Project Neighbors & Clustering.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "house_data = pd.read_csv('Data/house_rental_data.csv.txt', index_col='Unnamed: 0')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Sqft</th>\n",
       "      <th>Floor</th>\n",
       "      <th>TotalFloor</th>\n",
       "      <th>Bedroom</th>\n",
       "      <th>Living.Room</th>\n",
       "      <th>Bathroom</th>\n",
       "      <th>Price</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1177.698</td>\n",
       "      <td>2</td>\n",
       "      <td>7</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>62000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2134.800</td>\n",
       "      <td>5</td>\n",
       "      <td>7</td>\n",
       "      <td>4</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>78000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1138.560</td>\n",
       "      <td>5</td>\n",
       "      <td>7</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>58000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1458.780</td>\n",
       "      <td>2</td>\n",
       "      <td>7</td>\n",
       "      <td>3</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>45000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>967.776</td>\n",
       "      <td>11</td>\n",
       "      <td>14</td>\n",
       "      <td>3</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>45000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>1127.886</td>\n",
       "      <td>11</td>\n",
       "      <td>12</td>\n",
       "      <td>4</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>148000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>1352.040</td>\n",
       "      <td>5</td>\n",
       "      <td>7</td>\n",
       "      <td>3</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>58000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>757.854</td>\n",
       "      <td>5</td>\n",
       "      <td>14</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>48000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>1152.792</td>\n",
       "      <td>10</td>\n",
       "      <td>12</td>\n",
       "      <td>3</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>45000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>1423.200</td>\n",
       "      <td>4</td>\n",
       "      <td>5</td>\n",
       "      <td>4</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>65000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>668.904</td>\n",
       "      <td>4</td>\n",
       "      <td>11</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>31000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>711.600</td>\n",
       "      <td>4</td>\n",
       "      <td>7</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>29002</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>1352.040</td>\n",
       "      <td>9</td>\n",
       "      <td>19</td>\n",
       "      <td>4</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>39000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>818.340</td>\n",
       "      <td>4</td>\n",
       "      <td>13</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>48000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>2134.800</td>\n",
       "      <td>10</td>\n",
       "      <td>19</td>\n",
       "      <td>3</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>55000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>2768.124</td>\n",
       "      <td>6</td>\n",
       "      <td>19</td>\n",
       "      <td>3</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>100000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>711.600</td>\n",
       "      <td>5</td>\n",
       "      <td>13</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>48000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>462.540</td>\n",
       "      <td>5</td>\n",
       "      <td>13</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>25000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>2739.660</td>\n",
       "      <td>6</td>\n",
       "      <td>19</td>\n",
       "      <td>3</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>90000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>1174.140</td>\n",
       "      <td>3</td>\n",
       "      <td>9</td>\n",
       "      <td>3</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>33000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>2490.600</td>\n",
       "      <td>19</td>\n",
       "      <td>21</td>\n",
       "      <td>5</td>\n",
       "      <td>3</td>\n",
       "      <td>4</td>\n",
