1</td>\n",
       "      <td>4</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>6</td>\n",
       "      <td>...</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>white</td>\n",
       "      <td>blue</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>177</th>\n",
       "      <td>Tuvalu</td>\n",
       "      <td>6</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>5</td>\n",
       "      <td>...</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>9</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>white</td>\n",
       "      <td>blue</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>178</th>\n",
       "      <td>UAE</td>\n",
       "      <td>5</td>\n",
       "      <td>1</td>\n",
       "      <td>84</td>\n",
       "      <td>1</td>\n",
       "      <td>8</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>4</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>red</td>\n",
       "      <td>black</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>179</th>\n",
       "      <td>Uganda</td>\n",
       "      <td>4</td>\n",
       "      <td>1</td>\n",
       "      <td>236</td>\n",
       "      <td>13</td>\n",
       "      <td>10</td>\n",
       "      <td>5</td>\n",
       "      <td>0</td>\n",
       "      <td>6</td>\n",
       "      <td>5</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>black</td>\n",
       "      <td>red</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>180</th>\n",
       "      <td>UK</td>\n",
       "      <td>3</td>\n",
       "      <td>4</td>\n",
       "      <td>245</td>\n",
       "      <td>56</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>...</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>white</td>\n",
       "      <td>red</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>181</th>\n",
       "      <td>Uruguay</td>\n",
       "      <td>2</td>\n",
       "      <td>3</td>\n",
       "      <td>178</td>\n",
       "      <td>3</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>9</td>\n",
       "      <td>3</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>white</td>\n",
       "      <td>white</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>182</th>\n",
       "      <td>US-Virgin-Isles</td>\n",
       "      <td>1</td>\n",
       "      <td>4</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>6</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>white</td>\n",
       "      <td>white</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>183</th>\n",
       "      <td>USA</td>\n",
       "      <td>1</td>\n",
       "      <td>4</td>\n",
       "      <td>9363</td>\n",
       "      <td>231</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>13</td>\n",
       "      <td>3</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>50</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>blue</td>\n",
       "      <td>red</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>184</th>\n",
       "      <td>USSR</td>\n",
       "      <td>5</td>\n",
       "      <td>1</td>\n",
       "      <td>22402</td>\n",
       "      <td>274</td>\n",
       "      <td>5</td>\n",
       "      <td>6</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>red</td>\n",
       "      <td>red</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>185</th>\n",
       "      <td>Vanuatu</td>\n",
       "      <td>6</td>\n",
       "      <td>2</td>\n",
       "      <td>15</td>\n",
       "      <td>0</td>\n",
       "      <td>6</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>4</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>black</td>\n",
       "      <td>green</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>186</th>\n",
       "      <td>Vatican-City</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>6</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>4</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>gold</td>\n",
       "      <td>white</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>187</th>\n",
       "      <td>Venezuela</td>\n",
       "      <td>2</td>\n",
       "      <td>4</td>\n",
       "      <td>912</td>\n",
       "      <td>15</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>7</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>7</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>gold</td>\n",
       "      <td>red</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>188</th>\n",
       "      <td>Vietnam</td>\n",
       "      <td>5</td>\n",
       "      <td>1</td>\n",
       "      <td>333</td>\n",
       "      <td>60</td>\n",
       "      <td>10</td>\n",
       "      <td>6</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>red</td>\n",
       "      <td>red</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>189</th>\n",
       "      <td>Western-Samoa</td>\n",
       "      <td>6</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>5</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>blue</td>\n",
       "      <td>red</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>190</th>\n",
       "      <td>Yugoslavia</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>256</td>\n",
       "      <td>22</td>\n",
       "      <td>6</td>\n",
       "      <td>6</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>4</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>blue</td>\n",
       "      <td>red</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>191</th>\n",
       "      <td>Zaire</td>\n",
       "      <td>4</td>\n",
       "      <td>2</td>\n",
       "      <td>905</td>\n",
       "      <td>28</td>\n",
       "      <td>10</td>\n",
       "      <td>5</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>4</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>green</td>\n",
       "      <td>green</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>192</th>\n",
       "      <td>Zambia</td>\n",
       "      <td>4</td>\n",
       "      <td>2</td>\n",
       "      <td>753</td>\n",
       "      <td>6</td>\n",
       "      <td>10</td>\n",
       "      <td>5</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>4</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>green</td>\n",
       "      <td>brown</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>193</th>\n",
       "      <td>Zimbabwe</td>\n",
       "      <td>4</td>\n",
       "      <td>2</td>\n",
       "      <td>391</td>\n",
       "      <td>8</td>\n",
       "      <td>10</td>\n",
       "      <td>5</td>\n",
       "      <td>0</td>\n",
       "      <td>7</td>\n",
       "      <td>5</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>green</td>\n",
       "      <td>green</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>60 rows × 30 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                    name  landmass  zone   area  population  language  \\\n",
       "134     Papua-New-Guinea         6     2    463           3         1   \n",
       "135              Parguay         2     3    407           3         2   \n",
       "136                 Peru         2     3   1285          14         2   \n",
       "137          Philippines         6     1    300          48        10   \n",
       "138               Poland         3     1    313          36         5   \n",
       "139             Portugal         3     4     92          10         6   \n",
       "140          Puerto-Rico         1     4      9           3         2   \n",
       "141                Qatar         5     1     11           0         8   \n",
       "142              Romania         3     1    237          22         6   \n",
       "143               Rwanda         4     2     26           5        10   \n",
       "144           San-Marino         3     1      0           0         6   \n",
       "145             Sao-Tome         4     1      0           0         6   \n",
       "146         Saudi-Arabia         5     1   2150           9         8   \n",
       "147              Senegal         4     4    196           6         3   \n",
       "148           Seychelles         4     2      0           0         1   \n",
       "149         Sierra-Leone         4     4     72           3         1   \n",
       "150            Singapore         5     1      1           3         7   \n",
       "151      Soloman-Islands         6     2     30           0         1   \n",
       "152              Somalia         4     1    637           5        10   \n",
       "153         South-Africa         4     2   1221          29         6   \n",
       "154          South-Korea         5     1     99          39        10   \n",
       "155          South-Yemen         5     1    288           2         8   \n",
       "156                Spain         3     4    505          38         2   \n",
       "157            Sri-Lanka         5     1     66          15        10   \n",
       "158            St-Helena         4     3      0           0         1   \n",
       "159       St-Kitts-Nevis         1     4      0           0         1   \n",
       "160             St-Lucia         1     4      0           0         1   \n",
       "161           St-Vincent         1     4      0           0         1   \n",
       "162                Sudan         4     1   2506          20         8   \n",
       "163              Surinam         2     4     63           0         6   \n",
       "164            Swaziland         4     2     17           1        10   \n",
       "165               Sweden         3     1    450           8         6   \n",
       "166          Switzerland         3     1     41           6         4   \n",
       "167                Syria         5     1    185          10         8   \n",
       "168               Taiwan         5     1     36          18         7   \n",
       "169             Tanzania         4     2    945          18        10   \n",
       "170             Thailand         5     1    514          49        10   \n",
       "171                 Togo         4     1     57           2         3   \n",
       "172                Tonga         6     2      1           0        10   \n",
       "173      Trinidad-Tobago         2     4      5           1         1   \n",
       "174              Tunisia         4     1    164           7         8   \n",
       "175               Turkey         5     1    781          45         9   \n",
       "176  Turks-Cocos-Islands         1     4      0           0         1   \n",
       "177               Tuvalu         6     2      0           0         1   \n",
       "178                  UAE         5     1     84           1         8   \n",
       "179               Uganda         4     1    236          13        10   \n",
       "180                   UK         3     4    245          56         1   \n",
       "181              Uruguay         2     3    178           3         2   \n",
       "182      US-Virgin-Isles         1     4      0           0         1   \n",
       "183                  USA         1     4   9363         231         1   \n",
       "184                 USSR         5     1  22402         274         5   \n",
       "185              Vanuatu         6     2     15           0         6   \n",
       "186         Vatican-City         3     1      0           0         6   \n",
       "187            Venezuela         2     4    912          15         2   \n",
       "188              Vietnam         5     1    333          60        10   \n",
       "189        Western-Samoa         6     3      3           0         1   \n",
       "190           Yugoslavia         3     1    256          22         6   \n",
       "191                Zaire         4     2    905          28        10   \n",
       "192               Zambia         4     2    753           6        10   \n",
       "193             Zimbabwe         4     2    391           8        10   \n",
       "\n",
       "     religion  bars  stripes  colours  ...  saltires  quarters  sunstars  \\\n",
       "134         5     0        0        4  ...         0         0         5   \n",
       "135         0     0        3        6  ...         0         0         1   \n",
       "136         0     3        0        2  ...         0         0         0   \n",
       "137         0     0        0        4  ...         0         0         4   \n",
       "138         6     0        2        2  ...         0         0         0   \n",
       "139         0     0        0        5  ...         0         0         0   \n",
       "140         0     0        5        3  ...         0         0         1   \n",
       "141         2     0        0        2  ...         0         0         0   \n",
       "142         6     3        0        7  ...         0         0         2   \n",
       "143         5     3        0        4  ...         0         0         0   \n",
       "144         0     0        2        2  ...         0         0         0   \n",
       "145         0     0        3        4  ...         0         0         2   \n",
       "146         2     0        0        2  ...         0         0         0   \n",
       "147         2     3        0        3  ...         0         0         1   \n",
       "148         1     0        0        3  ...         0         0         0   \n",
       "149         5     0        3        3  ...         0         0         0   \n",
       "150         3     0        2        2  ...         0         0         5   \n",
       "151         1     0        0        4  ...         0         0         5   \n",
       "152         2     0        0        2  ...         0         0         1   \n",
       "153         1     0        3        5  ...         1         0         0   \n",
       "154         7     0        0        4  ...         0         0         0   \n",
       "155         2     0        3        4  ...         0         0         1   \n",
       "156         0     0        3        2  ...         0         0         0   \n",
       "157         3     2        0        4  ...         0         0         0   \n",
       "158         1     0        0        7  ...         1         1         0   \n",
       "159         1     0        0        5  ...         0         0         2   \n",
       "160         1     0        0        4  ...         0         0         0   \n",
       "161         1     5        0        4  ...         0         0         0   \n",
       "162         2     0        3        4  ...         0         0         0   \n",
       "163         1     0        5        4  ...         0         0         1   \n",
       "164         1     0        5        7  ...         0         0         0   \n",
       "165         1     0        0        2  ...         0         0         0   \n",
       "166         1     0        0        2  ...         0         0         0   \n",
       "167         2     0        3        4  ...         0         0         2   \n",
       "168         3     0        0        3  ...         0         1         1   \n",
       "169         5     0        0        4  ...         0         0         0   \n",
       "170         3     0        5        3  ...         0         0         0   \n",
       "171         7     0        5        4  ...         0         1         1   \n",
       "172         1     0        0        2  ...         0         1         0   \n",
       "173         1     0        0        3  ...         0         0         0   \n",
       "174         2     0        0        2  ...         0         0         1   \n",
       "175         2     0        0        2  ...         0         0         1   \n",
       "176         1     0        0        6  ...         1         1         0   \n",
       "177         1     0        0        5  ...         1         1         9   \n",
       "178         2     1        3        4  ...         0         0         0   \n",
       "179         5     0        6        5  ...         0         0         0   \n",
       "180         1     0        0        3  ...         1         0         0   \n",
       "181         0     0        9        3  ...         0         1         1   \n",
       "182         1     0        0        6  ...         0         0         0   \n",
       "183         1     0       13        3  ...         0         1        50   \n",
       "184         6     0        0        2  ...         0         0         1   \n",
       "185         1     0        0        4  ...         0         0         0   \n",
       "186         0     2        0        4  ...         0         0         0   \n",
       "187         0     0        3        7  ...         0         0         7   \n",
       "188         6     0        0        2  ...         0         0         1   \n",
       "189         1     0        0        3  ...         0         1         5   \n",
       "190         6     0        3        4  ...         0         0         1   \n",
       "191         5     0        0        4  ...         0         0         0   \n",
       "192         5     3        0        4  ...         0         0         0   \n",
       "193         5     0        7        5  ...         0         0         1   \n",
       "\n",
       "     crescent  triangle  icon  animate text  topleft  botright  \n",
       "134         0         1     0        1    0      red     black  \n",
       "135         0         0     1        1    1      red      blue  \n",
       "136         0         0     0        0    0      red       red  \n",
       "137         0         1     0        0    0     blue       red  \n",
       "138         0         0     0        0    0    white       red  \n",
       "139         0         0     1        0    0    green       red  \n",
       "140         0         1     0        0    0      red       red  \n",
       "141         0         0     0        0    0    white     brown  \n",
       "142         0         0     1        1    1     blue       red  \n",
       "143         0         0     0        0    1      red     green  \n",
       "144         0         0     0        0    0    white      blue  \n",
       "145         0         1     0        0    0    green     green  \n",
       "146         0         0     1        0    1    green     green  \n",
       "147         0         0     0        0    0    green       red  \n",
       "148         0         0     0        0    0      red     green  \n",
       "149         0         0     0        0    0    green      blue  \n",
       "150         1         0     0        0    0      red     white  \n",
       "151         0         1     0        0    0     blue     green  \n",
       "152         0         0     0        0    0     blue      blue  \n",
       "153         0         0     0        0    0   orange      blue  \n",
       "154         0         0     1        0    0    white     white  \n",
       "155         0         1     0        0    0      red     black  \n",
       "156         0         0     0        0    0      red       red  \n",
       "157         0         0     1        1    0     gold      gold  \n",
       "158         0         0     1        0    0    white      blue  \n",
       "159         0         1     0        0    0    green       red  \n",
       "160         0         1     0        0    0     blue      blue  \n",
       "161         0         0     1        1    1     blue     green  \n",
       "162         0         1     0        0    0      red     black  \n",
       "163         0         0     0        0    0    green     green  \n",
       "164         0         0     1        0    0     blue      blue  \n",
       "165         0         0     0        0    0     blue      blue  \n",
       "166         0         0     0        0    0      red       red  \n",
       "167         0         0     0        0    0      red     black  \n",
       "168         0         0     0        0    0     blue       red  \n",
       "169         0         1     0        0    0    green      blue  \n",
       "170         0         0     0        0    0      red       red  \n",
       "171         0         0     0        0    0      red     green  \n",
       "172         0         0     0        0    0    white       red  \n",
       "173         0         1     0        0    0    white     white  \n",
       "174         1         0     0        0    0      red       red  \n",
       "175         1         0     0        0    0      red       red  \n",
       "176         0         0     1        1    0    white      blue  \n",
       "177         0         0     0        0    0    white      blue  \n",
       "178         0         0     0        0    0      red     black  \n",
       "179         0         0     0        1    0    black       red  \n",
       "180         0         0     0        0    0    white       red  \n",
       "181         0         0     0        0    0    white     white  \n",
       "182         0         0     1        1    1    white     white  \n",
       "183         0         0     0        0    0     blue       red  \n",
       "184         0         0     1        0    0      red       red  \n",
       "185         0         1     0        1    0    black     green  \n",
       "186         0         0     1        0    0     gold     white  \n",
       "187         0         0     1        1    0     gold       red  \n",
       "188         0         0     0        0    0      red       red  \n",
       "189         0         0     0        0    0     blue       red  \n",
       "190         0         0     0        0    0     blue       red  \n",
       "191         0         0     1        1    0    green     green  \n",
       "192         0         0     0        1    0    green     brown  \n",
       "193         0         1     1        1    0    green     green  \n",
       "\n",
       "[60 rows x 30 columns]"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "flag.tail(60) # To get last few rows from the columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "92b72943",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "             name  landmass  zone  area  population  language  religion  bars  \\\n",
      "0     Afghanistan         5     1   648          16        10         2     0   \n",
      "1         Albania         3     1    29           3         6         6     0   \n",
      "2         Algeria         4     1  2388          20         8         2     2   \n",
      "3  American-Samoa         6     3     0           0         1         1     0   \n",
      "4         Andorra         3     1     0           0         6         0     3   \n",
      "\n",
      "   stripes  colours  ...  saltires  quarters  sunstars  crescent  triangle  \\\n",
      "0        3        5  ...         0         0         1         0         0   \n",
      "1        0        3  ...         0         0         1         0         0   \n",
      "2        0        3  ...         0         0         1         1         0   \n",
      "3        0        5  ...         0         0         0         0         1   \n",
      "4        0        3  ...         0         0         0         0         0   \n",
      "\n",
      "   icon  animate text  topleft  botright  \n",
      "0     1        0    0    black     green  \n",
      "1     0        1    0      red       red  \n",
      "2     0        0    0    green     white  \n",
      "3     1        1    0     blue       red  \n",
      "4     0        0    0     blue       red  \n",
      "\n",
      "[5 rows x 30 columns]\n"
     ]
    }
   ],
   "source": [
    "print(flag.head()) # To get the first few initial rows from the columns"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "fd8c0f6b",
   "metadata": {},
   "source": [
    "# Checking the types of loaded columns\n",
    "Here, `flag.dtypes` function is used to check the datatypes of the `flag` dataset's loaded columns. As an output, few variables are found which are objects that known as `name`,`mainhue`,`topleft`,`botright`. On the other hand, some integer numbers also have obtained which are named as `landmass`,`zone`,`area`,`population`,`language`,`religion`,`bars`,`stripes`,`colours`,`red`,`green`,`blue`,`gold`, `white`,`black`,`orange`,`circles`,`crosses`,`saltires`,`quarters`,`sunstars`,`crescent`,`triangle`,`icon`,`animate`,`text`."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "9b858398",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "name          object\n",
       "landmass       int64\n",
       "zone           int64\n",
       "area           int64\n",
       "population     int64\n",
       "language       int64\n",
       "religion       int64\n",
       "bars           int64\n",
       "stripes        int64\n",
       "colours        int64\n",
       "red            int64\n",
       "green          int64\n",
       "blue           int64\n",
       "gold           int64\n",
       "white          int64\n",
       "black          int64\n",
       "orange         int64\n",
       "mainhue       object\n",
       "circles        int64\n",
       "crosses        int64\n",
       "saltires       int64\n",
       "quarters       int64\n",
       "sunstars       int64\n",
       "crescent       int64\n",
       "triangle       int64\n",
       "icon           int64\n",
       "animate        int64\n",
       "text           int64\n",
       "topleft       object\n",
       "botright      object\n",
       "dtype: object"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "flag.dtypes "
   ]
  },
  {
   "cell_type": "markdown",
   "id": "17acbcbc",
   "metadata": {},
   "source": [
    "The entire `flag` dataframe's structure has been checked using `info()` function where 194 observations(rows) and 30 variables(columns) have been found as an output. Apart from this, 26 int64 and 4 object data types have been found from this `flag` dataset which are enlightened in the below output pane."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "97ef00eb",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 194 entries, 0 to 193\n",
      "Data columns (total 30 columns):\n",
      " #   Column      Non-Null Count  Dtype \n",
      "---  ------      --------------  ----- \n",
      " 0   name        194 non-null    object\n",
      " 1   landmass    194 non-null    int64 \n",
      " 2   zone        194 non-null    int64 \n",
      " 3   area        194 non-null    int64 \n",
      " 4   population  194 non-null    int64 \n",
      " 5   language    194 non-null    int64 \n",
      " 6   religion    194 non-null    int64 \n",
      " 7   bars        194 non-null    int64 \n",
      " 8   stripes     194 non-null    int64 \n",
      " 9   colours     194 non-null    int64 \n",
      " 10  red         194 non-null    int64 \n",
      " 11  green       194 non-null    int64 \n",
      " 12  blue        194 non-null    int64 \n",
      " 13  gold        194 non-null    int64 \n",
      " 14  white       194 non-null    int64 \n",
      " 15  black       194 non-null    int64 \n",
      " 16  orange      194 non-null    int64 \n",
      " 17  mainhue     194 non-null    object\n",
      " 18  circles     194 non-null    int64 \n",
      " 19  crosses     194 non-null    int64 \n",
      " 20  saltires    194 non-null    int64 \n",
      " 21  quarters    194 non-null    int64 \n",
      " 22  sunstars    194 non-null    int64 \n",
      " 23  crescent    194 non-null    int64 \n",
      " 24  triangle    194 non-null    int64 \n",
      " 25  icon        194 non-null    int64 \n",
      " 26  animate     194 non-null    int64 \n",
      " 27  text        194 non-null    int64 \n",
      " 28  topleft     194 non-null    object\n",
      " 29  botright    194 non-null    object\n",
      "dtypes: int64(26), object(4)\n",
      "memory usage: 45.6+ KB\n"
     ]
    }
   ],
   "source": [
    "flag.info() "
   ]
  },
  {
   "cell_type": "markdown",
   "id": "7567feba",
   "metadata": {},
   "source": [
    "## Full Description of the dataset:"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f751a71b",
   "metadata": {},
   "source": [
    "This data file contains details of various nations and their flags. In this file the fields are separated by commas. With this data, it might happens to predict the religion of a country from its size and the colours in its flag. Here, 10 attributes are numeric-valued and the remainder are either Boolean-or nominal-valued. Apart from this, number of instances are 194 and number of attributes are 30 (overall). This dataset has been found from the following link:\n",
    "https://archive.ics.uci.edu/ml/datasets/Flags\n",
    "\n",
    "Attributes Information:\n",
    "\n",
    "1) name: Name of the country concerned\n",
    "2) landmass: 1=N.America, 2=S.America, 3=Europe, 4=Africa, 5=Asia, 6=Oceania\n",
    "3) zone: Geographic quadrant, based on Greenwich and the Equator 1=NE, 2=SE, 3=SW, 4=NW\n",
    "4) area: in thousands of square km\n",
    "5) population: in round millions\n",
    "6) language: 1=English, 2=Spanish, 3=French, 4=German, 5=Slavic, 6=Other Indo-European, 7=Chinese, 8=Arabic, 9=Japanese/Turkish/Finnish/Magyar, 10=Others\n",
    "7) religion: 0=Catholic, 1=Other Christian, 2=Muslim, 3=Buddhist, 4=Hindu, 5=Ethnic, 6=Marxist, 7=Others\n",
    "8) bars: Number of vertical bars in the flag\n",
    "9) stripes: Number of horizontal stripes in the flag\n",
    "10) colours: Number of different colours in the flag\n",
    "11) red: 0 if red absent, 1 if red present in the flag\n",
    "12) green: same for green\n",
    "13) blue: same for blue\n",
    "14) gold: same for gold (also yellow)\n",
    "15) white: same for white\n",
    "16) black: same for black\n",
    "17) orange: same for orange (also brown)\n",
    "18) mainhue: predominant colour in the flag (tie-breaks decided by taking the topmost hue, if that fails then the most central hue, and if that fails the leftmost hue)\n",
    "19) circles: Number of circles in the flag\n",
    "20) crosses: Number of (upright) crosses\n",
    "21) saltires: Number of diagonal crosses\n",
    "22) quarters: Number of quartered sections\n",
    "23) sunstars: Number of sun or star symbols\n",
    "24) crescent: 1 if a crescent moon symbol present, else 0\n",
    "25) triangle: 1 if any triangles present, 0 otherwise\n",
    "26) icon: 1 if an inanimate image present (e.g., a boat), otherwise 0\n",
    "27) animate: 1 if an animate image (e.g., an eagle, a tree, a human hand)present, 0 otherwise\n",
    "28) text: 1 if any letters or writing on the flag (e.g., a motto or slogan), 0 otherwise\n",
    "29) topleft: colour in the top-left corner (moving right to decide tie-breaks)\n",
    "30) botright: Colour in the bottom-left corner (moving left to decide tie-breaks).\n",
    "Missing values: None."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b1b6c27c",
   "metadata": {},
   "source": [
    "In the following step, conversion process has been done where `name`, `mainhue`, `topleft` and `botright`variables are converted from object datatypes to category types using `astype()` function."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "ca30a19b",
   "metadata": {},
   "outputs": [],
   "source": [
    "flag['name'] = flag['name'].astype('category')\n",
    "flag['mainhue'] = flag['mainhue'].astype('category')\n",
    "flag['topleft'] = flag['topleft'].astype('category')\n",
    "flag['botright'] = flag['botright'].astype('category')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "173e9de7",
