ML 2 Heirachicaly clustering modelling.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "cf253d55",
   "metadata": {},
   "outputs": [],
   "source": [
    "#Import basic library libraries\n",
    "\n",
    "import pandas as pd \n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "sns.set()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "3b639f7b",
   "metadata": {},
   "outputs": [],
   "source": [
    "cars=pd.read_csv(r'C:\\Users\\ASUS\\Downloads\\cars_clus (1).csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "896e015e",
   "metadata": {},
   "outputs": [
    {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>manufact</th>\n",
       "      <th>model</th>\n",
       "      <th>sales</th>\n",
       "      <th>resale</th>\n",
       "      <th>type</th>\n",
       "      <th>price</th>\n",
       "      <th>engine_s</th>\n",
       "      <th>horsepow</th>\n",
       "      <th>wheelbas</th>\n",
       "      <th>width</th>\n",
       "      <th>length</th>\n",
       "      <th>curb_wgt</th>\n",
       "      <th>fuel_cap</th>\n",
       "      <th>mpg</th>\n",
       "      <th>lnsales</th>\n",
       "      <th>partition</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Acura</td>\n",
       "      <td>Integra</td>\n",
       "      <td>16.919</td>\n",
       "      <td>16.360</td>\n",
       "      <td>0</td>\n",
       "      <td>21.50</td>\n",
       "      <td>1.8</td>\n",
       "      <td>140</td>\n",
       "      <td>101.2</td>\n",
       "      <td>67.3</td>\n",
       "      <td>172.4</td>\n",
       "      <td>2.639</td>\n",
       "      <td>13.2</td>\n",
       "      <td>28.0</td>\n",
       "      <td>2.828</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Acura</td>\n",
       "      <td>TL</td>\n",
       "      <td>39.384</td>\n",
       "      <td>19.875</td>\n",
       "      <td>0</td>\n",
       "      <td>28.40</td>\n",
       "      <td>3.2</td>\n",
       "      <td>225</td>\n",
       "      <td>108.1</td>\n",
       "      <td>70.3</td>\n",
       "      <td>192.9</td>\n",
       "      <td>3.517</td>\n",
       "      <td>17.2</td>\n",
       "      <td>25.0</td>\n",
       "      <td>3.673</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Acura</td>\n",
       "      <td>RL</td>\n",
       "      <td>8.588</td>\n",
       "      <td>29.725</td>\n",
       "      <td>0</td>\n",
       "      <td>42.00</td>\n",
       "      <td>3.5</td>\n",
       "      <td>210</td>\n",
       "      <td>114.6</td>\n",
       "      <td>71.4</td>\n",
       "      <td>196.6</td>\n",
       "      <td>3.850</td>\n",
       "      <td>18.0</td>\n",
       "      <td>22.0</td>\n",
       "      <td>2.150</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Audi</td>\n",
       "      <td>A4</td>\n",
       "      <td>20.397</td>\n",
       "      <td>22.255</td>\n",
       "      <td>0</td>\n",
       "      <td>23.99</td>\n",
       "      <td>1.8</td>\n",
       "      <td>150</td>\n",
       "      <td>102.6</td>\n",
       "      <td>68.2</td>\n",
       "      <td>178.0</td>\n",
       "      <td>2.998</td>\n",
       "      <td>16.4</td>\n",
       "      <td>27.0</td>\n",
       "      <td>3.015</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Audi</td>\n",
       "      <td>A6</td>\n",
       "      <td>18.780</td>\n",
       "      <td>23.555</td>\n",
       "      <td>0</td>\n",
       "      <td>33.95</td>\n",
       "      <td>2.8</td>\n",
       "      <td>200</td>\n",
       "      <td>108.7</td>\n",
       "      <td>76.1</td>\n",
       "      <td>192.0</td>\n",
       "      <td>3.561</td>\n",
       "      <td>18.5</td>\n",
       "      <td>22.0</td>\n",
       "      <td>2.933</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
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      ],
      "text/plain": [
       "  manufact    model   sales  resale  type  price  engine_s  horsepow  \\\n",
       "0    Acura  Integra  16.919  16.360     0  21.50       1.8       140   \n",
       "1    Acura       TL  39.384  19.875     0  28.40       3.2       225   \n",
       "2    Acura       RL   8.588  29.725     0  42.00       3.5       210   \n",
       "3     Audi       A4  20.397  22.255     0  23.99       1.8       150   \n",
       "4     Audi       A6  18.780  23.555     0  33.95       2.8       200   \n",
       "\n",
       "   wheelbas  width  length  curb_wgt  fuel_cap   mpg  lnsales  partition  \n",
       "0     101.2   67.3   172.4     2.639      13.2  28.0    2.828          0  \n",
       "1     108.1   70.3   192.9     3.517      17.2  25.0    3.673          0  \n",
       "2     114.6   71.4   196.6     3.850      18.0  22.0    2.150          0  \n",
       "3     102.6   68.2   178.0     2.998      16.4  27.0    3.015          0  \n",
       "4     108.7   76.1   192.0     3.561      18.5  22.0    2.933          0  "
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cars.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "6b51be9b",
   "metadata": {},
   "outputs": [],
   "source": [
    "cars_cleaned=cars.drop(columns=['manufact','model'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "0c1264cd",
   "metadata": {},
   "outputs": [
    {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>sales</th>\n",
       "      <th>resale</th>\n",
       "      <th>type</th>\n",
       "      <th>price</th>\n",
       "      <th>engine_s</th>\n",
       "      <th>horsepow</th>\n",
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       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>16.919</td>\n",
       "      <td>16.360</td>\n",
       "      <td>0</td>\n",
       "      <td>21.50</td>\n",
       "      <td>1.8</td>\n",
       "      <td>140</td>\n",
       "      <td>101.2</td>\n",
       "      <td>67.3</td>\n",
       "      <td>172.4</td>\n",