       "      <td>140000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>2768.124</td>\n",
       "      <td>6</td>\n",
       "      <td>19</td>\n",
       "      <td>3</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>100000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>747.180</td>\n",
       "      <td>7</td>\n",
       "      <td>15</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>55000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>1668.702</td>\n",
       "      <td>4</td>\n",
       "      <td>12</td>\n",
       "      <td>3</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>105000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>3664.740</td>\n",
       "      <td>19</td>\n",
       "      <td>19</td>\n",
       "      <td>3</td>\n",
       "      <td>2</td>\n",
       "      <td>3</td>\n",
       "      <td>200000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26</th>\n",
       "      <td>1779.000</td>\n",
       "      <td>10</td>\n",
       "      <td>12</td>\n",
       "      <td>4</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>98000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27</th>\n",
       "      <td>3664.740</td>\n",
       "      <td>19</td>\n",
       "      <td>19</td>\n",
       "      <td>3</td>\n",
       "      <td>2</td>\n",
       "      <td>3</td>\n",
       "      <td>200000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28</th>\n",
       "      <td>3059.880</td>\n",
       "      <td>21</td>\n",
       "      <td>24</td>\n",
       "      <td>4</td>\n",
       "      <td>2</td>\n",
       "      <td>3</td>\n",
       "      <td>145000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29</th>\n",
       "      <td>533.700</td>\n",
       "      <td>3</td>\n",
       "      <td>4</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>25000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>30</th>\n",
       "      <td>853.920</td>\n",
       "      <td>6</td>\n",
       "      <td>7</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>24028</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",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>619</th>\n",
       "      <td>1857.276</td>\n",
       "      <td>2</td>\n",
       "      <td>14</td>\n",
       "      <td>3</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>45000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>620</th>\n",
       "      <td>1102.980</td>\n",
       "      <td>6</td>\n",
       "      <td>7</td>\n",
       "      <td>3</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>65000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>621</th>\n",
       "      <td>925.080</td>\n",
       "      <td>2</td>\n",
       "      <td>4</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>32000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>622</th>\n",
       "      <td>2312.700</td>\n",
       "      <td>11</td>\n",
       "      <td>14</td>\n",
       "      <td>3</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>99999</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>623</th>\n",
       "      <td>925.080</td>\n",
       "      <td>4</td>\n",
       "      <td>9</td>\n",
       "      <td>3</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>45000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>624</th>\n",
       "      <td>711.600</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>12000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>625</th>\n",
       "      <td>4198.440</td>\n",
       "      <td>15</td>\n",
       "      <td>19</td>\n",
       "      <td>7</td>\n",
       "      <td>3</td>\n",
       "      <td>4</td>\n",
       "      <td>180000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>626</th>\n",
       "      <td>3095.460</td>\n",
       "      <td>1</td>\n",
       "      <td>6</td>\n",
       "      <td>4</td>\n",
       "      <td>2</td>\n",
       "      <td>3</td>\n",
       "      <td>100000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>627</th>\n",
       "      <td>2590.224</td>\n",
       "      <td>5</td>\n",
       "      <td>15</td>\n",
       "      <td>3</td>\n",
       "      <td>2</td>\n",
       "      <td>3</td>\n",
       "      <td>110000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>628</th>\n",
       "      <td>1885.740</td>\n",
       "      <td>8</td>\n",
       "      <td>13</td>\n",
       "      <td>3</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>120000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>629</th>\n",
       "      <td>1245.300</td>\n",
       "      <td>1</td>\n",
       "      <td>14</td>\n",