   "metadata": {},
   "source": [
    "The entire flag dataframe's structure has been checked using `info()` function again where 194 observations(rows) and 30 variables(columns) have been found as an output. Apart from this, 26 `int64` and 4 `category` data types have been found from this flag dataset which are enlightened in the below output pane."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "c509f6d6",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 194 entries, 0 to 193\n",
      "Data columns (total 30 columns):\n",
      " #   Column      Non-Null Count  Dtype   \n",
      "---  ------      --------------  -----   \n",
      " 0   name        194 non-null    category\n",
      " 1   landmass    194 non-null    int64   \n",
      " 2   zone        194 non-null    int64   \n",
      " 3   area        194 non-null    int64   \n",
      " 4   population  194 non-null    int64   \n",
      " 5   language    194 non-null    int64   \n",
      " 6   religion    194 non-null    int64   \n",
      " 7   bars        194 non-null    int64   \n",
      " 8   stripes     194 non-null    int64   \n",
      " 9   colours     194 non-null    int64   \n",
      " 10  red         194 non-null    int64   \n",
      " 11  green       194 non-null    int64   \n",
      " 12  blue        194 non-null    int64   \n",
      " 13  gold        194 non-null    int64   \n",
      " 14  white       194 non-null    int64   \n",
      " 15  black       194 non-null    int64   \n",
      " 16  orange      194 non-null    int64   \n",
      " 17  mainhue     194 non-null    category\n",
      " 18  circles     194 non-null    int64   \n",
      " 19  crosses     194 non-null    int64   \n",
      " 20  saltires    194 non-null    int64   \n",
      " 21  quarters    194 non-null    int64   \n",
      " 22  sunstars    194 non-null    int64   \n",
      " 23  crescent    194 non-null    int64   \n",
      " 24  triangle    194 non-null    int64   \n",
      " 25  icon        194 non-null    int64   \n",
      " 26  animate     194 non-null    int64   \n",
      " 27  text        194 non-null    int64   \n",
      " 28  topleft     194 non-null    category\n",
      " 29  botright    194 non-null    category\n",
      "dtypes: category(4), int64(26)\n",
      "memory usage: 47.1 KB\n"
     ]
    }
   ],
   "source": [
    "flag.info()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "74f60ca2",
   "metadata": {},
   "source": [
    "In the next step, extra-whitespace errors has been checked using `str.strip()` function for the given variables such as `name`, `mainhue`, `topleft` and `botright`."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "0ab81843",
   "metadata": {},
   "outputs": [],
   "source": [
    "flag['name']= flag['name'].str.strip()\n",
    "flag['mainhue']= flag['mainhue'].str.strip()\n",
    "flag['topleft']= flag['topleft'].str.strip()\n",
    "flag['botright']= flag['botright'].str.strip()\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "cacbfb90",
   "metadata": {},
   "source": [
    "As this `Malagasy` is an Austronesian language of `Sunda islands` and as `Malaysia` is one of the country of the part of `Sunda islands`, therefore this `Malagasy` language name has been changed as a country name `Malaysia` to enhance the readability and consistency of this dataset. During this manpulation, the `replace()` function has been applied in the following step."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "f7dcc990",
   "metadata": {},
   "outputs": [],
   "source": [
    "flag['name'].replace('Malagasy', 'Malaysia', inplace=True) "
   ]
  },
  {
   "cell_type": "markdown",
   "id": "45dc1f61",
   "metadata": {},
   "source": [
    "Here,it has been found that `0`is redundant value compare with the other values in the `area` variable which can also make \n",
    "inconsistency in this`flag` dataset. Hence, to extend the sanity of the dataset, 'area' variable's values have been \n",
    "modified using the above function."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "a2c25aa3",
   "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>name</th>\n",
       "      <th>landmass</th>\n",
       "      <th>zone</th>\n",
       "      <th>area</th>\n",
       "      <th>population</th>\n",
       "      <th>language</th>\n",
       "      <th>religion</th>\n",
       "      <th>bars</th>\n",
       "      <th>stripes</th>\n",
       "      <th>colours</th>\n",
       "      <th>...</th>\n",
       "      <th>saltires</th>\n",
       "      <th>quarters</th>\n",
       "      <th>sunstars</th>\n",
       "      <th>crescent</th>\n",
       "      <th>triangle</th>\n",
       "      <th>icon</th>\n",
       "      <th>animate</th>\n",
       "      <th>text</th>\n",
       "      <th>topleft</th>\n",
       "      <th>botright</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Afghanistan</td>\n",
       "      <td>5</td>\n",
       "      <td>1</td>\n",
       "      <td>648</td>\n",
       "      <td>16</td>\n",
       "      <td>10</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>5</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>black</td>\n",
       "      <td>green</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Albania</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>29</td>\n",
       "      <td>3</td>\n",
       "      <td>6</td>\n",
       "      <td>6</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>red</td>\n",
       "      <td>red</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Algeria</td>\n",
       "      <td>4</td>\n",
       "      <td>1</td>\n",
       "      <td>2388</td>\n",
       "      <td>20</td>\n",
       "      <td>8</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>green</td>\n",
       "      <td>white</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>Angola</td>\n",
       "      <td>4</td>\n",
       "      <td>2</td>\n",
       "      <td>1247</td>\n",
       "      <td>7</td>\n",
       "      <td>10</td>\n",
       "      <td>5</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>3</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>red</td>\n",
       "      <td>black</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>Argentina</td>\n",
       "      <td>2</td>\n",
       "      <td>3</td>\n",
       "      <td>2777</td>\n",
       "      <td>28</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>2</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>blue</td>\n",
       "      <td>blue</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <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>189</th>\n",
       "      <td>Western-Samoa</td>\n",
       "      <td>6</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>5</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>blue</td>\n",
       "      <td>red</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>190</th>\n",
       "      <td>Yugoslavia</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>256</td>\n",
       "      <td>22</td>\n",
       "      <td>6</td>\n",
       "      <td>6</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>4</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>blue</td>\n",
       "      <td>red</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>191</th>\n",
       "      <td>Zaire</td>\n",
       "      <td>4</td>\n",
       "      <td>2</td>\n",
       "      <td>905</td>\n",
       "      <td>28</td>\n",
       "      <td>10</td>\n",
       "      <td>5</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>4</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>green</td>\n",
       "      <td>green</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>192</th>\n",
       "      <td>Zambia</td>\n",
       "      <td>4</td>\n",
       "      <td>2</td>\n",
       "      <td>753</td>\n",
       "      <td>6</td>\n",
       "      <td>10</td>\n",
       "      <td>5</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>4</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>green</td>\n",
       "      <td>brown</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>193</th>\n",
       "      <td>Zimbabwe</td>\n",
       "      <td>4</td>\n",
       "      <td>2</td>\n",
       "      <td>391</td>\n",
       "      <td>8</td>\n",
       "      <td>10</td>\n",
       "      <td>5</td>\n",
       "      <td>0</td>\n",
       "      <td>7</td>\n",
       "      <td>5</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>green</td>\n",
       "      <td>green</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>160 rows × 30 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "              name  landmass  zone  area  population  language  religion  \\\n",
       "0      Afghanistan         5     1   648          16        10         2   \n",
       "1          Albania         3     1    29           3         6         6   \n",
       "2          Algeria         4     1  2388          20         8         2   \n",
       "5           Angola         4     2  1247           7        10         5   \n",
       "8        Argentina         2     3  2777          28         2         0   \n",
       "..             ...       ...   ...   ...         ...       ...       ...   \n",
       "189  Western-Samoa         6     3     3           0         1         1   \n",
       "190     Yugoslavia         3     1   256          22         6         6   \n",
       "191          Zaire         4     2   905          28        10         5   \n",
       "192         Zambia         4     2   753           6        10         5   \n",
       "193       Zimbabwe         4     2   391           8        10         5   \n",
       "\n",
       "     bars  stripes  colours  ...  saltires  quarters  sunstars  crescent  \\\n",
       "0       0        3        5  ...         0         0         1         0   \n",
       "1       0        0        3  ...         0         0         1         0   \n",
       "2       2        0        3  ...         0         0         1         1   \n",
       "5       0        2        3  ...         0         0         1         0   \n",
       "8       0        3        2  ...         0         0         0         0   \n",
       "..    ...      ...      ...  ...       ...       ...       ...       ...   \n",
       "189     0        0        3  ...         0         1         5         0   \n",
       "190     0        3        4  ...         0         0         1         0   \n",
       "191     0        0        4  ...         0         0         0         0   \n",
       "192     3        0        4  ...         0         0         0         0   \n",
       "193     0        7        5  ...         0         0         1         0   \n",
       "\n",
       "     triangle  icon  animate text  topleft  botright  \n",
       "0           0     1        0    0    black     green  \n",
       "1           0     0        1    0      red       red  \n",
       "2           0     0        0    0    green     white  \n",
       "5           0     1        0    0      red     black  \n",
       "8           0     0        0    0     blue      blue  \n",
       "..        ...   ...      ...  ...      ...       ...  \n",
       "189         0     0        0    0     blue       red  \n",
       "190         0     0        0    0     blue       red  \n",
       "191         0     1        1    0    green     green  \n",
       "192         0     0        1    0    green     brown  \n",
       "193         1     1        1    0    green     green  \n",
       "\n",
       "[160 rows x 30 columns]"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "new_flag = flag[flag.area != 0]\n",
    "new_flag"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "9e0f00d2",
   "metadata": {},
   "source": [
    " Here,it has been found that `0` is also redundant value compare with the other values in the `population`variable which can also make inconsistency in this `flag` dataset. Hence, to extend the sanity of the dataset, the above variables' values have been modified using the following functions."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "bb1cb879",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <th></th>\n",
       "      <th>name</th>\n",
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       "      <td>Afghanistan</td>\n",
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       "      <th>1</th>\n",
       "      <td>Albania</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>29</td>\n",
       "      <td>3</td>\n",
       "      <td>6</td>\n",
       "      <td>6</td>\n",
       "      <td>0</td>\n",
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       "      <td>red</td>\n",
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       "      <th>2</th>\n",
       "      <td>Algeria</td>\n",
       "      <td>4</td>\n",
       "      <td>1</td>\n",
       "      <td>2388</td>\n",
       "      <td>20</td>\n",
       "      <td>8</td>\n",
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       "      <td>green</td>\n",
       "      <td>white</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>Angola</td>\n",
       "      <td>4</td>\n",
       "      <td>2</td>\n",
       "      <td>1247</td>\n",
       "      <td>7</td>\n",
       "      <td>10</td>\n",
       "      <td>5</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>3</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
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       "      <td>1</td>\n",
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       "      <td>0</td>\n",
       "      <td>red</td>\n",
       "      <td>black</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>Argentina</td>\n",
       "      <td>2</td>\n",
       "      <td>3</td>\n",
       "      <td>2777</td>\n",
       "      <td>28</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>2</td>\n",
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       "      <td>blue</td>\n",
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       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>188</th>\n",
       "      <td>Vietnam</td>\n",
       "      <td>5</td>\n",
       "      <td>1</td>\n",
       "      <td>333</td>\n",
       "      <td>60</td>\n",
       "      <td>10</td>\n",
       "      <td>6</td>\n",
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       "      <td>red</td>\n",
       "      <td>red</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>190</th>\n",
       "      <td>Yugoslavia</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>256</td>\n",
       "      <td>22</td>\n",
       "      <td>6</td>\n",
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       "      <td>0</td>\n",
       "      <td>0</td>\n",
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       "      <td>0</td>\n",
       "      <td>blue</td>\n",
       "      <td>red</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>191</th>\n",
       "      <td>Zaire</td>\n",
       "      <td>4</td>\n",
       "      <td>2</td>\n",
       "      <td>905</td>\n",
       "      <td>28</td>\n",
       "      <td>10</td>\n",
       "      <td>5</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>4</td>\n",
       "      <td>...</td>\n",
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       "      <td>0</td>\n",
       "      <td>0</td>\n",
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       "      <td>green</td>\n",
       "      <td>green</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>192</th>\n",
       "      <td>Zambia</td>\n",
       "      <td>4</td>\n",
       "      <td>2</td>\n",
       "      <td>753</td>\n",
       "      <td>6</td>\n",
       "      <td>10</td>\n",
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       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>green</td>\n",
       "      <td>brown</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>193</th>\n",
       "      <td>Zimbabwe</td>\n",
       "      <td>4</td>\n",
       "      <td>2</td>\n",
       "      <td>391</td>\n",
       "      <td>8</td>\n",
       "      <td>10</td>\n",
       "      <td>5</td>\n",
       "      <td>0</td>\n",
       "      <td>7</td>\n",
       "      <td>5</td>\n",
       "      <td>...</td>\n",
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       "      <td>0</td>\n",
       "      <td>1</td>\n",
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       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>green</td>\n",
       "      <td>green</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>138 rows × 30 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "            name  landmass  zone  area  population  language  religion  bars  \\\n",
       "0    Afghanistan         5     1   648          16        10         2     0   \n",
       "1        Albania         3     1    29           3         6         6     0   \n",
       "2        Algeria         4     1  2388          20         8         2     2   \n",
       "5         Angola         4     2  1247           7        10         5     0   \n",
       "8      Argentina         2     3  2777          28         2         0     0   \n",
       "..           ...       ...   ...   ...         ...       ...       ...   ...   \n",
       "188      Vietnam         5     1   333          60        10         6     0   \n",
       "190   Yugoslavia         3     1   256          22         6         6     0   \n",
       "191        Zaire         4     2   905          28        10         5     0   \n",
       "192       Zambia         4     2   753           6        10         5     3   \n",
       "193     Zimbabwe         4     2   391           8        10         5     0   \n",
       "\n",
       "     stripes  colours  ...  saltires  quarters  sunstars  crescent  triangle  \\\n",
       "0          3        5  ...         0         0         1         0         0   \n",
       "1          0        3  ...         0         0         1         0         0   \n",
       "2          0        3  ...         0         0         1         1         0   \n",
       "5          2        3  ...         0         0         1         0         0   \n",
       "8          3        2  ...         0         0         0         0         0   \n",
       "..       ...      ...  ...       ...       ...       ...       ...       ...   \n",
       "188        0        2  ...         0         0         1         0         0   \n",
       "190        3        4  ...         0         0         1         0         0   \n",
       "191        0        4  ...         0         0         0         0         0   \n",
       "192        0        4  ...         0         0         0         0         0   \n",
       "193        7        5  ...         0         0         1         0         1   \n",
       "\n",
       "     icon  animate text  topleft  botright  \n",
       "0       1        0    0    black     green  \n",
       "1       0        1    0      red       red  \n",
       "2       0        0    0    green     white  \n",
       "5       1        0    0      red     black  \n",
       "8       0        0    0     blue      blue  \n",
       "..    ...      ...  ...      ...       ...  \n",
       "188     0        0    0      red       red  \n",
       "190     0        0    0     blue       red  \n",
       "191     1        1    0    green     green  \n",
       "192     0        1    0    green     brown  \n",
       "193     1        1    0    green     green  \n",
       "\n",
       "[138 rows x 30 columns]"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "new_flag= flag[flag.population != 0]\n",
    "new_flag"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "c61023d9",
   "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>name</th>\n",
       "      <th>landmass</th>\n",
       "      <th>zone</th>\n",
       "      <th>area</th>\n",
       "      <th>population</th>\n",
       "      <th>language</th>\n",
       "      <th>religion</th>\n",
       "      <th>bars</th>\n",
       "      <th>stripes</th>\n",
       "      <th>colours</th>\n",
       "      <th>...</th>\n",
       "      <th>saltires</th>\n",
       "      <th>quarters</th>\n",
       "      <th>sunstars</th>\n",
       "      <th>crescent</th>\n",
       "      <th>triangle</th>\n",
       "      <th>icon</th>\n",
       "      <th>animate</th>\n",
       "      <th>text</th>\n",
       "      <th>topleft</th>\n",
       "      <th>botright</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Afghanistan</td>\n",
       "      <td>5</td>\n",
       "      <td>1</td>\n",
       "      <td>648</td>\n",
       "      <td>16</td>\n",
       "      <td>10</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>5</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>black</td>\n",
       "      <td>green</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Albania</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>29</td>\n",
       "      <td>3</td>\n",
       "      <td>6</td>\n",
       "      <td>6</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>red</td>\n",
       "      <td>red</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Algeria</td>\n",
       "      <td>4</td>\n",
       "      <td>1</td>\n",
       "      <td>2388</td>\n",
       "      <td>20</td>\n",
       "      <td>8</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>green</td>\n",
       "      <td>white</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>Angola</td>\n",
       "      <td>4</td>\n",
       "      <td>2</td>\n",
       "      <td>1247</td>\n",
       "      <td>7</td>\n",
       "      <td>10</td>\n",
       "      <td>5</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>3</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>red</td>\n",
       "      <td>black</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>Argentina</td>\n",
       "      <td>2</td>\n",
       "      <td>3</td>\n",
       "      <td>2777</td>\n",
       "      <td>28</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>2</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>blue</td>\n",
       "      <td>blue</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <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>188</th>\n",
       "      <td>Vietnam</td>\n",
       "      <td>5</td>\n",
       "      <td>1</td>\n",
       "      <td>333</td>\n",
       "      <td>60</td>\n",
       "      <td>10</td>\n",
       "      <td>6</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>red</td>\n",
       "      <td>red</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>190</th>\n",
       "      <td>Yugoslavia</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>256</td>\n",
       "      <td>22</td>\n",
       "      <td>6</td>\n",
       "      <td>6</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>4</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>blue</td>\n",
       "      <td>red</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>191</th>\n",
       "      <td>Zaire</td>\n",
       "      <td>4</td>\n",
       "      <td>2</td>\n",
       "      <td>905</td>\n",
       "      <td>28</td>\n",
       "      <td>10</td>\n",
       "      <td>5</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>4</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>green</td>\n",
       "      <td>green</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>192</th>\n",
       "      <td>Zambia</td>\n",
       "      <td>4</td>\n",
       "      <td>2</td>\n",
       "      <td>753</td>\n",
       "      <td>6</td>\n",
       "      <td>10</td>\n",
       "      <td>5</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>4</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>green</td>\n",
       "      <td>brown</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>193</th>\n",
       "      <td>Zimbabwe</td>\n",
       "      <td>4</td>\n",
       "      <td>2</td>\n",
       "      <td>391</td>\n",
       "      <td>8</td>\n",
       "      <td>10</td>\n",
       "      <td>5</td>\n",
       "      <td>0</td>\n",
       "      <td>7</td>\n",
       "      <td>5</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>green</td>\n",
       "      <td>green</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>138 rows × 30 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "            name  landmass  zone  area  population  language  religion  bars  \\\n",
       "0    Afghanistan         5     1   648          16        10         2     0   \n",
       "1        Albania         3     1    29           3         6         6     0   \n",
       "2        Algeria         4     1  2388          20         8         2     2   \n",
       "5         Angola         4     2  1247           7        10         5     0   \n",
       "8      Argentina         2     3  2777          28         2         0     0   \n",
       "..           ...       ...   ...   ...         ...       ...       ...   ...   \n",
       "188      Vietnam         5     1   333          60        10         6     0   \n",
       "190   Yugoslavia         3     1   256          22         6         6     0   \n",
       "191        Zaire         4     2   905          28        10         5     0   \n",
       "192       Zambia         4     2   753           6        10         5     3   \n",
       "193     Zimbabwe         4     2   391           8        10         5     0   \n",
       "\n",
       "     stripes  colours  ...  saltires  quarters  sunstars  crescent  triangle  \\\n",
       "0          3        5  ...         0         0         1         0         0   \n",
       "1          0        3  ...         0         0         1         0         0   \n",
       "2          0        3  ...         0         0         1         1         0   \n",
       "5          2        3  ...         0         0         1         0         0   \n",
       "8          3        2  ...         0         0         0         0         0   \n",
       "..       ...      ...  ...       ...       ...       ...       ...       ...   \n",
       "188        0        2  ...         0         0         1         0         0   \n",
       "190        3        4  ...         0         0         1         0         0   \n",
       "191        0        4  ...         0         0         0         0         0   \n",
       "192        0        4  ...         0         0         0         0         0   \n",
       "193        7        5  ...         0         0         1         0         1   \n",
       "\n",
       "     icon  animate text  topleft  botright  \n",
       "0       1        0    0    black     green  \n",
       "1       0        1    0      red       red  \n",
       "2       0        0    0    green     white  \n",
       "5       1        0    0      red     black  \n",
       "8       0        0    0     blue      blue  \n",
       "..    ...      ...  ...      ...       ...  \n",
       "188     0        0    0      red       red  \n",
       "190     0        0    0     blue       red  \n",
       "191     1        1    0    green     green  \n",
       "192     0        1    0    green     brown  \n",
       "193     1        1    0    green     green  \n",
       "\n",
       "[138 rows x 30 columns]"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "new_flag.dropna(inplace=True)\n",
    "new_flag # Here, `dropna()` function has been used with the `inplace=True` argument to ensure that all missing values are \n",
    "# dropped from this `flag` dataset."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "4508d967",
   "metadata": {},
   "source": [
    "To check the null values, the `isnull()` and `values.any()` functions are applied. As a result, `False` is highlighted as the evidence of not having any missing values in the output."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "3dfac646",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "False"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "new_flag.isnull().values.any() "
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b1d51dcf",
   "metadata": {},
   "source": [
    "## Task 2: Data Exploration"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "1d09c11b",
   "metadata": {},
   "source": [
    "## 1. Pie chart"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "9134d244",
   "metadata": {},
   "source": [
    "Here,`value.counts()` function has been used for the `botright` variable and different kinds of values have obtained from the output pane which are highlighting as red, green, blue, white, black, gold, orange, brown values and this values observation numbers are 50, 30, 28,  12, 9, 7, 1, 1 respectively."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "985d3c18",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "red       50\n",
       "green     30\n",
       "blue      28\n",
       "white     12\n",
       "black      9\n",
       "gold       7\n",
       "orange     1\n",
       "brown      1\n",
       "Name: botright, dtype: int64"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "new_flag['botright'].value_counts()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "91a95a97",
   "metadata": {},
   "source": [
    "Here, the libraries numpy, matplotlib and matplotlib.pyplot are imported as np, mpl and plt respectively to explore the pie plot. In this pie plot, all the values of the `botright` variable are subsetted using `array()` function which is added with the numpy and character as the all values are showing character attributes and all these have been defined by `botright_values` object.Again, the `botright_value_counts` object is made by applying the `array()` function which is added with the numpy and  highlighted all the observation numbers orderly for all values of the `botright` variable. Apart from this, different types of colours have been used in order using the `colors` object. Here, the `porcent` object has been generated to get all the observation numbers' percentage accurately. After that, the `plt.pie()` function is applied to obtained the perfect pie plot which is accumulated with the `startangle=90` by defining every slice is rotated counter-clockwise with the 90 degrees, and the first slice starts on the positive y-axis and the radius parameter which is highllighted as 1.2 in the output pane. Here, `labels` object is used by highlighting this claculation `{0} - {1:1.2f} %` to get all values auto percentages and `format()` function has been used for i and j which are mentioning by zipping with the `botright_values` and `porcent` respectively. Here, `sort_legend=True` argument has been used to keep the legend in orderwise and `dummy` object is used by applying the `zip()` function to preserve all the same properties where `key=lamda` and `reverse=True` arguments are highlighted. Additionally, the `plt.legend()` function is applied  by representing the `fontsize`, `loc`, and `bbox_to_anchor` arguments where the fontsize is 15, the loc parameter specifies in which corner of the bounding box(bbox) legend will be placed and the default for loc is `loc= best` which gives unpredictable results when the `bbox_to_anchor` argument is used where the default for `bbox_to_anchor=(-0.1,1.)` is mentioning a bounding box over the complete axes. According to the comparing of the values proportion,red, green, blue, white, black, gold, orange, brown values are showing '36.23%', '21.74%', '20.29%', '8.70%', '6.52%', '5.07%', '0.72' and '0.72%' respectively from the following pie chart. Finally, the `plt.title()` function has been used to highlight the title name of the pie chart which named as `new_flag_botright_Info` and the `plt.savefig()` function is used to save the pie chart figure appropriately which are highlighted in the output pane. As it is a circular statistical graph, it does not have any x-axis and y-axis."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "10982934",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import numpy as np\n",