       "      <td>2.639</td>\n",
       "      <td>13.2</td>\n",
       "      <td>28.0</td>\n",
       "      <td>2.828</td>\n",
       "      <td>0</td>\n",
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       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>39.384</td>\n",
       "      <td>19.875</td>\n",
       "      <td>0</td>\n",
       "      <td>28.40</td>\n",
       "      <td>3.2</td>\n",
       "      <td>225</td>\n",
       "      <td>108.1</td>\n",
       "      <td>70.3</td>\n",
       "      <td>192.9</td>\n",
       "      <td>3.517</td>\n",
       "      <td>17.2</td>\n",
       "      <td>25.0</td>\n",
       "      <td>3.673</td>\n",
       "      <td>0</td>\n",
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       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>8.588</td>\n",
       "      <td>29.725</td>\n",
       "      <td>0</td>\n",
       "      <td>42.00</td>\n",
       "      <td>3.5</td>\n",
       "      <td>210</td>\n",
       "      <td>114.6</td>\n",
       "      <td>71.4</td>\n",
       "      <td>196.6</td>\n",
       "      <td>3.850</td>\n",
       "      <td>18.0</td>\n",
       "      <td>22.0</td>\n",
       "      <td>2.150</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>20.397</td>\n",
       "      <td>22.255</td>\n",
       "      <td>0</td>\n",
       "      <td>23.99</td>\n",
       "      <td>1.8</td>\n",
       "      <td>150</td>\n",
       "      <td>102.6</td>\n",
       "      <td>68.2</td>\n",
       "      <td>178.0</td>\n",
       "      <td>2.998</td>\n",
       "      <td>16.4</td>\n",
       "      <td>27.0</td>\n",
       "      <td>3.015</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>18.780</td>\n",
       "      <td>23.555</td>\n",
       "      <td>0</td>\n",
       "      <td>33.95</td>\n",
       "      <td>2.8</td>\n",
       "      <td>200</td>\n",
       "      <td>108.7</td>\n",
       "      <td>76.1</td>\n",
       "      <td>192.0</td>\n",
       "      <td>3.561</td>\n",
       "      <td>18.5</td>\n",
       "      <td>22.0</td>\n",
       "      <td>2.933</td>\n",
       "      <td>0</td>\n",
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      "text/plain": [
       "    sales  resale  type  price  engine_s  horsepow  wheelbas  width  length  \\\n",
       "0  16.919  16.360     0  21.50       1.8       140     101.2   67.3   172.4   \n",
       "1  39.384  19.875     0  28.40       3.2       225     108.1   70.3   192.9   \n",
       "2   8.588  29.725     0  42.00       3.5       210     114.6   71.4   196.6   \n",
       "3  20.397  22.255     0  23.99       1.8       150     102.6   68.2   178.0   \n",
       "4  18.780  23.555     0  33.95       2.8       200     108.7   76.1   192.0   \n",
       "\n",
       "   curb_wgt  fuel_cap   mpg  lnsales  partition  \n",
       "0     2.639      13.2  28.0    2.828          0  \n",
       "1     3.517      17.2  25.0    3.673          0  \n",
       "2     3.850      18.0  22.0    2.150          0  \n",
       "3     2.998      16.4  27.0    3.015          0  \n",
       "4     3.561      18.5  22.0    2.933          0  "
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cars_cleaned.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "8bfe7096",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 117 entries, 0 to 116\n",
      "Data columns (total 14 columns):\n",
      " #   Column     Non-Null Count  Dtype  \n",
      "---  ------     --------------  -----  \n",
      " 0   sales      117 non-null    float64\n",
      " 1   resale     117 non-null    float64\n",
      " 2   type       117 non-null    int64  \n",
      " 3   price      117 non-null    float64\n",
      " 4   engine_s   117 non-null    float64\n",
      " 5   horsepow   117 non-null    int64  \n",
      " 6   wheelbas   117 non-null    float64\n",
      " 7   width      117 non-null    float64\n",
      " 8   length     117 non-null    float64\n",
      " 9   curb_wgt   117 non-null    float64\n",
      " 10  fuel_cap   117 non-null    float64\n",
      " 11  mpg        117 non-null    float64\n",
      " 12  lnsales    117 non-null    float64\n",
      " 13  partition  117 non-null    int64  \n",
      "dtypes: float64(11), int64(3)\n",
      "memory usage: 12.9 KB\n"
     ]
    }
   ],
   "source": [
    "cars_cleaned.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "12193cb1",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "sales        0\n",
       "resale       0\n",
       "type         0\n",
       "price        0\n",
       "engine_s     0\n",
       "horsepow     0\n",
       "wheelbas     0\n",
       "width        0\n",
       "length       0\n",
       "curb_wgt     0\n",
       "fuel_cap     0\n",
       "mpg          0\n",
       "lnsales      0\n",
       "partition    0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cars_cleaned.isnull().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "e1ac2413",
   "metadata": {},
   "outputs": [
    {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>sales</th>\n",
       "      <th>resale</th>\n",
       "      <th>type</th>\n",
       "      <th>price</th>\n",
       "      <th>engine_s</th>\n",
       "      <th>horsepow</th>\n",
       "      <th>wheelbas</th>\n",
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       "      <th>curb_wgt</th>\n",
       "      <th>fuel_cap</th>\n",
       "      <th>mpg</th>\n",
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       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>117.000000</td>\n",
       "      <td>117.000000</td>\n",
       "      <td>117.000000</td>\n",
       "      <td>117.000000</td>\n",
       "      <td>117.000000</td>\n",
       "      <td>117.000000</td>\n",
       "      <td>117.000000</td>\n",
       "      <td>117.000000</td>\n",