       "      <td>3</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>55000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>630</th>\n",
       "      <td>1174.140</td>\n",
       "      <td>10</td>\n",
       "      <td>12</td>\n",
       "      <td>3</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>41000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>631</th>\n",
       "      <td>1458.780</td>\n",
       "      <td>6</td>\n",
       "      <td>7</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>75000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>632</th>\n",
       "      <td>1305.786</td>\n",
       "      <td>9</td>\n",
       "      <td>12</td>\n",
       "      <td>3</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>42000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>633</th>\n",
       "      <td>1316.460</td>\n",
       "      <td>12</td>\n",
       "      <td>12</td>\n",
       "      <td>3</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>45000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>634</th>\n",
       "      <td>1167.024</td>\n",
       "      <td>2</td>\n",
       "      <td>12</td>\n",
       "      <td>3</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>38000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>635</th>\n",
       "      <td>996.240</td>\n",
       "      <td>3</td>\n",
       "      <td>5</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>28000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>636</th>\n",
       "      <td>1099.422</td>\n",
       "      <td>2</td>\n",
       "      <td>7</td>\n",
       "      <td>4</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>57777</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>637</th>\n",
       "      <td>2042.292</td>\n",
       "      <td>6</td>\n",
       "      <td>18</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>82000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>638</th>\n",
       "      <td>843.246</td>\n",
       "      <td>2</td>\n",
       "      <td>18</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>53000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>639</th>\n",
       "      <td>1889.298</td>\n",
       "      <td>18</td>\n",
       "      <td>21</td>\n",
       "      <td>3</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>70000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>640</th>\n",
       "      <td>1266.648</td>\n",
       "      <td>6</td>\n",
       "      <td>7</td>\n",
       "      <td>3</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>40000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>641</th>\n",
       "      <td>1707.840</td>\n",
       "      <td>9</td>\n",
       "      <td>9</td>\n",
       "      <td>4</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>60000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>642</th>\n",
       "      <td>1707.840</td>\n",
       "      <td>9</td>\n",
       "      <td>15</td>\n",
       "      <td>3</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>80000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>643</th>\n",
       "      <td>2846.400</td>\n",
       "      <td>5</td>\n",
       "      <td>12</td>\n",
       "      <td>4</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>138888</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>644</th>\n",
       "      <td>1359.156</td>\n",
       "      <td>7</td>\n",
       "      <td>15</td>\n",
       "      <td>3</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>45000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>645</th>\n",
       "      <td>377.148</td>\n",
       "      <td>4</td>\n",
       "      <td>10</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>24800</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>646</th>\n",
       "      <td>740.064</td>\n",
       "      <td>13</td>\n",
       "      <td>14</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>45000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>647</th>\n",
       "      <td>1707.840</td>\n",
       "      <td>3</td>\n",
       "      <td>14</td>\n",
       "      <td>3</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>65000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>648</th>\n",
       "      <td>1376.946</td>\n",
       "      <td>6</td>\n",
       "      <td>7</td>\n",
       "      <td>3</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>36000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>645 rows × 7 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "         Sqft  Floor  TotalFloor  Bedroom  Living.Room  Bathroom   Price\n",