    "import matplotlib as mpl \n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "botright_values = np.char.array(['red','green','blue','white','black','gold','orange','brown'])\n",
    "botright_value_counts= np.array([50, 30, 28, 12, 9, 7, 1, 1])\n",
    "colors = ['red','green','blue','lightgrey','black','gold','orange','brown']\n",
    "porcent = 100.*botright_value_counts/botright_value_counts.sum()\n",
    "\n",
    "patches, text = plt.pie(botright_value_counts, colors=colors, startangle=90, radius=1.2)\n",
    "labels = ['{0} - {1:1.2f} %'.format(i,j) for i,j in zip(botright_values, porcent)]\n",
    "\n",
    "sort_legend = True\n",
    "if sort_legend:\n",
    "    patches, labels, dummy =  zip(*sorted(zip(patches, labels,botright_value_counts),\n",
    "                                          key=lambda botright_values: botright_values[2],\n",
    "                                          reverse=True))\n",
    "\n",
    "plt.legend(patches,labels, loc='best', bbox_to_anchor=(-0.1, 1.),\n",
    "           fontsize=15)\n",
    "plt.title('new_flag_botright_Info')\n",
    "plt.savefig('piechart.png', bbox_inches='tight')\n",
    "# https://matplotlib.org/stable/api/_as_gen/matplotlib.pyplot.pie.html"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "5267f375",
   "metadata": {},
   "source": [
    "## 2. Bar chart"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b68eeb0a",
   "metadata": {},
   "source": [
    "Here, the libraries pandas, matplotlib and matplotlib.pyplot are imported as pd, mpl and plt respectively to explore the bar plot."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "f0de317f",
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import matplotlib as mpl \n",
    "import matplotlib.pyplot as plt"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "924c7b79",
   "metadata": {},
   "source": [
    "Here,`value.counts()` function has been used for the `mainhue` variable and different kinds of values have obtained from the output pane which are highlighting as red, green, blue, white, gold, black, orange, brown values and this values observation numbers are 56, 24, 21,  14, 13, 5, 4, 1 respectively."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "bd068124",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "red       56\n",
       "green     24\n",
       "blue      21\n",
       "white     14\n",
       "gold      13\n",
       "black      5\n",
       "orange     4\n",
       "brown      1\n",
       "Name: mainhue, dtype: int64"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "new_flag['mainhue'].value_counts() "
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e1e563c3",
   "metadata": {},
   "source": [
    "To generate this bar plot, the `mainhue` variable which is categorical and subsetted with the `new_flag` dataset as well as added with the `value.counts()` function where 'X' object has been made with those functions. After that,the `plot()` function has been applied using the various argument such as kind='bar',figsize=(10,8) and fontsize=(15) to get the appropriate bar plot. Here, the `plt.title()`,`plt.xlabel()`, `plt.ylabel()`, `plt.grid()` and `plt.legend()` functions have been used to get the title of the barplot as `flag_mainhue-Info`, xlabel as `mainhue`, ylabel as `count`, background of the barplot as `grid`, and the legend which explores the symbol of the colours of the bars respectively. Apart from this, the `plt.show()`function is applied to demonstrate the bar plot adequately. From this barplot, it is represented that the number of the `red` value of the `mainhue` variable is cpmparatively higher than the other values which is shown over `50` where `brown` value is showing it's lowest demonstration in this bar plot."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "bdace3c6",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x576 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "X = new_flag['mainhue'].value_counts()\n",
    "X.plot(kind='bar',figsize=(10,8), fontsize=(15),color='purple')\n",
    "plt.title('new_flag_mainhue-Info')\n",
    "plt.xlabel('mainhue')\n",
    "plt.ylabel('count')\n",
    "plt.grid()\n",
    "plt.legend()\n",
    "plt.show()\n",
    "# https://www.analyticsvidhya.com/blog/2021/08/understanding-bar-plots-in-python-beginners-guide-to-data-visualization/"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "9c215f87",
   "metadata": {},
   "source": [
    "## 3. Histogram"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "84416eb4",
   "metadata": {},
   "source": [
    "To check the number of the values, the `value_counts()` function is used for the `language` variable of the `new_flag` dataset. As an output, various numbers of the values have been found for the `language` variable such as 10,2,6,8,1,3,4,5,7,9 and which are named as 1=English, 2=Spanish, 3=French, 4=German, 5=Slavic, 6=Other Indo-European, 7=Chinese, 8=Arabic, 9=Japanese/Turkish/Finnish/Magyar, 10=Others."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "6dd08bd6",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "10    38\n",
       "2     21\n",
       "6     19\n",
       "8     17\n",
       "1     15\n",
       "3     12\n",
       "4      4\n",
       "5      4\n",
       "7      4\n",
       "9      4\n",
       "Name: language, dtype: int64"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "new_flag['language'].value_counts() "
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f0c6b1d0",
   "metadata": {},
   "source": [
    "Here, the libraries pandas, matplotlib and matplotlib.pyplot are imported as pd, mpl and plt respectively to explore the plot. In the following histogram plot,the `language` variable is subsetted with the `new_flag` dataframe where the `plot()` function is highlighting the kind of hist which means histogram and bins= 10 means 10 different range of languages.After that, the `plt.title()` function has been used to get the title name of the histogram chart which named as `new_flag-language Info` and highlighted in the output pane. Apart from this, the histogram has shown the xlabel or x-axis as `language` using the `plt.xlabel()` function whether it does not have any `ylabel` by default. Here, the `plt.legend()` function is applied to get the explicit information about the number of languages where `10` number means `other` languages frequency which is higher than the rest of the number of languages in this `new_flag` dataset. Here, the `language` variable is describing, 1=English, 2=Spanish, 3=French, 4=German, 5=Slavic, 6=Other Indo-European, 7=Chinese, 8=Arabic, 9=Japanese/Turkish/Finnish/Magyar, 10=Others. Finally, the `plt.grid()`function is used to demonstrate the background of the histogram as grid and the `plt.show()` function is applied to explore the histogram graph explicitly."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "c433d827",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "import pandas as pd\n",
    "import matplotlib as mpl \n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "new_flag['language'].plot(kind='hist',bins= 10,fontsize=(12))\n",
    "plt.title('new_flag - language Info')\n",
    "plt.xlabel('language')\n",
    "plt.legend()\n",
    "plt.grid()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "bb76035e",
   "metadata": {},
   "source": [
    "## 4. Boxplot"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "06dafa6b",
   "metadata": {},
   "source": [
    "In the next step, the `value_counts()` function is used for the `religion` variable of the `new_flag` dataset. As an output, various numbers of the values have been found for the `religion` variable such as 0,1,2,3,4,5,6,7 and which are mentioned as 0=Catholic, 1=Other Christian, 2=Muslim, 3=Buddhist, 4=Hindu, 5=Ethnic, 6=Marxist, 7=Others."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "d00c6b9d",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "2    30\n",
       "0    29\n",
       "5    26\n",
       "1    22\n",
       "6    15\n",
       "3     8\n",
       "4     4\n",
       "7     4\n",
       "Name: religion, dtype: int64"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "new_flag['religion'].value_counts()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "284f407c",
   "metadata": {},
   "source": [
    "According to the boxplot, it will give the information about the distribution of the data. Here, the boxplot is created using the `boxplot()` function in terms of detecting the outliers of the `religion` variable of `new_flag` dataset. Apart from this, `dropna()` function has been used in the following step to double-check and to drop the missing values during this operation time. After using those above functions, it is found that there is no outlier highlighting both in the upper fence and lower fence of the boxplot. Here, the `plt.title()` function is used to generate the title `new_flag_religion-Info`whether it does not have any legend by default which are shown in the following boxplot."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "65b5bb59",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 576x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "new_flag.dropna().boxplot(column = 'religion',figsize=(8,5), fontsize=(15))\n",
    "plt.title('new_flag_religion-Info')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f1cef128",
   "metadata": {},
   "source": [
    "## 5. Pie chart"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "4b791ce1",
   "metadata": {},
   "source": [
    "Here, the `value.counts()` function has been used for the `topleft` variable of the `new_flag` dataset and different kinds of values have obtained from the output pane which are demonstrating as red, blue, green, white, black, gold, orange values and this values observation numbers are 46, 29, 26, 18, 10, 5, 4 respectively."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "id": "c1efd50a",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "red       46\n",
       "blue      29\n",
       "green     26\n",
       "white     18\n",
       "black     10\n",
       "gold       5\n",
       "orange     4\n",
       "Name: topleft, dtype: int64"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "new_flag['topleft'].value_counts()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "fc1f2494",
   "metadata": {},
   "source": [
    "Here, the libraries numpy, matplotlib and matplotlib.pyplot are imported as np, mpl and plt respectively to explore the pie plot. In this pie plot, all the values of the `topleft` variable are subsetted using `array()` function which is added with the numpy and character as the all values are showing character attributes and all these have been defined by `topleft_values` object.Again, the `topleft_value_counts` object is made by applying the `array()` function which is added with the numpy and  highlighted all the observation numbers orderly for all values of the `topleft` variable. Apart from this, different types of colours have been used in order using the `colors` object. Here, the `porcent` object has been generated to get all the observation numbers' percentage accurately. After that, the `plt.pie()` function is applied to obtained the perfect pie plot which is accumulated with the `startangle=90` by defining every slice is rotated counter-clockwise with the 90 degrees, and the first slice starts on the positive y-axis and the radius parameter which is highllighted as 1.2 in the output pane. Here, `labels` object is used by highlighting this claculation `{0} - {1:1.2f} %` to get all values auto percentages and `format()` function has been used for i and j which are mentioning by zipping with the `topleft_values` and `porcent` respectively. Here, `sort_legend=True` argument has been used to keep the legend in orderwise and `dummy` object is used by applying the `zip()` function to preserve all the same properties where `key=lamda` and `reverse=True` arguments are highlighted. Additionally, the `plt.legend()` function is applied  by representing the `fontsize`, `loc`, and `bbox_to_anchor` arguments where the fontsize is 15, the loc parameter specifies in which corner of the bounding box(bbox) legend will be placed and the default for loc is `loc= best` which gives unpredictable results when the `bbox_to_anchor` argument is used where the default for `bbox_to_anchor=(-0.1,1.)` is mentioning a bounding box over the complete axes. According to the comparing of the values proportion,red, blue, white, black, gold, orange, green values are showing '33.33%', '21.01%', '18.84%', '13.04%', '7.25%', '3.62%', and '2.90%' respectively from the following pie chart. Finally, the `plt.title()` function has been used to highlight the title name of the pie chart which named as `new_flag_botright_Info` and the `plt.savefig()` function is used to save the pie chart figure appropriately which are highlighted in the output pane. As it is a circular statistical graph, it does not have any x-axis and y-axis."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "id": "92690179",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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79u3rYG5u3lZTU1O+f//+iPz8fLJ48WLLNWvWvBMIBM3qj6nJIYQNYA4AfwDaKo6m3klZpa8pKyxlZamSYCxr/d1sDeAAgLkIJV/CibmtxKjUWpMfZgQAExMTSVEiA4CnT59qxMXF8UaOHJkikUhQdBs4cGBmfn4+efjwoQAAgoODtfr3759WsncyatSotKqOJ5VKUbJdubzyS1U6dOiQGxQU9OLEiRMvJ0yYkLh48WLrHTt2fFCp++eff46+fPly6Pbt219nZ2ez+vXr1yonJ6fKYcYZM2YkBQUFvQgICAgXi8U5I0aMaPnw4cMPhiMuX74cev78+bCVK1e+u3jxot6ECROsK2vX0dGxICIiIiQ4ODjk/fv3wcOGDctYt26diZmZmWT8+PFp58+f13ZwcHDW19d3GzdunHVVsyMpNUKIK4A7AH5AM0hkAFDARn7J+5ospKskEIJX0C934kdNeAK4gVCyubkMPTaLZGZkZCQpeT8+Pp4DAKNGjWrF4/E8im5OTk6uAPDmzRseACQlJXFNTExKnfMRiUSl2ipPly5dHEu2e/bs2UpPIguFQrmXl1fO0KFDM3/99de3n3zyScqqVas+6P25urrm9+rVK3vmzJkpgYGBr168eKG5c+fOypanAABYW1tLvby8csaOHZt++fLlV7q6urLVq1d/UO6pW7duOf37989atWpV/Lp1694GBAQYP3v2rNL1orhcLtq0aZOvo6Mjj4uL42zdutVs69atb3Nzc8nnn3/ews/PL+7Vq1chT58+1dy8ebNKKqFTNUAIF4SsAvAQQHsVR9Og8jgoNVqhx6r3UlblM8V7JbXEgqJq/2OEkg5KalNtNYthRsXK6f8xMjKSAcD333//pkOHDh+c9HVwcMgv3E6SkJBQ6j2KjY2t8jLKXbt2RaWnpxeP/7m6utaoJI67u3v2H3/8YZifn080NDTKHapzcHAo0NXVlUZGRtZocUIulwsnJ6ec6OjoSvfr2LFjNgC8evWK5+Likl/ZtkUWLFhgMWjQoFRPT8/c27dvC6RSKZk6dWoqAIwePTo5KChIuGTJksSaxEs1IEIcoKjk3lbFkahELhelfqialOmpNZAUmCn9R4QTgFsIJesA+MOJqfIHeWPULJJZWW5ubnkmJiaSqKgojQULFiRVtF2bNm2yAwMD9eRyeUzRUOORI0f0qtF+nf4Ibt++rW1qaiqpKJEBQHBwsEZaWhrHzs6uRlW2c3JySEhIiFb79u2zKtvu6tWr2gDQqlWrarV/9+5dwblz5/SeP3/+rOgxiUTCkkql4HA4yM7OZjPN63x040LIOAC/oJkMKZYnhwtZyftWqihlpYsnYKFHPbTMAbAcQD+EkjFwYl7XwzFUqlkmMzabjdWrV7+dOXOmXUZGBmvQoEEZGhoa8vDwcI1Tp07pnT59OlJHR0e+ePHi9z179mw9ePDgFlOmTEl68uSJICAgwFhZcbx8+ZI3fvx425EjR6a0bNkyPzMzk3XixAn906dPG6xfv754Ysr06dMtORwO06lTp2x9fX3Zs2fP+D/++KOZlZVV/pQpU4pnJY4cOdLmzp07OtHR0SEAsGPHDoPz588L+/fvn2FpaSmJiYnh7ty50zgpKYm7aNGi4qGM7t27t+rZs2eGWCzO5XA4uH79uvaOHTtMBw8enFrdXtncuXOt5s2bF2dubi4FFD8Y+Hy+/H//+59l7969M3/77TfjWbNmNevrYNQSIQIAP0FR4LZZy+SVTma23Ppf2boMKSzrfVmcjlAMO06HE3O0no/VoGqXzBjmoZLjaHDTpk1L1dXVla1fv9786NGjRmw2G5aWlvn9+vVL4/P5cgDw8vLK2bVrV+SqVassxo0b11IsFmcHBARE9OjRQyklewwNDWWmpqaSTZs2mSclJXF1dHRkLVu2zD18+HD4qFGjik8+t2/fPvuXX34xDQgIMC4oKCBmZmYFgwYNSv3222/fC4XC4l+PMpmMyGSy4j9AFxeXvEOHDhksW7bMKiMjg21sbCxp27Zt9o4dO563b9++eOizXbt22YcOHTKKiYnhsdlsxsrKKn/p0qUxCxcurNaQ4N69e/USEhK4X331VfH2mpqazJ49eyJ9fX2tDx8+bDRo0KDU6rZHNRBCWkNxzZiLqkNRBxkapXtiLRq6lJUG7kMDnRvgSLoAjiCU9AXgCyemSVToIZUN/QQHB0e5ublVOAxHUeoqODjYyM3NzVbVcagtQj6DYlhRleWaVO05AOeiO384I+jTkf9V3DgjwpNBWmjTYNG0RAiEEDfY8RQeAhgCJ0ZZk05UplnMZqQoqpBiiZbVUKwv1pwT2QfSykxgt+E04AXTLDxTQSIDAA8AdxBKnKvcUs3RZEZRzQUhGlCsYrxU1aGoo1R+6XNkDVrKyhxpDXasD9lAUY2/pwpjqDOazCiqOSDEEMAlAGNUHYq6SuWjZDmdXF12gxUZfg+TGpWuqg96AM4jlHym4jhqjSYzimrqCGkF4DaAbqoORZ2lCv6bEMcBkhvswPoIA4GqlwEFAB6A/Qgly1UdSG3QZEZRTRkhnlAksiZX5V7ZSiazBixllQdLlZwrq8y3CCW/IpQ0qku3aDKjqKaKkE4ALgIqW8akUUnj/zcVX5+FSosKKI0A98FVy3+fyVAsAKqaVQNqgSYzimqKCOkM4AKgoiVMGqE0PopLvJmyUaPKOrVmrdardPcFcAGhRHULlNYATWYU1dQQ0gVAIKDCVZIboZLJzKpMaat6wca/0IJDvR+nbjoCOINQolnllipGkxlFNSWEdAVwHjSR1VgmD8Vf2LacBvhutGig3l/ddQfwt7ovJdPkk9nw4cNtxWJxleWnCCEea9euVVrdRWVISUlhzZs3T+Tq6tpaR0enrZGRkVvfvn3tnzx5UqrifWxsLGfixIlWrq6urblcbjsLCwvX6h4jNzeXTJs2zdLAwMBNIBC49+jRo2VYWFipMj7Xrl3THD58uK2tra2YxWJ5DB8+3LY6bb9584bbpUuXVtra2u7dunVrFR0dXeqEckhIiIZQKGwbERGhDjO5Gj9Fj4wmslrK0PgvmdV7KSuCaBg2qiV2ejMM/nRpSdT2b7VWs1UIgYeyA6kOhkGjrwlZExEREbyAgACjMWPGJPXo0SMrOzubtXHjRrNu3bq1fvTo0bOWLVtKACAqKop76tQpAzc3t2wASElJqfa/65QpU6zPnj2rv3r16rcmJiaS1atXi/r16+fw4sWLZ5qamgwABAUFad+/f1/b3d09Oycnp9o/gGbNmmUFAPv27YvYuHGj2axZs6xOnTpVXK3b19fXcvr06fH29vZNckmKBkWIE4DTaMZV7+uCAZhsXqlkVr/DaoZ4DYJKF79VJwwD2fJtED6PwAFCyBiGYRp+RYEqNKqpl82No6NjQWRk5FNtbe3iApp9+/bNtLOza/N///d/Rps2bYoDgI4dO+YmJycHA4oK+2fOnNGvTvsRERHco0ePGm3ZsiVq9uzZyYBi1WsHBwfXX375xXD+/PlJALBkyZKE5cuXJwBAdXq5RW7evKnz119/vfL29s7R09OTDR8+vGXRc3/99ZfOixcvNE+ePBlZ3faoChBiAuAsgGr9u1PlymXIfwnMhgvdejxWFkSNZ804hkH+Fyvx765jxdcpJgKYrcqYytPkhxmL7N+/X8/Ozs5FQ0OjnYeHh+PDhw8rHf+1sLBwnT59umXJx7Zt22ZICPFIT08vft/i4+PZY8eOtTE0NHTT0NBo5+7u7nTlyhWl1LwTCoXykokMAExNTWUikaggISGhuLvPZrM/3LkaTp06JQSAzz//PLXoMTs7O4mHh0dWYGBg8R9zbduXSCQsTU1NOQBoa2vLJRIJCwCkUikWLVpk/c0337wr+/qoGiJEE4oemZ2qQ2nM5ASlKsdb1GcpKy08BKdek6XSMAyyPvXFs13H0LHEw7MIIUtUFlQFmkUyi4mJ4S1evNjKz88vbufOnZGZmZnswYMHO+Tk5NRpvaLc3FzSs2dPh2vXrgm/+eabdwEBAeEGBgYSHx8fh7Lnh5QlNjaWEx0dreHs7Jxb17ZCQ0MFpqamBbq6uqWGDFq1apUXERFR55O9Li4u2Vu3bjVJTExkb9myxdjV1TUbADZu3GgsFAql06ZNS62qDaoShLAAHALQQdWhNHay0sksR8iqt+FaBtawqae2lUouR2qfSXjz50W0K+fp1YSQjxs8qEo0i2HGtLQ0ztGjR0P79u2bDQCdO3fOcXZ2dv3pp5+M/Pz8ar3G1i+//GL46tUrwaNHj565urrmA8DHH3+c0aJFC/GaNWvMduzY8U5Zr6HIrFmzLDU1NeWTJk1KqXrryqWlpbF1dHQ+mIKsr68vzcjIqF13rITvv//+3dChQ1sFBAQYGxoaSk+ePPkyMTGRvXHjRtHJkydf1bV9ClsBfKTqIJoCKQvFi9ByFaWs6uecGRcPIFD/Hx8yGeI7jkLGw2cVrnVHAOwnhHRmGOZZBds0qGbRMzMwMJAWJTIAcHBwKHB2ds6+f/9+nYYDr1y5ouPi4pLj5OSUL5FIIJEo5jF06tQp699//63wj0EqlaJo+6J9qmP9+vXGJ0+eNNy2bVuUmZmZUq6DIeTDzinDMEpZYbd79+457969e/Lvv/+GvHv37knXrl1zFy1aJOrTp09a9+7dc/bu3atnY2MjNjIycvP19RUp45jNBiGzoIbnLRqrAjaKF6vVZNVjBXtL9f/OLZDgrfMQ5D98VmUJNB0AJwkhanGutln0zAwNDT/IGEZGRtL4+Pg6TTNNTk7mBAcHa/F4vA9md1pZWeWXtw8A2NjYuMbGxhZP/Q0NDX3q6OhY6TUnAQEBukuXLrVeunTpu/Hjx6fVJe4ienp6svJ6YGlpaWyhUKiUZCkQCBg3N7d8AHj06BH/xIkThk+fPg15+/YtZ9asWXbHjx9/6eDgUNC1a1enTp06ZY8ZM6ahauI1XoR0ALBZ1WE0JXkcFH9H6LNQPysvE0RAXzUzwasrNw/hDgOh/e49zKq5iz2AI4SQgQzD1P+F5pVoFsksOTn5g6SVlJTEcXR0zCtvewDg8XjygoKCUj2UslPe9fX1ZS4uLjk///zzm7L78/n8Cic2nDhxIjwvL6+4bRsbm0q7ZxcvXtSaOnVqi7Fjxyb6+/vHV7ZtTTg5OeXu3r3bNCMjgyUUCovPm4WHh/Pt7e0rfG9qy9fX12r27Nnvra2tpQcOHNCztbXN69evXzYADB48OPXKlSs6NJlVgRA9AEeBer4OqpnJ5f6XzEw59XQxswlioPjyV0sZWXjWoi9EyWk1nhXbF8BGAPPrIaxqU/surzKkpKRwLl68WDyk+OrVK97z5881O3TokF3RPubm5pKXL1+WmgRx5cqVUnXuevbsmREdHa1hb29f4OXllVPy5unpWeEEDU9Pz9yS21aW+B48eMAfMWJEq+7du2fs2bMnunqvuHp8fHwyAODAgQN6RY9FRUVxHzx4oN2/f3+lJpVDhw7pvnnzRmPZsmXFyTg3N7f485ednc1iGDqxsRr2ALBVdRBNTTYX0qL/t+LUSymrVJip70XSCcl4bOENm1oksiLzCCGfKDWoGmoWPTM9PT3p5MmT7ZYuXRqrqakpX716tcjAwEA6e/bspIr28fHxSV26dKn1119/bdaxY8ecP/74Q+/Vq1elktvMmTOTf/vtN2MvLy/HOXPmvG/ZsmVBUlIS++7du1pmZmaSlStXJtQl7piYGM6QIUMcNDU1ZXPnzk24evVqcULW09OTeXh4FPee9uzZow8oelW5ubmsovv9+/fPFIlEUgDo3LmzAwDcvn37JQDY29tLRo4cmbR06VIrhmFgamoq9ff3F4lEooIZM2YUr+cUGxvLCQwM1AGA9PR09rt373hF7U+aNKnKGYn5+flk8eLFVmvWrHknEAgYAPDy8sqKiYnRWL58uamdnV3B33//bfDLL7+8rqqtZo2QLwEMVXEUTVIWD8UjE3Yc1Hny0weECAYbPZTerhJExeCu0yC0zS+ARtVbV2oXIeQOwzBxSgmshmqVzBpbJQ4LC4uCRYsWxa1cudIyNjaWJxaLcwICAl4WVbgoz/z585MiIiI0du/ebfrzzz+T4cOHJy9YsCBu0aJFxdNqNTU1mevXr79cuHCh6LvvvrNITk7mGBgYSN3c3LI/+eSTtLrG/fjxY0HReT0fH59SBUk7dOiQde/evbCi+5MnT25R8vmi+6dOnXopEokyAUAmk30wsePXX3+NnjNnjmzZsmVWeXl5LE9Pz8wjR45ElnxvHj16JCjZ/rt37zTu3bunAwCTJk2q8rOwbt06E3Nz84LPP/88regxa2tr6Y8//vh61apVltnZ2ezx48cnjhs3jg4xVoSQjgA2qDqMpipD479k1oKn9IUypbBSz4LCT1/iZrth6CiVKaVjYwjgd0LIAEYFwyyksmMGBwdHubm5Vdh7oSh1FRwcbOTm5mar6jiUghAtAE9BL4xWtucAnAHgmDOujhyp6DmdF+Fpfy1Uu75plTRwGy7orLT2lOTafQT1GA8vRjHNXpm+ZBhmq5LbrFKzOGdGUY3cWtBEVq/S+f99oSu9lJUllFIRSJn+CMRV7/HwrodEBgDfEUIafPVsmswoSp0pFtmk15PVsxT+f9+FlsosZcXCc+iijdLaqyOGAbP9III+/bJez9/xARwkhNT1HFyN0GRGUepK8WXwK+jfab1LFRS/x9naLCX2pMygNiXbGAayVT/h5mx/eDfA4VwB+DXAcYrRPxKKUl/LAFR7lQKq9tL4igkQhaWslCUBJupRuophUDDzW9z/9ufiyvcNYTEhpMGGx2kyoyh1REgbAF+pOozmIlWgmMGoxYLyZtTq4zlYqr+4nWGQPWo+nv5yGJ0a+NACAD821MFoMqModaMomLkLUPoUcaoCqXzFe63EUlb5sKywSG+DkcuR1m8qIo+dV1kZrcGEkKENcSCazChK/YwF4KnqIJqTdL7igmEzDiqsqVojAjwAF8ZKaauWZDIkdByFxEu3lHiZQe1sJYp19+oVTWYUpU4Ukz7WqDqM5iaNDz4AWHIgr2rbarGCkVLaqSWJFO/EHyH3QUiVle8bgjWA5fV9EJrMKEq9zAUax+KNTUmGBgQAYMdVQikrNoKhDcc6t1NLufmIaNEXnNBItfoczSOEWNfnAWgyoyh1QYgBALVbjr45yOQpFuO05yphwoYIdV4FvrYys/Hcuif0a7CES0PRAPBNfR6gVvW4QkJCVHIyUSwW13tNyG3bthn6+vrapqWlPdbV1a1wyMHT09PRwMBAcv78+UgAOH78uDAkJIS/YsWKOhUXrqm8vDzi6+tr8eDBA61nz55p5efnE4ZhPnif8vLyyLJly8yOHj1qmJCQwDMxMSkYPnx4ytq1a+OKiv9WZOfOnfpbtmwxi4qK4mtra8u6du2a8cMPP8TY2tqWu3TN5MmTrfbs2WMybdq0+J07d1a62vb+/fv1Fi9ebJWTk8OaNGlSwqZNm0oVKV24cKH548ePNS9fvhxRnfejkVsOQE/VQTQ3DMBkFyYzO26dV5h+ByPVTMdPSsW/LfrCPjMbOqo4fjWMJ4R8X18rU9OeWS393//935uNGzfGFN0/f/68cMeOHaYNHUdWVhbr0KFDRgKBQO7u7p5V0XazZ8+22L59u9nkyZMTjx079mrSpEmJ//d//2c2c+ZMy8raDwgI0P3iiy9atG/fPvvQoUPhq1atenfv3j2dQYMGtZTJPlwp4+HDh/yjR48aaWtrV7mMRlxcHOeLL76wmzBhQuKGDRuid+7caXr8+PHiZXZev37N3bVrl+nWrVsrTYhNAiEtAMxUdRjNVA5DFGWdrDl1/DFhiAiQeqi6X4XoWNyz6gEnNU5kgCLf1FvvrFksAVMfSi6/okpGRkaytLS0f1ksFtauXWt8586dcj/Mf/31l+Fnn32WuGrVqngA8PHxyYyJieGeOHHCEMDbito/dOiQobOzc86+ffuK11LT1dWVffbZZy2Dg4P57dq1K/U+zJkzx3rKlCnxf/zxR5Ulga5evaolEokK1qxZ8x4A/vnnH53AwEDhsGHDMgDA19fXcvTo0UlisVg5M8zU2wrQBTdVQk6QCyiqftSxlFU2LNBWKUHVwLNw3Gw7VGmV7+vbMEKIG8MwwcpuuEn3zP7++28dQohHVFRU8fU6bdu2dWKz2R5JSUnFv54cHByc58yZY1Fy37CwMI0uXbq0EggE7nZ2di579+7VK/m8p6en44ABA1oAwPz580W7du0yjY2N5RFCPAghHsOHD7ct2jYwMFC7Q4cOjgKBwF1PT6/t6NGjbVJTU5X23rNYVTcllUqJrq5uqd6Snp6erKqVGiQSCdHR0Sm1n4GBgQwAyu67Z88e/fDwcL6/v//76sRdUFBA+Hx+8VCupqZm8erely9f1rp165Zw3bp1sdVpq1EjxAbAOFWH0VzJSPG1ZVlarDoMM2rhIThKLlJchRsPcc3VB10aSSIDFIWNV9ZHw006mfXq1SuLw+EwFy9e1AaAzMxM1rNnzzS5XC5z6dIlbQCIj49nh4eHC7y9vTNL7vvZZ5/ZDR48OD0gICDCzs4uf+rUqS0iIiLKvYh11qxZiT4+PilGRkaSS5cuhV66dCn022+/jQOACxcuaPn4+DiYmJhIfv/998g1a9ZEX7lyRXfMmDG29fzySxkzZkzivn37jC9cuKCVnp7OOn/+vPbevXuNJ0+eXOk5vkmTJiU9ePBA+6effjJMSUlhPXnyRGP58uUWHTt2zCzZO83KyiJLly61XLly5TuhUFit6c0dO3bMefnypeDUqVM6oaGhvLNnz+q3b98+Wy6XY968eVZLliyJMTAwUM5UafW2EHSURGWkLOQBdS5lxcAaVkoKqVr+uoyr3T+rlyVc6tvQ+qiq36T/gLS1tRkXF5ec69ev60ybNi31n3/+0SqcwJB57do17dGjR6dfunRJmxCCXr16lTrfNHPmzPgvv/wyGQC6dOmSbWlp2fbPP//U8/PzSyx7HHt7e4mZmZmEx+MxvXv3zi753OLFiy3d3d2zzpw5E1n0mJWVleTjjz92uH//Pr9Dhw4NMly5ffv2mNzcXFb//v2dih777LPPEr///vtKV4UdPXp0empqatSXX35pO2fOHFsAcHd3zz59+nR4ye2WLVtmbmxsLPnf//6XUt2YnJycCubOnRv30UcfOQCAt7d3+vTp01O2b99uKJVKia+vb9NfS48QYwBTVB1Gc1bARgFQx1JWXDyEAO2VFlQVdhxB0IxV6rlydTUQKC5Bma7MRpt0zwwAOnXqlHX37l1tALh69apO+/bts7p3755ZdG4pKChIx9HRMadsD8DHxyej6P/NzMxkBgYGknfv3tWovFBmZiYrODhYe9iwYakSiQRFt379+mVxOBzmzp075VbnlsvlKLm9VCqt+QsvY8WKFaYnTpwwXLNmTfTZs2fDVq9e/favv/4y+PLLL0WV7Xfq1CmdhQsX2kyePDn+1KlTL3fu3BmZlpbGHjx4sH1RXKGhobwdO3aYbt68+W11hjxL+v777+NiY2ODw8LCnl69ejU8JyeH5e/vb7Fp06a3EomEjB071lpfX9/NwcHBOTAwULv274DamglFDTtKRfI4imRmUJdSVhYN0ztiGMi+/RnXZ6xqkMr39ekzQojyltpBM0hm3t7emeHh4YKkpCT27du3tbt27ZrVq1evzJCQEM2cnBxy584d7Y4dO34wC9DIyKjUeSIul8vk5eXV6P1KSkpiy2QyfP3119Y8Hs+j6CYQCNpJpVLy9u3bck/4nz17Vqfk9l26dKnTBZhxcXGc9evXW6xYseLdkiVLEgcOHJi1dOnShBUrVrz76aefzGJiYirsoX/11VeWvXv3Tvu///u/mCFDhmROmzYt9cSJE+H37t3TCQgI0AOABQsWWHp5eWW4ubnlJSUlsZOSkthyuRz5+fmk6P8rY25uLnVwcCgAFD289u3bZw0cODDr+++/N37+/Lnmy5cvQxYuXBj3+eeft8jNzW1sQyoVI4QPOoNR5XK4imRmVpjUaowgEvpop9SgysEwKJjtj/srf0T3+j5WAxBAyT2zJj3MCAB9+vTJYhgG586d0/n333+11q9f/87DwyNPU1NT/vfffwufP3+uOW/evPj6OLahoaGMEIL58+fHfvTRRx8MYVhbW5d7nVbXrl2zg4KCXhTdLztxo6ZCQ0N5UqmUtG/fvtQvz/bt2+fIZDISHh7Os7CwKLf79/r1a/7w4cNLDR26ubnl8/l8eXh4uAYARERE8MPCwgTGxsZtS263b98+k3379pmEh4c/sbe3L/e1lhQWFsbbu3ev8f37958Dil7zyJEjk42NjWXTp09P/eqrr6yfPn3K9/T0VNlFqUo2DoCJqoNo7nK4kAGAVW1LWRnjHQhaKDWoMhgGOWMW4MWRcw1e+b4+zSSEbGQYpu5DT2gGyczY2FjWsmXL3G3btpmy2Wx06dIlh8ViwcPDI3PTpk1mMpmM9O7dO7PqlirH4/GY/Pz8Uj03oVAod3Nzy3758iXfy8ur0nNTJenr68u9vLyUVb0bLVu2LACAe/fuaXp7exe3e+/ePS0AaNWqVYW/SM3NzQv+/fffUjO8Hj16xM/Ly2PZ2dkVAMDOnTujMjIySl1bM3HixBYdO3bM/N///pcoEomq9WH19fW1nDRpUoKjo2NxPDk5OSwAkEqlKCgoYFU1+7KRUeovU6p2MjUUScy2dqWs0mFevxXp5QzSB05D9IWbKqt8X18sAQwHcEQZjTX5ZAYozpvt37/fuFu3bhkcjuIld+3aNcvf39/SxsYm39raus6/DJycnPKSk5M527ZtM2zbtm2uqamp1NHRseC777575+Pj4zB06FCMGDEiVSgUyqKionjnzp3TXb9+fUybNm3qfA3V0aNHhdnZ2ezg4GBNQDFFvvA1Zjs4OBRYWVlJ+/Tpk/btt99a5uXlsdq2bZv76NEjwffffy8aOHBgalGyefnyJc/Z2dl1y5YtUbNnz04GgEmTJiWuXLnSatq0aZLBgwenx8XFcdevXy8SiUQFI0aMSAeA8hKvhoaG3MLComDIkCHV+qFw5swZ7cePH2sfPXo0quix7t27Z+7YscPUxcUl7/LlyzpaWlqyNm3aqMX1fXVGSGvQyvhqIaMwmdWqlJUO/gW7/s5fyeRI7DoGKXefqLzyfX3xhSqTWUOUlVKm7t27Z+7fv9+4a9euxV+sPXr0yPL390eHDh0qrJpRE5MnT065evWqzqpVqyxTU1M5w4YNS/7zzz+j+vfvn3X+/PmwlStXimbMmGEnk8kgEokKevbsmV7R0F5NzZs3zyY2Nrb4D3Hy5MktAGDr1q1RDg4OyQBw5MiR119//bVo586dJomJiTwTE5OCcePGJZW8jothGMhkMpQ8x7V06dIEHo/H/Prrr8YHDhww1tHRkXXo0CFr48aN1Z6CXxWZTIaFCxdar1ixolSbixYtSgwJCRFMnz7dztjYWLJnz57IqkpvNSKTVB0ApZChofivHRc1nWAkgzXslR5QIYkU79oOhfR5hOqKFjeAzoQQR4ZhwuraEKls2CY4ODjKzc2t6U+Pppqc4OBgIzc3N1tVx1EuQjhQVF1Rt2Kwzc1zAM472yHoi4/g/coG71ryUGl5t1J4uANx/ZzDystHpNMgCN7Ewrw+2lcz/gzDrKhrI01+NiNFqaEBoIlMbaQKFNPqRTUtZWVVP5dUZGbjhXUv6DaTRAYoFqOtM5rMKKrhTVR1ANR/0vhgA8jUZNUgObEQCl24KTuW5DQEW/aARWJKnWpENjb2hJA693BpMqOohqRYs8xH1WFQ/0kRgMOraSkrUyj99Mvb97hv1QMOGVkQVr11k1Pn2qQ0mVFUwxoMWh1fraTywdFiIaPqLYslwlS5a5Y9j8DNFn3gnpvfbKvBjCKKc8m1RpMZRTUs2itTM+l8aBiwkV31loX08BwsaCjr+Lce45qrDzo3osr39cEYQO+6NECTGUU1FEK4APqrOgyqtFQBNMzYqLJCTaECWMFZWcf++wqCuo6Fl5yh38UABtVlZ/oGUlTD8QKa5fkQtZauAQ1rDqp3/SIf98GFsTKOu/sPXP14VqMvGKxMA+qyM01mFNVwhqg6AOpDGRrQtONW87vQGgZ1PR7DQL7mF1yftrzRLuFSXxwIIXa13ZkmM4pqODSZqaFMDWi24FbjHBgbT6CN1nU5FsNA8uU63F22tUlUvq8PtR6Gb/LJbPjw4bZisbjKDyAhxGPt2rVKGT4ocvr0aR1CiMf9+/f5ymy3SGxsLGfixIlWrq6urblcbjsLC4ty67eNHTvW2s7OzkVTU9NdKBS2bd++veNff/2lU51jXLhwQatNmzZOfD6/nYWFhevq1asrrfLep08f++q+lxs2bDA2MTFpY2Ji0mbTpk1GZZ//9NNPbSdNmtSgq/fWG0IcAbRUdRhUaQzA5HAhsOOi3LUFSzGvw3pnUFS+/8wPwdv2o3Nd2mniaj3UWKvZM4QQlVRvZhimUdWErG9RUVHcU6dOGbi5uWUDQEpKSrn/nnl5eaxp06YltG7dOj8/P5/89ttvRiNGjGgVGBgYVnZl7JJCQkI0hg4d6tCrV6/01atXx9y5c0dr1apVVpqamvL58+d/cJ3N8ePHhf/++2+16ts9fPiQv3z5cqt169ZFMwyDxYsXW/fs2TOrXbt2eQBw/fp1zUuXLum+fPkypHrvhtqj50bUUw5DoGXDgW4V28XAuPbT8RkG6YO/wJtz1xtuNepGqhchhMswTHUn5BRrzlNBG72OHTvmJicnBwPA9OnTLc+cOaNf3nbHjx+PKnl/xIgR6VZWVq779+83qCyZrV271szExERy4sSJSC6Xi48++igzOjqat2HDBtGXX36ZVHJV6fz8fLJw4UKrpUuXxsyfP9+mqtgDAwN1OnfunFGUFE+ePKkfGBioU5TMvvzySys/P79YY2PjOq3lpkbor3E1JCfIAaAl4uCDkYFSDPEKBBa1OYZMjsTu45B8+1+0qc3+zYwOgI4AbtR0xyY/zFhk//79enZ2di4aGhrtPDw8HB8+fFjp0N/hw4d1u3Tp0srAwMBNW1vb3c3Nzen48eMfzES7e/euoFevXi11dHTaampquru6urY+ceJEhTPWdu7cqc/lcttt2LChzkOabHZtll8COBwOdHR0ZAUFBZWu2vzPP/8IBw0alMrlcosfGzduXEp8fDz3wYMHpS7uXLNmjQmfz5fPnTu3WpURCgoKWHw+v7hCvkAgkBfFs3PnTv2MjAzOokWLEmv0wtQbTWZqSKZIZukCFir7PsiBBdrWpn2JFDFuHyPr9r9wqlWAzVOX2uzULJJZTEwMb/HixVZ+fn5xO3fujMzMzGQPHjzYIScnp8Iv88jISN6gQYPSd+3a9Xr//v0RHTp0yPr0009bXbhwoXhs/fHjx/xevXo5JSQkcDdt2vQmICAgYsiQIalv3rzhltfmtm3bDGfNmmW3adOmN35+fg36RS2XyyGRSPD+/Xv2N998YxIdHc2fNm1ahYknIyOD9f79e56Tk1Op9cPc3NzyACAkJKT4jz86Oprzww8/mG/atOltdRNshw4dsm/evCm8e/eu4Pbt24Jbt27pdOzYMScrK4usWrXKcuPGjdFFa881eooSVg6qDoP6kISNfB6QUulGmngADvRq2nZ+AV47DAB5Fo5az9BrpmpVp7GJfFtULi0tjXP06NHQvn37ZgNA586dc5ydnV1/+ukno4qSypIlS4ofl8lkGDJkSEZoaKhg165dxv369csGgBUrVphra2vL7ty5E6qtrc0AwCeffFJuWZwNGzYYL1u2zGr79u2vp0+fnqr8V1m53bt363/xxRctAEUv6Ndff43o2bNnhSe0k5OT2QCgr69fapjP2NhYCgApKSnFWcvX19eye/fuGQMHDqz22nCDBw/OGjhwYGqnTp2cAWDMmDFJAwYMyFqwYIF569atc4YOHVrn1b/VSCcAlfaCKdUoYCNfm4XKFnxlYF3z4cWsHITa94NRQnIVw5fUB1gsVq0uSm8WyczAwEBalMgAwMHBocDZ2Tn7/v37WgDKTWYRERHcRYsWWdy4cUOYlJTELVr3rV27dsVf2Ldu3RIOGzYsuSiRVWTz5s2mx48fN9y9e3fk+PHj0yrbVi6XQyb7L3+wWKxaDyeW9Mknn2Q4OTm9SEhI4Bw4cMBw6tSpLXR1dcOrWgmakPK/g4sev3Tpktb58+f1g4ODn9U0pqNHj755/fp1LCEEtra2koiICO6uXbtMb9269SIpKYk9fvx4m5s3bwrt7Ozyfv311ygPD4/Guso0HWJUU3kcFBiwUVDhBhw8giZqNOEtOQ3B9v1gm55Z5aQSCsjV0dEJd3JySunWrRvHy8vLwt7e3iEkJEQkFotjq979P80imRkaGn4wM8bIyEgaHx9f7nCgTCaDj49Py+zsbPbixYtjHRwc8nR0dOTLly8XJScnF++Tnp7OMTc3r3LWzblz5/Stra3zfXx8qixm+tNPPxn6+vraFt0vWrG6qv2qYmxsLDM2Ns4BgBEjRmR07tzZYdWqVaIhQ4aUu8KroaGhDABSU1NLZdLExEQO8F+Pbf78+VZjx45NMjAwkCUlJRVvm5uby0pOTmYXtVMROzu74vdv3rx5lmPHjk0Si8X5U6dOtZRKpeTt27dP1q9fbzJhwgS7kJCQF7V9/SpWLws4UnWXy4HUrLJkZoEaraYeE48HrfrDpRkXDK6MlM/nR9jb2yd06dIF3t7epi4uLi04HE55lxS5A6DJrKySCahIUlISx9HRsdxf+s+ePdN48eKF5rFjx16NGDGiOAHl5eWVOseoq6srjYuLKzchlrR79+5IX19fm/79+7e6cuXKy8p6ciNHjkxr27Zt8Ze2qamptKr2a8PV1TXn5MmTFVYzEAqFcjMzs4LQ0NBSJ8afPHnCBwCxWJwHAK9fv+Y/ffpU67fffit1/dnq1ast161bZymVSqt1OcWlS5e0bt++rRMWFhYCADdv3hSuWLEiRigUyv38/BLWrl1rkZKSwjIwMKjRl4uaaKvqAKjyZfMgta7oL5jgNQyqP5U+7DVuuX6EDhIpqvxOaAYYLpcbZW1tHevp6Snr0aOHfrt27Vry+XxHAI7V2L8dgDM1OWCzSGYpKSmcixcvahUNNb569Yr3/Plzzc8++6zcNYyys7NZAKChoVGcdF6+fMl79OiRtqOjY27RY126dMk4deqUQU5OToympmaFCcrGxqbg4sWLYT169HAaMmSIfWBgYETJtksyMzOTmZmZ1enizKrI5XI8ePBA29LSMr+y7Xr27Jlx9uxZPalUGlM0GePgwYMGZmZmBe3bt88FgD/++CNcIpGUGov08fFxmDhxYsKIESOqdW5QLpdj3rx51kuWLIktmayK/h0yMjIa70QlxeQPet5ETWXyILPjVDARzhjRINWbvHHvCa53Ho2uzbVgMIvFijM3N3/j4eGR36NHDx1PT097XV1dO6DWk1/a1XSHZpHM9PT0pJMnT7ZbunRprKampnz16tUiAwMD6ezZs8udzde2bds8U1NTyVdffWWZnZ0dm5GRwVq7dq2FiYlJqSHF1atXx3bt2tW5Y8eOTnPnzn1vbGwsffjwoaahoaH0yy+/LJUonZ2dC86fP/+yd+/ejsOGDbP7+++/I5VxLmzPnj36ABAeHs7Pzc1lFd3v379/pkgkkp4/f177+++/Nx06dGiara1tQVJSEnvfvn1GwcHBWgcPHgwvaufMmTPaH3/8sePJkyfDBg8enAUAS5Ysee/p6en8ySef2E2fPj3p7t27mgcPHjTasGFDdNE1Zv379y930kerVq3yitqpyvbt2w1lMhlKTuvv3Llz5ubNm8309fVle/fuNWzdunVOI+2VVedXKKUimRpgWnDLHRJMh3n1zpWdCcLVITOaT51FQkiqoaFhhJubW7a3t7ega9eutiYmJuYAzJV4mBpfylCrZNbYKnFYWFgULFq0KG7lypWWsbGxPLFYnBMQEPCyot6UQCBgjhw5Ej579mybiRMn2puamhYsWLAgLigoSCcsLKz4g+/m5pZ/+fLlUD8/P4svv/zSFgDs7e1z/f39Y8prt127dnmnT59+OWDAAMcxY8bYHD58+E3JC49rY/LkyS3Ku3/q1KmXIpEo087OroDD4TD+/v4WqampHH19famzs3NOYGBgaJ8+fYonxTAMQ2QyGRiGKe5licXi/BMnTrxauHCh1YgRI1oZGRlJVq1a9a686h+1lZ6ezvL397fYu3dvqeS+fv362LFjx9pOmDDB3tbWNm/v3r2vlXXMBkan5KuxND7QorxSVjr4F+yqq7b8fgJBk5Y06USWLRQKw52dndO8vLy43bt3t7S1tbUG6r2SSZWFF8oiRbP0yhMcHBzl5uam9OXBKaq+BQcHG7m5udmqOg4QsgbAElWHQZXr+Q4PJPadg5YtuKWm38vhjBjwUWFdUIaBfP1u3Fi8GV4NEGdDkWhqaoa3bNkyqVu3bsTLy8vUycmpBVsZQ0i1YyYWi+Oru3GzGGakKBWiPTM1lsYHEbHLnNPk4T746FjRPgwDyYL1uP/D3kadyOQ8Hu+1jY3N+86dO8u8vb0N27Zt25LH49VpVQAlswVAkxlFqQl6zkyN5WtAxmeVWf7FsuLlYBgGuRO+Rsj+v2tXcklV2Gz2O5FI9K5Dhw553t7eep6envba2tr2AOxVHVslbAHcre7GNJlRVP2ipYzUmEyA0teJshAGvfIvpWAYZAyZgddnr9W+en5DIIQkGRsbv3Z3d8/28vLS6tq1q52hoaElAEtVx1ZDNfrbocmMouoLIQIA1VoSh1INlgClL+o3RSLK6U3L5Ujy+hyJNx/BraFiq6ZMfX39cBcXl/Tu3btrdO/e3crKysoSTeNyENuabEyTGUXVH0NVB0BVjqOFkjPgkmD6Ya9LKkWs+zDkh7yq2yrTSpCvpaUV7ujomNS1a1e2l5eXuYODgx2LxXJXcVz1xbYmG9NkRlH1pyn8Om7SNLRLFIDWRQhYpafZ5xcgqvVgcF+/a/DhYpmGhkZkixYt3nfq1Inx9vY2btOmTUsul+vSwHGokqgmG9NkRlH1hyYzNaepjaJp5xJYle55ZecgzL4/DOKTUOe1B6vC4XCiLS0t33Xo0EHSo0cPvfbt27fU1NRsBaBVfR9bjdVoiJ4mM4qqP3SYUc1paxfOXOTjPnj/zVBMTccTu76wqY/K9ywWK8HU1DTK3d09x9vbW7tz584t9PX1rQFYK/tYjRxNZhSlJmjPTM0JdQpLWVn9t/hmbAIetOoP55w8aCrhEOkGBgYRrq6uGV5eXvxu3brZiEQicwAmVe5JfViZpRI0mVFU/alwVQJK9WQAY6gFbbDxFDpwBYCXUbgt9kH7Wla+L7s2l8je3t6WEFLjorkUAEAQEhLCEovF1arJWrtkFkpqtFid0jiptiakp6eno4GBgeT8+fORFW2Tnp7O0tPTc9+6dWvU3Llzy63KXxOElP9ec7lcpqCg4FFF+/3222/6Bw4cMAwJCdHMyspi29nZ5c2dOzf+iy++KLVEvIWFhWtsbCyv5GOGhobSpKSk4Mri2r9/v97ixYutcnJyWJMmTUrYtGlTXMnnFy5caP748WPNy5cvR1T9KpssftWbUKqSzQMsudCHOV4DwIMQXO84stqV78uuzWXi4uJiX8HaXFTtEACaAKpVsJz2zNTcpUuXQss+NmLEiJYeHh6V/gNv27bN1MrKKn/t2rVvTUxMpKdPn9adMWOGXVJSEmfp0qUJJbf18fFJ8fX1LX6souVpisTFxXG++OILu3nz5sXZ2dnlz5s3z6Zr167Zw4YNywCA169fc3ft2mV68+bNxrqYprKUv0w3pRayeGBEHBTAGJ7nr+PqwOkVFgwuuTaX1Nvb28DDw6Mma3NRtacNmsyaht69e2eXvP/PP/9opqWlcUaNGpVS0T4AcO7cuXBzc/PihT0/+uijzLi4OO7PP/9sWjaZmZmZScoepzJXr17VEolEBWvWrHlfGJNOYGCgsCiZ+fr6Wo4ePTpJLBZXul5aM9As17ZqLDh8QMMQr/eexOuJi/9LZEVrc7Vr1y6vR48euh07dmxRx7W5qNqr9nmzZvHHtnbtWmMzM7M2AoHAvU+fPvYnT57UIYR4nD59Wqdom8zMTNbEiROtjIyM3DQ0NNqJxeLWx48fF1bV9u+//65na2sr5vP57dq3b+8YHBxcr0NLAQEBBgKBQD569Oj0yrYrmciKtG3bNiclJaXOP2AKCgoIn88vHsfW1NSUFxQUEAC4fPmy1q1bt4Tr1q2r0ZLnTRTtmakxLW1g8yVu3sJNRtq9evW6+s0339y7fPlyQnBwsPn58+c7rV27tke/fv3cdXV1lT6jkaq28taaK1eTT2b79u3TW7p0qXXfvn3TAgICIsRice7MmTNty243btw4m2PHjhnNmzcvbv/+/eEikahg5MiRLQMDAyucHnrjxg3NqVOn2rdu3Tpn37594YMGDUobO3ZsvRXulMvlOH36tEGfPn3SdHR0arxQ5Z07d7Tt7e3zyj5+5MgRIy6X205HR6ftgAEDWrx8+ZJX3v5FOnbsmPPy5UvBqVOndEJDQ3lnz57Vb9++fXbhitFWS5YsiWmkC2kqW5P/+2qsciDI9U78N6nP+Ecd/vnnn/Zbt27tMWzYME8TExM6y1C9FFR3wyY/zLhhwwZzb2/v9P3790cDwLBhwzKSkpI4AQEBxRdCPnr0iH/69GmDrVu3Rs2ZMycZAIYPH57h5OTk4u/vb96/f/9X5bW9du1aMxsbm7wzZ85EslgsjBw5MiM/P59s2LDBorzt6yowMFA7Pj6eO3r06EqHGMtz8uRJncuXL+tt2bIlquTj/fr1S+vUqVO2jY1NQUhICH/Dhg0ib29vx5CQkOeGhoay8tpycnIqmDt3btxHH33kAADe3t7p06dPT9m+fbuhVColvr6+dA08BZrM1FAMRAmt8SIxM1vY4/Dh2Gtjx6Y05qVcmrrc6m7YpP/YpFIpQkNDBYMHD04r+fjQoUNL3b9165YWwzCYOHFiatFjbDYbH330UcrDhw8r7JkFBwdr9e/fP63katGjRo1Kq2j7knFJJJLim1xevU7MgQMHDIRCoazo3FR1hYWF8SZPntyid+/eaWVnWO7Zs+ftF198kTJgwICshQsXJp05c+ZlYmIib/v27ZVe8Pv999/HxcbGBoeFhT29evVqeE5ODsvf399i06ZNbyUSCRk7dqy1vr6+m4ODg3Nlvdsmjg4zqpkH8HhliyhJJoQuALBxo3lniQRvVB0XVaEPRpIq0qSTWWxsLEcmkxETE5NS549MTU1L3Y+Li+NqamrKyw7dmZqaSvPy8li5ubnlfiklJSVxy7YtEokk5W1bUpcuXRx5PJ5H0e3s2bM6Ve0jkUhw7tw5/YEDB6by+fxKZxuWFB8fzx44cGArc3Pzgj///PN1Vdt36NAhz9bWNu/x48dVXjBqbm4udXBwKACAZcuWmbdv3z5r4MCBWd9//73x8+fPNV++fBmycOHCuM8//7xFRe9hE1flZ4FqOAEY+6AD7ptKwS0eOZFKCXfzZjN6fld90Z4ZAIhEIimbzWYSEhJKDafGx8eXum9ubi7JyclhZWZmsspux+fz5QKBoNzkYWRkJCnbdmxsbJUXW+7atSsqKCjoRdGta9euVc4k/Pvvv4WpqamcsWPHVnuIMTMzkzVgwIBWEomEBAYGvhIKhdU+j0VI9XNPWFgYb+/evcY//PDDOwAICgrSGTlyZLKxsbFs+vTpqRKJhDx9+rQ5XnOVpuoAKIWF2HjtMxxwB8gHk7oOHDDslJXFeqaKuKgq0WQGABwOB05OTrmnT5/WK/n4X3/9Vep+ly5dsgkh2Lt3r37RY3K5HKdOndKv7HquNm3aZAcGBuqVHCY8cuSIXkXbF3Fzc8v38vLKKbrp6+tXmWQOHjxoYGRkJBk8eHBmVdsCip6cj49Pi6ioKI0zZ868srCw+GB2Y3nu37/Pj4qK4rdr1y6nOtsDiqn4kyZNSnB0dCw+WZuTk8MCFEOqBQUFLIapdmeyKUmtehOqPsnAkvfAP9c2YaEXQNjlb0XI119b0l60+pGIxeJyz9uXp8lPAPHz84ubMGGC/fjx462HDh2adv36de0rV67oAgCbzWYAoF27dnlDhgxJ+frrr60zMjJYDg4O+bt27TKOjIzk//jjj9EVtb148eL3PXv2bD148OAWU6ZMSXry5Img5MQSZcnNzSUXLlzQ+/TTT5PZ7A//Hl++fMlzdnZ23bJlS9Ts2bOTAWD8+PE2QUFBuv7+/m8TExM5ly9fLv637tKlS45AIGAOHz6sGxAQYDBo0KB0S0tLyfPnz/mbNm0yNzMzK5g5c2a1JnGcOXNG+/Hjx9pHjx6NKnqse/fumTt27DB1cXHJu3z5so6WlpasTZs21R77bkJoMlOhTGhnOeP583ewqnKCR1CQsG1cHPe+ublErVeRbmaq3SsDapvMVFxWqibGjx+f9vbt27fbtm0zO3bsmKGnp2fm6tWr302ZMqWFrq5ucdYPCAh4M3v2bOmmTZtEmZmZbAcHh9wjR46E9+/fv8KemZeXV86uXbsiV61aZTFu3LiWYrE4OyAgIKJHjx5KXcTvjz/+0M3KymKPGzeu3CFGhmEgk8lKTSS5du2aEACWL19uVXb70NDQp46OjgV2dnYFSUlJ3CVLllhlZmay9fT0ZN7e3umbNm2q1tR6mUyGhQsXWq9YseJdySHMRYsWJYaEhAimT59uZ2xsLNmzZ09kRUO1TVyaqgNorqJgE+eCZxk50PKs7j5z5lgbHDsWISMEFfTgqAZWo2RGKhv+CQ4OjnJzc2ty06z9/PzMt23bZp6UlPRYW1u7OX7JNnnBwcFGbm5utioNghBPAHdVGkMzdANdX/TAVX0ZOGY13ffgwYjrrq653esjLqrGnojFYrfqbtzkhxljY2M5K1asMOvVq1emlpaWPCgoSOfnn382GzlyZBJNZFQ9o8OMDWwXpt6djp2uAKnV8i1ffmntcOlSWDYhNVt+hKoXb2uycZNPZhoaGsyrV6/4f/75p2FmZibb2NhYMnny5PgffviBTsel6luNL26nam86dgTtwrTuAKn1xLaEBK7ppUvCoL59M7yVGRtVKzSZlWRoaCgLCgoKV3UcVDPEMMkgJAdQyiKPVAUk4Ei74ubt+/BUSgJatszCo3fvjEQWC0qfzEXVSI2SWZOemk9RaoBWl6hHadBNt8Lb4PvwVNp5rpwctva+fYbNffkidVDhTPLy0GRGUfUrStUBNFUv0eqtOeKS4mGm9MWCt2wx61JQQKqsmEPVK9ozoyg10pxX2q43F9HnaWu84OdBUC+rVMhkhLN+vVlC1VtS9YgmM4pSI+WuuEDV3mbMu9UPF1rJwa7Xc1pHjxp2zMhgPanPY1AVYgC8q8kONJlRVP16qeoAmpKxCAhagE2dAdIgtT4XLrRujgWy1UGUWCyu9lpmAE1mFFXfwlQdQFOQD16BK57cPISx3kANqmDX0e3b2q5v33LvNNTxqGKParoDTWbNzIMHD/jdu3dvZWJi0obH47UzNzd3HTVqlM2bN2+qrPYPAPfu3RP07NmzpY6OTlstLS13V1fX1tevXy+eev7bb7/p9+rVq6WJiUkbTU1NdxcXl9Y7duwwqKrd1NRU1qBBg1ro6Oi0dXNzc3ry5IlGyecTExPZBgYGbiWP1Ui8AVCj9eeo0hJhlCJC7IsQuHZVxfFnz7YxYxhUq1A3pTQ1Tma1u87sIFH67KFqGdt4akKqq9TUVLa1tXX+uHHjki0tLSURERG8DRs2iAYMGKD15MmT51xuxTnt1q1bgr59+zr16dMnbc+ePZEAcPfuXa2iCvkAsG3bNlMrK6v8tWvXvjUxMZGePn1ad8aMGXZJSUmcpUuXVnhCfenSpeaRkZH833//PfL33383nDBhgt3jx49Di5738/MT9e7dO7179+7VruavFhhGDkIeAOil6lAaoxC4vG6HRywJeNUua6RskZF823//1bzm7p5DV6RuODVOZrWrzdgMkllOTg7R1NRsFuWuTpw4IRw2bFir69evv+jWrVuFycLNzc3J2to6/9SpUxVOWY6Li+OYm5uX+hXr4+Nj9+jRI+2YmJinFe0nFotbz58///3kyZNT3759y7G2tnZLT09/LBQK5Y8ePeL37NnT6enTpyHW1tbV+oWsFrUZixCyDsDXqg6jsfkbPv8OxV82DFj6VW9dvwwNJYn//BPGJwRVLqRLKYWpWCyu0WzSZjHMuHv3bn0HBwdnHo/XzszMrM2cOXMsJJL/li/atm2bISHE459//tH09PR05PP57VauXGkGADNnzrRwcHBw1tTUdDc1NW3z0Ucf2UVHR5fq0VpYWLhOnz7d8ptvvjExNTVtIxQK2w4ZMqRFUlJSqerbd+/eFbi7uztpaGi0a9mypcuRI0d0xWJx6+HDh9uW3C4wMFC7Q4cOjgKBwF1PT6/t6NGjbVJTU+vt38rY2FgKAPn5+RWei3j48CH/yZMnWr6+vpV+wMomMgBo27ZtTkpKSqWjAAUFBUQgEMgBQFtbW14yHl9fX6vZs2e/r24iU0P3VB1AY/Mtlt/4GCdd1CGRAUByMtf43DndGvcWqFqJqWkiA5pBMjt+/Lhw2rRpLcRicc7BgwfDp06dGr9jxw7TCRMmWJfddvz48S0GDBiQduzYsVdDhw5NA4DExETuggUL3h87duzVd9999zY6OlqjZ8+ejlJp6e/VU6dO6V+9elW4bdu2NytWrHh35coV3S+//LJ4efbMzEzWkCFDWuXl5bF27doVuWjRorivvvrKKjY2lleynQsXLmj5+Pg4mJiYSH7//ffINWvWRF+5ckV3zJgxtsp8X2QyGfLy8khwcLDGV199ZSEWi3N69OhR4YrXN27c0AKA5ORkjqOjozOHw/GwsrIS//DDD0ZVHevOnTva9vb2la5n1qZNm5zdu3cbvX//nr1u3TpTS0vLfGNjY9nhw4d1o6KiNJYtWxZf81epNmgyqyY5CPMRTgatxLfdAFKt87gNZeVKiw4yGRrz57CxqNUIXJOvzfjNN9+IPD09M48fPx4FACNGjMgAgHXr1ln6+/vH2dvbF3fRpk+fnrB8+fJSvwiOHTsWVfT/UqkUPXr0yGrRokWbixcvag8cOLB4rTMOh8NcuHAhvOic0/PnzwV///23AQpLsvz000+GaWlpnHv37r2ws7OTAICDg0Ner169Sq19tnjxYkt3d/esM2fORBY9ZmVlJfn4448d7t+/z+/QoYNSFrns0aNHqxs3bggBwMXFJScwMPBVeQt/Fnn//j0XAGbMmGE7a9as9x07dsw5evSo/vz5821EIpFk1KhR6eXtd/LkSZ3Lly/rbdmyJaqyeNasWRPbt29fB3Nz87aampry/fv3R+Tn55PFixdbrlmz5l2jXg+NYWJASCwAkapDUWe54Oe1wZPH4WillkV+8/JYmrt3Gz/84otEU1XH0sTVqgfcpHtmUqkUz58/1/zkk09KLcUxfvz4VLlcjqCgIO2Sj3/yyScffCEfPXpU6O7u7qSjo9OWy+V6tGjRog0AvHjxotR1Ll26dMksOXnC2dk5NyUlhZOXl0cA4MGDB1ouLi45RYkMAHr27JljaGhY3MXLzMxkBQcHaw8bNixVIpGg6NavX78sDofD3Llzp9xlKeRyOUpuX7bXWJ6ff/45+vLly6Hbt29/nZ2dzerXr1+rnJycCocZixb+HDNmTNLq1avjfXx8Mvfv3x/dsWPHzA0bNpS7blRYWBhv8uTJLXr37p02d+7c5MricXR0LIiIiAgJDg4Oef/+ffCwYcMy1q1bZ2JmZiYZP3582vnz57UdHByc9fX13caNG2dd9L42IrR3VolYmCea4X1EOFp1VnUslfn5Z5MueXmEFi6vX7drs1OTTmZxcXEcqVRKzMzMJCUft7S0lABAcnJyqa6IhYVFqe2CgoI0x44d29Lc3Lzgl19+eX3p0qXQy5cvhwJA2S/TkqtWAwCPx2MYhik+75OQkMA1MDD4IMsYGBgUHzMpKYktk8nw9ddfW/N4PI+im0AgaCeVSsnbt295ZfcHgLNnz+qU3L5Lly6OVb03rq6u+b169cqeOXNmSmBg4KsXL15o7ty5s8Ip9AYGBjIA6N27d2bJx728vDIjIiIEZbePj49nDxw4sJW5uXnBn3/+Wa0ad1wuF23atMnX0dGRx8XFcbZu3Wq2devWt7m5ueTzzz9v4efnF/fq1auQp0+fam7evLnK4U01c0PVAairR3APt8Gb/Azouqg6lqrI5YS9erWIrlNXf3IBXKvNjk16mNHc3FzK4XCY+Pj4UmPv79694wKK5WFKPs5ilc7tx44d0zcwMJCePn06sui5ly9flptQqmJiYiKJiIj4oGpBSkpKcWyGhoYyQgjmz58f+9FHH33QS7S2tpaUfQwAunbtmh0UFFRc5btsYq2Kg4NDga6urjQyMlKjom1cXFzKHd5kGAaEkFJDgJmZmawBAwa0kkgkJDAw8JVQKJTXJB4AWLBggcWgQYNSPT09c2/fvi2QSqVk6tSpqQAwevTo5KCgIOGSJUsSa9quCp0H8L2qg1A3hzHq4RgcagUQoapjqa6TJ/U7LFjw/l99fVlbVcfSBAWJxeJanUpp0smMw+HAxcUl58SJE/pfffVV8Rff/v379VksFry9vbMq2z83N5fF4XCYkknut99+q/IC4PK0b98+++TJkwavX7/mFg01/vPPP5rJycnF/wZCoVDu5uaW/fLlS76Xl1dcddvW19eXe3l51fr6q+DgYI20tDSOnZ1dheVj+vTpkyUUCmUXL14UDh8+vPgi4KCgIGHr1q1zi+5LJBL4+Pi0iIqK0ggKCgq1sLCo8QzEu3fvCs6dO6f3/PnzZyXaZUmlUnA4HGRnZ7Mru6RELTHMMxASDeCDiUfN1Vf47toG+HUBSKP7Hpo/35r322+vGULQ2Ia71V1gbXdsdB+imlqxYkXs8OHDW40YMcJ2zJgxKcHBwYINGzZYjBo1KrHk5I/y9OvXL+O3334zmTx5stXHH3+cduPGDe1jx44Z1iaO2bNnJ2/evNl80KBBrZYsWRKbk5PDWrdunUhfX19aMll+991373x8fByGDh2KESNGpAqFQllUVBTv3LlzuuvXr49p06ZNfm2OX2T69OmWHA6H6dSpU7a+vr7s2bNn/B9//NHMysoqf8qUKcUrI48cOdLmzp07OtHR0SEAwOfzmQULFsT6+/tb6unpSTt27Jhz7NgxvQcPHmifPXu2uGTT+PHjbYKCgnT9/f3fJiYmci5fvlz8GevSpUtOdSZyzJ0712revHlxRdP83dzc8vh8vvx///ufZe/evTN/++0341mzZjXGWWXnAHyh6iBUTQaWvC8uXv8HvdRyokd1PHig5RwVxbttZ1eg1uf4GqHztd2xdsmsEVXiGDZsWMbOnTsjN27caH7y5EkDAwMD6fTp0+M3b94cU9W+o0aNSn/69Om73bt3mx46dMjI3d09+9SpU6/atGkjrmkcOjo68r///vvVzJkzbaZMmdJCJBIV+Pv7v1u+fLmljo5O8bBg//79s86fPx+2cuVK0YwZM+xkMhlEIlFBz54902vTyymrffv22b/88otpQECAcUFBATEzMysYNGhQ6rfffvu+5HCgTCYjMpms1K/OFStWJMjlcrJr1y6TTZs2ce3s7PL27NkTMWDAgOIe7rVr14QAsHz5cquyxw4NDX3q6OhYafHQvXv36iUkJHBL9qQ1NTWZPXv2RPr6+lofPnzYaNCgQakLFy5sTEOMRZp9MsuCVrYznj97C+tGm8iKzJljY3Hq1KsCQlCrUw/UB96IxeLQqjcrX+0qgFBKERoayhOLxeJNmza98fX1rXS2H1UzalUBpAgh2gCSgeb55RcNqzhnPE/PhraTqmNRll9/fX3N0zOblrlSjp1isbjWP/aa/DCjOlm8eLGZSCSS2NnZFURFRfE2b95sZmBgIP3888/p7KjmgGGyQMh1AL1VHUpDu4XOoV64pisDp8kkMgBYsMBKfO1aaDoh0FV1LE1Arc+XATSZNShCCNavXy9KTEzk8ng8efv27bM2b978zsDAoMaz/ahG6xSaWTL7FZPvTcVuF4CUe51kY5aWxjH46y+9oE8+SWv0w6YqlgPgYl0aoMOMVJOklsOMAECIKYAYABWXW2lC/oefg37BjO4AabLXtPJ48ry7d5+ncDi0wksdHBSLxePq0kCT/YBRlFpimHgAF1QdRn2TgCPtjFvXfsH/vJtyIgOAggIW///+z6RahQGoCu2rawNN+kNGUWqqzn+46iwdwgxrRAffQedmMzFi1y7jzrm5hK4qXjuxAC7VtRGazCiq4f2FJrr6dDjs35nhfcJ7mKtmzUMVYRjCWrnSotIiDFSFAsRicY2qFpWHJjOKamgMkwfgD1WHoWyX0DvEEWEaeRC0VHUsqnDunJ5HUhKn0VyDq0b2KqMRmswoSjWa1FDjVsy93RcXW8rBNlZ1LKrk62utzTBoZLXWVOqxWCx+VvVmVaPJjKJU4xqAJrGUyGfYH/QltnQCyAeFtJubJ080HcPDNW6pOo5GRGk/6mgyoyhVUFwTs1XVYdRFPngFbvj3RgA+8wYILbhbaM4cG1uGgVIW0W3icqDEZFari6bJN0QlJ3eZlY2nJmRDS0lJYfn7+5tdunRJNyoqSkNDQ4Nxd3fP2rRp07vqFCfetGmT0Y8//mgWGxvLs7S0zF+4cGHczJkzU0puk5ubS+bOnWvx559/Gubm5rI6duyYuWPHjuiq6i1u2LDB+PvvvzcHgK+++ip2wYIFpa5d/PTTT221tbVle/bseVub196I7QHwLQB9VQdSU0kwTHVC6JtkGHVTdSzqJiaGZ3HzpnZQt25Z9ELqyv0uFotTqt6semjPrImIiIjgBQQEGPXq1St93759kVu2bHmTkJDA7datW+vw8HBuZfvu2LHDYNGiRTaDBg1KPXz4cHjPnj0zZs+ebbdv3z69kttNmTLF+tixY0bffvvtu71790akpKRw+vXr51DZCtUPHz7kL1++3Orrr7+O/eqrr2IXL15s/ejRo+LhqOvXr2teunRJd8OGDbF1fhMaG4bJBrBD1WHU1DM4R1kgJj0ZRm1VHYu6+uorq7ZyOWiZuorJAWxRZoO1qgDSHHpmWVlZRFtbu9GcyM3IyGCxWCymZMzx8fFsOzu7Nv/73//eb9q0qcL10WxtbcXu7u5ZJ06ciCp6rF+/fvavX7/mv3r16hkAREREcB0dHdts2bIlavbs2ckA8Pr1a66Dg4Pr+vXro+fPn19upZi1a9caX7hwQffq1avhAODl5dVq4MCBaYsXL04EAA8PD8cRI0akFN1XFrWtAFIWISIAUQAq/cGhLk5jcPBH+NuKAatW6/o1J8uWxQSNGpVKe2fl+1ssFn+szAabRc9s7dq1xmZmZm0EAoF7nz597E+ePKlDCPE4ffq0TtE2hBCPVatWmU6ePNlKX1/fzdnZ2QUAcnJyyIwZMyzNzMza8Hi8do6Ojs5Hjhz5oKjo5s2bjVq2bOnC4/HaiUQi12XLlpmWfH748OG2YrG49YkTJ4QODg7OAoHA3cPDw/HBgwdKOWkuFArlZZOvqampTCQSFSQkJFT4RZmZmcmKjo7W6N27d2bJx/v06ZMRHh7ODwsL4wHAqVOnhABQsiiynZ2dxMPDIyswMLDCIqsFBQUsPp9fXHtSIBDICwoKCADs3LlTPyMjg7No0aLGuJyLcjBMLIDDqg6jOtZgyQ0fnGpNE1n1rF9v3lkqxTtVx6GmvlN2g00+me3bt09v6dKl1n379k0LCAiIEIvFuTNnzrQtb9vt27ebxsfHc3ft2vV648aNbwFg8ODB9kePHjWcN29e3OHDh8Pbtm2bPW7cuJa3bt0SFO23fPlyUz8/P+uBAwemHTlyJHzixImJGzZssFi7dm2pacqxsbG8xYsXWy5atChu9+7dkcnJydzRo0fby+X1U2c4NjaWEx0dreHs7Jxb0TZ5eXmEYRjweLxSQWhoaMgB4OnTp3wACA0NFZiamhbo6uqW2q5Vq1Z5ERERFSbkDh06ZN+8eVN49+5dwe3btwW3bt3S6dixY05WVhZZtWqV5caNG6M5nGZf73qTqgOojByEGYoTQcuwphtAmuXyNbUhkbB4W7eaRqs6DjV0VSwW31Z2o03+W2TDhg3m3t7e6fv3748GFIt1JiUlcQICAj64HsbIyEh65syZyKL7J0+e1Ll69aru6dOnwwYPHpxVtH/79u35/v7+5ufOnYtMSUlhbdq0SeTr6xtXNJT3ySefZOTk5LA2b94s8vPzSyz6ss7IyOAEBQWFurq65gOAXC4n48ePtw8ODua7u7srffbTrFmzLDU1NeWTJk2q8CSrsbGxTFdXV3b//n2t6dOnF/e67t27pwUASUlJHABIS0tjl1xEtIi+vr40IyOjwqK5gwcPzho4cGBqp06dnAFgzJgxSQMGDMhasGCBeevWrXOGDh2aWdG+zQbDBIOQ0wCGqDqUsnLBz2uLfx+9hCMdLquF33836jxjRuILLS15a1XHokbW1kejTbpnJpVKERoaKhg8eHBayceHDh2aVt72ffv2LfX4hQsXhEZGRpJ+/fplSSQSFN28vb0znjx5ogUA//zzj3Zubi5r7NixqSW36dOnT2ZycjInMjKy+JesSCTKL0pkAODm5pYLAG/evKlwGFAqlaJku9W1fv1645MnTxpu27YtyszMrNJSMZ9//nliQECA8d69e/USExPZO3bsMDhx4oQhALDZ7OKhS1LO7GuGYaqckn306NE3kZGRT16/fv3k4MGDbyIiIri7du0y3bp167ukpCT2oEGDWujq6rZt27at08OHD5vrtUqLoTgprjbewzTRHHHhL+HYRdWxNF6ELFliSafp/+eeWCyu01IvFWnSPbPY2FiOTCYjJiYm0pKPm5qaSsvbvuzjycnJnKSkJC6Px/tgwgubreiMJCQkcACgffv2LuW1+fr1a56Dg0MBAAiFwlJJRUNDgwGA3NzcCn9U2NjYuMbGxhYnxNDQ0KdVTYUPCAjQXbp0qfXSpUvfjR8/Pq2ybQFg7dq1cRERERoTJ060BwBdXV3ZwoULY/39/S3Nzc2lAKCnpycrrweWlpbGLvu6ymNnZ1eciefNm2c5duzYJLFYnD916lRLqVRK3r59+2T9+vUmEyZMsAsJCXlRVXtNDsOEgJADAMarOhQAeIy2EZ64pyEFV6zqWBq7K1eE7vHxnPumptIOqo5FDSysr4abdDITiURSNpvNFCWcIvHx8eW+bkJIqQkU+vr6UhMTE8mxY8cqrNRgaGgoBYBDhw6Fi0SiD7pOrq6udfpVduLEifC8vLzi3o+NjU2l3bOLFy9qTZ06tcXYsWMT/f3946tzDB0dHfnZs2cj3759y3n//j3HxcUl/8iRI3pcLpfp0qVLNgA4OTnl7t692zQjI4MlFAqLexDh4eF8e3v7ar/GS5cuad2+fVsnLCwsBABu3rwpXLFiRYxQKJT7+fklrF271iIlJYXVTBcsXQ5gFAANVQZxFJ8+HIUjLQFCV09WkjlzbPSPHImQEdI81rGrwHGxWHy9vhpv0smMw+HAyckp9/Tp03qLFi0qnjr+119/6VVn/759+2bu2rXLTEdHR17ROa1evXpl8/l8eUxMDHf06NHpSgq9mKenZ4WTN8p68OABf8SIEa26d++esWfPnhqfeLayspJaWVlJZTIZdu3aZTxgwIDUoqTi4+OTsXDhQhw4cECv6GLqqKgo7oMHD7TXr19frWPJ5XLMmzfPesmSJbElk1V2djYLUFxeUNOYmxSGiQYhPwOYp6oQFmPt9e/wdWeANOnvhob24oWg5YsX/BvOznnN9SLzAgB+9XmAJv+B9fPzi5swYYL9+PHjrYcOHZp2/fp17StXrugCpc8HlWfo0KEZ3bp1Sx84cKDD3Llz41xdXfPS09PZjx8/FuTl5bG2b98eY2RkJFuwYEHssmXLrN68ecPr0aNHllwuR2hoKD8oKEjn4sWLEQ3xOmNiYjhDhgxx0NTUlM2dOzfh6tWrxUvU6+npyTw8PPIA4MyZM9off/yx48mTJ4sntRw6dEg3KiqK5+Likvf+/XvOnj17jCMiIvgBAQHFCw7a29tLRo4cmbR06VIrhmFgamoq9ff3F4lEooIZM2YkVyfG7du3G8pkMsydO7f4h0Xnzp0zN2/ebKavry/bu3evYevWrXOaaa+syBoAUwAIG/KgMrDkA3D++iX0pRM96omvr7X9hQsvcwiBpqpjUYGfxGJxvX4X1iqZNaayUuPHj097+/bt223btpkdO3bM0NPTM3P16tXvpkyZ0kJXV7fScz0sFgvnzp2LWLJkifkvv/xiGhcXx9PV1ZW1bt06Z9asWQlF261evTpeJBJJtm/fbrpz504zHo8nt7W1zRs2bFiDVQB4/PixID4+ngsAPj4+DiWf69ChQ9a9e/fCAMWEDZlMVmriBpfLZfbs2WP89u1bDR6Px3h5eaXv27cvquR5LgD49ddfo+fMmSNbtmyZVV5eHsvT0zPzyJEjkZqamlVeXJ6ens7y9/e32Lt3b2TR+UYAWL9+fezYsWNtJ0yYYG9ra5u3d+/e5r1iL8Mkg5DvUE8zvsqTBa1sMUJC3sCWJrJ69P49z/zqVZ2gnj0zm9v7nAzAv74PUqsKII2dn5+f+bZt28yTkpIeN6YqH1T1NZoKIOUhhAfgXwD1Pp37LSzft8aL1Gxo06njDUBbW5Zx8+aLAhYLRqqOpQHNFYvFP9b3QZr8OYrY2FjO1KlTLQ8ePKh78uRJnfnz54u2bdtmPnLkyCSayCi1xDAFAL4A6nddrNvoFGaH1wxNZA0nK4stDAgwfK7qOBpQKID/a4gDNflzZhoaGsyrV6/4f/75p2FmZibb2NhYMnny5Pgffvih+RW2pRoPhrkOQnYDmFYfze/BxHuT8ZszQLTro32qYps3m3UePTrlDZfL2Kg6lnomBzBNLBaXeymUsjX5ZGZoaCgLCgpqEosgUs2OHwAfAGbKbHQWfgr6GTO7A6TJj8yoI6mUcDduNItdsiSuqSezrWKx+EZDHYx+mClKXTFMGoAvldWcFGxZV9y49jNmedNEplqHDhl2zsxkhag6jnoUBmBpQx6wqg+0XC6X0xVkqUal8DPbNKb3M8wRAGfq2kw6hBnWiH58C129lBAVpQR+flZN4zP6ITmAiWKxuNrXyCpDpcmMEPI+Nze3udbKoxqp3NxcPiHkvarjUKJpAGq9TE4EWrwzR1x8HETtlRgTVUc3bui0iYnh3lN1HPXge7FYfKehD1ppMpNKpd9ERUXxsrOzBbSHRqk7uVxOsrOzBVFRUTypVPqNquNRGoaJg6JmY41nN/6DHs8c8FIjF5qtlB8YVVdz5tgYMwwaZIJEA3kOYIUqDlzpdWYA8OjRo/4cDmclwzBmoOfYKPUmJ4S8l0ql37Rr1y5Q1cEoHSEbUYNCrT9h1u05+LEtQARVbkypTEBAxPU2bXK7qzoOJZAC6CIWi++r4uBVJjOKotQEIVwANwB4VrXpBPwetA/jvYBy1u2h1IqRkSTxypUwASFo7JdJ+IrF4m2qOjjtaVFUY8EwEgCjAVRY0LoAXIk7Ht3YhwneNJE1DklJXOMLF4SNpkRgBfapMpEBtGdGUY0PIZ8COFr24WQYpDkiLCoZRm0bPiiqLgQCefbt28+z2WyYqDqWWngEoKtYLFbpIqS0Z0ZRjQ3DHAPwfcmHXsDpjQixqTSRNU65uSytPXuMwlQdRy0kAfhE1YkMoD0zimqcCGEBOAlgyFkMfDIEpy0ZsAxUHRZVeywWI7t37/kbDQ2mhapjqSYZgH5isfiKqgMBaM+MohonhpEDGLsBi44OxhknmsgaP7mcsNeuNW9Mq5R8pS6JDKA9M4pq1AiBNYA7AMxVHQulHDduvAjW1ZW5qTqOKvwmFounqDqIkmjPjKIaMYZBNIBBALJUHQulHPPnW6l7Afg/AUxXdRBl0WRGUY0cw+BfAJ8CTaqSRLN17562S3Q0r8HLQVXTBQBjxWKxTNWBlEWTGUU1AQyD8wAmQHFSnmrkZs+2NmcYSFQdRxm3AQwTi8UFqg6kPDSZUVQTwTA4CGAcaA+t0Xv9mm/z8KHmLVXHUcITAIPEYnG2qgOpCJ0AQlFNDCEYAeAQmsHiu02Zvr40OSgolEsIhCoOJRxAN7FYHK/iOCpFe2YU1cQwDP4AMApQu2EqqgZSUzmGp07pPVZxGOEA+qh7IgNoz4yimixC8DEUZa94qo6Fqh0NDXnu3bvP09hslVx68RjAALFYnKCCY9cY7ZlRVBPFMDgJYDgAtTxhT1UtP58l2LHDOEIFh/4HQI/GksgA2jOjqCaPEAyAooemo+pYqJojhJHfu/c8gs9nGmqB1T8BjBOLxfkNdDyloD0zimriCqftdwHwWtWxUDXHMIT1zTeiCpf9UbIdAEY2tkQG0J4ZRTUbhMAIwHEATWFV42YnKOjFYwMDmXs9HmK1WCxeXo/t1yvaM6OoZoJhkASgD4DfVB0LVXNffmnNZxjUR+8jG4qqHo02kQE0mVFUs8IwKGAYTAGwAIBc1fFQ1ff4sVbryEiN20puNgxAR7FYfEjJ7TY4mswoqhliGGwG4AMgQ9WxUNU3Z461NcNAWeezjgPoIBaLnympPZWiyYyimimGwVkAHQDcV3UsVPW8fatheeeOVl2LEMsA+InF4uFisThTGXGpAzoBhKKaOULAAbAcwFIAbBWHQ1VBKJSmXb8eChYLerXYPR7AaLFYfFW5Uake7ZlRVDPHMJAyDFYC6ArglarjoSqXkcHRO3FCP7gWux4D0KYpJjKA9swoiiqBEGgB2ATgC1XHQlWMy5Xn37v3PJHDgWU1No8HMFMsFh+v77hUifbMKIoqxjDIZhjMADAEii9BSg1JJCyNn34yfVONTfcDcG7qiQygPTOKoipQeJH1WgBTQH/4qiGGuXv3eZimJuNUzpPvAHwhFovPNnRUqkI/oBRFlYthkMQwmA7AA0CQquOhyiJk6VLLnDIPygD8AsClOSUygPbMKIqqJkLwKYCNAGxUHQv1nytXQh8aG0s9AFwEsEAsFj9VdUyqQJMZRVHVRgj4ABYC+BqAlorDoQC4u2cH7tv3+kexWHxG1bGoEk1mFEXVGCGwALAGwDgAHBWH01y9AbASwH6GoaXJaDKjKKrWCIEtFD21yQAEqo2m2YgD8B2AXxiGLrxahCYziqLqjBAYA/AFMAuoVWUKqmqPAGwBcIQmsQ/RZEZRlNIQAh0oLrieB0Ck4nCaAjmAvwH8wDC4pupg1BlNZhRFKR0h4EFxPm0ygG4qDqcxyoRi3bltDINIVQfTGNBkRlFUvSIELQCMB/A5gBYqDkedMQBuAjgMxaQOujxPDdBkRlFUgyAEBIpe2ngAIwEIVRuR2rgH4AiAowyDd6oOprGiyYyiqAZHCAQAPi689QVgqNqIGlwwFD2wIwyD16oOpimgyYyiKJUiBCwA7QD0A9AfQGcAXJUGpXyJUJQECwJwgWHwUsXxNDk0mVEUpVYIgTaAnlAktt4AHAEQlQZVc0XJ62rh7TnDgH7Z1iOazCiKUmuEQAjAHYqCxx4A3AA4QD16bwyAaAAvCm/PAdxmGDxTaVTNEE1mFEU1OoSAC6AVADEUPTfzEjezwpuGkg6XBSC58PYGpRNXGMMgW0nHoeqAJjOKopokQqCP/xKcAQB24Y1V4v9LPlaA/5JW8Y1W22gcaDKjKIqiGj26OCdFURTV6NFkRlEURTV6NJlRFEVRjR5NZhRFUVSjR5MZRVEU1ejRZEZRFEU1ejSZURRFUY0eTWYURVFUo0eTGUVRFNXo0WRGURRFNXr/D/2CP8gMr6ehAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import numpy as np\n",
    "import matplotlib as mpl \n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "topleft_values = np.char.array(['red','blue','white','black','gold','orange','green'])\n",
    "topleft_value_counts= np.array([46, 29, 26, 18, 10, 5, 4])\n",
    "colors = ['red','blue','lightgrey','black','gold','orange','green']\n",
    "porcent = 100.*topleft_value_counts/topleft_value_counts.sum()\n",
    "\n",
    "patches, text = plt.pie(botright_value_counts, colors=colors, startangle=90, radius=1.2)\n",
    "labels = ['{0} - {1:1.2f} %'.format(i,j) for i,j in zip(topleft_values, porcent)]\n",
    "\n",
    "sort_legend = True\n",
    "if sort_legend:\n",
    "    patches, labels, dummy =  zip(*sorted(zip(patches, labels, topleft_value_counts),\n",
    "                                          key=lambda topleft_values: topleft_values[2],\n",
    "                                          reverse=True))\n",
    "\n",
    "plt.legend(patches,labels, loc='best', bbox_to_anchor=(-0.1, 1.),\n",
    "           fontsize=15)\n",
    "plt.title('new_flag_topleft-Info')\n",
    "plt.savefig('piechart.png', bbox_inches='tight')\n",
    "# https://stackoverflow.com/questions/23577505/how-to-avoid-overlapping-of-labels-autopct-in-a-matplotlib-pie-chart\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d8019471",
   "metadata": {},
   "source": [
    "## 6. Bar chart"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "63b462ab",
   "metadata": {},
   "source": [
    "Here,the `value.counts()` function is applied for the `icon` variable of the `new_flag` dataset and only two values have found from the output pane as the numbers of `0` and `1` which are mentioning `1` if an inanimate image present (e.g., a boat), otherwise `0` "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "id": "684adba9",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0    110\n",
       "1     28\n",
       "Name: icon, dtype: int64"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "new_flag['icon'].value_counts()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "14b9e6f5",
   "metadata": {},
   "source": [
    "To generate this bar plot, the `icon` variable is subsetted with the `new_flag` dataset as well as added with the `value.counts()` function where 'X' object has been made with those functions. After that, the `plot()` function is applied using the various argument such as kind='bar',figsize=(10,8), fontsize=(15) and color= `violet` to get the appropriate bar plot. Here, the `plt.title()`,`plt.xlabel()`, `plt.ylabel()`, `plt.grid()` and `plt.legend()` functions have been used to get the title of the barplot as `new_flag_icon-Info`, xlabel as `icon`, ylabel as `count`, background of the barplot as 'grid', and the legend which explores the symbol of the colours of the bars respectively. Apart from this, the `plt.show()`function is applied to demonstrate the bar plot adequately. From this barplot, it is highlighted that the number of the `0` value of the `icon` variable is cpmparatively higher which is shown over 100 than the number of `1` value which is showing it's lowest representation by demonstrating it's figure only over 20 through the following bar plot."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "id": "2d2d7630",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x576 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "X = new_flag['icon'].value_counts()\n",
    "X.plot(kind='bar',figsize=(10,8), fontsize=(15),color='violet')\n",
    "plt.title('new_flag_icon-Info')\n",
    "plt.xlabel('icon')\n",
    "plt.ylabel('count')\n",
    "plt.grid()\n",
    "plt.legend() \n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "43de2979",
   "metadata": {},
   "source": [
    "## 7. Box plot"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "88381e85",
   "metadata": {},
   "source": [
    "Here,the `value.counts()` function has been used for the `colours`variable and different kinds of values have obtained from the output pane which are highlighting as the number of 3, 2, 4, 5, 7, 6, 1."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "id": "87157c71",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "3    57\n",
       "2    34\n",
       "4    31\n",
       "5     9\n",
       "7     4\n",
       "6     2\n",
       "1     1\n",
       "Name: colours, dtype: int64"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "new_flag['colours'].value_counts()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "acff8ed6",
   "metadata": {},
   "source": [
    "According to the boxplot, it will give the information about the distribution of the data. Here, the boxplot is created using the `boxplot()` function in terms of detecting the outliers of the `colours` variable of the `new_flag` dataset. Apart from this, `dropna()` function has been used in the following step to double-check and to drop the missing values during the operation time. After using above those functions, it is found that one outlier is highlighting in the upper fence of the boxplot. This outlier indicates that it's value range is very inconsistent compare to the other values of the `colours` variable which is really uncountable and known as outlier. Apart from this, `3` number values are showing more colours than the other values of the `colours` varable. Here, the `plt.title()` function is used to generate the title `new_flag_colours-Info` whether it does not have any legend by default which are shown in the given boxplot."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "id": "e270b31f",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 576x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "new_flag.dropna().boxplot(column = 'colours',figsize=(8,5), fontsize=(15))\n",
    "plt.title('new_flag_colours-Info')\n",
    "plt.show() "
   ]
  },
  {
   "cell_type": "markdown",
   "id": "7ad98b6b",
   "metadata": {},
   "source": [
    "## 8. Histogram"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e60cea1e",
   "metadata": {},
   "source": [
    "In the next step, the `value_counts()` function is used for the `landmass`  variable of the `new_flag` dataset. As an output, various numbers of the values have been found for the `landmass` variable such as 4,5,3,2,1,6 and which are mentioned as 1=N.America, 2=S.America, 3=Europe, 4=Africa, 5=Asia, 6=Oceania."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "id": "2b899e22",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "4    45\n",
       "5    35\n",
       "3    25\n",
       "2    14\n",
       "1    13\n",
       "6     6\n",
       "Name: landmass, dtype: int64"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "new_flag['landmass'].value_counts()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "91a545d0",
   "metadata": {},
   "source": [
    " Here, the libraries pandas, matplotlib and matplotlib.pyplot are imported as pd, mpl and plt respectively to explore the plot. In the following histogram graph,the `language` variable is subsetted with the `new_flag` dataframe where the `plot()` function is highlighting the kind of hist which means histogram and bins= 8 means 8 different range of `landmass`.After that, the the `plt.title()` function has been used to get the title name of the histogram chart which named as `new_flag-language Info` and highlighted in the output pane. Apart from this, the histogram has shown the xlabel or x-axis as `landmass` using the `plt.xlabel()` function whether it does not have any ylabel by default.Here, the `plt.legend()` function is applied to get the explicit information about the number of landmass where `4` number value's frequency is higher compare to the rest of the number of landmass in this `new_flag` dataset as the `landmass`variable is describing, 1=N.America, 2=S.America, 3=Europe, 4=Africa, 5=Asia, 6=Oceania. Finally, the `plt.grid()` and `plt.show()` functions are applied to demonstrate the background as grid and to show the histogram graph appropriately."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "id": "a71f86f2",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "import pandas as pd\n",
    "import matplotlib as mpl \n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "new_flag['landmass'].plot(kind='hist',bins= 8,fontsize=(12))\n",
    "plt.title('new_flag - landmass Info')\n",
    "plt.xlabel('landmass')\n",
    "plt.legend()\n",
    "plt.grid()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f600f877",
   "metadata": {},
   "source": [
    "## 9. Bar plot"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "39784f78",
   "metadata": {},
   "source": [
    "Here, the `value.counts()` function is applied for the `triangle` variable of the `new_flag` dataset and only two values are found from the output pane as the numbers of `0` and `1` which are mentioning if any triangle present, it will be `1` otherwise it will be `0`."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "id": "fe9951d7",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0    123\n",
       "1     15\n",
       "Name: triangle, dtype: int64"
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "new_flag['triangle'].value_counts()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "2fb68a0a",
   "metadata": {},
   "source": [
    "To generate this bar plot, the `triangle` variable is subsetted with the `new_flag` dataset as well as added with the `value.counts()` function where 'X' object has been made with those functions. After that, the `plot()` function is applied using the various argument such as kind='bar',figsize=(10,8), fontsize=(15) and color='deeppink' to get the appropriate bar plot. Here, the `plt.title()`,`plt.xlabel()`, `plt.ylabel()`, `plt.grid()` and `plt.legend()` functions have been used to get the title of the barplot as `new_flag_triangle-Info`, xlabel as `triangle`, ylabel as `count`, background of the barplot as `grid`, and the legend which explores the symbol of the colours of the bars respectively. Apart from this, the `plt.show()` function is applied to demonstrate the bar plot adequately. From this barplot, it is represented that the number of the `0` value of the `triangle` variable is cpmparatively higher which is shown over 120 than the number of `1` value which is showing it's lowest representation by demonstrating it's figure only over 10 through the following bar plot."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "id": "5ffa44d9",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x576 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "X = new_flag['triangle'].value_counts()\n",
    "X.plot(kind='bar',figsize=(10,8), fontsize=(15), color='deeppink')\n",
    "plt.title('new_flag_triangle-Info')\n",
    "plt.xlabel('triangle')\n",
    "plt.ylabel('count')\n",
    "plt.grid()\n",
    "plt.legend() \n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f8dc19af",
   "metadata": {},
   "source": [
    "## 10. Countplot(Bar plot)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e59a257c",
   "metadata": {},
   "source": [
    "In the next step, the `value_counts()` function is used for the `zone` variable of the `new_flag` dataset. As an output, few numbers of the values have been found for the `zone` variable such as 1,4,2,3 and which are mentioned as 1=NE, 2=SE, 3=SW, 4=NW."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "id": "b0e3d026",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1    73\n",
       "4    34\n",
       "2    22\n",
       "3     9\n",
       "Name: zone, dtype: int64"
      ]
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "new_flag['zone'].value_counts()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "63e106e7",
   "metadata": {},
   "source": [
    "Here, the libraries seaborn, numpy, matplotlib.pyplot are imported as sns, np and plt respectively to explore the plot.As the count plot is one of the bar plot, therefore to generate this bar plot, the `zone` variable is subsetted with the `new_flag` dataset. Here, the `countplot()` function has been used with the seaborn package. Apart from this, the `plt.figure()`, `plt.title()`, `plt.xlabel()`, and `plt.ylabel()` functions are applied where figsize are x-axis=10 and y-axis=5, the title name of the plot is `new_flag_zone & saltires-Info`, xlabel as `zone` and ylabel as `count`respectively whether it does not have any `legend` by default. Apart from this, the `plt.show()`function has been used to demonstrate the count plot adequately. From this count plot, it is represented that zone number `1` which means north east(NE) zone is higher than the other zone of the flag dataset which is highlighted over the number of 70 where `3` number value is showing it's lowest demonstration in this bar plot or count plot."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "37a9808e",
   "metadata": {},
   "source": [