       "      <td>117.000000</td>\n",
       "      <td>117.000000</td>\n",
       "      <td>117.000000</td>\n",
       "      <td>117.000000</td>\n",
       "      <td>117.000000</td>\n",
       "      <td>117.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>59.112316</td>\n",
       "      <td>18.031538</td>\n",
       "      <td>0.247863</td>\n",
       "      <td>25.969487</td>\n",
       "      <td>3.048718</td>\n",
       "      <td>181.282051</td>\n",
       "      <td>107.326496</td>\n",
       "      <td>71.189744</td>\n",
       "      <td>187.717949</td>\n",
       "      <td>3.324051</td>\n",
       "      <td>17.812821</td>\n",
       "      <td>24.117949</td>\n",
       "      <td>3.401179</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>75.058933</td>\n",
       "      <td>11.605632</td>\n",
       "      <td>0.433629</td>\n",
       "      <td>14.149699</td>\n",
       "      <td>1.055169</td>\n",
       "      <td>58.591786</td>\n",
       "      <td>8.050588</td>\n",
       "      <td>3.530151</td>\n",
       "      <td>13.849926</td>\n",
       "      <td>0.597177</td>\n",
       "      <td>3.794609</td>\n",
       "      <td>4.404164</td>\n",
       "      <td>1.337862</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>0.110000</td>\n",
       "      <td>5.160000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>9.235000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>55.000000</td>\n",
       "      <td>92.600000</td>\n",
       "      <td>62.600000</td>\n",
       "      <td>149.400000</td>\n",
       "      <td>1.895000</td>\n",
       "      <td>10.300000</td>\n",
       "      <td>15.000000</td>\n",
       "      <td>-2.207000</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>16.767000</td>\n",
       "      <td>11.240000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>16.980000</td>\n",
       "      <td>2.200000</td>\n",
       "      <td>140.000000</td>\n",
       "      <td>102.400000</td>\n",
       "      <td>68.500000</td>\n",
       "      <td>177.500000</td>\n",
       "      <td>2.911000</td>\n",
       "      <td>15.300000</td>\n",
       "      <td>22.000000</td>\n",
       "      <td>2.819000</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>32.299000</td>\n",
       "      <td>14.010000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>21.665000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>175.000000</td>\n",
       "      <td>107.000000</td>\n",
       "      <td>70.400000</td>\n",
       "      <td>187.800000</td>\n",
       "      <td>3.340000</td>\n",
       "      <td>17.200000</td>\n",
       "      <td>24.000000</td>\n",
       "      <td>3.475000</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>76.029000</td>\n",
       "      <td>19.875000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>29.465000</td>\n",
       "      <td>3.800000</td>\n",
       "      <td>210.000000</td>\n",
       "      <td>111.600000</td>\n",
       "      <td>73.600000</td>\n",
       "      <td>196.500000</td>\n",
       "      <td>3.823000</td>\n",
       "      <td>19.800000</td>\n",
       "      <td>26.000000</td>\n",
       "      <td>4.331000</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>540.561000</td>\n",
       "      <td>67.550000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>82.600000</td>\n",
       "      <td>8.000000</td>\n",
       "      <td>450.000000</td>\n",
       "      <td>138.700000</td>\n",
       "      <td>79.300000</td>\n",
       "      <td>224.500000</td>\n",
       "      <td>5.115000</td>\n",
       "      <td>32.000000</td>\n",
       "      <td>45.000000</td>\n",
       "      <td>6.293000</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "            sales      resale        type       price    engine_s    horsepow  \\\n",
       "count  117.000000  117.000000  117.000000  117.000000  117.000000  117.000000   \n",
       "mean    59.112316   18.031538    0.247863   25.969487    3.048718  181.282051   \n",
       "std     75.058933   11.605632    0.433629   14.149699    1.055169   58.591786   \n",
       "min      0.110000    5.160000    0.000000    9.235000    1.000000   55.000000   \n",
       "25%     16.767000   11.240000    0.000000   16.980000    2.200000  140.000000   \n",
       "50%     32.299000   14.010000    0.000000   21.665000    3.000000  175.000000   \n",
       "75%     76.029000   19.875000    0.000000   29.465000    3.800000  210.000000   \n",
       "max    540.561000   67.550000    1.000000   82.600000    8.000000  450.000000   \n",
       "\n",
       "         wheelbas       width      length    curb_wgt    fuel_cap         mpg  \\\n",
       "count  117.000000  117.000000  117.000000  117.000000  117.000000  117.000000   \n",
       "mean   107.326496   71.189744  187.717949    3.324051   17.812821   24.117949   \n",
       "std      8.050588    3.530151   13.849926    0.597177    3.794609    4.404164   \n",
       "min     92.600000   62.600000  149.400000    1.895000   10.300000   15.000000   \n",
       "25%    102.400000   68.500000  177.500000    2.911000   15.300000   22.000000   \n",
       "50%    107.000000   70.400000  187.800000    3.340000   17.200000   24.000000   \n",
       "75%    111.600000   73.600000  196.500000    3.823000   19.800000   26.000000   \n",
       "max    138.700000   79.300000  224.500000    5.115000   32.000000   45.000000   \n",
       "\n",
       "          lnsales  partition  \n",
       "count  117.000000      117.0  \n",
       "mean     3.401179        0.0  \n",
       "std      1.337862        0.0  \n",
       "min     -2.207000        0.0  \n",
       "25%      2.819000        0.0  \n",
       "50%      3.475000        0.0  \n",
       "75%      4.331000        0.0  \n",
       "max      6.293000        0.0  "