       "1    1177.698      2           7        2            2         2   62000\n",
       "2    2134.800      5           7        4            2         2   78000\n",
       "3    1138.560      5           7        2            2         1   58000\n",
       "4    1458.780      2           7        3            2         2   45000\n",
       "5     967.776     11          14        3            2         2   45000\n",
       "6    1127.886     11          12        4            2         2  148000\n",
       "7    1352.040      5           7        3            2         1   58000\n",
       "8     757.854      5          14        1            0         1   48000\n",
       "9    1152.792     10          12        3            2         2   45000\n",
       "10   1423.200      4           5        4            2         2   65000\n",
       "11    668.904      4          11        1            1         1   31000\n",
       "12    711.600      4           7        2            1         1   29002\n",
       "13   1352.040      9          19        4            2         2   39000\n",
       "14    818.340      4          13        2            2         1   48000\n",
       "15   2134.800     10          19        3            2         2   55000\n",
       "16   2768.124      6          19        3            2         2  100000\n",
       "17    711.600      5          13        2            2         1   48000\n",
       "18    462.540      5          13        1            1         1   25000\n",
       "19   2739.660      6          19        3            2         2   90000\n",
       "20   1174.140      3           9        3            2         1   33000\n",
       "21   2490.600     19          21        5            3         4  140000\n",
       "22   2768.124      6          19        3            2         2  100000\n",
       "23    747.180      7          15        2            2         1   55000\n",
       "24   1668.702      4          12        3            2         2  105000\n",
       "25   3664.740     19          19        3            2         3  200000\n",
       "26   1779.000     10          12        4            2         2   98000\n",
       "27   3664.740     19          19        3            2         3  200000\n",
       "28   3059.880     21          24        4            2         3  145000\n",
       "29    533.700      3           4        1            1         1   25000\n",
       "30    853.920      6           7        2            1         1   24028\n",
       "..        ...    ...         ...      ...          ...       ...     ...\n",
       "619  1857.276      2          14        3            2         2   45000\n",
       "620  1102.980      6           7        3            2         2   65000\n",
       "621   925.080      2           4        2            1         1   32000\n",
       "622  2312.700     11          14        3            2         2   99999\n",
       "623   925.080      4           9        3            2         2   45000\n",
       "624   711.600      1           1        3            1         1   12000\n",
       "625  4198.440     15          19        7            3         4  180000\n",
       "626  3095.460      1           6        4            2         3  100000\n",
       "627  2590.224      5          15        3            2         3  110000\n",
       "628  1885.740      8          13        3            2         2  120000\n",
       "629  1245.300      1          14        3            2         2   55000\n",
       "630  1174.140     10          12        3            2         2   41000\n",
       "631  1458.780      6           7        2            2         2   75000\n",
       "632  1305.786      9          12        3            2         2   42000\n",
       "633  1316.460     12          12        3            2         2   45000\n",
       "634  1167.024      2          12        3            2         2   38000\n",
       "635   996.240      3           5        3            1         1   28000\n",
       "636  1099.422      2           7        4            2         2   57777\n",
       "637  2042.292      6          18        2            2         2   82000\n",
       "638   843.246      2          18        1            1         1   53000\n",
       "639  1889.298     18          21        3            2         2   70000\n",
       "640  1266.648      6           7        3            2         2   40000\n",
       "641  1707.840      9           9        4            2         2   60000\n",
       "642  1707.840      9          15        3            2         2   80000\n",