    "The `countplot` is one of the bar plot. Here, `plt.legend()` is not giving any result by default. In this countplot, no.`1` zone is shown "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "id": "6cf3630a",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAl4AAAFNCAYAAADRi2EuAAAAOXRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjMuNCwgaHR0cHM6Ly9tYXRwbG90bGliLm9yZy8QVMy6AAAACXBIWXMAAAsTAAALEwEAmpwYAAAXVklEQVR4nO3df7BfdX3n8ecLAkX5sRC5SSM/jLrUSrsS9MqiGYSCtrSiYV2h0qUbXLapO9XirOiiM7utbZnS2a5bW52trApRKUoFJFqqZsLPWotcFBAEhbIISEwuCAVqBwu894/vSbnG/PhekvP5fu/l+Zj5zvf8Pq/7ne9kXjnnfM9JVSFJkqT+7TLqAJIkSc8WFi9JkqRGLF6SJEmNWLwkSZIasXhJkiQ1YvGSJElqxOIlaewkWZ7kjiSPJTkxyVVJ/vOoc42TJP8lyYbuM3reqPNIGo7FS9I4+j3gQ1W1V1V9btRhWkhyWpK/GXLZ3YAPAL/YfUYP9ptO0s5i8ZI0jl4A3DrqEGNsMbAHfkbSnGPxkrRVSe5OcmaSm5P8Q5LPJNmjm3dCkhuTPJzkb5O8rJv+1iSfn7GNO5NcNGP83iTLtrHPvwdeBHy+O432U5vNf3GSK5I8mOSBJBck2XfG/Jcn+UaSR5P8ZZf5D7bzd27a16bXU0lO6+a9Osn13d9/fZJXz1jvqiS/n+Qr3f6+nGT/GfOP7D6bh5PclOSYbX7gP55pi599kp8Bvt0t9nCSK7aXU9L4sHhJ2p6TgeOBFwIvA05L8nLg48BvAs8DPgKs6UrS1cBRSXZJsgTYDVgOkORFwF7AzVvbWVW9GLgHeEN3Gu3xzRYJ8IfA84GXAgcBv9ttf3fgUuB8YCFwIfDvtvcHVtWmfe0FvBn4PrAuyULgr4A/7f7ODwB/tdk1Vb8GvBVYBOwOnNllOaBb9w+6LGcCFyeZ2F6eGX7is6+q7wA/183ft6qOHTKnpDFg8ZK0PX9aVfdX1Q+AzwPLgN8APlJV11XVk1W1GngcOLKq7gIe7ZY7GvgS8L0kP9uNX1tVTz3TMFV1Z1WtrarHq2qaQck4upt9JLCgy/zPVXUJ8LVht90dTfoE8KtVdS/weuCOqvpkVT1RVRcCtwNvmLHaeVX1nar6J+Ci7u8GOBW4vKour6qnqmotMAX8yiz+3C199lsyTE5JY2DBqANIGnvfnzH8QwZHmhYCK5O8Y8a83bt5MDjqdQzwr7vhhxmUo1d1489YkkUMjuwcBezN4D+QD3Wznw98r6pqxir3DrndfwVcBvz3qrp2xva+u9mi3wUOmDG++eezVzf8AuCkJDPLz27AlUmOAv560/aq6ufYsi199lsyTE5JY8AjXpKeiXuBs6tq3xmv53ZHWuDp4nVUN3w1g+J1NDtYvBicZizgZVW1D4MjS+nmrQcOSJIZyx+0vQ0m2QX4C+DKqvrIjFn3MyhQMx0MfG+InPcCn9zsM9qzqs6pqms3ndrcRumajR3JKakhi5ekZ+L/Am9L8m8zsGeS1yfZu5t/NfALwHOq6j7gWgbXKj0P+MYO7ntv4DEGF5YfALx7xryvAk8Cb0+yIMkK4Ightnk2sCdwxmbTLwd+Jsmvddv7VeBQ4AtDbPNTwBuS/FKSXbsL449JcuAQ687WjuSU1JDFS9KsVdUUg+u8PsTgNN+dwGkz5n+HQTm6tht/BLgL+EpVPbmDu38/8HLgHxhcUH7JjP3+CHgTcDqD05unMigfm1+gv7lTGFwf9tCMXzb+h+7+WCcA7wIeBN4DnFBVD2wvZHeN2ArgfcA0gyNg76aHf3d3JKektvLjl0JI0vyS5Drgz6vqvFFnkSSPeEmaV5IcneSnu1NuKxnchuGLo84lSWDxkjQCSY7a7Ial//LaCZt/CXATg1OR7wLeXFXrk7xvK/v8621vTpJ2Hk81SpIkNeIRL0mSpEYsXpIkSY3MiTvX77///rV06dJRx5AkSdquG2644YGq2uJzWedE8Vq6dClTU1OjjiFJkrRdSTZ/hNe/8FSjJElSIxYvSZKkRixekiRJjVi8JEmSGrF4SZIkNWLxkiRJasTiJUmS1IjFS5IkqRGLlyRJUiMWL0mSpEYsXpIkSY3MiWc1PlOvePcnRh1BO+CG//kfRx1BkqSdyiNekiRJjVi8JEmSGrF4SZIkNWLxkiRJasTiJUmS1IjFS5IkqZHeileSlyS5ccbrkSTvTLIwydokd3Tv+/WVQZIkaZz0Vryq6ttVtayqlgGvAH4IXAqcBayrqkOAdd24JEnSvNfqVONxwN9X1XeBFcDqbvpq4MRGGSRJkkaqVfF6C3BhN7y4qtYDdO+LGmWQJEkaqd6LV5LdgTcCfznL9VYlmUoyNT093U84SZKkhloc8fpl4OtVtaEb35BkCUD3vnFLK1XVuVU1WVWTExMTDWJKkiT1q0XxOoWnTzMCrAFWdsMrgcsaZJAkSRq5XotXkucCrwMumTH5HOB1Se7o5p3TZwZJkqRxsaDPjVfVD4HnbTbtQQa/cpQkSXpW8c71kiRJjVi8JEmSGrF4SZIkNWLxkiRJasTiJUmS1IjFS5IkqRGLlyRJUiMWL0mSpEYsXpIkSY1YvCRJkhqxeEmSJDVi8ZIkSWrE4iVJktSIxUuSJKkRi5ckSVIjFi9JkqRGLF6SJEmNWLwkSZIasXhJkiQ1YvGSJElqxOIlSZLUiMVLkiSpEYuXJElSIxYvSZKkRixekiRJjVi8JEmSGum1eCXZN8lnk9ye5LYkr0qyMMnaJHd07/v1mUGSJGlc9H3E64PAF6vqZ4HDgNuAs4B1VXUIsK4blyRJmvd6K15J9gFeA3wMoKp+VFUPAyuA1d1iq4ET+8ogSZI0Tvo84vUiYBo4L8k3knw0yZ7A4qpaD9C9L+oxgyRJ0tjos3gtAF4O/J+qOhz4R2ZxWjHJqiRTSaamp6f7yihJktRMn8XrPuC+qrquG/8sgyK2IckSgO5945ZWrqpzq2qyqiYnJiZ6jClJktRGb8Wrqr4P3JvkJd2k44BvAWuAld20lcBlfWWQJEkaJwt63v47gAuS7A7cBbyVQdm7KMnpwD3AST1nkCRJGgu9Fq+quhGY3MKs4/rcryRJ0jjyzvWSJEmNWLwkSZIasXhJkiQ1YvGSJElqxOIlSZLUiMVLkiSpEYuXJElSIxYvSZKkRixekiRJjVi8JEmSGrF4SZIkNWLxkiRJasTiJUmS1IjFS5IkqRGLlyRJUiMWL0mSpEYsXpIkSY1YvCRJkhqxeEmSJDVi8ZIkSWrE4iVJktSIxUuSJKkRi5ckSVIjFi9JkqRGLF6SJEmNWLwkSZIasXhJkiQ1sqDPjSe5G3gUeBJ4oqomkywEPgMsBe4GTq6qh/rMIUmSNA5aHPH6hapaVlWT3fhZwLqqOgRY141LkiTNe6M41bgCWN0NrwZOHEEGSZKk5vouXgV8OckNSVZ10xZX1XqA7n1RzxkkSZLGQq/XeAHLq+r+JIuAtUluH3bFrqitAjj44IP7yidJktRMr0e8qur+7n0jcClwBLAhyRKA7n3jVtY9t6omq2pyYmKiz5iSJElN9Fa8kuyZZO9Nw8AvArcAa4CV3WIrgcv6yiBJkjRO+jzVuBi4NMmm/fxFVX0xyfXARUlOB+4BTuoxgyRJ0tjorXhV1V3AYVuY/iBwXF/7lSRJGlfeuV6SJKkRi5ckSVIjFi9JkqRGLF6SJEmNWLwkSZIasXhJkiQ1YvGSJElqxOIlSZLUiMVLkiSpEYuXJElSIxYvSZKkRixekiRJjVi8JEmSGrF4SZIkNWLxkiRJasTiJUmS1IjFS5IkqRGLlyRJUiMWL0mSpEYsXpIkSY1YvCRJkhqxeEmSJDVi8ZIkSWrE4iVJktTIUMUrybphpkmSJGnrFmxrZpI9gOcC+yfZD0g3ax/g+T1nkyRJmle2WbyA3wTeyaBk3cDTxesR4MPD7CDJrsAU8L2qOiHJQuAzwFLgbuDkqnpotsElSZLmmm2eaqyqD1bVC4Ezq+pFVfXC7nVYVX1oyH2cAdw2Y/wsYF1VHQKs68YlSZLmve0d8QKgqv4syasZHKVaMGP6J7a1XpIDgdcDZwP/tZu8AjimG14NXAX8t1lkliRJmpOGKl5JPgm8GLgReLKbXMA2ixfwJ8B7gL1nTFtcVesBqmp9kkWzyCtJkjRnDVW8gEng0KqqYTec5ARgY1XdkOSY2QZLsgpYBXDwwQfPdnVJkqSxM+x9vG4BfnqW214OvDHJ3cCngWOTfArYkGQJQPe+cUsrV9W5VTVZVZMTExOz3LUkSdL4GbZ47Q98K8mXkqzZ9NrWClX13qo6sKqWAm8BrqiqU4E1wMpusZXAZc8wuyRJ0pwy7KnG392J+zwHuCjJ6cA9wEk7cduSJElja9hfNV69IzupqqsY/HqRqnoQOG5HtidJkjQXDfurxkcZ/IoRYHdgN+Afq2qfvoJJkiTNN8Me8Zp5OwiSnAgc0UcgSZKk+WrYi+t/TFV9Djh250aRJEma34Y91fimGaO7MLiv19D39JIkSdLwv2p8w4zhJxg83HrFTk8jSZI0jw17jddb+w4iSZI03w11jVeSA5NcmmRjkg1JLu4egC1JkqQhDXtx/XkM7jj/fOAA4PPdNEmSJA1p2OI1UVXnVdUT3et8wAcoSpIkzcKwxeuBJKcm2bV7nQo82GcwSZKk+WbY4vWfgJOB7wPrgTcDXnAvSZI0C8PeTuL3gZVV9RBAkoXAHzMoZJIkSRrCsEe8XrapdAFU1Q+Aw/uJJEmSND8NW7x2SbLfppHuiNewR8skSZLE8OXpfwF/m+SzDB4VdDJwdm+pJEmS5qFh71z/iSRTDB6MHeBNVfWtXpNJkiTNM0OfLuyKlmVLkqQhXf2ao0cdQTvg6Guu3unbHPYaL0mSJO0gi5ckSVIjFi9JkqRGLF6SJEmNWLwkSZIasXhJkiQ1YvGSJElqxOIlSZLUiMVLkiSpEYuXJElSI70VryR7JPlakpuS3Jrk/d30hUnWJrmje9+vrwySJEnjpM8jXo8Dx1bVYcAy4PgkRwJnAeuq6hBgXTcuSZI07/VWvGrgsW50t+5VwApgdTd9NXBiXxkkSZLGSa/XeCXZNcmNwEZgbVVdByyuqvUA3fuiray7KslUkqnp6ek+Y0qSJDXRa/GqqierahlwIHBEkp+fxbrnVtVkVU1OTEz0llGSJKmVJr9qrKqHgauA44ENSZYAdO8bW2SQJEkatT5/1TiRZN9u+DnAa4HbgTXAym6xlcBlfWWQJEkaJwt63PYSYHWSXRkUvIuq6gtJvgpclOR04B7gpB4zSJIkjY3eildV3QwcvoXpDwLH9bVfSZKkceWd6yVJkhqxeEmSJDVi8ZIkSWrE4iVJktSIxUuSJKkRi5ckSVIjFi9JkqRGLF6SJEmNWLwkSZIasXhJkiQ1YvGSJElqxOIlSZLUiMVLkiSpEYuXJElSIxYvSZKkRixekiRJjVi8JEmSGrF4SZIkNWLxkiRJasTiJUmS1IjFS5IkqRGLlyRJUiMWL0mSpEYWjDqANA7u+b1/M+oI2gEH/49vjjqCJA3FI16SJEmNWLwkSZIa6a14JTkoyZVJbktya5IzuukLk6xNckf3vl9fGSRJksZJn0e8ngDeVVUvBY4EfivJocBZwLqqOgRY141LkiTNe70Vr6paX1Vf74YfBW4DDgBWAKu7xVYDJ/aVQZIkaZw0ucYryVLgcOA6YHFVrYdBOQMWtcggSZI0ar0XryR7ARcD76yqR2ax3qokU0mmpqen+wsoSZLUSK/FK8luDErXBVV1STd5Q5Il3fwlwMYtrVtV51bVZFVNTkxM9BlTkiSpiT5/1RjgY8BtVfWBGbPWACu74ZXAZX1lkCRJGid93rl+OfDrwDeT3NhNex9wDnBRktOBe4CTeswgSZI0NnorXlX1N0C2Mvu4vvYrSZI0rrxzvSRJUiMWL0mSpEYsXpIkSY1YvCRJkhqxeEmSJDVi8ZIkSWrE4iVJktSIxUuSJKkRi5ckSVIjFi9JkqRGLF6SJEmNWLwkSZIasXhJkiQ1YvGSJElqxOIlSZLUiMVLkiSpEYuXJElSIxYvSZKkRixekiRJjVi8JEmSGrF4SZIkNWLxkiRJasTiJUmS1IjFS5IkqRGLlyRJUiMWL0mSpEYsXpIkSY30VrySfDzJxiS3zJi2MMnaJHd07/v1tX9JkqRx0+cRr/OB4zebdhawrqoOAdZ145IkSc8KvRWvqroG+MFmk1cAq7vh1cCJfe1fkiRp3LS+xmtxVa0H6N4XbW3BJKuSTCWZmp6ebhZQkiSpL2N7cX1VnVtVk1U1OTExMeo4kiRJO6x18dqQZAlA976x8f4lSZJGpnXxWgOs7IZXApc13r8kSdLILOhrw0kuBI4B9k9yH/A7wDnARUlOB+4BTupr/5LUl+V/tnzUEbQDvvKOr4w6gp7FeiteVXXKVmYd19c+JUmSxtnYXlwvSZI031i8JEmSGrF4SZIkNWLxkiRJasTiJUmS1IjFS5IkqRGLlyRJUiMWL0mSpEYsXpIkSY1YvCRJkhqxeEmSJDVi8ZIkSWrE4iVJktSIxUuSJKkRi5ckSVIjFi9JkqRGLF6SJEmNWLwkSZIasXhJkiQ1YvGSJElqxOIlSZLUiMVLkiSpEYuXJElSIxYvSZKkRixekiRJjVi8JEmSGhlJ8UpyfJJvJ7kzyVmjyCBJktRa8+KVZFfgw8AvA4cCpyQ5tHUOSZKk1kZxxOsI4M6ququqfgR8GlgxghySJElNjaJ4HQDcO2P8vm6aJEnSvLZgBPvMFqbVTyyUrAJWdaOPJfl2r6nmpv2BB0Ydoi/545WjjjCfzOvvCr+zpX9WtAPm9fclv+33ZSea198V8oy/Ky/Y2oxRFK/7gINmjB8I3L/5QlV1LnBuq1BzUZKpqpocdQ6NP78rmg2/LxqW35XZG8WpxuuBQ5K8MMnuwFuANSPIIUmS1FTzI15V9USStwNfAnYFPl5Vt7bOIUmS1NooTjVSVZcDl49i3/OMp2I1LL8rmg2/LxqW35VZStVPXNcuSZKkHvjIIEmSpEYsXnNQko8n2ZjkllFn0XhLclCSK5PcluTWJGeMOpPGU5I9knwtyU3dd+X9o86k8ZZk1yTfSPKFUWeZSyxec9P5wPGjDqE54QngXVX1UuBI4Ld8RJe24nHg2Ko6DFgGHJ/kyNFG0pg7A7ht1CHmGovXHFRV1wA/GHUOjb+qWl9VX++GH2Xwj6RPitBPqIHHutHdupcXAWuLkhwIvB746KizzDUWL+lZIslS4HDguhFH0ZjqTh3dCGwE1laV3xVtzZ8A7wGeGnGOOcfiJT0LJNkLuBh4Z1U9Muo8Gk9V9WRVLWPwRJEjkvz8iCNpDCU5AdhYVTeMOstcZPGS5rkkuzEoXRdU1SWjzqPxV1UPA1fhtaTasuXAG5PcDXwaODbJp0Ybae6weEnzWJIAHwNuq6oPjDqPxleSiST7dsPPAV4L3D7SUBpLVfXeqjqwqpYyeOzfFVV16ohjzRkWrzkoyYXAV4GXJLkvyemjzqSxtRz4dQb/I72xe/3KqENpLC0BrkxyM4Nn6q6tKm8TIO1k3rlekiSpEY94SZIkNWLxkiRJasTiJUmS1IjFS5IkqRGLlyRJUiMWL0mSpEYsXpIkSY1YvCTNK0neNuNmsf8vyZVJTknyzSS3JPmjGcs+luTsJDcl+bski7vpE0kuTnJ991o+ur9I0nxi8ZI0r1TVn3cPen4lcB9wPvBHwLHAMuCVSU7sFt8T+LuqOgy4BviNbvoHgf9dVa8E/j3w0UbxJc1zC0YdQJJ68kHgCuBh4KqqmgZIcgHwGuBzwI+ATY/FuQF4XTf8WuDQwaMuAdgnyd5V9WiT5JLmLYuXpHknyWnAC4C3A2/cxqL/XE8/N+1Jnv43cRfgVVX1T72FlPSs5KlGSfNKklcAZwKnVtVTwHXA0Un2T7IrcApw9XY282UGpW3TNpf1FFfSs4xHvCTNN28HFgJXdqcKp4D3AlcCAS6vqsu2s43fBj6c5GYG/05eA7ytt8SSnjXy9FF2SZIk9clTjZIkSY1YvCRJkhqxeEmSJDVi8ZIkSWrE4iVJktSIxUuSJKkRi5ckSVIjFi9JkqRG/j/dUrMl2FNaEgAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 720x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "import seaborn as sns\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "import warnings\n",
    "warnings.filterwarnings('ignore') # As the error message is highlighted here, the `warnings` is imported and error message \n",
    "# has been removed using the `filterwarnings('ignore')` function.\n",
    "\n",
    "plt.figure(figsize=(10, 5))\n",
    "sns.countplot(new_flag['zone'])\n",
    "plt.title('new_flag_zone-Info')\n",
    "plt.xlabel('zone')\n",
    "plt.ylabel('count')\n",
    "plt.show() "
   ]
  },
  {
   "cell_type": "markdown",
   "id": "8a201a43",
   "metadata": {},
   "source": [
    "## Task 2(Subtask 2): The relationships between columns"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a16400cd",
   "metadata": {},
   "source": [
    "### Relationship no.1"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "2bed903d",
   "metadata": {},
   "source": [
    "In the next step, the `value_counts()` function is used for the `landmass` variable of the `new_flag` dataset. As an output, various numbers of the values have been found for the landmass variable such as 4,5,3,2,1,6 and which are mentioned as 1=N.America, 2=S.America, 3=Europe, 4=Africa, 5=Asia, 6=Oceania."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "id": "d151e365",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "4    45\n",
       "5    35\n",
       "3    25\n",
       "2    14\n",
       "1    13\n",
       "6     6\n",
       "Name: landmass, dtype: int64"
      ]
     },
     "execution_count": 37,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "new_flag['landmass'].value_counts()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a87331c4",
   "metadata": {},
   "source": [
    "Here, the value.counts() function has been used for the topleft variable of the new_flag dataset and different kinds of values have obtained from the output pane which are demonstrating as red, blue, green, white, black, gold, orange values and this values observation numbers are 46, 29, 26, 18, 10, 5, 4 respectively."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "id": "6136a1ff",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "red       46\n",
       "blue      29\n",
       "green     26\n",
       "white     18\n",
       "black     10\n",
       "gold       5\n",
       "orange     4\n",
       "Name: topleft, dtype: int64"
      ]
     },
     "execution_count": 38,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "new_flag['topleft'].value_counts()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "57850284",
   "metadata": {},
   "source": [
    "According to the boxplot, it will give the information about the distribution of the data. Here, the boxplot is created using the `boxplot()` function to detect the outliers of the `topleft` variable of the `new_flag` dataset as well as the boxplot is generated in terms of the `landmass` by `topleft` where the `landmass` is plotting onto the `topleft` and seven groups of colours have been formed with the black, blue, gold, green, orange, red and white using the groupby effectively. Apart from this, `dropna()` function has been used in the below step to double-check and to drop the missing values during the operation time. After using those above functions, it is found that few groups of colours are showing their outliers in both the upper and lower fence which are known as `gold` and `green`. Here, these `gold` and `green` colours groups are shown flat plot instead of boxplot because of their all number of values inconsistency with the other groups of colours' values which are highlighting in the following figure. In this case, `black` group is also highlighted it's one outlier in the lower fence which indicates it's value ranges inconsistency compare to the other values of the colours which is really uncountable and known as outlier. However, according to the median explanation, `red` colours group is demonstrated more in the landmass than the other groups of colours which is found from the following boxplots. Here, the `plt.title()`, `plt.xlabel()` and `plt.ylabel()` functions are applied to form the title `new_flag_landmass & topleft-relation`, x-axis `topleft`' and y-axis`landmass` whether it does not have any legend by default which are represented in the given boxplot."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "id": "773738ce",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 576x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "new_flag.dropna().boxplot(column = 'landmass', by = 'topleft', figsize=(8,5), fontsize=(15))\n",
    "plt.title('new_flag_landmass & topleft-relation')\n",
    "plt.xlabel('topleft')\n",
    "plt.ylabel('landmass')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "74911c8c",
   "metadata": {},
   "source": [
    "### Relationship no.2"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b4981cf4",
   "metadata": {},
   "source": [
    "In the next step, the `value_counts()` function is used for the `zone` variable of the `new_flag` dataset. As an output, few numbers of the values have been found for the `zone` variable such as 1,4,2,3 and which are mentioned as 1=NE, 2=SE, 3=SW, 4=NW."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "id": "e1947227",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1    73\n",
       "4    34\n",
       "2    22\n",
       "3     9\n",
       "Name: zone, dtype: int64"
      ]
     },
     "execution_count": 40,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "new_flag['zone'].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "id": "5e462c8a",
   "metadata": {},
   "outputs": [],
   "source": [
    "colors_palette = {1:'red', 4:'green', 2:'blue', 3:'yellow'}\n",
    "# To find the color for each number of zone, different colors have been entitled with the different number of zone and\n",
    "# made an object as `colors_palette`."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "21390b8e",
   "metadata": {},
   "source": [
    "This `colors_palette` object has been subsetted with the `integer_label` and operated with the other subsetted function \n",
    "named as `new_flag['zone']` to find the color for each number of zones where 'colors' is defined as object."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "id": "f4a7f98a",
   "metadata": {},
   "outputs": [],
   "source": [
    "colors = [colors_palette[integer_label] \n",
    "          for integer_label in new_flag['zone']]"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "740d65c3",
   "metadata": {},
   "source": [
    "Here,the `value.counts()` function has been used for the `stripes` variable and different kinds of values have obtained from the output pane which are highlighting as the number of 0,3,2,5,9,4,6,7,11,13,14."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "id": "81285909",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0     70\n",
       "3     38\n",
       "2     11\n",
       "5     11\n",
       "9      2\n",
       "4      1\n",
       "6      1\n",
       "7      1\n",
       "11     1\n",
       "13     1\n",
       "14     1\n",
       "Name: stripes, dtype: int64"
      ]
     },
     "execution_count": 43,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "new_flag['stripes'].value_counts()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ebdcf733",
   "metadata": {},
   "source": [
    "In the following scatter plot, the `plot()` has been used with the `new_flag`dataframe where `plot()` function is highlighting the kind of scatter and added with x='stripes', y='area', c='colors', figsize=(8,5). Here, x and y is plotting the x-axis and y-axis using the `plt.xlabel()` and `plt.ylabel()` functions as well as colors and figsize are highlighting the different colors of stripes and the different size of the plotting figure respectively. After that, the `plt.title()` function has been used to get the title name of the scatter plot which named as `stripes & area relation-Info` and highlighted in the output pane. Here, the `plt.legend()` function is applied to get the explicit information about the number of stripes' flags onto the number of zone where the number of 1 zone means north-east(NE) zone's flags which are known as red dots and highlighted none stripes or 0 stripes flags in the broader area and indicating over 20000 square km in the following scatter plot. Although, green dots' flags that is number of 4 zone and which known as north-west(NW) zone's flags are also comparatively highlighted in the less-wider area but having the number of stripes at least 3 is more convenient which has been observed from the given scatter plot and found that relatively all zones are used 3 stripes flags than other number of stripes flags.Therefore, it may decide that the number of 3 stripes' flags will be more acceptable and readable to the respected users than the other number of stripes' flags. Finally, the `plt.grid()` and `plt.show()` functions are applied to demonstrate the grid and to show the scatter plot explicitly."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "id": "5dfe6fcc",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 576x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "new_flag.plot(kind='scatter',\n",
    "                   x='stripes', y='area',\n",
    "                   c=colors, figsize=(8,5))\n",
    "plt.title('stripes & area relation-Info')\n",
    "plt.legend('colors_palette')\n",
    "plt.xlabel('stripes')\n",
    "plt.ylabel('area')\n",
    "plt.grid()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "cae67ab6",
   "metadata": {},
   "source": [
    "### Relationship no.3"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a9472d3b",
   "metadata": {},
   "source": [
    "In the next step, the `value_counts()` function is used for the `religion` variable of the `new_flag` dataset. As an output, various numbers of the values have been found for the `religion` variable such as 0,1,2,3,4,5,6,7 and which are mentioned as 0=Catholic, 1=Other Christian, 2=Muslim, 3=Buddhist, 4=Hindu, 5=Ethnic, 6=Marxist, 7=Others."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "id": "4c68f96d",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "2    30\n",
       "0    29\n",
       "5    26\n",
       "1    22\n",
       "6    15\n",
       "3     8\n",
       "4     4\n",
       "7     4\n",
       "Name: religion, dtype: int64"
      ]
     },
     "execution_count": 45,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "new_flag['religion'].value_counts()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "da2737e0",
   "metadata": {},
   "source": [
    "In the next step, the `value_counts()` function is used for the `zone` variable of the `new_flag` dataset. As an output, few numbers of the values have been found for the `zone` variable such as 1,4,2,3 and which are mentioned as 1=NE, 2=SE, 3=SW, 4=NW."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "id": "82d4684f",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1    73\n",
       "4    34\n",
       "2    22\n",
       "3     9\n",
       "Name: zone, dtype: int64"
      ]
     },
     "execution_count": 46,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "new_flag['zone'].value_counts()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "762a663f",
   "metadata": {},
   "source": [
    "Here, the libraries numpy, matplotlib.pyplot and seaborn are imported as np, plt and sns respectively to explore the plot.To generate this bar plot, the `religion` and `zone` variables are subsetted with the `new_flag` dataset where both functions are made an object as `religion` and `zone` respectively. Here, the positions are also set as x-axis=0 and y-axis=1. After that, the `plt.figure()` and `plt.bar()` functions have been used to demonstrate the bar plot where figsize are 10 for x=axis and 5 for y-axis as well as the `color` argument is set as `gold`and width argument is set as 0.5 gradually. Here, the `plt.xlabel()`, `plt.ylabel()`, `plt.title()` functions have been used to get the xlabel as `religion`, ylabel as `zone` and the title of the barplot as `religion & zone relation-Info` respectively. Apart from this,the `plt.grid()` and `plt.show()` functions are applied to demonstrate the grid and to show the bar plot adequately whether it does not have any legend by default. From this barplot, it is represented that `3=Buddhist` and `7=Others` religions ranges are very lower than the rest of the religions. As a result, it is found from the `new_flag` dataset that `Buddist` and `other` religions are dwelling only the `1=NE` zone whereas rest of the religions are located all of the zones from `1=NE` to `4=NW` which are demonstrating in the following bar plot.  "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "id": "bd1e58c8",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "# Dataset generation\n",
    "religion=new_flag['religion']\n",
    "zone=new_flag['zone']\n",
    "positions=[0,1]\n",
    "fig = plt.figure(figsize = (10, 5))\n",
    "#  Bar plot\n",
    "plt.bar(religion, zone, color ='gold',\n",
    "        width = 0.5)\n",
    "plt.xlabel(\"religion\")\n",
    "plt.ylabel(\"zone\")\n",
    "plt.title(\"religion & zone relation-Info\")\n",
    "plt.grid()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "599dec30",
   "metadata": {},
   "source": [
    "### Relationship no.4"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "28b3fa3a",
   "metadata": {},
   "source": [
    "In the next step, the `value_counts()` function is used for the `religion` variable of the `new_flag` dataset. As an output, various numbers of the values have been found for the `religion` variable such as 0,1,2,3,4,5,6,7 and which are mentioned as 0=Catholic, 1=Other Christian, 2=Muslim, 3=Buddhist, 4=Hindu, 5=Ethnic, 6=Marxist, 7=Others."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "id": "53191957",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "2    30\n",
       "0    29\n",
       "5    26\n",
       "1    22\n",
       "6    15\n",
       "3     8\n",
       "4     4\n",
       "7     4\n",
       "Name: religion, dtype: int64"
      ]
     },
     "execution_count": 48,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "new_flag['religion'].value_counts()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c7cd2661",
   "metadata": {},
   "source": [
    "In the next step, the `value_counts()` function is used for the `zone` variable of the `new_flag` dataset. As an output, few numbers of the values have been found for the `zone` variable such as 1,4,2,3 and which are mentioned as 1=NE, 2=SE, 3=SW, 4=NW."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "id": "19b75de3",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1    73\n",
       "4    34\n",
       "2    22\n",
       "3     9\n",
       "Name: zone, dtype: int64"
      ]
     },
     "execution_count": 49,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "new_flag['zone'].value_counts()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d894d3b2",
   "metadata": {},
   "source": [
    "In the following scatter plot, the `plot()` has been used with the `new_flag` dataframe where `plot()` function is highlighting the kind of scatter and added with x='religion', y='population', c='colors', figsize=(8,5). Here, x and y is plotting the x-axis and y-axis with `religion` and `population` as well as `colors` and `figsize` are highlighting the different colors of `zone` and the different size of the plotting figure respectively. After that, the `plt.title()` function has been used to get the title name of the scatter plot which named as `religion & population relation-Info` and highlighted in the output pane. Apart from this, the scatter plot has shown the xlabel or x-axis and ylable or y-axis as `religion` and `population` using the `plt.xlabel()` and `plt.ylabel()` function respectively. Here, the `plt.legend()` function is applied to get the details information about the number of zone's religion where `6=Marxist` religion's population are more in the `1=NE` zone rather than other zones which is indicating around 1000 number of population and highlighting in the given scatter plot. Finally, the `plt.grid()` and `plt.show()` functions are applied to demonstrate the background as grid and to show the scatter plot explicitly."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "id": "76889460",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 576x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "new_flag.plot(kind='scatter',\n",
    "                   x='religion', y='population',\n",
    "                   c=colors, figsize=(8,5), fontsize=(15))\n",
    "plt.title('religion & population relation-Info')\n",
    "plt.legend('colors_palette')\n",
    "plt.xlabel('religion')\n",
    "plt.ylabel('population')\n",
    "plt.grid()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "8586f0f6",
   "metadata": {},
   "source": [
    "### Relationship no.5"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ae746c40",
   "metadata": {},
   "source": [
    "Here, the `value.counts()` function is applied for the `red` variable of the `new_flag` dataset and only two values are found from the output pane as the numbers of `1` and `0` which are mentioning if any `red` colour present, it will be `1` otherwise it will be `0`."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "id": "51eaf968",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1    109\n",
       "0     29\n",
       "Name: red, dtype: int64"
      ]
     },
     "execution_count": 51,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "new_flag['red'].value_counts()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "2d073c8e",
   "metadata": {},
   "source": [
    "Here, the libraries numpy, matplotlib.pyplot and seaborn are imported as np, plt and sns respectively to explore the plot.To create this bar plot, the `red` and `area` variables are subsetted with the `new_flag` dataset where both functions are made an object as `red` and `area` respectively. Here, the `positions` are also set as x-axis=0 and y-axis=1. After that, the `plt.figure()` and `plt.bar()` functions have been used to demonstrate the bar plot where figsize are 10 for x=axis and 5 for y-axis as well as the `color` argument is set as `red`and `width` argument is set as 0.5 gradually. Here, the `plt.xlabel()`, `plt.ylabel()`, `plt.title()` functions have been used to get the xlabel as `red`, ylabel as `area` and the title of the barplot as `red & area relation-Info` respectively. Apart from this, the `plt.grid()` and `plt.show()` functions are applied to demonstrate the background as grid and to show the bar plot adequately whether it does not have any legend by default. From this barplot, it is represented that maximum areas of the flag are highlighted `red` colour which are indicating over 20000 square km from the `area` values of the `new-flag` dataset and demonstrating in the following bar plot.  "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "id": "83e4d4eb",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 720x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "# Dataset generation\n",
    "red=new_flag['red']\n",
    "area=new_flag['area']\n",
    "positions=[0,1]\n",
    "fig = plt.figure(figsize = (10, 5))\n",
    "#  Bar plot\n",
    "plt.bar(red, area, color ='red',\n",
    "        width = 0.5)\n",
    "plt.xlabel(\"red\")\n",
    "plt.ylabel(\"area\")\n",
    "plt.title(\"red & area relation-Info\")\n",
    "plt.grid()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "7d1c5928",
   "metadata": {},
   "source": [
    "### Relationship no.6"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "2929f6ce",
   "metadata": {},
   "source": [
    "Here,`value.counts()` function has been used for the `botright` variable and different kinds of values have obtained from the output pane which are highlighting as red, green, blue, white, black, gold, orange, brown values and this values observation numbers are 50, 30, 28,  12, 9, 7, 1, 1 respectively."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "id": "0cbc082e",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "red       50\n",
       "green     30\n",
       "blue      28\n",
       "white     12\n",
       "black      9\n",
       "gold       7\n",
       "orange     1\n",
       "brown      1\n",
       "Name: botright, dtype: int64"
      ]
     },
     "execution_count": 53,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "new_flag['botright'].value_counts()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "1d4bcb90",
   "metadata": {},
   "source": [
    "According to the boxplot, it will give the information about the distribution of the data. Here, the boxplot is created using the `boxplot()` function to detect the outliers of the `botright` variable of the `new_flag` dataset as well as the boxplot is generated in terms of the `zone` by `botright` where the `zone` is plotting onto the `botright` and `eight` groups of colours have been formed with the black, blue, brown, gold, green, orange, red, and white using the groupby effectively. Apart from this, `dropna()` function has been used in the below step to double-check and to drop the missing values during the operation time. After using those above functions, it is found that few groups of colours such as `black`, `brown`, and `orange` are shown flat plot instead of boxplot because of their all number of values inconsistency with the other groups of colours' values which are highlighting in the following figure. In this case, `black` and `gold` colours groups are also highlighted their one outlier in the upper fence which indicates it's value ranges inconsistency compare to the other values of the colours and which is really uncountable. However, according to the median explanation, `blue`, `green`, and `red` colours group are demonstrated onto the more zones than the other groups of colours which is found from the following boxplots. Here, the `plt.title()`, `plt.xlabel()` and `plt.ylabel()` functions are applied to form the title `zone & botright_relation-Info`, x-axis `botright`' and y-axis`zone` whether it does not have any legend by default which are represented in the given boxplot."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "id": "c4703d13",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 576x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "new_flag.dropna().boxplot(column = 'zone', by = 'botright', figsize=(8,5), fontsize=(10))\n",
    "plt.title('zone & botright_relation-Info')\n",
    "plt.xlabel('botright')\n",
    "plt.ylabel('zone')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "937af3d9",
   "metadata": {},
   "source": [
    "### Relationship no.7"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b4f9fed2",
   "metadata": {},
   "source": [
    "Here, the `value.counts()` function is applied for the `icon` variable of the `new_flag` dataset and only two values are found from the output pane as the numbers of `0` and `1` which are mentioning `1` if an inanimate image present (e.g., a boat), otherwise it will be `0`."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "id": "b90c0cd9",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0    110\n",
       "1     28\n",
       "Name: icon, dtype: int64"
      ]
     },
     "execution_count": 55,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "new_flag['icon'].value_counts()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e1957723",
   "metadata": {},
   "source": [
    "Here, the libraries numpy and matplotlib.pyplot are imported as np and plt respectively to explore the plot.To create this bar plot, the `icon` and `area` variables are subsetted with the `new_flag` dataset where both functions are made an object as `icon` and `area` respectively. Here, the `positions` are also set as x-axis=0 and y-axis=1. After that, the `plt.figure()` and `plt.bar()` functions have been used to demonstrate the bar plot where figsize are 10 for x=axis and 5 for y-axis as well as the `color` argument is set as `orange`and `width` argument is set as 0.5 gradually. Here, the `plt.xlabel()`, `plt.ylabel()`, `plt.title()` functions have been used to get the xlabel as `icon`, ylabel as `area` and the title of the barplot as `red & area relation-Info` respectively. Apart from this, the `plt.grid()` and `plt.show()` functions are applied to demonstrate the background as grid and to show the bar plot adequately whether it does not have any legend by default. From this barplot, it is represented that maximum areas of the flags are highlighted `an inanimate image` which are indicating over 20000 square km from the `area` values of the `new-flag` dataset and demonstrating in the given bar plot.  "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "id": "bae488b7",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 720x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "# Dataset generation\n",
    "icon=new_flag['icon']\n",
    "area=new_flag['area']\n",
    "positions=[0,1]\n",
    "fig = plt.figure(figsize = (10, 5))\n",
    "#  Bar plot\n",
    "plt.bar(icon, area, color ='orange',\n",
    "        width = 0.5)\n",
    "plt.xlabel(\"icon\")\n",
    "plt.ylabel(\"area\")\n",
    "plt.title(\"icon & area relation_Info\")\n",
    "plt.grid()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a764583e",
   "metadata": {},
   "source": [
    "### Relationship no.8"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ff8f07cc",
   "metadata": {},
   "source": [
    "Here, the `value.counts()` function is applied for the `saltires` variable of the `new_flag` dataset and only two values are found from the output pane as the numbers of `0` and `1`."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "id": "cda831ec",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0    130\n",
       "1      8\n",
       "Name: saltires, dtype: int64"
      ]
     },
     "execution_count": 57,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "new_flag['saltires'].value_counts()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a81338ec",