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cars_cleaned.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "a4b4d33b",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='price', ylabel='mpg'>"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#Plotting the data\n",
    "\n",
    " \n",
    "\n",
    "sns.scatterplot(data=cars_cleaned,x=\"price\",y=\"mpg\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "bf6bdd49",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='sales', ylabel='Count'>"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "\n",
    "\n",
    "#visualising the distribution of sales\n",
    "\n",
    " \n",
    "\n",
    "sns.histplot(cars_cleaned[\"sales\"])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "ad1ca08d",
   "metadata": {},
   "outputs": [],
   "source": [
    "#Lets preprocess the data\n",
    "\n",
    " \n",
    "\n",
    "from sklearn.preprocessing import MinMaxScaler\n",
    "\n",
    "scaling = MinMaxScaler()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "bdaf5ca7",
   "metadata": {},
   "outputs": [],
   "source": [
    "car_scaled=scaling.fit_transform(cars_cleaned)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "5f393cc5",
   "metadata": {},
   "outputs": [],
   "source": [
    "car_scaled=pd.DataFrame(car_scaled,columns=cars_cleaned.columns)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "896d7f03",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "\n",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>sales</th>\n",
       "      <th>resale</th>\n",
       "      <th>type</th>\n",
       "      <th>price</th>\n",
       "      <th>engine_s</th>\n",
       "      <th>horsepow</th>\n",
       "      <th>wheelbas</th>\n",
       "      <th>width</th>\n",
       "      <th>length</th>\n",
       "      <th>curb_wgt</th>\n",
       "      <th>fuel_cap</th>\n",
       "      <th>mpg</th>\n",
       "      <th>lnsales</th>\n",
       "      <th>partition</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0.031102</td>\n",
       "      <td>0.179516</td>\n",
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       "      <td>0.0</td>\n",
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       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0.072669</td>\n",
       "      <td>0.235855</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.261228</td>\n",
       "      <td>0.314286</td>\n",
       "      <td>0.430380</td>\n",
       "      <td>0.336226</td>\n",
       "      <td>0.461078</td>\n",
       "      <td>0.579228</td>\n",
       "      <td>0.503727</td>\n",
       "      <td>0.317972</td>\n",
       "      <td>0.333333</td>\n",
       "      <td>0.691765</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0.015687</td>\n",
       "      <td>0.393733</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.446603</td>\n",
       "      <td>0.357143</td>\n",
       "      <td>0.392405</td>\n",
       "      <td>0.477223</td>\n",
       "      <td>0.526946</td>\n",
       "      <td>0.628495</td>\n",
       "      <td>0.607143</td>\n",
       "      <td>0.354839</td>\n",
       "      <td>0.233333</td>\n",
       "      <td>0.512588</td>\n",
       "      <td>0.0</td>\n",
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       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0.037537</td>\n",
       "      <td>0.274002</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.201118</td>\n",
       "      <td>0.114286</td>\n",
       "      <td>0.240506</td>\n",
       "      <td>0.216920</td>\n",
       "      <td>0.335329</td>\n",
       "      <td>0.380826</td>\n",
       "      <td>0.342547</td>\n",
       "      <td>0.281106</td>\n",
       "      <td>0.400000</td>\n",
       "      <td>0.614353</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0.034545</td>\n",
       "      <td>0.294839</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.336877</td>\n",
       "      <td>0.257143</td>\n",
       "      <td>0.367089</td>\n",
       "      <td>0.349241</td>\n",
       "      <td>0.808383</td>\n",
       "      <td>0.567244</td>\n",
       "      <td>0.517391</td>\n",
       "      <td>0.377880</td>\n",
       "      <td>0.233333</td>\n",
       "      <td>0.604706</td>\n",
       "      <td>0.0</td>\n",
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       "    <tr>\n",
       "      <th>112</th>\n",
       "      <td>0.017857</td>\n",
       "      <td>0.100417</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.077217</td>\n",
       "      <td>0.142857</td>\n",
       "      <td>0.151899</td>\n",
       "      <td>0.136659</td>\n",
       "      <td>0.341317</td>\n",
       "      <td>0.185087</td>\n",
       "      <td>0.270807</td>\n",
       "      <td>0.193548</td>\n",
       "      <td>0.366667</td>\n",
       "      <td>0.527647</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>113</th>\n",
       "      <td>0.154706</td>\n",
       "      <td>0.129508</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.101752</td>\n",
       "      <td>0.142857</td>\n",
       "      <td>0.151899</td>\n",
       "      <td>0.136659</td>\n",
       "      <td>0.341317</td>\n",
       "      <td>0.304927</td>\n",
       "      <td>0.297516</td>\n",
       "      <td>0.193548</td>\n",
       "      <td>0.366667</td>\n",
       "      <td>0.780471</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>114</th>\n",
       "      <td>0.094351</td>\n",
       "      <td>0.185366</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.163089</td>\n",
       "      <td>0.114286</td>\n",
       "      <td>0.240506</td>\n",
       "      <td>0.299349</td>\n",
       "      <td>0.353293</td>\n",
       "      <td>0.462051</td>\n",
       "      <td>0.356522</td>\n",
       "      <td>0.281106</td>\n",
       "      <td>0.400000</td>\n",