       "643  2846.400      5          12        4            2         2  138888\n",
       "644  1359.156      7          15        3            2         2   45000\n",
       "645   377.148      4          10        1            1         1   24800\n",
       "646   740.064     13          14        1            1         1   45000\n",
       "647  1707.840      3          14        3            2         2   65000\n",
       "648  1376.946      6           7        3            2         1   36000\n",
       "\n",
       "[645 rows x 7 columns]"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "house_data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.neighbors import NearestNeighbors"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "nn = NearestNeighbors(n_neighbors=3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.preprocessing import StandardScaler"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "ss = StandardScaler()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/awantik/anaconda3/lib/python3.7/site-packages/sklearn/preprocessing/data.py:645: DataConversionWarning: Data with input dtype int64, float64 were all converted to float64 by StandardScaler.\n",
      "  return self.partial_fit(X, y)\n",
      "/home/awantik/anaconda3/lib/python3.7/site-packages/sklearn/base.py:464: DataConversionWarning: Data with input dtype int64, float64 were all converted to float64 by StandardScaler.\n",
      "  return self.fit(X, **fit_params).transform(X)\n"
     ]
    }
   ],
   "source": [
    "X_tf = ss.fit_transform(house_data)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "NearestNeighbors(algorithm='auto', leaf_size=30, metric='minkowski',\n",
       "         metric_params=None, n_jobs=None, n_neighbors=3, p=2, radius=1.0)"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "nn.fit(X_tf)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [],
   "source": [
    "houses = nn.kneighbors(X_tf[:1])[1][0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "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>Sqft</th>\n",
       "      <th>Floor</th>\n",
       "      <th>TotalFloor</th>\n",
       "      <th>Bedroom</th>\n",
       "      <th>Living.Room</th>\n",
       "      <th>Bathroom</th>\n",
       "      <th>Price</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1177.698</td>\n",
       "      <td>2</td>\n",
       "      <td>7</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>62000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>181</th>\n",
       "      <td>1494.360</td>\n",
       "      <td>2</td>\n",
       "      <td>7</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>49800</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>334</th>\n",
       "      <td>1494.360</td>\n",
       "      <td>2</td>\n",
       "      <td>7</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>48800</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "         Sqft  Floor  TotalFloor  Bedroom  Living.Room  Bathroom  Price\n",
       "1    1177.698      2           7        2            2         2  62000\n",
       "181  1494.360      2           7        2            2         2  49800\n",
       "334  1494.360      2           7        2            2         2  48800"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "house_data.iloc[houses]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[-4.56393027e-01, -1.01489712e+00, -7.72346989e-01,\n",
       "        -8.28956338e-01,  4.02693633e-01,  2.76918224e-01,\n",
       "         3.70055758e-04]])"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X_tf[:1]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.datasets import fetch_olivetti_faces"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "downloading Olivetti faces from https://ndownloader.figshare.com/files/5976027 to /home/awantik/scikit_learn_data\n"
     ]
    }
   ],
   "source": [
    "faces = fetch_olivetti_faces()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [],
   "source": [
    "targets = faces.target"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.image.AxesImage at 0x7f1f2f4bae48>"
      ]
     },
     "execution_count": 46,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.imshow(faces.images[100], cmap='gray')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "400"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(faces.images)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(64, 64)"
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "faces.images[0].shape"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Dataset description\n",
    "* 40 persons\n",