   "metadata": {},
   "source": [
    "In the next step, the `value_counts()` function is used for the `zone` variable of the `new_flag` dataset. As an output, few numbers of the values have been found for the `zone` variable such as 1,4,2,3 and which are mentioned as 1=NE, 2=SE, 3=SW, 4=NW."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "id": "f5ce1188",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1    73\n",
       "4    34\n",
       "2    22\n",
       "3     9\n",
       "Name: zone, dtype: int64"
      ]
     },
     "execution_count": 58,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "new_flag['zone'].value_counts()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e61fe486",
   "metadata": {},
   "source": [
    "In the following scatter plot, the `plot()` has been used with the `new_flag` dataframe where `plot()` function is highlighting the kind of scatter and added with x='religion', y='population', c='colors', figsize=(8,5). Here, x and y is plotting the x-axis and y-axis with `saltires` and `area` as well as `colors` and `figsize` are highlighting the different colors of `zone` and the different size of the plotting figure respectively. After that, the `plt.title()` function has been used to get the title name of the scatter plot which named as `saltires & area relation-Info` and highlighted in the output pane. Apart from this, the scatter plot has shown the xlabel or x-axis and ylable or y-axis as `saltires` and `area` using the `plt.xlabel()` and `plt.ylabel()` function respectively. Here, the `plt.legend()` function is used to get the details information about the number of zone's saltires or diagonal crosses where red dot or `1=NE` zone's saltires or diagonal crosses are `0` which means no diagonal crosses are highlighted in the flags whereas the `blue` dot zone(`2=SE`) is represented in less `area` and indicated the `area` lower than 10000 square km by demonstrating at least one diagonal crosses in the particular areas flags and highlighting in the given scatter plot. Finally, the `plt.grid()` and `plt.show()` functions are used to demonstrate the background as grid and to show the scatter plot appropriately."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "id": "58a195f6",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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iJElSJYYISZJUiSFCkiRVYoiQJEmVGCIkSVIlhghJklSJIUKSJFViiJAkSZUYIiRJUiWGCEmSVIkhQpIkVWKIkCRJlRgiJElSJYYISZJUiSFCkiRVYoiQJEmVGCIkSVIlhghJklSJIUKSJFViiJAkSZU0NURExE4R8c2IuDMi1kVEWzd15kdEdnk82k29XSPiuohYGRGPRMQZETG8S52IiFMi4qGIWBURN0bE3lXakiRJmzaiye3vBrwRuBXYYhP1LgbOq3n+dO3GiJgMXAvcDRwCvAD4MkUIOrWm6snAp4ATgXnA8cC1EbF7Zj7ax7YkSdImNDtEXJaZPwWIiB8BUzZSb1Fm3rqJdt4PjAEOzcxlwDURMQGYExFfyMxlETGaIkSclZnnl33eAswHPsSGgNBjW/W8YEmShoqmXs7IzPUNauog4KouB/hLKMLAfuXzfYAJwKU1/a8ALiv370tbkiSpBwNlYeV7I+LpiHgyIn4UEc/vsn1nissTf5OZC4CV5bbOOuuA+7rse09Nnd62JUmSetDsyxm98VOKNRMLgV2A04D/jYg9MvPJss5kYGk3+7aX2zrrdGTmum7qjI2ILTLz6V62JUmSetDyEJGZH6l5+r8RcTPwf8BRwFdqq3aze3Qp31idrtt609aGDRHHAscCTJs2jba2tu6qVdLR0dHQ9oYi57B+zmH9nMPGcB7r159z2PIQ0VVm/jEi7gVeWlPcDkzqpvpENpxVaAfGR8TwLmcjJgErM3NNH9rqOqYLgAsAZs6cmbNmzer5hfRSW1sbjWxvKHIO6+cc1s85bAznsX79OYcDZU1Ed2rPCsyjy3qFiNgeGMeG9Q3zgOHATl3a6boGojdtSZKkHgy4EBERuwMvBu6oKb4CODAixteUHQ6sAm4on98MLAPeVtPWWODgcv++tCVJknrQ1MsZ5UH8jeXT5wITIuKw8vkvgP2BI4DLgUcozhCcCiwAvlvT1Fzgw8CPI+LzwI7AHOCczrdqZubqiDgb+FREtLPhZlPDeOaNrHpsS5Ik9azZayKmAj/sUtb5fAfgobLOVyjWKSwGrgROqT2gZ2Z7RMwGzqe478NS4FyKg3+tsylCwyeArYHbgddn5mMV2pIkSZvQ1BCRmfPZ8O6IjZndy7buBg7ooU4CZ5aPutqSJEmbNuDWREiSpMHBECFJkioxREiSpEoMEZIkqRJDhCRJqsQQIUmSKjFESJKkSgwRkiSpEkOEJEmqxBAhSZIqMURIkqRKDBGSJKkSQ4QkSarEECFJkioxREiSpEoMEZIkqRJDhCRJqsQQIUmSKjFESJKkSgwRkiSpEkOEJEmqxBAhSZIqMURIkqRKDBGSJKkSQ4QkSarEECFJkioZ0duKEbE7sCswurMsMy9sxqAkSdLA16sQERGnAbMoQsQvgIOAXwGGCEmShqjeXs44DJgNPJqZRwF7AaOaNipJkjTg9TZErMrM9cDaiJgA/BXYsXnDkiRJA11v10TcHhGTgH8H7gA6gNuaNShJkjTw9SpEZOYHy2/nRsSVwITM/H3zhiVJkga6Xl3OiMIREfHpzJwPLI2IVzR3aJIkaSDr7ZqIbwCvBt5ePl8OfL0pI5IkSYNCb9dEvDIzXxoRvwPIzPaI2KKJ45IkSQNcb89ErImI4UACRMQ2wPqmjUqSJA14vQ0RXwP+B5gaEWdS3Gjqc00blSRJGvB6vJwREcOAB4CTKG44FcBbM/OeJo9NkiQNYD2GiMxcHxFfzsxXA/P6YUySJGkQ6O3ljKsj4h8jIpo6GkmSNGj09t0ZxwPjKG57vZrikkZm5oSmjUySJA1ovb1j5fiI2Ap4ITUfBS5Jkoau3t6x8hjgBuBKYE759dO92G+niPhmRNwZEesioq2bOhERp0TEQxGxKiJujIi9u6m3a0RcFxErI+KRiDijfNtpU9qSJEmb1ts1ER8BXg48mJn7Ay8BnujFfrsBbwT+VD66czLwKeDzwMEUH+51bURs21khIiYD11Lcp+IQ4Azg48DpTWxLkiRtQm9DxOrMXA0QEaMycx7w4l7sd1lmbp+ZbwPu6roxIkZTHPjPyszzM/Na4G0UB/gP1VR9PzAGODQzr8nMuRQH/ePLjyZvaFuSJKlnvQ0RC8uPAv8JcE1E/BR4pKedMrOnu1ruA0wALq3ZZwVwGXBQTb2DgKsyc1lN2SUUYWC/JrTVdKvWrOL6B65n+dPLWb12dX91K0lSw/QqRGTmP2Tm0sycQ3G54NvAWxvQ/87AOuC+LuX3lNtq6z3jHhWZuQBYWVOvkW011eMrHme3b+zGWy95K/cvuZ89/m0Plqxa0h9dS5LUML09E/E3mXlDZv4sM59uQP+TgY7MXNelvB0YW/MhX5OBpd3s315ua3RbTXXSNSexcNlClj+9nPW5ngVLF3DytSf3R9eSJDVMb+8T0UzZTVl0s21j9XpTp0pbGzZEHAscCzBt2jTa2tq6q9Zrr1z7SnbfaXcApo+azud2+hxbPr1l3e0OVR0dHc5dnZzD+jmHjeE81q8/57DVIaIdGB8Rw7ucQZgErMzMNTX1JnWz/0Q2nFVoZFvPkJkXABcAzJw5M2fNmrWJl9Szy6++nG/85husWruKL73oS3zqL5/iI6/8CMfNOq6udoeqtrY26v2ZDHXOYf2cw8ZwHuvXn3PY58sZDTYPGA7s1KW867qFeXRZrxAR21PcRXNeTZ1GtdVUnz3gs3x0yYu59sLgRYvhY8t2Y86sOf3RtSRJDdPqEHEzsIzirZgARMRYins8XFFT7wrgwIgYX1N2OLCK4iZYjW6rqUZffT1nzv0Ts/+SjH8KzjzvLkZd3y9dS5LUME0NERExNiIOi4jDgOcC23Q+j4ix5b0nzgZOiYh/iYjZwA/LcZ1X09Rc4CngxxHxunKNwhzgnM63ajayrab78pe5b8xKTpsFi8bD/WNWwTnn9EvXkiQ1SrPXREylOJDX6ny+AzCf4sA/DPgEsDVwO/D6zHysc4fMbC9DwfkU931YCpxLcfCv1ci2mubOcct5zXGwagR8YUt4yXFwy7wOdu+vAUiS1ABNDRGZOZ8N747YWJ0Eziwfm6p3N3BAf7XVTKfODla0U8xMwIqR8On94cetGpAkSRW0ek3EkNQ+fuQzolUOgycnjGrdgCRJqsAQ0QJH7HkEY0eO/dvzsSPH8s4939nCEUmS1Hetvk/EkHTcy45j6eqlfPXWrzJy2EhOn3U6R+19VKuHJUlSn3gmogUigpP3PZlFJyxiz2l7csI+JxCxyaUjkiQNOIYISZJUiSFCkiRVYohomQQWAmvYyOd+SZI0oBkiWmIlsD/wQuAPwOuB1S0dkSRJfWWIaIlTgV9TBIek+NiPOa0ckCRJfWaIaIlbeOaZh1UUQUKSpMHDENESuwAja55vAezaorFIklSNIaIlvgg8HxhP8SPYATirpSOSJKmvvGNlS2xNsaDyNmAZcCfgZ2dIkgYXz0S0zGjg74AtMUBIkgYjQ4QkSarEECFJ0mZi6VLIfrx/oSFCkqRB7vHH4aUvhalT4Xe/g9NP759+DRGSJA1y73wn/OEPsGZNcSbii1+Eyy9vfr+GCEmSBrnbboO1azc8X7ECbrml+f0aIiRJGuS22+6Zz8eOhec9r/n9GiIkSRrkvvc9GD++eAwbBnvtBUcd1fx+vdmUJEmD3MtfDvfeCzfdBFtsATfeCCP64QjvmQhJkjYDz3kOHHYYTJjQPwECDBGSJKkiQ4QkSarEECFJkioxREiSpEoMEZIkqRJDhCRJqsQQIUmSKjFESJKkSgwRkiSpEkOEJEmqxBAhSZIqMURIkqRKDBGSJKkSQ4QkSarEECFJkioxREiSpEoMEZIkqRJDhCRJqsQQIUmSKjFESJKkSloeIiLiPRGR3TzeX1MnIuKUiHgoIlZFxI0RsXc3be0aEddFxMqIeCQizoiI4V3q9KotSZK0aSNaPYAaBwCrap7/peb7k4FPAScC84DjgWsjYvfMfBQgIiYD1wJ3A4cALwC+TBGUTu1LW5IkqWcDKUT8JjM7uhZGxGiKA/9ZmXl+WXYLMB/4EBsCwvuBMcChmbkMuCYiJgBzIuILmbmsD21JkqQetPxyRi/sA0wALu0syMwVwGXAQTX1DgKuKgNEp0sogsV+fWxLkiT1YCCFiD9HxNqIuDcijqsp3xlYB9zXpf495bbaevNqK2TmAmBlTb3etiVJknowEC5nLKJYo3AbMBx4OzA3IsZm5rnAZKAjM9d12a8dGBsRW2Tm02W9pd20315uow9tPUNEHAscCzBt2jTa2tr6/io3oqOjo6HtDUXOYf2cw/o5h43hPNavP+ew5SEiM68CrqopuiIiRgGnRsRXO6t1s2t0s21j9XpTZ2PbyMwLgAsAZs6cmbNmzequWiVtbW00sr2hyDmsn3NYP+ewMZzH+vXnHA6kyxm1fgRsBcygOEswvutbNYFJwMrMXFM+by/LuprIhjMUvW1LkiT1YKCGiE5Jsc5hOLBTl21d10DMo8u6hojYHhhXU6+3bUmSpB4M1BDxj8ATwIPAzcAy4G2dGyNiLHAwcEXNPlcAB0bE+JqywynuPXFD+by3bUmSpB60fE1ERPw3xaLK31OcJTi8fHw4M9cDqyPibOBTEdHOhhtEDQPOq2lqLvBh4McR8XlgR2AOcE7n2z4zs7dtSZKkHrQ8RAD3Au8FtqdY4Hg38O7MvKimztkUB/pPAFsDtwOvz8zHOitkZntEzAbOp7jvw1LgXIogQV/akiRJPWt5iMjMU4BTeqiTwJnlY1P17qa4fXbdbUmSpE0bqGsiJEnSAGeIkCRJlRgiJElSJYYISZJUiSFCkiRVYoiQJEmVGCIkSVIlhghJklRJy282NWStXAk33wyrVhWPMWNaPSJJkvrEMxGt8PjjsNtucOihcP/9sMcesHhxq0clSVKfGCJa4cQT4eGHYflyWL8eFiyAT3yi1aOSJKlPDBGtcO+9sGbNhudr1hRlkiQNIoaIVth332eugRgzBl7zmtaNR5KkCgwRrfCZz8BrXwujRkEE7L8/nHZaq0clSVKfGCJaYfQWcGXAgqdhz4SfD4NRI1s9KkmS+sQQ0RLvgrgKpmb5JtvLgaNbPCZJkvrGENESl3dT9tN+H4UkSfUwRLRCjoLPAdOA3wNfKMskSRpEDBGtMPctcCbwV2ANcDrwH4e2dkySJPWRIaIV5v4GVtY8XwnMva1Vo5EkqRJDRCssWvTssocf7v9xSJJUB0NEK0yf/uyy7bfv/3FIklQHQ0QrvOhFzy7beef+H4ckSXUwRLTA+gULnlWWDz7YgpFIklSdIaIF7h62lHWx4fnagLuivXUDkiSpAkNECxyz+yiWbwGrRsD6gGWj4H27jG71sCRJ6hNDRAs8sWYie3wATngDLJwAe3wAljw1odXDkiSpTwwRLfAf/9vBczrgOcth5Dp47nL41s0drR6WJEl9MqLVAxiK9n38z/z6W5DAjYfArd8Ctr6v1cOSJKlPDBEtMGzNGgA611YOAyjLJEkaLAwRrTBlCoxbAW8BpgDbABO2afGgJEnqG9dEtMC6Dx5E3gN8Bdge8m5Y98GDWzwqSdJglQlf/zrsuy/cfz/8/vf9068hogWe3PXfYQIwjuInMAnadzy/tYOSJA1aZ54JJ50EN90ETz4Jr3lNESaazRDRAqOmriNqLiTFCBi1zdrWDUiSNKh97WuwsubToVetgv/8z+b3a4hogftugp/+zyz2eONc7r7/efziZ6/lvptbPSpJ0mAV0buyRjNEtMCxP/173vq+ufzx2newatnWvOnouXzgyte1eliSpEHq+ONh7NgNz8eOhXe8o/n9+u6MFrhzwb9A+wxYPwpyGCx5Ab9/4F9aPSxJ0iB10knFG/8uvhgmT4Zbb4Udd2x+v56JaIH17TsWAeJvBaNY92Q//LQlSZulCDj6aLjuuiI87Lpr//RriGiBMavHUtyvslMyduW4Vg1HkqRKDBEtsPPqRWy4XyVAsPvqha0ajiRJlRgiWuD1477HaDa8F2c0K5k99qIWjkiSpL4zRLTAfk/fznqG03lJYz3D2Xf9Ha0dlCRpUFu1Cn77W3jqqeIOlv1hSIaIiNg1Iq6LiJUR8UhEnBERw/ur/3PWnM7TjKLzksbTjOS81Z/ur+4lSZuZP9y7gskfOJSX/c8Y/vjEnezzge+xfn3z+x1yISIiJgPXUpwGOAQ4A/g4cHp/jaGDCV1KhrGC8f3VvSRpMzPrnKN5avovYMRqGLaWW6d8gNP+48am9zvkQgTwfmAMcGhmXpOZcykCxPER0fXo3hRHjfwOY1nxt+djWcF7tvhuf3QtSdoMLdnqahj51IaCEau5/sGrm97vUAwRBwFXZeaymrJLKILFfv0xgKNmfoez+ATPZz5b8DRf5ASOeOUP+qNrSdJmaNjTXc5mrx3F1mMnNr/fpvcw8OwMzKstyMwFwMpyW9Otewt8eOp5zB+1A3vEH/jgtnN5+g390bMkaXM0qSNhzWhYu0VxJ+TVE4jf/K7p/Ub21xLOASIi1gAnZuZXupQvBC7MzFO62edY4FiAadOmveySSy6pawzL776D8c8D1kBHTGfLEQtZtgAm7Pqyutodqjo6Othyyy1bPYxBzTmsn3PYGM5jNf/38B2si4D1I5g+dlsWrn6IiavGsdML6v/beP/9978jM2d2t22ofnZGd8kpNlJOZl4AXAAwc+bMnDVrVl2dv/f0wznprr8y4zlwy3FfYpvTT+CiXbbl222L6mp3qGpra6Pen8lQ5xzWzzlsDOexmk++61B+M6OdNSPgSy/6Ep++6wQ+ufwUjjn6/U3tdyhezmgHJnVTPhFY2h8DOPXc63jP6yZywEy4dxwcu/9kPvPvfha4JKma73/0BvZYNIrh6yES3n3vqzjlq59ter9D8UzEPLqsfYiI7YFxdFkr0Sw77r07t3y/nVt/ejlLhiW/ungxMawfPvhdkrRZ2uFle3DH3A5W3T+PXy9cxPE/+ni/9DsUz0RcARwYEbVLWQ8HVgE39NcgYljw6n84mHETJxggJEn1GzGCMTvvDiNG9luXQzFEzAWeAn4cEa8rF03OAc7p8rZPSZK0CUPuckZmtkfEbOB84DKKdRDnUgQJSZLUS0MuRABk5t3AAa0ehyRJg9lQvJwhSZIawBAhSZIqMURIkqRKDBGSJKkSQ4QkSapkyH0AV70i4nHgwQY2OQV4ooHtDUXOYf2cw/o5h43hPNav0XP4/MzcprsNhogWi4jbN/bpaOod57B+zmH9nMPGcB7r159z6OUMSZJUiSFCkiRVYohovQtaPYDNgHNYP+ewfs5hYziP9eu3OXRNhCRJqsQzEZIkqRJDRJNExK4RcV1ErIyIRyLijIgY3ov9JkbEdyKiPSKejIgfRMTW/THmgabKHEbEy8v5u7/c796IOC0iRvfXuAeSqr+HNfsPi4g7IiIj4s3NHOtAVs88RsShEfGbiFgVEYsj4sqIGNfsMQ80dfyfODMiri7nbklEXBsRr+yPMQ80EbFTRHwzIu6MiHUR0dbL/Zp2XBmSn+LZbBExGbgWuBs4BHgB8GWK0HZqD7v/F/Bi4BhgPfB54CfAa5s03AGpjjk8vKz7eeA+YE/gM+XXf2zikAecOn8POx0DPLcpAxwk6pnHiDgGOB/4AnAiMJniE4SH1P+9VecwIrYv9/st8O6y+ETg6ojYMzMbec+ewWA34I3ArcAWfdiveceVzPTR4AfwCaAdmFBTdhKwsrasm/1eDSTwdzVlryjLXtfq1zVI5nCbbsqOLefw+a1+XYNhDmvqTgYeB44u5+/NrX5Ng2keKW74sxx4X6tfQ6sfdczh+4F1wKSassll2Qda/bpaMI/Dar7/EdDWi32aelzxckZzHARclZnLasouAcYA+/Ww32OZeWNnQWbeBjxQbhtKKs1hZj7eTfHvyq9TGze8QaHq72GnzwA3Adc1YWyDSdV5/Kfy6/eaNbBBpOocjgTWAh01ZR1lWTR6kANdZq6vsFtTjyuGiObYGZhXW5CZCyhS98592a90Tw/7bY6qzmF39qE4hXdvY4Y2aFSew4jYEzgKOKFpoxs8qs7jKyl+546OiIURsSYifh0R+zRvqANW1Tn877LOlyNiakRMBc6lOKvxwyaNdXPT1OOKIaI5JgNLuylvL7c1er/NUUPmIiK2BT4JXNTlr6ChoJ45PA/4embe3+hBDUJV53FbiuvQpwL/ChwMrACujIhpDR7jQFdpDjPzEWB/ivVMj5WPQ4EDN3LWUc/W1OOKIaJ5ursBR2ykvBH7bY7qmouI2AK4lOL058caOK7BpM9zGBH/THHw+2yzBjUIVfldHAZsCRydmT/IzCuBt1Jcz/9Qw0c48FX5XXwOxbX/OyhOvR9Ufv/ziHheMwa5mWraccUQ0RztwKRuyifSfSLsab9JPey3Oao6hwBERAAXUq5mzsz2Rg5ukOjzHEbESOCLFKu3h0XEJGBCuXlcRIxv+CgHvqq/i0vKr22dBeXZsDuAXRsztEGj6hyeSPFOlsMy88oyiP0jRRDzUlvvNPW4Yohojnl0udZUvlVpHN1fm9rofqWNXdPanFWdw07nUryV7JDMHGpz16nKHI4DpgPnUPzn0w7cWW67hA2LVIeSqr+L91D8pdd1AWBQrNEZSqrO4c7AXZm5prMgM58G7qJ4m6h61tTjiiGiOa4ADuzyV9vhwCrghh722zYi9u0siIiZwI7ltqGk6hwSEZ8A/h9wRGb+qnlDHPCqzGEHxTXo2sfby22nAO9szlAHtKq/i5dTBIb9OwsiYiLwMjYEs6Gi6hw+COxeXpoEICJGAbsD85swzs1Rc48rrX7f6+b4oFissgi4BngdxX0KOoDPdql3P/DtLmVXAn+hWDz0VorV3f/b6tc0WOYQeAfFX3/fAV7V5fGse0hszo96fg+7bJ/B0L5PRD3/nn9S7nsk8CaKA+bjwORWv67BMIcUgWsN8PNy/t5MceBbA+zV6tfVgnkcCxxWPm6hOCPT+Xxsd3NYljXtuNLySdlcHxTXPK+nSNqLKN5zP7xLnfnAd7uUTSoPgEuBZcDFwJRWv57BMofAd8sDXneP97T6NQ2GOeymjSEdIuqZR4qFlf8GLC73vRbYo9WvZ5DN4WzgRoo1JksogtisVr+eFs1h57/F7h4zNjGHTTuu+CmekiSpEtdESJKkSgwRkiSpEkOEJEmqxBAhSZIqMURIkqRKDBGSJKkSQ4SkASMivhsRh5XffzQixtZs+0X5WR6SBghDhKSB6qMUd+gDIDPfmJlLaytEwf/HpBbxH5+kpoqIcRHx84i4MyL+GBGHR8SnI+I35fMLyk9drd3nw8B2wC8j4pdl2fyImBIRMyLinoj4BvBbYPuIOLFs7/cRcfrG+u3v1y5t7gwRkprt74FHMnOvzNyd4j7+52fmy8vnYyg+E+FvMvNrwCPA/pm5/7NahBcDF2bmS8rvXwi8AtgbeFlE/N1G+pXUQIYISc32B+B1EfH5iHhtZj4J7B8Rv46IPwAHALv1sc0HM/PW8vs3lI/fUZyZ2JkiVHTXr6QGGtHqAUjavGXmnyLiZcAbgbMi4mrgX4CZmflQRMwBRvex2RU13wdwVmZ+s2ulrv1m5hmVXoSkbnkmQlJTRcR2wMrM/D7wJeCl5aYnImJLio8x7s5yYHwvurgKeG/ZFhHx3IiYuol+JTWIZyIkNdsewBcjYj2wBvgA8FaKyw3zgd9sZL8LgCsiYtFG1kUAkJlXR8QuwC3l+swO4Ahgp276ldRAfhS4JEmqxMsZkiSpEkOEJEmqxBAhSZIqMURIkqRKDBGSJKkSQ4QkSarEECFJkioxREiSpEr+P7lh9m8NpzFnAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 576x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "new_flag.plot(kind='scatter',\n",
    "                   x='saltires', y='area',\n",
    "                   c=colors, figsize=(8,5), fontsize=(15))\n",
    "plt.title('saltires & area relation-Info')\n",
    "plt.legend('colors_palette')\n",
    "plt.xlabel('saltires')\n",
    "plt.ylabel('area')\n",
    "plt.grid()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "5efbab9f",
   "metadata": {},
   "source": [
    "### Relationship no. 9"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f7b3b908",
   "metadata": {},
   "source": [
    "Here, the `value.counts()` function is applied for the `animate` variable of the `new_flag` dataset and only two values are found from the output pane as the numbers of `0` and `1` which are mentioning `1` if an animate image (e.g., an eagle, a tree, a human hand) present, otherwise it will be `0`."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "id": "d442c9ec",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0    117\n",
       "1     21\n",
       "Name: animate, dtype: int64"
      ]
     },
     "execution_count": 60,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "new_flag['animate'].value_counts()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a281b6f9",
   "metadata": {},
   "source": [
    "Here, the libraries numpy and matplotlib.pyplot are imported as np and plt respectively to explore the plot.To create this bar plot, the `animate` and `area` variables are subsetted with the `new_flag` dataset where both functions are made an object as `animate` and `area` respectively. Here, the `positions` are also set as x-axis=0 and y-axis=1. After that, the `plt.figure()` and `plt.bar()` functions have been used to demonstrate the bar plot where figsize are 10 for x=axis and 5 for y-axis as well as the `color` argument is set as `red`and `width` argument is set as 0.5 gradually. Here, the `plt.xlabel()`, `plt.ylabel()`, `plt.title()` functions have been used to get the xlabel as `animate`, ylabel as `area` and the title of the barplot as `animate & area relation-Info` respectively. Apart from this, the `plt.grid()` and `plt.show()` functions are applied to demonstrate the background as grid and to show the bar plot adequately whether it does not have any legend by default. From this barplot, it is found that maximum areas of the flags are highlighted non animate image which means `0` values of the `animate` variable and which are indicating over 20000 square km from the `area` values of the `new-flag` dataset and demonstrating in the following bar plot.  "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "id": "e811419e",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "# Dataset generation\n",
    "religion=new_flag['animate']\n",
    "zone=new_flag['area']\n",
    "positions=[0,1]\n",
    "fig = plt.figure(figsize = (10, 5))\n",
    "#  Bar plot\n",
    "plt.bar(religion, zone, color ='red',\n",
    "        width = 0.5)\n",
    "plt.xlabel(\"animate\")\n",
    "plt.ylabel(\"area\")\n",
    "plt.title(\"animate & area relation_Info\")\n",
    "plt.grid()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "891e2396",
   "metadata": {},
   "source": [
    "### Relationship no.10"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "77b21bef",
   "metadata": {},
   "source": [
    "Here,the `value.counts()` function has been used for the `circles` variable and different kinds of number of values have obtained from the output pane which are highlighting as 0,1,2,4."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "id": "9fec860f",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0    119\n",
       "1     17\n",
       "2      1\n",
       "4      1\n",
       "Name: circles, dtype: int64"
      ]
     },
     "execution_count": 62,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "new_flag['circles'].value_counts()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c28e7a72",
   "metadata": {},
   "source": [
    "In the next step, the `value_counts()` function is used for the `zone` variable of the `new_flag` dataset. As an output, few numbers of the values have been found for the `zone` variable such as 1,4,2,3 and which are mentioned as 1=NE, 2=SE, 3=SW, 4=NW."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "id": "7bde15dc",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1    73\n",
       "4    34\n",
       "2    22\n",
       "3     9\n",
       "Name: zone, dtype: int64"
      ]
     },
     "execution_count": 63,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "new_flag['zone'].value_counts()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f77b4c11",
   "metadata": {},
   "source": [
    "Here, the libraries numpy and matplotlib.pyplot are imported as np and plt respectively to explore the plot.To generate this bar plot, the `zone` and `circles` variables are subsetted with the `new_flag` dataset where both functions are made an object as `zone` and `circles` respectively. Here, the `positions` are also set as x-axis=0 and y-axis=1. After that, the `plt.figure()` and `plt.bar()` functions have been used to demonstrate the bar plot where figsize are 10 for x=axis and 5 for y-axis as well as the `color` argument is set as `purple`and `width` argument is set as 0.5 gradually. Here, the `plt.xlabel()`, `plt.ylabel()`, `plt.title()` functions have been used to get the xlabel as `zone`, ylabel as `circles` and the title of the barplot as `zone & circles relation-Info` respectively. Apart from this, the `plt.grid()` and `plt.show()` functions are applied to demonstrate the background as grid and to show the bar plot adequately whether it does not have any legend by default. From this barplot, it is found that number `1=NE` zone flags is demonstrated maximum circles rather than other zones which is represented in the given bar plot. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "id": "65c21719",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "# Dataset generation\n",
    "zone=new_flag['zone']\n",
    "circles=new_flag['circles']\n",
    "positions=[0,1]\n",
    "fig = plt.figure(figsize = (10, 5))\n",
    "#  Bar plot\n",
    "plt.bar(zone, circles, color ='purple',\n",
    "        width = 0.5)\n",
    "plt.xlabel(\"zone\")\n",
    "plt.ylabel(\"circles\")\n",
    "plt.title(\"zone & circles relation_Info\")\n",
    "plt.grid()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "5410db27",
   "metadata": {},
   "source": [
    "### Relationship no.11"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "8917d7a0",
   "metadata": {},
   "source": [
    "To create this heatmap chart, library seaborn has been imported as sns. The `plt.figure()` function has been used to get the accurate figure. In this given chart, peers & correlation coefficient relationship has been made with all numerical variables where all variables are relate with diagonally. Here, the deep red colour box are highlighted strong relationship with each variable and dark blue colour box are represented weak relation with each other."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "id": "dd695f0f",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x720 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "import seaborn as sns\n",
    "plt.figure(figsize=(12,10))\n",
    "chart = sns.heatmap(new_flag.corr(), vmin=-0.5, vmax=0.5,annot=True, fmt='.1f', linewidths=.5, cmap=\"coolwarm\")\n",
    "# heatmap chart"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ce83504f",
   "metadata": {},
   "source": [
    "## Task 3(subtask 1): Data Modelling"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "id": "179872c7",
   "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>name</th>\n",
       "      <th>landmass</th>\n",
       "      <th>zone</th>\n",
       "      <th>area</th>\n",
       "      <th>population</th>\n",
       "      <th>language</th>\n",
       "      <th>religion</th>\n",
       "      <th>bars</th>\n",
       "      <th>stripes</th>\n",
       "      <th>colours</th>\n",
       "      <th>...</th>\n",
       "      <th>saltires</th>\n",
       "      <th>quarters</th>\n",
       "      <th>sunstars</th>\n",
       "      <th>crescent</th>\n",
       "      <th>triangle</th>\n",
       "      <th>icon</th>\n",
       "      <th>animate</th>\n",
       "      <th>text</th>\n",
       "      <th>topleft</th>\n",
       "      <th>botright</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Afghanistan</td>\n",
       "      <td>5</td>\n",
       "      <td>1</td>\n",
       "      <td>648</td>\n",
       "      <td>16</td>\n",
       "      <td>10</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>5</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>black</td>\n",
       "      <td>green</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Albania</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>29</td>\n",
       "      <td>3</td>\n",
       "      <td>6</td>\n",
       "      <td>6</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>red</td>\n",
       "      <td>red</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Algeria</td>\n",
       "      <td>4</td>\n",
       "      <td>1</td>\n",
       "      <td>2388</td>\n",
       "      <td>20</td>\n",
       "      <td>8</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>green</td>\n",
       "      <td>white</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>Angola</td>\n",
       "      <td>4</td>\n",
       "      <td>2</td>\n",
       "      <td>1247</td>\n",
       "      <td>7</td>\n",
       "      <td>10</td>\n",
       "      <td>5</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>3</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>red</td>\n",
       "      <td>black</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>Argentina</td>\n",
       "      <td>2</td>\n",
       "      <td>3</td>\n",
       "      <td>2777</td>\n",
       "      <td>28</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>2</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>blue</td>\n",
       "      <td>blue</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 30 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "          name  landmass  zone  area  population  language  religion  bars  \\\n",
       "0  Afghanistan         5     1   648          16        10         2     0   \n",
       "1      Albania         3     1    29           3         6         6     0   \n",
       "2      Algeria         4     1  2388          20         8         2     2   \n",
       "5       Angola         4     2  1247           7        10         5     0   \n",
       "8    Argentina         2     3  2777          28         2         0     0   \n",
       "\n",
       "   stripes  colours  ...  saltires  quarters  sunstars  crescent  triangle  \\\n",
       "0        3        5  ...         0         0         1         0         0   \n",
       "1        0        3  ...         0         0         1         0         0   \n",
       "2        0        3  ...         0         0         1         1         0   \n",
       "5        2        3  ...         0         0         1         0         0   \n",
       "8        3        2  ...         0         0         0         0         0   \n",
       "\n",
       "   icon  animate text  topleft  botright  \n",
       "0     1        0    0    black     green  \n",
       "1     0        1    0      red       red  \n",
       "2     0        0    0    green     white  \n",
       "5     1        0    0      red     black  \n",
       "8     0        0    0     blue      blue  \n",
       "\n",
       "[5 rows x 30 columns]"
      ]
     },
     "execution_count": 66,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "new_flag.dropna(inplace=True)\n",
    "new_flag.head() # To create the model the `new-flag` dataset has been selected and checked few rows of this dataset using `head()`\n",
    "# function. Apart from this `dropna()` function is used to drop the missing values again if there is any here."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a02acfaa",
   "metadata": {},
   "source": [
    "# Encoding the categorical data"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "092e57b5",
   "metadata": {},
   "source": [
    "Here, two models have been chosen to do the classification algorithm. One is `KNN(KNearestNeighbors)` and another one is `Decision tree` model. To make these models, the encoding has been done for categorical variables of the `new_flag` dataset using the following functions. Apart from this, the library `One HotEncoder` is imported from `sklearn.preproceesing`package. It is highlighted that all categorical variables have been encoded as the numerical variables using the `astype().cat.codes` function and checked the dataset again applying `head()` function which are demonstrated in the output pane."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "id": "201195fd",
   "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>name</th>\n",
       "      <th>landmass</th>\n",
       "      <th>zone</th>\n",
       "      <th>area</th>\n",
       "      <th>population</th>\n",
       "      <th>language</th>\n",
       "      <th>religion</th>\n",
       "      <th>bars</th>\n",
       "      <th>stripes</th>\n",
       "      <th>colours</th>\n",
       "      <th>...</th>\n",
       "      <th>saltires</th>\n",
       "      <th>quarters</th>\n",
       "      <th>sunstars</th>\n",
       "      <th>crescent</th>\n",
       "      <th>triangle</th>\n",
       "      <th>icon</th>\n",
       "      <th>animate</th>\n",
       "      <th>text</th>\n",
       "      <th>topleft</th>\n",
       "      <th>botright</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0</td>\n",
       "      <td>5</td>\n",
       "      <td>1</td>\n",
       "      <td>648</td>\n",
       "      <td>16</td>\n",
       "      <td>10</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>5</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>29</td>\n",
       "      <td>3</td>\n",
       "      <td>6</td>\n",
       "      <td>6</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>5</td>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2</td>\n",
       "      <td>4</td>\n",
       "      <td>1</td>\n",
       "      <td>2388</td>\n",
       "      <td>20</td>\n",
       "      <td>8</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>3</td>\n",
       "      <td>4</td>\n",
       "      <td>2</td>\n",
       "      <td>1247</td>\n",
       "      <td>7</td>\n",
       "      <td>10</td>\n",
       "      <td>5</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>3</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>5</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>4</td>\n",
       "      <td>2</td>\n",
       "      <td>3</td>\n",
       "      <td>2777</td>\n",
       "      <td>28</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>2</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 30 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "   name  landmass  zone  area  population  language  religion  bars  stripes  \\\n",
       "0     0         5     1   648          16        10         2     0        3   \n",
       "1     1         3     1    29           3         6         6     0        0   \n",
       "2     2         4     1  2388          20         8         2     2        0   \n",
       "5     3         4     2  1247           7        10         5     0        2   \n",
       "8     4         2     3  2777          28         2         0     0        3   \n",
       "\n",
       "   colours  ...  saltires  quarters  sunstars  crescent  triangle  icon  \\\n",
       "0        5  ...         0         0         1         0         0     1   \n",
       "1        3  ...         0         0         1         0         0     0   \n",
       "2        3  ...         0         0         1         1         0     0   \n",
       "5        3  ...         0         0         1         0         0     1   \n",
       "8        2  ...         0         0         0         0         0     0   \n",
       "\n",
       "   animate  text  topleft  botright  \n",
       "0        0     0        0         4  \n",
       "1        1     0        5         6  \n",
       "2        0     0        3         7  \n",
       "5        0     0        5         0  \n",
       "8        0     0        1         1  \n",
       "\n",
       "[5 rows x 30 columns]"
      ]
     },
     "execution_count": 67,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.preprocessing import OneHotEncoder\n",
    "new_flag.name=new_flag.name.astype('category').cat.codes\n",
    "new_flag.mainhue=new_flag.mainhue.astype('category').cat.codes\n",
    "new_flag.topleft=new_flag.topleft.astype('category').cat.codes\n",
    "new_flag.botright=new_flag.botright.astype('category').cat.codes\n",
    "new_flag.head() "
   ]
  },
  {
   "cell_type": "markdown",
   "id": "be27e70b",
   "metadata": {},
   "source": [
    "# Task 3(Subtask 2): Model Training"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "9be15e48",
   "metadata": {},
   "source": [
    "## Generating Train/Test set: For KNN Model"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "555c63e9",
   "metadata": {},
   "source": [
    "## Loaded packages"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "1b77c7ec",
   "metadata": {},
   "source": [
    "To train model, the `train_test_split` library has been imported from the `sklearn.model_selection` package. Apart from this, the convenience plotting library `pylab` is imported as `plt`. Here, the 'train' set will be used to train the classifier, and the 'test' set will be used for evaluation purposes."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "id": "7df072c2",
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.model_selection import train_test_split\n",
    "import pylab as plt"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "277c998a",
   "metadata": {},
   "source": [
    "## Splitted the data into a training set and the testing set where splitted the data 50% for training and 50% for testing"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "5f9b9d04",
   "metadata": {},
   "source": [
    "In the following step,\n",
    "\n",
    "X_train includes all independent variables which will be used to train the model, also as it specified the test size=0.50, this means 50% of observations from the complete data will be used to train/fit the model and rest 50% will be used to test the model.\n",
    "\n",
    "X_test is remaining 50% portion of the independent variables from the data which will not be used in the training phase and will be used to make predictions to test the accuracy of the model.\n",
    "\n",
    "y_train includes the dependent variable which needs to be predicted by this model. Apart from this, it has category labels against the independent variables. It needs to be specified the dependent variable while training or fitting the modl.\n",
    "\n",
    "y_test has category label for the test data which will be used to test the accuracy between actual and prediction categories.\n",
    "\n",
    "Here, random_state is showing reproducible behaviour which is ensured that those parameters are set exact the same from one run to next. If the random state is not used here, the train and test value will come different in every single time."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "id": "5e97c3e4",
   "metadata": {},
   "outputs": [],
   "source": [
    "X_train, X_test, y_train, y_test = train_test_split(new_flag,\n",
    "                                                    new_flag.mainhue,\n",
    "                                                    test_size=0.50,\n",
    "                                                    random_state=4)\n",
    "\n",
    "# Here, this `train_test_split()` function returns the four things from four variables such as `X_train`, `X_test`, `y_train`, \n",
    "# and `y_test` and passing into a sign that has correct value for it. Here, target variables has been set as `mainhue` to \n",
    "# classify the predominant colours of the flag from the `new_flag` dataset."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 70,
   "id": "2993ceea",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Training dataset size: 69\n"
     ]
    }
   ],
   "source": [
    "print(f'Training dataset size: {X_train.shape[0]}') # Here, checked the `X_train` varaibles' shape using the `print()` function."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 71,
   "id": "8c7dc51a",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Testing dataset size: 69\n"
     ]
    }
   ],
   "source": [
    "print(f'Testing dataset size: {X_test.shape[0]}') # Here, checked the `X_test` varaibles' shape using the `print()` function."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a12a807d",
   "metadata": {},
   "source": [
    "# Task 3 (Subtask 3): Model Evaluation"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "91d6a4b8",
   "metadata": {},
   "source": [
    "## Selected a k-Nearest Neighbor Value"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "9f6d24c5",
   "metadata": {},
   "source": [
    "To select the KNearestNeighbors and evaluate the model adequately, the `KNeighborsClassifier` library has been imported from the `sklearn.neighbors` package. Here, the closest neighbors numbers have been chosen as `8` to obtained the accuracy of the model. As low accuracy had shown with `less` nearest neighbors or less K-value, hence, the neighbors numbers have been increased to get the better accuracy of this model. During this operation, the `KNeighborsClassifier()` function has been used and `clf` is generated as object to explore the KNearestNeighbors value. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 72,
   "id": "993ec2e2",
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.neighbors import KNeighborsClassifier\n",
    "clf = KNeighborsClassifier(8) "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 73,
   "id": "0cd1e2b9",
   "metadata": {},
   "outputs": [],
   "source": [
    "fit = clf.fit(X_train, y_train) # Fitted the data using the '.fit()' function and added with the `clf` object to form the `fit` object appropriately."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 74,
   "id": "75dd9db0",
   "metadata": {},
   "outputs": [],
   "source": [
    "predicted = fit.predict(X_test) #Predicted on unseen data using the `predict()` function which is added with the `fit` object \n",
    "# and also created another `object` as `predicted`."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "81c84977",
   "metadata": {},
   "source": [
    "To find the classification report, the `classification_report ` library is imported from the `sklearn.metrics ` package and the `classification_report()`function has been applied to get the explicit report."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 75,
   "id": "e6a76bf6",
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.metrics import classification_report \n",
    "report=classification_report(y_test, predicted)\n",
    "import warnings\n",
    "warnings.filterwarnings('ignore') # As the error message is highlighted here, the `warnings` is imported and error message \n",
    "# has been removed using the `filterwarnings('ignore')` function."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c3755208",
   "metadata": {},
   "source": [
    "### Testing the accuracy using the Confusion Matrix"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "666b3695",
   "metadata": {},
   "source": [
    "To form the `confusion matrix`, the `confusion_matrix` library has been imported from the `sklearn.metrics` package. Here, the `confusion_matrix()` function is applied with the actual values'y_test' and predicted values as `predicted` to perform the measurement for the model classification and to calculate the Recall, Precision and accuracy. As a result, more than two classes are demonstrated in the output pane."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 76,
   "id": "1cea70b0",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[ 0,  1,  0,  0,  1,  0,  2,  0],\n",
       "       [ 0,  4,  0,  0,  0,  0,  7,  0],\n",
       "       [ 0,  1,  0,  0,  0,  0,  0,  0],\n",
       "       [ 0,  1,  0,  1,  0,  0,  3,  0],\n",
       "       [ 0,  1,  0,  0,  2,  0,  7,  0],\n",
       "       [ 0,  1,  0,  0,  0,  0,  1,  0],\n",
       "       [ 0,  9,  0,  2,  1,  0, 17,  0],\n",
       "       [ 0,  1,  0,  0,  0,  0,  6,  0]], dtype=int64)"
      ]
     },
     "execution_count": 76,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.metrics import confusion_matrix\n",
    "confusion_matrix(y_test, predicted) "
   ]
  },
  {
   "cell_type": "markdown",
   "id": "2eca6799",
   "metadata": {},
   "source": [
    "## Classification Error Rate"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "740cf9b8",
   "metadata": {},
   "source": [
    "Here, to get the best Nearest Neighbors, for loop has been used with the range of number from 1 to 18 using `i in range()` \n",
    "function. With the for loop, it has been found some result using the range, K=1 to 18 which is shown in the next plot.Apart from this, the `append()` function is used with the error_rate which is highlighting in the next step."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 77,
   "id": "15829b99",
   "metadata": {},
   "outputs": [],
   "source": [
    "error_rate = []\n",
    "for i in range(1, 18):\n",
    " \n",
    " knn = KNeighborsClassifier(n_neighbors=i)\n",
    " knn.fit(X_train,y_train)\n",
    " pred_i = knn.predict(X_test)\n",
    " error_rate.append(np.mean(pred_i != y_test))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "be285a22",
   "metadata": {},
   "source": [
    "To get the `Elbow` shape from this graph, Elbow method has been applied using the `plt.plot()` function where `plot()` function is highlighting the specific range from 1 to 18 with the `range()` function, color='blue', linestyle='dashed', marker='0',markerfacecolor='red', markersize=10. Here, the `plt.figure()`, `plt.title()`, `plt.xlabel()`,\n",
    "`plt.ylabel()`, `plt.xticks()` functions are used to explore the figure adequately by highlighting the title as `Elboe Method` x-axis as `Number of clusters` and y-axis `Error rate`respectively. Finally, the `plt.show()` function has been used to represents the plot appropriately. From this given plot, it is highlighted that the first error rate is found as `0.77` from the `first KNearestNeighbor` or `cluster 1`. But, however, as the 'K' has been increased and the best KNearestNeighbors have been taken as `8`, hence the classifcation error rate has decreased dramatically from `0.84` to `0.66` which is demonstrating in the following plot. Therefore, in terms of elbow position, the most decreasing error rate is `0.66` which is represented by the best KNearestNeighbors value `8`."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 78,
   "id": "19528835",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(12,6))\n",
    "plt.plot(range(1, 18),error_rate,color='blue', linestyle='dashed', marker='o',\n",
    " markerfacecolor='red', markersize=10)\n",
    "plt.title('Elbow Method')\n",
    "plt.xlabel('Number of clusters')\n",
    "plt.ylabel('Error rate')\n",
    "plt.xticks(np.arange(0, 18, step=1)) \n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "8b158ffb",
   "metadata": {},
   "source": [
    "To get the accuracy correctly, the `accuracy_score` library has been imported from the `sklearn.metrics` package and applied \n",
    "the `accuracy_score()` function to measure the accuracy appropriately. In the following output pane, the accuracy result has been obtained as `0.34782608695652173` using the `print()` function."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 79,
   "id": "d295ab12",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.34782608695652173\n"
     ]
    }
   ],
   "source": [
    "from sklearn.metrics import accuracy_score\n",
    "accuracy = accuracy_score(y_test,predicted)\n",
    "print(accuracy)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 80,
   "id": "e5627231",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.6521739130434783\n"
     ]
    }
   ],
   "source": [
    "classification_error_rate=1-accuracy\n",
    "print(classification_error_rate)\n",
    "# In this step, the classification error rate is obtained by subtracting the accuracy result with `1` and the result has been \n",
    "# found as `0.6521739130434783` in the below output pane. "
   ]
  },
  {
   "cell_type": "markdown",
   "id": "6c42ee81",
   "metadata": {},
   "source": [
    "### Precision, Recall and F1-score"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "9a3147ba",
   "metadata": {},
   "source": [
    "Here, the `Precision` is mentioned as the predict value and the `Recall` is for actual values. As it is difficult to compare two models with low precision and high recall values, therefore, f1-score helps 'to measure the recall and precision using the Harmonic Mean in place of Arithmatic Mean' which has been highlighted in the output pane using the `print()` function."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 81,
   "id": "039a4b14",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       0.00      0.00      0.00         4\n",
      "           1       0.21      0.36      0.27        11\n",
      "           2       0.00      0.00      0.00         1\n",
      "           3       0.33      0.20      0.25         5\n",
      "           4       0.50      0.20      0.29        10\n",
      "           5       0.00      0.00      0.00         2\n",
      "           6       0.40      0.59      0.47        29\n",
      "           7       0.00      0.00      0.00         7\n",
      "\n",
      "    accuracy                           0.35        69\n",
      "   macro avg       0.18      0.17      0.16        69\n",
      "weighted avg       0.30      0.35      0.30        69\n",
      "\n"
     ]
    }
   ],
   "source": [
    "print(report)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "0e084b3e",
   "metadata": {},
   "source": [
    "## Task 3(Subtask 2): Model Training"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "0d8e9452",
   "metadata": {},
   "source": [
    "### Splitted the data into a training set and the testing set where splitted the data 60% for training and 40% for testing"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "95b02473",
   "metadata": {},
   "source": [
    "In the following step,X_train includes all independent variables which will be used to train the model, also as it specified the test size=0.40, this means 60% of observations from the complete data will be used to train/fit the model and rest 40% will be used to test the model."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 82,
   "id": "daa12364",
   "metadata": {},
   "outputs": [],
   "source": [
    "X_train, X_test, y_train, y_test = train_test_split(new_flag,\n",
    "                                                    new_flag.mainhue,\n",
    "                                                    test_size=0.40,\n",
    "                                                    random_state=4)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 83,
   "id": "c32c5529",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Training dataset size: 82\n"
     ]
    }
   ],
   "source": [
    "print(f'Training dataset size: {X_train.shape[0]}')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 84,
   "id": "9fe5b193",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Testing dataset size: 56\n"
     ]
    }
   ],
   "source": [
    "print(f'Testing dataset size: {X_test.shape[0]}')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c124d494",
   "metadata": {},
   "source": [
    "## Task 3 (Subtask 3): Model Evaluation"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f428ec40",
   "metadata": {},
   "source": [
    "### Selected a k-Nearest Neighbor Value"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "1403a57d",
   "metadata": {},
   "source": [
    "To select the KNearestNeighbors and evaluate the model adequately, the `KNeighborsClassifier` library has been imported from the `sklearn.neighbors` package. Here, the closest neighbors numbers have been chosen as `8` to obtained the accuracy of the model. As low accuracy had shown with `less` nearest neighbors or less K-value, hence, the neighbors numbers have been increased to get the better accuracy of this model. During this operation, the `KNeighborsClassifier()` function has been used and `clf` is generated as object to explore the KNearestNeighbors value. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 85,
   "id": "e3b35371",
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.neighbors import KNeighborsClassifier\n",
    "clf = KNeighborsClassifier(8) "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 86,
   "id": "8fdeb699",
   "metadata": {},
   "outputs": [],
   "source": [
    "fit = clf.fit(X_train, y_train) # Fitted the data using the '.fit()' function and added with the `clf` object to form the `fit` object appropriately."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 87,
   "id": "92c1ed99",
   "metadata": {},
   "outputs": [],
   "source": [
    "predicted = fit.predict(X_test) "
   ]
  },
  {
   "cell_type": "markdown",
   "id": "de688394",
   "metadata": {},
   "source": [
    "To find the classification report, the `classification_report ` library is imported from the `sklearn.metrics ` package and the `classification_report()`function has been applied to get the explicit report."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 88,
   "id": "a0eef93f",
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.metrics import classification_report\n",
    "report=classification_report(y_test, predicted)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "bf43da7d",
   "metadata": {},
   "source": [
    "### Testing the accuracy using the Confusion Matrix"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "9fc8556c",
   "metadata": {},
   "source": [
    "To form the `confusion matrix`, the `confusion_matrix` library has been imported from the `sklearn.metrics` package. Here, the `confusion_matrix()` function is applied with the actual values'y_test' and predicted values as `predicted` to perform the measurement for the model classification and to calculate the Recall, Precision and accuracy. As a result, more than two classes are demonstrated in the output pane."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 89,
   "id": "45c58b83",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[0, 1, 0, 0, 1, 2, 0],\n",
       "       [0, 3, 0, 0, 2, 3, 0],\n",
       "       [0, 1, 0, 0, 0, 0, 0],\n",
       "       [0, 1, 0, 0, 2, 2, 0],\n",
       "       [0, 1, 0, 0, 3, 4, 0],\n",
       "       [0, 8, 0, 0, 7, 9, 0],\n",
       "       [0, 1, 0, 0, 0, 5, 0]], dtype=int64)"
      ]
     },
     "execution_count": 89,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.metrics import confusion_matrix\n",
    "confusion_matrix(y_test, predicted) "
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d960a523",
   "metadata": {},
   "source": [
    "## Classification Error Rate"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "72bbec81",
   "metadata": {},
   "source": [
    "Here, to get the best Nearest Neighbors, for loop has been used with the range of number from 1 to 18 using `i in range()` \n",
    "function. With the for loop, it has been found some result using the range, K=1 to 18 which is shown in the next plot.Apart from this, the `append()` function is used with the error_rate which is highlighting in the next step."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 90,
   "id": "b5397db2",
   "metadata": {},
   "outputs": [],
   "source": [
    "error_rate = []# Will take some time\n",
    "for i in range(1, 18):\n",
    " \n",
    " knn = KNeighborsClassifier(n_neighbors=i)\n",
    " knn.fit(X_train,y_train)\n",
    " pred_i = knn.predict(X_test)\n",
    " error_rate.append(np.mean(pred_i != y_test))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "2beae8bf",
   "metadata": {},
   "source": [
    "To get the `Elbow` shape from this graph, Elbow method has been applied using the `plt.plot()` function where `plot()` function is highlighting the specific range from 1 to 18 with the `range()` function, color='blue', linestyle='dashed', marker='0',markerfacecolor='red', markersize=10. Here, the `plt.figure()`, `plt.title()`, `plt.xlabel()`,\n",
    "`plt.ylabel()`, `plt.xticks()` functions are used to explore the figure adequately by highlighting the title as `Elboe Method` x-axis as `Number of clusters` and y-axis `Error rate`respectively. Finally, the `plt.show()` function has been used to represents the plot appropriately. From this given plot, it is highlighted that the first error rate is found as `0.733` from the `first KNearestNeighbor` or `cluster 1`. But, however, as the 'K' has been increased and the best KNearestNeighbors have been taken as `8`, hence the classifcation error rate has decreased dramatically from `0.870` to `0.732` which is demonstrating in the following plot. Therefore, in terms of elbow position, the most decreasing error rate is `0.732` which is represented by the best KNearestNeighbors value `8`."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 91,
   "id": "503bf289",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(12,6))\n",
    "plt.plot(range(1,18),error_rate,color='blue', linestyle='dashed', marker='o',\n",
    " markerfacecolor='red', markersize=10)\n",
    "plt.title('Elbow Method')\n",
    "plt.xlabel('Number of clusters')\n",
    "plt.ylabel('Error rate')\n",
    "plt.xticks(np.arange(0, 18, step=1)) \n",
    "plt.show()\n",
    "# In this plot, in cluster "
   ]
  },
  {
   "cell_type": "markdown",
   "id": "3489225e",
   "metadata": {},
   "source": [
    "To get the accuracy correctly, the `accuracy_score` library has been imported from the `sklearn.metrics` package and applied \n",
    "the `accuracy_score()` function to measure the accuracy appropriately. In the following output pane, the accuracy result has been obtained as `0.26785714285714285` using the `print()` function."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 92,
   "id": "e80b83fd",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.26785714285714285\n"
     ]
    }
   ],
   "source": [
    "from sklearn.metrics import accuracy_score\n",
    "accuracy = accuracy_score(y_test, predicted)\n",
    "print(accuracy)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 93,
   "id": "f6fec2a4",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.7321428571428572\n"
     ]
    }
   ],
   "source": [
    "classification_error_rate=1-accuracy\n",
    "print(classification_error_rate)\n",
    "# In this step, the classification error rate is obtained by subtracting the accuracy result with `1` and the result has been \n",
    "# found as `0.6521739130434783` in the below output pane. "
   ]
  },
  {
   "cell_type": "markdown",
   "id": "0ea3a810",
   "metadata": {},
   "source": [
    "## Precision, Recall and F1-score"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "000c6b90",
   "metadata": {},
   "source": [
    "Here, the `Precision` is mentioned as the predict value and the `Recall` is for actual values. As it is difficult to compare two models with low precision and high recall values, therefore, f1-score helps 'to measure the recall and precision using the Harmonic Mean in place of Arithmatic Mean' which has been highlighted in the output pane using the `print()` function."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 94,
   "id": "9f2b9099",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       0.00      0.00      0.00         4\n",
      "           1       0.19      0.38      0.25         8\n",
      "           2       0.00      0.00      0.00         1\n",
      "           3       0.00      0.00      0.00         5\n",
      "           4       0.20      0.38      0.26         8\n",
      "           6       0.36      0.38      0.37        24\n",
      "           7       0.00      0.00      0.00         6\n",
      "\n",
      "    accuracy                           0.27        56\n",
      "   macro avg       0.11      0.16      0.13        56\n",
      "weighted avg       0.21      0.27      0.23        56\n",
      "\n"
     ]
    }
   ],
   "source": [
    "print(report) # (for KNN classifier algorithm)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b3e22d8a",
   "metadata": {},
   "source": [
    "## Task 3(Subtask 2): Model Training"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "476ec184",
   "metadata": {},
   "source": [
    "### Splitted the data into a training set and the testing set where splitted the data 80% for training and 20% for testing"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "cb9248d0",
   "metadata": {},
   "source": [
    "In the following step,X_train includes all independent variables which will be used to train the model, also as it specified the test size=0.20, this means 80% of observations from the complete data will be used to train/fit the model and rest 20% will be used to test the model."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 95,
   "id": "b49cff0d",
   "metadata": {},
   "outputs": [],
   "source": [
    "X_train, X_test, y_train, y_test = train_test_split(new_flag,\n",
    "                                                    new_flag.mainhue,\n",
    "                                                    test_size=0.20,\n",
    "                                                    random_state=4)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 96,
   "id": "f7f1c69c",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Training dataset size: 110\n"
     ]
    }
   ],
   "source": [
    "print(f'Training dataset size: {X_train.shape[0]}')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 97,
   "id": "8c20824d",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Testing dataset size: 28\n"
     ]
    }
   ],
   "source": [
    "print(f'Testing dataset size: {X_test.shape[0]}')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a77861bb",
   "metadata": {},
   "source": [
    "# Task 3 (Subtask 3): Model Evaluation"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "01acebb3",
   "metadata": {},
   "source": [
    "## Selected a k-Nearest Neighbor Value"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "29ef3387",
   "metadata": {},
   "source": [
    "To select the KNearestNeighbors and evaluate the model adequately, the `KNeighborsClassifier` library has been imported from the `sklearn.neighbors` package. Here, the closest neighbors numbers have been chosen as `8` to obtained the accuracy of the model. As low accuracy had shown with `less` nearest neighbors or less K-value, hence, the neighbors numbers have been increased to get the better accuracy of this model. During this operation, the `KNeighborsClassifier()` function has been used and `clf` is generated as object to explore the KNearestNeighbors value. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 98,
   "id": "d1a17371",
   "metadata": {},
   "outputs": [],
   "source": [
    "clf = KNeighborsClassifier(2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 99,
   "id": "830ff0ef",
   "metadata": {},
   "outputs": [],
   "source": [
    "fit = clf.fit(X_train, y_train) # Fitted the data using the '.fit()' function and added with the `clf` object to form the `fit` object appropriately."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 100,
   "id": "1f63af9b",
   "metadata": {},
   "outputs": [],
   "source": [
    "predicted = fit.predict(X_test) "
   ]
  },
  {
   "cell_type": "markdown",
   "id": "5789b9eb",
   "metadata": {},
   "source": [
    "To find the classification report, the `classification_report ` library is imported from the `sklearn.metrics ` package and the `classification_report()`function has been applied to get the explicit report."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 101,
   "id": "dca53b31",
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.metrics import classification_report\n",
    "report=classification_report(y_test, predicted)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "7d057f80",
   "metadata": {},
   "source": [
    "### Testing the accuracy using the Confusion Matrix"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "9a66d1a2",
   "metadata": {},
   "source": [
    "To form the `confusion matrix`, the `confusion_matrix` library has been imported from the `sklearn.metrics` package. Here, the `confusion_matrix()` function is applied with the actual values'y_test' and predicted values as `predicted` to perform the measurement for the model classification and to calculate the Recall, Precision and accuracy. As a result, more than two classes are demonstrated in the output pane."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 102,
   "id": "db31bb19",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[0, 1, 0, 1, 1, 0],\n",
       "       [1, 2, 0, 0, 0, 1],\n",
       "       [0, 0, 0, 2, 1, 0],\n",
       "       [0, 2, 0, 0, 1, 0],\n",
       "       [0, 4, 4, 3, 3, 0],\n",
       "       [0, 0, 0, 0, 1, 0]], dtype=int64)"
      ]
     },
     "execution_count": 102,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.metrics import confusion_matrix\n",
    "confusion_matrix(y_test, predicted) # Created confusion matrix(for KNN classifier)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "8e8bf935",
   "metadata": {},
   "source": [
    "## Classification Error Rate"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b3eb1b9a",
   "metadata": {},
   "source": [
    "Here, to get the best Nearest Neighbors, for loop has been used with the range of number from 1 to 18 using `i in range()` \n",
    "function. With the for loop, it has been found some result using the range, K=1 to 18 which is shown in the next plot.Apart from this, the `append()` function is used with the error_rate which is highlighting in the next step."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 103,
   "id": "78c7be3e",
   "metadata": {},
   "outputs": [],
   "source": [
    "error_rate = []# Will take some time\n",
    "for i in range(1,18):\n",
    " \n",
    " knn = KNeighborsClassifier(n_neighbors=i)\n",
    " knn.fit(X_train,y_train)\n",
    " pred_i = knn.predict(X_test)\n",
    " error_rate.append(np.mean(pred_i != y_test))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "55b709d7",
   "metadata": {},
   "source": [
    "To get the `Elbow` shape from this graph, Elbow method has been applied using the `plt.plot()` function where `plot()` function is highlighting the specific range from 1 to 18 with the `range()` function, color='blue', linestyle='dashed', marker='0',markerfacecolor='red', markersize=10. Here, the `plt.figure()`, `plt.title()`, `plt.xlabel()`,\n",
    "`plt.ylabel()`, `plt.xticks()` functions are used to explore the figure adequately by highlighting the title as `Elboe Method` x-axis as `Number of clusters` and y-axis `Error rate`respectively. Finally, the `plt.show()` function has been used to represents the plot appropriately. From this given plot, it is highlighted that the first error rate is found as `0.65` from the `first KNearestNeighbor` or `cluster 1`. But, unfortunately this eror rate has been increased up to `0.82` which is highlighted in the given plot. Although, the 'K' has been increased and the best KNearestNeighbors have been taken as `8` but the classifcation error rate is found as the same rate like `0.82`. Because, the trend is looking up and down sometimes but eventually the classifcation error rate has allocated as `0.82`. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 104,
   "id": "9bfcc36c",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(12,6))\n",
    "plt.plot(range(1,18),error_rate,color='blue', linestyle='dashed', marker='o',\n",
    " markerfacecolor='red', markersize=10)\n",
    "plt.title('Elbow Method')\n",
    "plt.xlabel('Number of clusters')\n",
    "plt.ylabel('Error rate')\n",
    "plt.xticks(np.arange(0, 18, step=1)) \n",
    "plt.show()\n",
    "# In this plot, in cluster "
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c558c63c",
   "metadata": {},
   "source": [
    "To get the accuracy correctly, the `accuracy_score` library has been imported from the `sklearn.metrics` package and applied \n",
    "the `accuracy_score()` function to measure the accuracy appropriately. In the following output pane, the accuracy result has been obtained as `0.17857142857142858` using the `print()` function."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 105,
   "id": "eec53924",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.17857142857142858\n"
     ]
    }
   ],
   "source": [
    "from sklearn.metrics import accuracy_score\n",
    "accuracy = accuracy_score(y_test, predicted)\n",
    "print(accuracy)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "95dd190b",
   "metadata": {},
   "source": [
    "In this step, the classification error rate is obtained by subtracting the accuracy result with `1` and the result has been found as `0.8214285714285714` in the below output pane. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 106,
   "id": "cf87f47e",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.8214285714285714\n"
     ]
    }
   ],
   "source": [
    "classification_error_rate=1-accuracy\n",
    "print(classification_error_rate)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "0f91a0af",
   "metadata": {},
   "source": [
    "## Precision, Recall and F1-score"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "0d9accb7",
   "metadata": {},
   "source": [
    "Here, the `Precision` is mentioned as the predict value and the `Recall` is for actual values. As it is difficult to compare two models with low precision and high recall values, therefore, f1-score helps 'to measure the recall and precision using the Harmonic Mean in place of Arithmatic Mean' which has been highlighted in the output pane using the `print()` function."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 107,
   "id": "994bcc81",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       0.00      0.00      0.00         3\n",
      "           1       0.22      0.50      0.31         4\n",
      "           3       0.00      0.00      0.00         3\n",
      "           4       0.00      0.00      0.00         3\n",
      "           6       0.43      0.21      0.29        14\n",
      "           7       0.00      0.00      0.00         1\n",
      "\n",
      "    accuracy                           0.18        28\n",
      "   macro avg       0.11      0.12      0.10        28\n",
      "weighted avg       0.25      0.18      0.19        28\n",
      "\n"