       "      <td>0.722471</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>115</th>\n",
       "      <td>0.017502</td>\n",
       "      <td>0.182962</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.146596</td>\n",
       "      <td>0.142857</td>\n",
       "      <td>0.151899</td>\n",
       "      <td>0.104121</td>\n",
       "      <td>0.245509</td>\n",
       "      <td>0.146471</td>\n",
       "      <td>0.367702</td>\n",
       "      <td>0.156682</td>\n",
       "      <td>0.366667</td>\n",
       "      <td>0.525412</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>116</th>\n",
       "      <td>0.010151</td>\n",
       "      <td>0.137843</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.112656</td>\n",
       "      <td>0.142857</td>\n",
       "      <td>0.151899</td>\n",
       "      <td>0.136659</td>\n",
       "      <td>0.341317</td>\n",
       "      <td>0.185087</td>\n",
       "      <td>0.269255</td>\n",
       "      <td>0.198157</td>\n",
       "      <td>0.366667</td>\n",
       "      <td>0.462235</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>117 rows × 14 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "        sales    resale  type     price  engine_s  horsepow  wheelbas  \\\n",
       "0    0.031102  0.179516   0.0  0.167178  0.114286  0.215190  0.186551   \n",
       "1    0.072669  0.235855   0.0  0.261228  0.314286  0.430380  0.336226   \n",
       "2    0.015687  0.393733   0.0  0.446603  0.357143  0.392405  0.477223   \n",
       "3    0.037537  0.274002   0.0  0.201118  0.114286  0.240506  0.216920   \n",
       "4    0.034545  0.294839   0.0  0.336877  0.257143  0.367089  0.349241   \n",
       "..        ...       ...   ...       ...       ...       ...       ...   \n",
       "112  0.017857  0.100417   0.0  0.077217  0.142857  0.151899  0.136659   \n",
       "113  0.154706  0.129508   0.0  0.101752  0.142857  0.151899  0.136659   \n",
       "114  0.094351  0.185366   0.0  0.163089  0.114286  0.240506  0.299349   \n",
       "115  0.017502  0.182962   0.0  0.146596  0.142857  0.151899  0.104121   \n",
       "116  0.010151  0.137843   0.0  0.112656  0.142857  0.151899  0.136659   \n",
       "\n",
       "        width    length  curb_wgt  fuel_cap       mpg   lnsales  partition  \n",
       "0    0.281437  0.306258  0.231056  0.133641  0.433333  0.592353        0.0  \n",
       "1    0.461078  0.579228  0.503727  0.317972  0.333333  0.691765        0.0  \n",
       "2    0.526946  0.628495  0.607143  0.354839  0.233333  0.512588        0.0  \n",
       "3    0.335329  0.380826  0.342547  0.281106  0.400000  0.614353        0.0  \n",
       "4    0.808383  0.567244  0.517391  0.377880  0.233333  0.604706        0.0  \n",
       "..        ...       ...       ...       ...       ...       ...        ...  \n",
       "112  0.341317  0.185087  0.270807  0.193548  0.366667  0.527647        0.0  \n",
       "113  0.341317  0.304927  0.297516  0.193548  0.366667  0.780471        0.0  \n",
       "114  0.353293  0.462051  0.356522  0.281106  0.400000  0.722471        0.0  \n",
       "115  0.245509  0.146471  0.367702  0.156682  0.366667  0.525412        0.0  \n",
       "116  0.341317  0.185087  0.269255  0.198157  0.366667  0.462235        0.0  \n",
       "\n",
       "[117 rows x 14 columns]"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "car_scaled"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "5934e2da",
   "metadata": {},
   "outputs": [],
   "source": [
    "#building heirachical model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "598c77a4",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "\n",
    "\n",
    "import scipy.cluster.hierarchy as sch\n",
    "\n",
    "\n",
    "\n",
    "dendogram=sch.dendrogram(sch.linkage(car_scaled,method=\"ward\"))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "392a6788",
   "metadata": {},
   "outputs": [],
   "source": [
    "\n",
    "\n",
    "from sklearn.cluster import AgglomerativeClustering"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "79f99a48",
   "metadata": {},
   "outputs": [],
   "source": [
    "hc=AgglomerativeClustering(n_clusters=2,affinity=\"euclidean\",linkage=\"ward\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "3ef74b18",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,\n",
       "       0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 1, 1,\n",
       "       1, 1, 1, 0, 0, 1, 1, 1, 0, 0, 0, 0, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0,\n",
       "       0, 0, 0, 1, 1, 0, 0, 0, 0, 1, 1, 0, 0, 0, 0, 0, 0, 0, 1, 1, 0, 0,\n",
       "       1, 1, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1,\n",
       "       1, 1, 0, 0, 0, 0, 0], dtype=int64)"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "clusters=hc.fit_predict(car_scaled)\n",
    "clusters"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "id": "bab935c0",
   "metadata": {},
   "outputs": [],
   "source": [
    "cars['cluster']=clusters"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "id": "aa218570",
   "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>manufact</th>\n",
       "      <th>model</th>\n",
       "      <th>sales</th>\n",
       "      <th>resale</th>\n",
       "      <th>type</th>\n",
       "      <th>price</th>\n",
       "      <th>engine_s</th>\n",
       "      <th>horsepow</th>\n",
       "      <th>wheelbas</th>\n",
       "      <th>width</th>\n",
       "      <th>length</th>\n",
       "      <th>curb_wgt</th>\n",
       "      <th>fuel_cap</th>\n",
       "      <th>mpg</th>\n",
       "      <th>lnsales</th>\n",
       "      <th>partition</th>\n",
       "      <th>cluster</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Acura</td>\n",
       "      <td>Integra</td>\n",
       "      <td>16.919</td>\n",