    "* 10 image per person"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(4096,)"
      ]
     },
     "execution_count": 50,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "faces.data[399].shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(400, 64, 64)"
      ]
     },
     "execution_count": 51,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "faces.images.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(400, 4096)"
      ]
     },
     "execution_count": 52,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "faces.data.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {},
   "outputs": [],
   "source": [
    "train = faces.data[targets < 30]\n",
    "test = faces.data[targets >= 30]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "metadata": {},
   "outputs": [],
   "source": [
    "trainX = train[:,:2048]\n",
    "trainY = train[:,2048:]\n",
    "testX = test[:,:2048]\n",
    "testY = test[:,2048:]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.image.AxesImage at 0x7f1f2f43dc50>"
      ]
     },
     "execution_count": 57,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.imshow(train[0][:2048].reshape(32,64))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.image.AxesImage at 0x7f1f2f2499b0>"
      ]
     },
     "execution_count": 59,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.imshow(trainY[0][:2048].reshape(32,64))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.neighbors import KNeighborsRegressor"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "metadata": {},
   "outputs": [],
   "source": [
    "nn_r = KNeighborsRegressor(n_neighbors=20,weights='distance', n_jobs=-1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "KNeighborsRegressor(algorithm='auto', leaf_size=30, metric='minkowski',\n",
       "          metric_params=None, n_jobs=-1, n_neighbors=20, p=2,\n",
       "          weights='distance')"
      ]
     },
     "execution_count": 65,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "nn_r.fit(trainX,trainY)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "metadata": {},
   "outputs": [],
   "source": [
    "gen = nn_r.predict(testX)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(100, 2048)"
      ]
     },
     "execution_count": 68,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "gen.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 78,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.image.AxesImage at 0x7f1f2e6dca20>"
      ]
     },
     "execution_count": 78,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "res = np.vstack([testX[11].reshape(32,64), gen[11,:].reshape(32,64)])\n",
    "plt.imshow(res, cmap='gray')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 79,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.datasets import load_digits"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 80,
   "metadata": {},
   "outputs": [],
   "source": [
    "digits = load_digits()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 84,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.image.AxesImage at 0x7f1f2f832c88>"
      ]
     },
     "execution_count": 84,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.imshow(digits.images[10], cmap='gray')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 86,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([ 0.,  0.,  5., 13.,  9.,  1.,  0.,  0.,  0.,  0., 13., 15., 10.,\n",
       "       15.,  5.,  0.,  0.,  3., 15.,  2.,  0., 11.,  8.,  0.,  0.,  4.,\n",
       "       12.,  0.,  0.,  8.,  8.,  0.,  0.,  5.,  8.,  0.,  0.,  9.,  8.,\n",
       "        0.,  0.,  4., 11.,  0.,  1., 12.,  7.,  0.,  0.,  2., 14.,  5.,\n",
       "       10., 12.,  0.,  0.,  0.,  0.,  6., 13., 10.,  0.,  0.,  0.])"
      ]
     },
     "execution_count": 86,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "digits.data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 87,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.cluster import KMeans, DBSCAN, AgglomerativeClustering"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 90,
   "metadata": {},
   "outputs": [],
   "source": [
    "km = KMeans(n_clusters=10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 91,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "KMeans(algorithm='auto', copy_x=True, init='k-means++', max_iter=300,\n",
       "    n_clusters=10, n_init=10, n_jobs=None, precompute_distances='auto',\n",
       "    random_state=None, tol=0.0001, verbose=0)"
      ]
     },
     "execution_count": 91,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "km.fit(digits.data)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 104,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(array([  15,   25,   32,   33,   35,   46,   71,   74,  102,  109,  117,\n",