     ]
    }
   ],
   "source": [
    "print(report)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "025f6843",
   "metadata": {},
   "source": [
    "# Task 3(Subtask 1): Decision Tree Model"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "db59909d",
   "metadata": {},
   "source": [
    "## Loaded packages"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a2a2f7b9",
   "metadata": {},
   "source": [
    "To train model, the `train_test_split` library has been imported from the `sklearn.model_selection` package. Apart from this, the convenience plotting library `pylab` is imported as `plt`. Here, the 'train' set will be used to train the classifier, and the 'test' set will be used for evaluation purposes."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 108,
   "id": "7a89a34f",
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.model_selection import train_test_split\n",
    "import pylab as plt"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "00754630",
   "metadata": {},
   "source": [
    "## Task 3(subtask 2): Model Training"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "320ed210",
   "metadata": {},
   "source": [
    "### Splitted the data into a training set and the testing set where splitted the data 50% for training and 50% for testing"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b4fd6913",
   "metadata": {},
   "source": [
    "In the following step,X_train includes all independent variables which will be used to train the model, also as it specified the test size=0.50, this means 50% of observations from the complete data will be used to train/fit the model and rest 50% will be used to test the model."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 109,
   "id": "3577763e",
   "metadata": {},
   "outputs": [],
   "source": [
    "X_train, X_test, y_train, y_test = train_test_split(new_flag,\n",
    "                                                    new_flag.mainhue,\n",
    "                                                    test_size=0.50,\n",
    "                                                    random_state=4)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "51ccfccd",
   "metadata": {},
   "source": [
    "## Task 3(Subtask 3): Model Evaluation"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "0e7215ec",
   "metadata": {},
   "source": [
    "### Selecting decision tree classifier(value)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "1401c3d4",
   "metadata": {},
   "source": [
    "To generate the decision tree and evaluate the model adequately, the `DecisionTreeClassifier` library has been imported from the `sklearn.tree` package. Here, the parameters have been added for decision tree classifier with `gini` criterion and parallelly other parameters have been checked to change their values which results the decision tree model's accuracy down. But, as the default parameters are generated the decision tree models accuracy is high, the default parameters are used in here appropriately. During this operation, the `DecisionTreeClassifier()` function has been used and `clf` is created as `object` to explore the DecisionTreeClassifier value adequately."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 110,
   "id": "6b2f3632",
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.tree import DecisionTreeClassifier\n",
    "clf= DecisionTreeClassifier(criterion='gini', max_depth=None, min_samples_split=2, min_samples_leaf=1, max_features=None, \n",
    "                            max_leaf_nodes=None)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 111,
   "id": "e27d91fe",
   "metadata": {},
   "outputs": [],
   "source": [
    "fit = clf.fit(X_train, y_train) "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 112,
   "id": "0eaeeefc",
   "metadata": {},
   "outputs": [],
   "source": [
    "y_pre = fit.predict(X_test) #Predicted on unseen data using the `predict()` function which is added with the `fit` object \n",
    "# and also created another `object` as `y_pre`."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c9d08960",
   "metadata": {},
   "source": [
    "### Measured confusion matrix and classification precision/recall/f1-score"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "fded6481",
   "metadata": {},
   "source": [
    "To create the `confusion matrix`, the `confusion_matrix` library has been imported from the `sklearn.metrics` package and to find the classification report, the `classification_report ` library is also imported from the `sklearn.metrics ` package. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 113,
   "id": "86bad838",
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.metrics import confusion_matrix\n",
    "from sklearn.metrics import classification_report"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 114,
   "id": "d897116a",
   "metadata": {},
   "outputs": [],
   "source": [
    "cm = confusion_matrix(y_test, y_pre) # To find the confusion matrix, the `confusion_matrix()` function is applied \n",
    "# by creating `cm` object."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 115,
   "id": "f2b0e147",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[ 0  2  0  2  0  0  0  0]\n",
      " [ 0 10  0  1  0  0  0  0]\n",
      " [ 0  1  0  0  0  0  0  0]\n",
      " [ 0  0  0  5  0  0  0  0]\n",
      " [ 0  0  0  0 10  0  0  0]\n",
      " [ 0  0  0  0  0  2  0  0]\n",
      " [ 0  0  0  0  0  0 29  0]\n",
      " [ 0  0  0  0  0  0  0  7]]\n"
     ]
    }
   ],
   "source": [
    "print(cm) # Here, the `print()` function is used to demonstrate the result of the `confusion_matrix` which is hsown in the given \n",
    "# output pane."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c0493af7",
   "metadata": {},
   "source": [
    "To find the classification report, the `classification_report()`function has been applied to get the explicit report using the `report `object."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 116,
   "id": "1fd82376",
   "metadata": {},
   "outputs": [],
   "source": [
    "report = classification_report(y_test, y_pre)\n",
    "warnings.filterwarnings('ignore')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "95038fa4",
   "metadata": {},
   "source": [
    "### Precision, Recall and F1-score"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "8e0cb933",
   "metadata": {},
   "source": [
    "Here, the `Precision` is mentioned as the predict value and the `Recall` is for actual values. As it is difficult to compare two models with low precision and high recall values, therefore, f1-score helps 'to measure the recall and precision using the Harmonic Mean in place of Arithmatic Mean' which has been highlighted in the output pane using the `print()` function."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 117,
   "id": "1ff1406c",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       0.00      0.00      0.00         4\n",
      "           1       0.77      0.91      0.83        11\n",
      "           2       0.00      0.00      0.00         1\n",
      "           3       0.62      1.00      0.77         5\n",
      "           4       1.00      1.00      1.00        10\n",
      "           5       1.00      1.00      1.00         2\n",
      "           6       1.00      1.00      1.00        29\n",
      "           7       1.00      1.00      1.00         7\n",
      "\n",
      "    accuracy                           0.91        69\n",
      "   macro avg       0.67      0.74      0.70        69\n",
      "weighted avg       0.86      0.91      0.88        69\n",
      "\n"
     ]
    }
   ],
   "source": [
    "print(report)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "be4e9b43",
   "metadata": {},
   "source": [
    "To get the accuracy correctly, the `accuracy_score` library has been imported from the `sklearn.metrics` package and applied \n",
    "the `accuracy_score()` function to measure the accuracy appropriately. In the following output pane, the accuracy result has been obtained as `0.9130434782608695` using the `print()` function."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 118,
   "id": "2812fc30",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.9130434782608695\n"
     ]
    }
   ],
   "source": [
    "from sklearn.metrics import accuracy_score\n",
    "accuracy = accuracy_score(y_test, y_pre)\n",
    "print(accuracy)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a741ccc9",
   "metadata": {},
   "source": [
    "In the next step, the classification error rate is obtained by subtracting the accuracy result with `1` and the result has been \n",
    "found as `0.08695652173913049` in the below output pane. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 119,
   "id": "1f06fe25",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.08695652173913049\n"
     ]
    }
   ],
   "source": [
    "classification_error_rate=1-accuracy\n",
    "print(classification_error_rate)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "848c5905",
   "metadata": {},
   "source": [
    "## Task 3(subtask 2): Model Training"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "2abcbee3",
   "metadata": {},
   "source": [
    "### Splitted the data into a training set and the testing set where splitted the data 60% for training and 40% for testing"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e2a53414",
   "metadata": {},
   "source": [
    "In the following step,X_train includes all independent variables which will be used to train the model, also as it specified the test size=0.40, this means 60% of observations from the complete data will be used to train/fit the model and rest 40% will be used to test the model."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 120,
   "id": "e4501f34",
   "metadata": {},
   "outputs": [],
   "source": [
    "X_train, X_test, y_train, y_test = train_test_split(new_flag,\n",
    "                                                    new_flag.mainhue,\n",
    "                                                    test_size=0.40,\n",
    "                                                    random_state=4)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a825a023",
   "metadata": {},
   "source": [
    "## Task 3(Subtask 3): Model Evaluation"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "9df9c26a",
   "metadata": {},
   "source": [
    "### Selecting decision tree classifier(value)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "7e2fd430",
   "metadata": {},
   "source": [
    "To generate the decision tree and evaluate the model adequately, the `DecisionTreeClassifier` library has been imported from the `sklearn.tree` package. Here, the parameters have been added for decision tree classifier with `gini` criterion and parallelly other parameters have been checked to change their values which results the decision tree model's accuracy down. But, as the default parameters are generated the decision tree models accuracy is high, the default parameters are used in here appropriately. During this operation, the `DecisionTreeClassifier()` function has been used and `clf` is created as `object` to explore the DecisionTreeClassifier value adequately."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 121,
   "id": "bfc935c2",
   "metadata": {},
   "outputs": [],
   "source": [
    "clf= DecisionTreeClassifier(criterion='gini', max_depth=None, min_samples_split=2, min_samples_leaf=1, max_features=None, \n",
    "                            max_leaf_nodes=None)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 122,
   "id": "4865d7e2",
   "metadata": {},
   "outputs": [],
   "source": [
    "fit = clf.fit(X_train, y_train) # Fitted the data with classifier using '.fit()' method"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 123,
   "id": "926f0ead",
   "metadata": {},
   "outputs": [],
   "source": [
    "y_pre = fit.predict(X_test) # Made prediction on test data by creating the object as `y_pre`."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "732d7369",
   "metadata": {},
   "source": [
    "### Measured confusion matrix and classification precision/recall/f1-score"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 124,
   "id": "35e28ef5",
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.metrics import confusion_matrix\n",
    "from sklearn.metrics import classification_report"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 125,
   "id": "df88be5a",
   "metadata": {},
   "outputs": [],
   "source": [
    "cm = confusion_matrix(y_test, y_pre) # To find the confusion matrix, the `confusion_matrix()` function is applied \n",
    "# by creating `cm` object."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 126,
   "id": "0784007d",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[ 1  2  0  1  0  0  0]\n",
      " [ 0  7  0  1  0  0  0]\n",
      " [ 0  1  0  0  0  0  0]\n",
      " [ 0  0  0  5  0  0  0]\n",
      " [ 0  0  0  0  8  0  0]\n",
      " [ 0  0  0  0  0 24  0]\n",
      " [ 0  0  0  0  0  0  6]]\n"
     ]
    }
   ],
   "source": [
    "print(cm) # Here, the `print()` function is used to demonstrate the result of the `confusion_matrix` which is hsown in the given \n",
    "# output pane."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ffda4c63",
   "metadata": {},
   "source": [
    "To find the classification report, the `classification_report()`function has been applied to get the explicit report using the `report `object."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 127,
   "id": "4f9482dd",
   "metadata": {},
   "outputs": [],
   "source": [
    "report = classification_report(y_test, y_pre)\n",
    "warnings.filterwarnings('ignore')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "173ad2b2",
   "metadata": {},
   "source": [
    "### Precision, Recall and F1-score"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f88a8196",
   "metadata": {},
   "source": [
    "Here, the `Precision` is mentioned as the predict value and the `Recall` is for actual values. As it is difficult to compare two models with low precision and high recall values, therefore, f1-score helps 'to measure the recall and precision using the Harmonic Mean in place of Arithmatic Mean' which has been highlighted in the output pane using the `print()` function."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 128,
   "id": "e7c36b27",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       1.00      0.25      0.40         4\n",
      "           1       0.70      0.88      0.78         8\n",
      "           2       0.00      0.00      0.00         1\n",
      "           3       0.71      1.00      0.83         5\n",
      "           4       1.00      1.00      1.00         8\n",
      "           6       1.00      1.00      1.00        24\n",
      "           7       1.00      1.00      1.00         6\n",
      "\n",
      "    accuracy                           0.91        56\n",
      "   macro avg       0.77      0.73      0.72        56\n",
      "weighted avg       0.91      0.91      0.89        56\n",
      "\n"
     ]
    }
   ],
   "source": [
    "print(report)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "66c6b840",
   "metadata": {},
   "source": [
    "To get the accuracy correctly, the `accuracy_score` library has been imported from the `sklearn.metrics` package and applied \n",
    "the `accuracy_score()` function to measure the accuracy appropriately. In the following output pane, the accuracy result has been obtained as `0.9107142857142857` using the `print()` function."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 129,
   "id": "248541ae",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.9107142857142857\n"
     ]
    }
   ],
   "source": [
    "from sklearn.metrics import accuracy_score\n",
    "accuracy = accuracy_score(y_test, y_pre)\n",
    "print(accuracy)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "7879c33d",
   "metadata": {},
   "source": [
    "In the next step, the classification error rate is obtained by subtracting the accuracy result with `1` and the result has been \n",
    "found as `0.0892857142857143` in the below output pane. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 130,
   "id": "591886a7",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.0892857142857143\n"
     ]
    }
   ],
   "source": [
    "classification_error_rate=1-accuracy\n",
    "print(classification_error_rate)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "006f104b",
   "metadata": {},
   "source": [
    "## Task 3(subtask 2): Model Training"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "5af133c1",
   "metadata": {},
   "source": [
    "### Splitted the data into a training set and the testing set where splitted the data 80% for training and 20% for testing"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "77c69f83",
   "metadata": {},
   "source": [
    "In the following step,X_train includes all independent variables which will be used to train the model, also as it specified the test size=0.20, this means 80% of observations from the complete data will be used to train/fit the model and rest 20% will be used to test the model."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 131,
   "id": "327a180c",
   "metadata": {},
   "outputs": [],
   "source": [
    "X_train, X_test, y_train, y_test = train_test_split(new_flag,\n",
    "                                                    new_flag.mainhue,\n",
    "                                                    test_size=0.20,\n",
    "                                                    random_state=4)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "725f2ae5",
   "metadata": {},
   "source": [
    "## Task 3(Subtask 3): Model Evaluation"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "4912dd93",
   "metadata": {},
   "source": [
    "### Selecting decision tree classifier(value)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "2c85ff44",
   "metadata": {},
   "source": [
    "To generate the decision tree and evaluate the model adequately, the `DecisionTreeClassifier` library has been imported from the `sklearn.tree` package. Here, the parameters have been added for decision tree classifier with `gini` criterion and parallelly other parameters have been checked to change their values which results the decision tree model's accuracy down. But, as the default parameters are generated the decision tree models accuracy is high, the default parameters are used in here appropriately. During this operation, the `DecisionTreeClassifier()` function has been used and `clf` is created as `object` to explore the DecisionTreeClassifier value adequately."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 132,
   "id": "0c6d3d77",
   "metadata": {},
   "outputs": [],
   "source": [
    "clf= DecisionTreeClassifier(criterion='gini', max_depth=None, min_samples_split=2, min_samples_leaf=1, max_features=None,\n",
    "                            max_leaf_nodes=None)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 133,
   "id": "72c024ea",
   "metadata": {},
   "outputs": [],
   "source": [
    "fit = clf.fit(X_train, y_train) # Fitted the data with classifier using '.fit()' method"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 134,
   "id": "7a99fa4a",
   "metadata": {},
   "outputs": [],
   "source": [
    "y_pre = fit.predict(X_test) # Made prediction on test data"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b1ec46f3",
   "metadata": {},
   "source": [
    "### Measured confusion matrix and classification precision/recall/f1-score"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e84651c7",
   "metadata": {},
   "source": [
    "To create the `confusion matrix`, the `confusion_matrix` library has been imported from the `sklearn.metrics` package and to find the classification report, the `classification_report ` library is also imported from the `sklearn.metrics ` package. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 135,
   "id": "d183fe0a",
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.metrics import confusion_matrix\n",
    "from sklearn.metrics import classification_report"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 136,
   "id": "79a15fda",
   "metadata": {},
   "outputs": [],
   "source": [
    "cm = confusion_matrix(y_test, y_pre) # To find the confusion matrix, the `confusion_matrix()` function is applied \n",
    "# by creating `cm` object."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 137,
   "id": "605cb268",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[ 1  2  0  0  0  0]\n",
      " [ 1  3  0  0  0  0]\n",
      " [ 0  0  3  0  0  0]\n",
      " [ 0  0  0  3  0  0]\n",
      " [ 0  0  0  0 14  0]\n",
      " [ 0  0  0  0  0  1]]\n"
     ]
    }
   ],
   "source": [
    "print(cm) # Here, the `print()` function is used to demonstrate the result of the `confusion_matrix` which is hsown in the given \n",
    "# output pane."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "2d698251",
   "metadata": {},
   "source": [
    "To find the classification report, the `classification_report()`function has been applied to get the explicit report using the `report `object."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 138,
   "id": "835dbd46",
   "metadata": {},
   "outputs": [],
   "source": [
    "report = classification_report(y_test, y_pre)\n",
    "warnings.filterwarnings('ignore')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "8185f3d8",
   "metadata": {},
   "source": [
    "### Precision, Recall and F1-score"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b72a26ac",
   "metadata": {},
   "source": [
    "Here, the `Precision` is mentioned as the predict value and the `Recall` is for actual values. As it is difficult to compare two models with low precision and high recall values, therefore, f1-score helps 'to measure the recall and precision using the Harmonic Mean in place of Arithmatic Mean' which has been highlighted in the output pane using the `print()` function."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 139,
   "id": "03168535",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       0.50      0.33      0.40         3\n",
      "           1       0.60      0.75      0.67         4\n",
      "           3       1.00      1.00      1.00         3\n",
      "           4       1.00      1.00      1.00         3\n",
      "           6       1.00      1.00      1.00        14\n",
      "           7       1.00      1.00      1.00         1\n",
      "\n",
      "    accuracy                           0.89        28\n",
      "   macro avg       0.85      0.85      0.84        28\n",
      "weighted avg       0.89      0.89      0.89        28\n",
      "\n"
     ]
    }
   ],
   "source": [
    "print(report) "
   ]
  },
  {
   "cell_type": "markdown",
   "id": "1a27bcb2",
   "metadata": {},
   "source": [
    "To get the accuracy correctly, the `accuracy_score` library has been imported from the `sklearn.metrics` package and applied \n",
    "the `accuracy_score()` function to measure the accuracy appropriately. In the following output pane, the accuracy result has been obtained as `0.8928571428571429` using the `print()` function."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 140,
   "id": "f67b0051",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.8928571428571429\n"
     ]
    }
   ],
   "source": [
    "from sklearn.metrics import accuracy_score\n",
    "accuracy = accuracy_score(y_test, y_pre)\n",
    "print(accuracy)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "2ebb991c",
   "metadata": {},
   "source": [
    "In the next step, the classification error rate is obtained by subtracting the accuracy result with `1` and the result has been \n",
    "found as `0.1071428571428571` in the below output pane. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 141,
   "id": "a032fa01",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.1071428571428571\n"
     ]
    }
   ],
   "source": [
    "classification_error_rate=1-accuracy\n",
    "print(classification_error_rate)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "bf86cfa5",
   "metadata": {},
   "source": [
    "# Task 3(Subtask 4): Comparing the two models(KNN & Decision Tree)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "5101d516",
   "metadata": {},
   "source": [
    "In this step, the two (KNN and Decision Tree) models' accuracy have been compared which are done in the above steps such as \n",
    "\n",
    "In 50% training and 50% testing splitting process, \n",
    "the KNN model's accuracy is 0.35 whereas the Decision Tree model's accuracy is 0.90\n",
    "In 60% training and 40% testing splitting process, \n",
    "the KNN model's accuracy is 0.27 whereas the Decision Tree model's accuracy is 0.91\n",
    "In 80% training and 20% testing splitting process, \n",
    "the KNN model's accuracy is 0.18 whereas the Decision Tree model's accuracy is 0.89\n",
    "\n",
    "Here, it is recommended that as the KNN model's accuracy is lower than the Decision tree model, therefore, the Decision Tree model would be perfect to use in the real world for the particular `new_flag` dataset."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "caee979e",
   "metadata": {},
   "source": [
    "## Visualising the new_flag data set with the Decision Tree Model"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "da9a5659",
   "metadata": {},
   "source": [
    "To visualize the Decision Tree model, the `confusion_matrix` library has been imported from the `sklearn.metrics` package. Apart from this, the convenience plotting library `pylab` is imported as `plt`."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 142,
   "id": "31e75e89",
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.metrics import confusion_matrix\n",
    "import pylab as plt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 143,
   "id": "74c1e1e5",
   "metadata": {},
   "outputs": [],
   "source": [
    "feature_names=['name', 'landmass', 'zone', 'area', 'population', 'language', 'religion',\n",
    "                             'bars', 'stripes', 'colours', 'red','green', 'blue', 'gold', 'white',\n",
    "                             'black', 'orange', 'mainhue', 'circles', 'crosses', 'saltires', 'quarters',\n",
    "                             'sunstars', 'crescent', 'triangle', 'icon', 'animate', 'text', 'topleft', 'botright']\n",
    "\n",
    "# Here, the `feature_names` object has been created by taking all columns of the `new_flag` dataset."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 144,
   "id": "ea9271ae",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "feature names: ['name', 'landmass', 'zone', 'area', 'population', 'language', 'religion', 'bars', 'stripes', 'colours', 'red', 'green', 'blue', 'gold', 'white', 'black', 'orange', 'mainhue', 'circles', 'crosses', 'saltires', 'quarters', 'sunstars', 'crescent', 'triangle', 'icon', 'animate', 'text', 'topleft', 'botright']\n"
     ]
    }
   ],
   "source": [
    "print(f\"feature names: {feature_names}\") # In this step, all columns has been highlighted using the `print()` function."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 145,
   "id": "979f3701",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Target values: [4, 6, 4, 6, 1, 1, 1, 6, 4, 3, 4, 5, 6, 1, 4, 6, 6, 6, 6, 3, 6, 3, 3, 6, 6, 3, 6, 1, 1, 7, 7, 6, 1, 3, 0, 1, 4, 1, 7, 7, 4, 6, 3, 0, 6, 1, 1, 3, 3, 4, 0, 1, 1, 6, 5, 6, 6, 6, 7, 7, 7, 7, 4, 7, 0, 6, 6, 4, 6, 6, 1, 6, 4, 6, 6, 6, 3, 4, 6, 4, 6, 6, 3, 2, 6, 1, 1, 5, 4, 1, 6, 6, 6, 4, 6, 0, 6, 6, 1, 7, 6, 6, 6, 6, 4, 4, 4, 7, 1, 5, 7, 6, 6, 3, 6, 1, 1, 6, 6, 6, 4, 6, 4, 6, 6, 6, 4, 3, 6, 7, 7, 6, 6, 6, 6, 4, 4, 4]\n"
     ]
    }
   ],
   "source": [
    "print(f\"Target values: {new_flag.mainhue.tolist()}\") # Here, all values of the `Target variable` are demonstrated in the \n",
    "# output pane applying the `print()` function and to use the `list()` function with the targeted variable."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 146,
   "id": "5daf6ed4",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "number of targets:  138\n"
     ]
    }
   ],
   "source": [
    "print(f\"number of targets:  {new_flag.mainhue.shape[0]}\") # The `print()` function is used to obtain the number of targets which\n",
    "# has shown in the output panel."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 147,
   "id": "4b834fc5",
   "metadata": {},
   "outputs": [],
   "source": [
    "target_names = ['red','green','blue','white','gold','black','orange','brown'] # Here, all values names of the target variable has\n",
    "# been subsetted by creating the `target_names` object."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 148,
   "id": "ebba2d32",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "target names: ['red', 'green', 'blue', 'white', 'gold', 'black', 'orange', 'brown']\n"
     ]
    }
   ],
   "source": [
    "print(f\"target names: {target_names}\") # All target names are printed using the `print()` function."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "146b980b",
   "metadata": {},
   "source": [
    "In this step, to visualize the Decision Tree model, the `tree` library has been imported from the `sklearn` package.\n",
    "Additionally, the `open()` function is applied as `f` which is indicated as object of the `export_graphviz()` function. Apart from different types of parameters have been used to get the explicit visualization of the Decision Tree Model.\n",
    "    "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 149,
   "id": "f21c323f",
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn import tree\n",
    "\n",
    "with open(\"new_flag.dot\",\"w\") as f:\n",
    "    f = tree.export_graphviz(clf, \n",
    "                             out_file=f,\n",
    "                             feature_names=feature_names,\n",
    "                             class_names=target_names,\n",
    "                             filled=True,\n",
    "                             rounded=True, \n",
    "                             special_characters=True)\n",
    "    "
   ]
  },
  {
   "cell_type": "markdown",
   "id": "aa25f4b3",
   "metadata": {},
   "source": [
    "the code above would generate `new_flag.dot` file, copy the content from it into the [web-page](https://dreampuf.github.io/GraphvizOnline/). It will produce the following graph."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c60ecc01",
   "metadata": {},
   "source": [
    "The explanations of the above codes are given as belows:\n",
    "\n",
    "`clf` : decision tree classifier The decision tree to be visualized\n",
    "\n",
    "`out_file` : file object or string, optional (default='tree.dot')exported to GraphViz.\n",
    "\n",
    "`feature_names` : list of strings, optional (default=None) Names of each of the features.\n",
    "\n",
    "`class_names`: list of string, bool or None, optional (default=None) Names of each of the target classes in ascending numerical order.\n",
    "\n",
    "`filled` : bool, optional (default=False) When set to True, paint nodes to indicate majority class for - classification, extremity of values for regression, or purity of node for multi-output.\n",
    "\n",
    "`rounded` : bool, optional (default=False) When set to True, draw node boxes with rounded corners and use Helvetica fonts instead of Times-Roman.\n",
    "\n",
    "`special_characters` : bool, optional (default=False) When set to False, ignore special characters for PostScript compatibility."
   ]
  }
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