       "      <td>16.360</td>\n",
       "      <td>0</td>\n",
       "      <td>21.50</td>\n",
       "      <td>1.8</td>\n",
       "      <td>140</td>\n",
       "      <td>101.2</td>\n",
       "      <td>67.3</td>\n",
       "      <td>172.4</td>\n",
       "      <td>2.639</td>\n",
       "      <td>13.2</td>\n",
       "      <td>28.0</td>\n",
       "      <td>2.828</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Acura</td>\n",
       "      <td>TL</td>\n",
       "      <td>39.384</td>\n",
       "      <td>19.875</td>\n",
       "      <td>0</td>\n",
       "      <td>28.40</td>\n",
       "      <td>3.2</td>\n",
       "      <td>225</td>\n",
       "      <td>108.1</td>\n",
       "      <td>70.3</td>\n",
       "      <td>192.9</td>\n",
       "      <td>3.517</td>\n",
       "      <td>17.2</td>\n",
       "      <td>25.0</td>\n",
       "      <td>3.673</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Acura</td>\n",
       "      <td>RL</td>\n",
       "      <td>8.588</td>\n",
       "      <td>29.725</td>\n",
       "      <td>0</td>\n",
       "      <td>42.00</td>\n",
       "      <td>3.5</td>\n",
       "      <td>210</td>\n",
       "      <td>114.6</td>\n",
       "      <td>71.4</td>\n",
       "      <td>196.6</td>\n",
       "      <td>3.850</td>\n",
       "      <td>18.0</td>\n",
       "      <td>22.0</td>\n",
       "      <td>2.150</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Audi</td>\n",
       "      <td>A4</td>\n",
       "      <td>20.397</td>\n",
       "      <td>22.255</td>\n",
       "      <td>0</td>\n",
       "      <td>23.99</td>\n",
       "      <td>1.8</td>\n",
       "      <td>150</td>\n",
       "      <td>102.6</td>\n",
       "      <td>68.2</td>\n",
       "      <td>178.0</td>\n",
       "      <td>2.998</td>\n",
       "      <td>16.4</td>\n",
       "      <td>27.0</td>\n",
       "      <td>3.015</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Audi</td>\n",
       "      <td>A6</td>\n",
       "      <td>18.780</td>\n",
       "      <td>23.555</td>\n",
       "      <td>0</td>\n",
       "      <td>33.95</td>\n",
       "      <td>2.8</td>\n",
       "      <td>200</td>\n",
       "      <td>108.7</td>\n",
       "      <td>76.1</td>\n",
       "      <td>192.0</td>\n",
       "      <td>3.561</td>\n",
       "      <td>18.5</td>\n",
       "      <td>22.0</td>\n",
       "      <td>2.933</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>112</th>\n",
       "      <td>Volkswagen</td>\n",
       "      <td>Golf</td>\n",
       "      <td>9.761</td>\n",
       "      <td>11.425</td>\n",
       "      <td>0</td>\n",
       "      <td>14.90</td>\n",
       "      <td>2.0</td>\n",
       "      <td>115</td>\n",
       "      <td>98.9</td>\n",
       "      <td>68.3</td>\n",
       "      <td>163.3</td>\n",
       "      <td>2.767</td>\n",
       "      <td>14.5</td>\n",
       "      <td>26.0</td>\n",
       "      <td>2.278</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>113</th>\n",
       "      <td>Volkswagen</td>\n",
       "      <td>Jetta</td>\n",
       "      <td>83.721</td>\n",
       "      <td>13.240</td>\n",
       "      <td>0</td>\n",
       "      <td>16.70</td>\n",
       "      <td>2.0</td>\n",
       "      <td>115</td>\n",
       "      <td>98.9</td>\n",
       "      <td>68.3</td>\n",
       "      <td>172.3</td>\n",
       "      <td>2.853</td>\n",
       "      <td>14.5</td>\n",
       "      <td>26.0</td>\n",
       "      <td>4.427</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>114</th>\n",
       "      <td>Volkswagen</td>\n",
       "      <td>Passat</td>\n",
       "      <td>51.102</td>\n",
       "      <td>16.725</td>\n",
       "      <td>0</td>\n",
       "      <td>21.20</td>\n",
       "      <td>1.8</td>\n",
       "      <td>150</td>\n",
       "      <td>106.4</td>\n",
       "      <td>68.5</td>\n",
       "      <td>184.1</td>\n",
       "      <td>3.043</td>\n",
       "      <td>16.4</td>\n",
       "      <td>27.0</td>\n",
       "      <td>3.934</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>115</th>\n",
       "      <td>Volkswagen</td>\n",
       "      <td>Cabrio</td>\n",
       "      <td>9.569</td>\n",
       "      <td>16.575</td>\n",
       "      <td>0</td>\n",
       "      <td>19.99</td>\n",
       "      <td>2.0</td>\n",
       "      <td>115</td>\n",
       "      <td>97.4</td>\n",
       "      <td>66.7</td>\n",
       "      <td>160.4</td>\n",
       "      <td>3.079</td>\n",
       "      <td>13.7</td>\n",
       "      <td>26.0</td>\n",
       "      <td>2.259</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>116</th>\n",
       "      <td>Volkswagen</td>\n",
       "      <td>GTI</td>\n",
       "      <td>5.596</td>\n",
       "      <td>13.760</td>\n",
       "      <td>0</td>\n",
       "      <td>17.50</td>\n",
       "      <td>2.0</td>\n",
       "      <td>115</td>\n",
       "      <td>98.9</td>\n",
       "      <td>68.3</td>\n",
       "      <td>163.3</td>\n",
       "      <td>2.762</td>\n",
       "      <td>14.6</td>\n",
       "      <td>26.0</td>\n",
       "      <td>1.722</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>117 rows × 17 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "       manufact    model   sales  resale  type  price  engine_s  horsepow  \\\n",
       "0         Acura  Integra  16.919  16.360     0  21.50       1.8       140   \n",
       "1         Acura       TL  39.384  19.875     0  28.40       3.2       225   \n",
       "2         Acura       RL   8.588  29.725     0  42.00       3.5       210   \n",
       "3          Audi       A4  20.397  22.255     0  23.99       1.8       150   \n",
       "4          Audi       A6  18.780  23.555     0  33.95       2.8       200   \n",