       "         120,  135,  145,  155,  162,  163,  165,  176,  201,  204,  230,\n",
       "         237,  245,  246,  271,  281,  288,  302,  327,  330,  365,  376,\n",
       "         391,  401,  411,  419,  432,  460,  488,  495,  503,  506,  521,\n",
       "         548,  587,  625,  633,  651,  661,  671,  679,  681,  692,  717,\n",
       "         720,  748,  755,  763,  766,  781,  791,  801,  808,  809,  811,\n",
       "         822,  847,  850,  878,  885,  893,  896,  910, 1003, 1034, 1044,\n",
       "        1054, 1061, 1062, 1064, 1075, 1098, 1101, 1129, 1136, 1144, 1147,\n",
       "        1162, 1172, 1182, 1189, 1190, 1192, 1195, 1197, 1203, 1228, 1231,\n",
       "        1233, 1259, 1264, 1266, 1274, 1277, 1302, 1312, 1319, 1320, 1322,\n",
       "        1333, 1358, 1389, 1396, 1407, 1420, 1489, 1532, 1535, 1550, 1560,\n",
       "        1568, 1571, 1575, 1576, 1578, 1580, 1582, 1589, 1614, 1617, 1643,\n",
       "        1650, 1656, 1659, 1662, 1672, 1682, 1690, 1699, 1700, 1702, 1713,\n",
       "        1738, 1765, 1776]),)"
      ]
     },
     "execution_count": 104,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.where(km.labels_ == 2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 105,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.image.AxesImage at 0x7f1f2d882b70>"
      ]
     },
     "execution_count": 105,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAPgAAAD8CAYAAABaQGkdAAAABHNCSVQICAgIfAhkiAAAAAlwSFlzAAALEgAACxIB0t1+/AAAADl0RVh0U29mdHdhcmUAbWF0cGxvdGxpYiB2ZXJzaW9uIDMuMC4zLCBodHRwOi8vbWF0cGxvdGxpYi5vcmcvnQurowAACuNJREFUeJzt3d+LXPUZx/HPpxtL468stDZINmRXkIAUsisSkIBJY1tiFZOLXiSg0FDIlaK2INq7/AOSXhQhRNOAqdJGDSJWK2iwQmtN4rYxbixp2JJttFFK/FXoEn16sZOSpitzNnPO98w8eb9gcWd3yPcZ49tzdnbmfB0RApDTV9oeAEBzCBxIjMCBxAgcSIzAgcQIHEiMwIHECBxIjMCBxBY18YfaLvbyuCVLlpRaSkuXLi22liQtWtTIX8+8Fi9eXGytko4cOVJ0vdnZ2WJrRYS73afcf0ENWbduXbG1HnjggWJrSdLw8HCxtVatWlVsrZLGxsaKrjc9PV10vW44RQcSI3AgMQIHEiNwIDECBxIjcCAxAgcSI3AgsUqB295g+13bx20/1PRQAOrRNXDbQ5J+Luk2STdI2mL7hqYHA9C7Kkfw1ZKOR8SJiJiV9JSkjc2OBaAOVQJfJunkebdnOl8D0OeqvNlkvnes/N+7xWxvk7St54kA1KZK4DOSlp93e0TSqQvvFBE7Je2Uyr5dFMCXq3KK/qak622P2f6qpM2Snmt2LAB16HoEj4iztu+R9JKkIUmPR8TRxicD0LNKF3yIiBckvdDwLABqxivZgMQIHEiMwIHECBxIjMCBxAgcSIzAgcQIHEhs4Hc22bp1a7G11q5dW2wtSfroo4+KrbV9+/Ziax04cKDYWv2200hpHMGBxAgcSIzAgcQIHEiMwIHECBxIjMCBxAgcSIzAgcSq7GzyuO3Ttt8uMRCA+lQ5gv9C0oaG5wDQgK6BR8Rrkv5ZYBYANeNncCCx2t5NxtZFQP+pLXC2LgL6D6foQGJVfk32pKTfS1ppe8b2j5ofC0AdquxNtqXEIADqxyk6kBiBA4kROJAYgQOJETiQGIEDiRE4kBiBA4kN/NZFk5OTxdYaHx8vtpZU9rHt2LGj2FpnzpwpttaljiM4kBiBA4kROJAYgQOJETiQGIEDiRE4kBiBA4kROJAYgQOJVbno4nLbr9qesn3U9n0lBgPQuyqvRT8r6ScRcdj2VZIO2X45It5peDYAPaqyN9l7EXG48/knkqYkLWt6MAC9W9C7yWyPSpqQ9MY832PrIqDPVA7c9pWSnpZ0f0R8fOH32boI6D+VnkW3fZnm4t4bEc80OxKAulR5Ft2SHpM0FRGPND8SgLpUOYKvkXS3pPW2Jzsf3294LgA1qLI32euSXGAWADXjlWxAYgQOJEbgQGIEDiRG4EBiBA4kRuBAYgQOJDbwe5OVtGLFirTrldwHbXR0tNhalzqO4EBiBA4kRuBAYgQOJEbgQGIEDiRG4EBiBA4kRuBAYlUuuvg123+0/afO1kXbSwwGoHdVXqr6b0nrI+LTzuWTX7f9m4j4Q8OzAehRlYsuhqRPOzcv63ywsQEwAKpufDBke1LSaUkvR8S8WxfZPmj7YN1DArg4lQKPiM8jYlzSiKTVtr81z312RsRNEXFT3UMCuDgLehY9Is5IOiBpQyPTAKhVlWfRr7E93Pl8saTvSDrW9GAAelflWfRrJe2xPaS5/yH8KiKeb3YsAHWo8iz6nzW3JziAAcMr2YDECBxIjMCBxAgcSIzAgcQIHEiMwIHECBxIzHPvBq35D7WLvZ10eHi41FLatGlTsbVK2717d7G1bBdbK7OI6PovkiM4kBiBA4kROJAYgQOJETiQGIEDiRE4kBiBA4kROJBY5cA710Z/yzbXYwMGxEKO4PdJmmpqEAD1q7qzyYik2yXtanYcAHWqegTfIelBSV80OAuAmlXZ+OAOSacj4lCX+7E3GdBnqhzB10i60/a0pKckrbf9xIV3Ym8yoP90DTwiHo6IkYgYlbRZ0isRcVfjkwHoGb8HBxKrsjfZf0XEAc3tLgpgAHAEBxIjcCAxAgcSI3AgMQIHEiNwIDECBxIjcCCxgd+6KLONGzcWW2v//v3F1pqYmCi21uTkZLG1SmPrIuASR+BAYgQOJEbgQGIEDiRG4EBiBA4kRuBAYgQOJFbpkk2dK6p+IulzSWe5ciowGBZyTbZvR8SHjU0CoHacogOJVQ08JP3W9iHb25ocCEB9qp6ir4mIU7a/Kell28ci4rXz79AJn/iBPlLpCB4Rpzr/PC3pWUmr57kPWxcBfabK5oNX2L7q3OeSvifp7aYHA9C7KqfoSyU9a/vc/X8ZES82OhWAWnQNPCJOSFpVYBYANePXZEBiBA4kRuBAYgQOJEbgQGIEDiRG4EBiBA4kNvBbFw0PD5daSmvXri22liTt2bOn2FrT09PF1hofHy+2VmZsXQRc4ggcSIzAgcQIHEiMwIHECBxIjMCBxAgcSIzAgcQqBW572PY+28dsT9m+uenBAPSu6nXRfybpxYj4ge2vSrq8wZkA1KRr4LavlnSLpB9KUkTMSpptdiwAdahyin6dpA8k7bb9lu1dneujA+hzVQJfJOlGSY9GxISkzyQ9dOGdbG+zfdD2wZpnBHCRqgQ+I2kmIt7o3N6nueD/B1sXAf2na+AR8b6kk7ZXdr50q6R3Gp0KQC2qPot+r6S9nWfQT0ja2txIAOpSKfCImJTEqTcwYHglG5AYgQOJETiQGIEDiRE4kBiBA4kROJAYgQOJETiQ2MDvTbZu3bpSS2n//v3F1pKkJUuWFFtrbGys2Fol90HLjL3JgEscgQOJETiQGIEDiRE4kBiBA4kROJAYgQOJETiQWNfAba+0PXnex8e27y8xHIDedL3oYkS8K2lckmwPSfq7pGcbngtADRZ6in6rpL9GxN+aGAZAvapeF/2czZKenO8btrdJ2tbzRABqU/kI3tn04E5Jv57v+2xdBPSfhZyi3ybpcET8o6lhANRrIYFv0ZecngPoT5UCt325pO9KeqbZcQDUqereZP+S9PWGZwFQM17JBiRG4EBiBA4kRuBAYgQOJEbgQGIEDiRG4EBiTW1d9IGkhb6l9BuSPqx9mP6Q9bHxuNqzIiKu6XanRgK/GLYPZn0nWtbHxuPqf5yiA4kROJBYPwW+s+0BGpT1sfG4+lzf/AwOoH79dAQHULO+CNz2Btvv2j5u+6G256mD7eW2X7U9Zfuo7fvanqlOtodsv2X7+bZnqZPtYdv7bB/r/N3d3PZMvWj9FL1zrfW/aO6KMTOS3pS0JSLeaXWwHtm+VtK1EXHY9lWSDknaNOiP6xzbP5Z0k6SrI+KOtuepi+09kn4XEbs6Fxq9PCLOtD3XxeqHI/hqSccj4kREzEp6StLGlmfqWUS8FxGHO59/ImlK0rJ2p6qH7RFJt0va1fYsdbJ9taRbJD0mSRExO8hxS/0R+DJJJ8+7PaMkIZxje1TShKQ32p2kNjskPSjpi7YHqdl1kj6QtLvz48cu21e0PVQv+iFwz/O1NE/t275S0tOS7o+Ij9uep1e275B0OiIOtT1LAxZJulHSoxExIekzSQP9nFA/BD4jafl5t0cknWppllrZvkxzce+NiCxXpF0j6U7b05r7cWq97SfaHak2M5JmIuLcmdY+zQU/sPoh8DclXW97rPOkxmZJz7U8U89sW3M/y01FxCNtz1OXiHg4IkYiYlRzf1evRMRdLY9Vi4h4X9JJ2ys7X7pV0kA/KbrQvclqFxFnbd8j6SVJQ5Iej4ijLY9VhzWS7pZ0xPZk52s/jYgXWpwJ3d0raW/nYHNC0taW5+lJ678mA9CcfjhFB9AQAgcSI3AgMQIHEiNwIDECBxIjcCAxAgcS+w99zJUAPz+1RwAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.imshow(digits.images[15], cmap='gray')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 106,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.image.AxesImage at 0x7f1f2d83d470>"
      ]
     },
     "execution_count": 106,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.imshow(digits.images[25],cmap='gray')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 108,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "5"
      ]
     },
     "execution_count": 108,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "digits.target[25]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 110,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(1797,)"
      ]
     },
     "execution_count": 110,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "digits.target.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 112,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.image.AxesImage at 0x7f1f14f1d198>"
      ]
     },
     "execution_count": 112,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "from skimage import io\n",
    "image = io.imread('Data/nadal.jpg')\n",
    "plt.imshow(image)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 113,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(428, 752, 3)"
      ]
     },
     "execution_count": 113,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "image.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 115,
   "metadata": {},
   "outputs": [],
   "source": [
    "image = image/255.0"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 118,
   "metadata": {},
   "outputs": [],
   "source": [
    "image_data = image.reshape(-1,3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 119,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(321856, 3)"
      ]
     },
     "execution_count": 119,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "image_data.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 120,
   "metadata": {},
   "outputs": [],
   "source": [
    "km = KMeans(n_clusters=3, n_jobs=-1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 121,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "KMeans(algorithm='auto', copy_x=True, init='k-means++', max_iter=300,\n",
       "    n_clusters=3, n_init=10, n_jobs=-1, precompute_distances='auto',\n",
       "    random_state=None, tol=0.0001, verbose=0)"
      ]
     },
     "execution_count": 121,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "km.fit(image_data)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 122,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[0.67232142, 0.70628033, 0.77136762],\n",
       "       [0.10816772, 0.10251858, 0.10226602],\n",
       "       [0.392361  , 0.33929145, 0.32520442]])"
      ]
     },
     "execution_count": 122,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "km.cluster_centers_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 130,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[0.10816772, 0.10251858, 0.10226602],\n",
       "       [0.10816772, 0.10251858, 0.10226602],\n",
       "       [0.67232142, 0.70628033, 0.77136762]])"
      ]
     },
     "execution_count": 130,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "km.cluster_centers_[[1,1,0]]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 125,
   "metadata": {},
   "outputs": [],
   "source": [
    "data = km.cluster_centers_[km.labels_]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 127,
   "metadata": {},
   "outputs": [],
   "source": [
    "img = data.reshape(428, 752, 3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 128,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.image.AxesImage at 0x7f1f146ca3c8>"
      ]
     },
     "execution_count": 128,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.imshow(img)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
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