       "..          ...      ...     ...     ...   ...    ...       ...       ...   \n",
       "112  Volkswagen     Golf   9.761  11.425     0  14.90       2.0       115   \n",
       "113  Volkswagen    Jetta  83.721  13.240     0  16.70       2.0       115   \n",
       "114  Volkswagen   Passat  51.102  16.725     0  21.20       1.8       150   \n",
       "115  Volkswagen   Cabrio   9.569  16.575     0  19.99       2.0       115   \n",
       "116  Volkswagen      GTI   5.596  13.760     0  17.50       2.0       115   \n",
       "\n",
       "     wheelbas  width  length  curb_wgt  fuel_cap   mpg  lnsales  partition  \\\n",
       "0       101.2   67.3   172.4     2.639      13.2  28.0    2.828          0   \n",
       "1       108.1   70.3   192.9     3.517      17.2  25.0    3.673          0   \n",
       "2       114.6   71.4   196.6     3.850      18.0  22.0    2.150          0   \n",
       "3       102.6   68.2   178.0     2.998      16.4  27.0    3.015          0   \n",
       "4       108.7   76.1   192.0     3.561      18.5  22.0    2.933          0   \n",
       "..        ...    ...     ...       ...       ...   ...      ...        ...   \n",
       "112      98.9   68.3   163.3     2.767      14.5  26.0    2.278          0   \n",
       "113      98.9   68.3   172.3     2.853      14.5  26.0    4.427          0   \n",
       "114     106.4   68.5   184.1     3.043      16.4  27.0    3.934          0   \n",
       "115      97.4   66.7   160.4     3.079      13.7  26.0    2.259          0   \n",
       "116      98.9   68.3   163.3     2.762      14.6  26.0    1.722          0   \n",
       "\n",
       "     cluster  \n",
       "0          0  \n",
       "1          0  \n",
       "2          0  \n",
       "3          0  \n",
       "4          0  \n",
       "..       ...  \n",
       "112        0  \n",
       "113        0  \n",
       "114        0  \n",
       "115        0  \n",
       "116        0  \n",
       "\n",
       "[117 rows x 17 columns]"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cars"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "id": "92a3ce49",
   "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>sales</th>\n",
       "      <th>resale</th>\n",
       "      <th>type</th>\n",
       "      <th>price</th>\n",
       "      <th>engine_s</th>\n",
       "      <th>horsepow</th>\n",
       "      <th>wheelbas</th>\n",
       "      <th>width</th>\n",
       "      <th>length</th>\n",
       "      <th>curb_wgt</th>\n",
       "      <th>fuel_cap</th>\n",
       "      <th>mpg</th>\n",
       "      <th>lnsales</th>\n",
       "      <th>partition</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>cluster</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>47.151841</td>\n",
       "      <td>18.639716</td>\n",
       "      <td>0.0</td>\n",
       "      <td>26.586625</td>\n",
       "      <td>2.938636</td>\n",
       "      <td>182.818182</td>\n",
       "      <td>105.554545</td>\n",
       "      <td>70.744318</td>\n",
       "      <td>186.848864</td>\n",
       "      <td>3.163875</td>\n",
       "      <td>16.539773</td>\n",
       "      <td>25.475</td>\n",
       "      <td>3.198625</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>95.406172</td>\n",
       "      <td>16.186034</td>\n",
       "      <td>1.0</td>\n",
       "      <td>24.096793</td>\n",
       "      <td>3.382759</td>\n",
       "      <td>176.620690</td>\n",
       "      <td>112.703448</td>\n",
       "      <td>72.541379</td>\n",
       "      <td>190.355172</td>\n",
       "      <td>3.810103</td>\n",
       "      <td>21.675862</td>\n",
       "      <td>20.000</td>\n",
       "      <td>4.015828</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "             sales     resale  type      price  engine_s    horsepow  \\\n",
       "cluster                                                                \n",
       "0        47.151841  18.639716   0.0  26.586625  2.938636  182.818182   \n",
       "1        95.406172  16.186034   1.0  24.096793  3.382759  176.620690   \n",
       "\n",
       "           wheelbas      width      length  curb_wgt   fuel_cap     mpg  \\\n",
       "cluster                                                                   \n",
       "0        105.554545  70.744318  186.848864  3.163875  16.539773  25.475   \n",
       "1        112.703448  72.541379  190.355172  3.810103  21.675862  20.000   \n",
       "\n",
       "          lnsales  partition  \n",
       "cluster                       \n",
       "0        3.198625        0.0  \n",
       "1        4.015828        0.0  "
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#cluster analysis\n",
    "cars.groupby('cluster').mean()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "id": "b260cc7f",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='type', ylabel='mpg'>"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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sSefcR7AnQ0pvp1FF2oqZYOP3+zFp0iS43e6qbcXFxbjrrrvQpk0brFq1CuPGjcO8efOwatWqKFZKREQUuwRRhLnvbefcx9z3Vs3Ws9FazDzu/dJLL8Futwdte/vtt2EymTB9+nQYDAZkZWXhyJEjWLhwIW688cYoVUpERBTbjJndgSsfrGEdm2SY+96qyTo2vzZ16nTNjhUTwearr77CW2+9hTVr1mDgwIFV27dv344ePXrAYPilzN69e+PVV19FYWEhUlJSolAtERFR7DNmdoehdbeorTwcLVEPNk6nE5MnT8YTTzyBZs2aBY3l5+cjOzs7aFtaWhoA4MSJE2EFG4Ohfv9gJUkM+i9FBvusHfZaG+yzNhpKnxVFqNfvE0QRhow/1Ot3nvN4wi//VdXQvkOShLDO0VEPNtOnT0eXLl0wdOjQamMejwcmkylom9lsBgB4vd6QjymKApKS7L+/YwgcDmtEvpeCsc/aYa+1wT5rI9b77PFIOHNGDPvkHm2hBEhFESCKIhISbLBYLCEfO6rBZs2aNdi+fTvWrl1b47jFYoHP5wvaVhlobDZbyMdVFBVOp/v3d6wDSRLhcFjhdFZAlvW5mmMsYJ+1w15rg33WRkPps8/nhaIokGUVgUDs1lkbQTjba1lW6nzFRpZVKIqC0lI3KirkauMOh/W8AlNUg82qVatQWFgYdF8NAEybNg2LFy9GRkYGCgoKgsYqf9+0adOwjh2pvzCyrDTIv4wNDfusHfZaG+yzNmK9z7Ic4vxNjKgMM6FOQwEIO9RFNdg8//zz8Hg8QduuuuoqPPTQQ7juuuvw/vvvY+XKlZBlGZIkAQC2bt2KzMxM3jhMRES6pYaTDBqo+vqZozqB17RpU7Ru3TroFwCkpKSgefPmuPHGG+FyuTB16lQcPHgQq1evxtKlS3HvvfdGs2wiIqKIqPxHvM8X+n2kDVXlzyxJ4V1zifrNw+eSkpKCRYsWYdasWRg2bBhSU1MxefJkDBs2LNqlERER1TtRlGC1xsHlOrv2jMlkhiDU75NSkaYoQp2m1FRVhc/nhctVDKs1DmKYj6PHXLDZty/4baOdO3fGW2+9FaVqiIiItOVwJANAVbhpaERRhKLU/R4ZqzWu6mcPR8wFGyIiosZMEAQkJKQgPj4JshyIdjl1IkkCEhJsKC111+mqjSQZwr5SU4nBhoiIKAaJoghRNP3+jjHEYBBhsVhQUSFH7emzhrv6DxEREdFvMNgQERGRbjDYEBERkW4w2BAREZFuMNgQERGRbjDYEBERkW4w2BAREZFuMNgQERGRbjDYEBERkW4w2BAREZFuMNgQERGRbjDYEBERkW4w2BAREZFuMNgQERGRbjDYEBERkW4w2BAREZFuMNgQERGRbjDYEBERkW4w2BAREZFuMNgQERGRbjDYEBERkW4w2BAREZFuMNgQERGRbjDYEBERkW4w2BAREZFuMNgQERGRbjDYEBERkW4w2BAREZFuMNgQERGRbjDYEBERkW4w2BAREZFuMNgQERGRbjDYEBERkW4w2BAREZFuMNgQERGRbjDYEBERkW4w2BAREZFuMNgQERGRbjDYEBERkW4w2BAREZFuMNgQERGRbjDYEBERkW4Yol1AYWEhnn32WWzevBlerxc9evTA5MmT0bZtWwDAY489htWrVwd9pmnTpti0aVM0yiUiIqIYFvVgc//990MURSxcuBA2mw3z5s3DyJEjsX79elitVuzbtw/33XcfRowYUfUZSZKiWDERERHFqqhORRUXF6NFixZ46qmn0KlTJ2RlZWHs2LE4ffo0Dhw4AFmWcfDgQXTq1AmpqalVv5KTk6NZNhEREcWoqF6xSUpKwpw5c6p+f+bMGSxevBjp6elo27YtDh8+DK/Xi6ysrChWSURERA1F1KeiKj355JN4++23YTKZsGDBAthsNuzfvx+CIGDp0qXYtGkTRFHEgAEDMH78eMTHx4d1PIOhfi9WSZIY9F+KDPZZO+y1NthnbbDP2oiFPguqqqpRO/qvHDx4EB6PBytWrMC6deuwfPlyfPzxx8jNzcUjjzyCAQMG4MiRI3juuefQrFkzLF26FKIYWuNUVYUgCPX8ExAREVG0xUywqaQoCoYOHYrOnTtj9uzZKCsrg8PhqBrfuXMnhg8fjrfffhs5OTkhHUOWFTidFfVVMoCz6dThsMLprIAsK/X63fQL9lk77LU22GdtsM/aiGSfHQ7reV0JiupUVGFhIbZu3Yprr7226kknURSRlZWFgoICCIIQFGoAIDs7GwCQn58fcrABgEAgMn+xZVmJ2HfTL9hn7bDX2mCftcE+ayOafY7qZGNBQQEmTpyIL7/8smqb3+/Hnj17kJWVhYkTJ2LUqFFBn9m9ezcAVK1zQ0RERFQpqsGmffv26NevH2bMmIHt27dj//79mDJlCpxOJ0aOHIkhQ4Zgy5YtWLBgAY4ePYqNGzfi8ccfx5AhQ/ikFBEREVUT1akoQRAwd+5cvPDCCxg/fjzKysrQvXt3vPnmm8jIyEBGRgbmzZuH3Nxc5ObmIj4+HkOHDsX48eOjWTYRERHFqJi7eVgLsqygqKi8Xr/TYBCRlGRHcXE5528jiH3WDnutDfZZG+yzNiLZ5+Rk+3ndPMwH+omIiEg3GGyIiIhINxhsiIiISDcYbIiIiEg3GGyIiIhINxhsiIiISDcYbIiIiEg3GGyIiIhINxhsiIiISDcYbIiIiEg3GGyIiIhINxhs6oHFaoTbr+LYqTJ4FcBiieq7RYmIiBotnoHDJIsi/vXJQXy47QjcngDaNHPgzsEd0CrNDkFpdO8XJSIiiipesQmDLAiYt/IbvLvxR7g9AQDA4ZNOzFj0BfJOlsFmM0a5QiIiosaFwSYMRWVefHeosMax19d+j1J3QOOKiIiIGjcGmxDZbCZ8X0uoAYATZ8rh9csaVkREREQMNiFSFAUOu6nWcUkUYJDYXiIiIi3xzBsijyeADpkpMEhCjeN9O2fAZpY0roqIiKhxY7AJg8UsYuJtF0MUg8NNi7Q4jLimPRRORREREWmKj3uHw6+gXasEvDTxMmzfm4/CUg86t22CVunxsEhAgPcOExERaYrBJlwBFVYJuLZ3a1itJpSVueHxyAw1REREUcCpqHri8fgBAIEAF+UjIiKKFgabeiCKAmQAJWVeCGLNNxMTERFR5HEqKkyKICAvvwzvfvojSlxedM5qgiH9MmE1ilD5SgUiIiJNMdiEQREEvPdZHt7bfKhq2/ECFz7efgzPPnAJkmxGKAw3REREmuFUVBgqfHJQqKnk9ct4bc1u8HYbIiIibTHYhEiSROz+8Uyt43sPF8MXUDSsiIiIiBhswvDbhfmq4X3EREREmmKwCZEsK+h4QZNaxzu1TYHZwPYSERFpiWfeMFiNIv5yZXa17TaLAWP+2Aki77EhIiLSFJ+KCoOgqriie0t0btsE7206hOIyL7pkp+Kyi1vAYhCgyEw2REREWmKwCZOoqmiWaMHYP3WC0WSEHPDD55UZaoiIiKKAU1H1QFXP/o84m5GBhoiIKIoYbOqJ1WoEABiNUpQrISIiarw4FRUuQYDLJ+ODjw+isLQCXbJT0b51MmxGEX6/HO3qiIiIGhUGm3BIInYeOIOX/rXz7HQUgP/tOI7UJCtm3tMHVglQuEYfERGRZjgVFQaPX8HL7+yqCjWVThdXYOn7e6CInJYiIiLSUkhXbNasWVPrmCAIsNvtaNWqFbKzq6/xohdms4SvvjtV60suv9xzCndc1wEWRkciIiLNhBRspk6dCuXnORb1V5crBEGo2iYIAnr16oUFCxbAarXWQ6mxRRBElFf4ax1XFBWyrAAikw0REZFWQjrrLlq0CFarFY888gg++eQTfPvtt/j0008xZcoUWK1WzJ49GwsWLEBeXh7mz59f3zXHBI/Hj05Ztb9SoVXTeFhMnIoiIiLSUkjB5rnnnsOYMWNwzz33ICMjAyaTCenp6Rg5ciTGjh2LZcuWYeDAgRg3bhw+/PDD+q45ZiTGmXBx+7Rq2wUBGHV9R9hMvFpDRESkpZDOvIcOHULnzp1rHPvDH/6AgwcPAgDatGmDM2fOhF5djJNUFff/qTNuv/YPSHZYIIkCOmQm45mx/dCyiR1eLx/3JiIi0lJI99i0bNkSH374IS655JJqY+vXr0ezZs0AAPn5+UhOTg6vwhgnKgqu6d0S/XIyAACSJMAIAbLMUENERKS1kILN6NGj8dhjj6GwsBBXX301UlJSUFhYiPXr12PDhg2YOXMm8vLyMHfuXPTv37++a4453ooArAYRSUl2FBeXIxBgqCEiIoqGkILNsGHDIAgC5s+fj48//rhqe6tWrfD3v/8dQ4YMwfvvv4+srCxMnDjxnN9VWFiIZ599Fps3b4bX60WPHj0wefJktG3bFgCwd+9ezJo1C9999x0SExNx++23Y9SoUaGUHVF8+ImIiCj6BFX97fJydXP06FEUFRUhPT0d6enpdf78n//8Z4iiiKlTp8Jms2HevHn4+uuvsX79eng8Hlx77bW44oorcNddd2Hnzp2YMWMGpk2bhhtvvDHkmmVZQVFRecif/zWj0YByXwAnC90oKfOidbN4xFuNkMJrK9XCEHRljMs6RxJ7rQ32WRvsszYi2efkZDsk6fevIoT1SoWNGzdix44dKC0tRZMmTdCnTx907979vD9fXFyMFi1a4P7778eFF14IABg7diyuv/56HDhwAFu3boXJZML06dNhMBiQlZWFI0eOYOHChWEFm/piMhlwvNCNp1/fBtev1rTpkp2KB27KgcT3KRAREWkqpGBTUlKCMWPGYPfu3TAYDEhMTERJSQleeeUVXHrppXj55ZdhMpl+93uSkpIwZ86cqt+fOXMGixcvRnp6Otq2bYuXXnoJPXr0gMHwS5m9e/fGq6++isLCQqSkpIRSfr0p8wYwY9EXqPAGgrbv3H8aazb+iOFXXIjAb8aIiIgockIKNrNnz8bRo0fx8ssv4/LLL4cgCFAUBRs2bMCTTz6JF198EVOmTKnTdz755JN4++23YTKZsGDBAthsNuTn51d7LUNa2tl1Y06cOBFWsDEYwrspxmCQsOfAmWqhptKGr45iSL9M2I28+aY+VV6GPJ/LkRQe9lob7LM22GdtxEKfQwo2GzduxKRJk3DFFVdUbRNFEVdddRWKiorw8ssv1znY3HnnnRg+fDhWrFiBBx54AMuXL4fH46l25cdsNgMAvF5vKKX/XKuApCR7yJ+vlF/krnXM65PhDyhISosP+zhUncOhv9d0xCr2WhvsszbYZ21Es88h32PTpEnNrxNo1qwZ3O7aT/i1qXwK6qmnnsLOnTuxbNkyWCwW+Hy+oP0qA43NZqvzMSopigqns+41/pokAdktE2sdT3ZYYDJKKC6un5uU6SxJEuFwWOF0Vpx9FxdFDHutDfZZG+yzNiLZZ4fDGrmbh4cNG4YFCxagZ8+esNt/ufIRCASwbNkyDBs27Ly+p7CwEFu3bsW1114LSTr7XiVRFJGVlYWCggKkp6ejoKAg6DOVv2/atGkopf+q1vAaHggA6ck2tEiLw/ECV7Xxv1yZjUSbAW537S/KpNDJssInGzTCXmuDfdYG+6yNaPY5pGBjsVhw+PBhDBo0CIMGDUJaWhqKi4vx2WefIT8/HwkJCXjssccAnH3j9+zZs2v8noKCAkycOBEpKSno06cPAMDv92PPnj0YNGgQmjRpgpUrV0KW5args3XrVmRmZkb9xmEAsBiAJ+/uhYVrdmPHvgKoKhBvM2L4le3QNTuVoYaIiEhjIQWb9957D3FxcQCAbdu2BY2lp6fj66+/rvq9IAi1fk/79u3Rr18/zJgxA08//TQcDgdyc3PhdDoxcuRImM1mLFq0CFOnTsXo0aPx7bffYunSpZgxY0YoZde7QECFRQLuv7EzKnwy/H4ZVrMB8TYDvBV8GoqIiEhrYS/QF66ysjK88MIL2LBhA8rKytC9e3c8+uijVevafPvtt5g1axb27NmD1NRU3H333RgxYkRYx6zPBfoqcfEnbbDP2mGvtcE+a4N91kYsLNAXUrApLS3F/Pnz8fXXX8PpdFb/UkHAhg0b6vq1mmGwabjYZ+2w19pgn7XBPmsjFoJNSFNRTz75JD7++GNceumlaN++fShfoRuiKCAAASUVAZxyFiHOaoTdJCHg54swiYiItBZSsPn8888xefJk3HnnnfVdT4NiMIgo9ymY//Y32Hu4CABgNkkYNqAtLu/Rkq9UICIi0lhIwcZutyMzM7O+a2lwKgIqpi/6Aqd+tVCf1ydj5fp9sFkMGNStOXx8pQIREZFmQlrz+LbbbsOSJUtQXt54F58TRQEnzriCQs2vrfrfAbgYaoiIiDQV0hWbESNG4N1338WAAQNwwQUXwGKxBI0LgoClS5fWS4GxymiUcOin6jdOVyp1+eDzKzDxXVFERESaCems+9e//hV5eXlITU2F2WyGqqpBv5RGcG+JLCtonlr7+6asZgNMYb5ok4iIiOompCs2n3zyCSZMmIB77rmnvutpMAIBBW0yHIi3GVFWwwrD1/RpA7vJAL+f01FERERaCemSgslkQqdOneq7lgbHZhAxfUwfJDuCp+L65WRgcN82DDVEREQaC+mKzQ033IAVK1agV69eEMXGO90SCChIjTfimbGXoLjMC1eFD02T7bCZJAiNYDqOiIgo1oQUbOLi4vD5559j0KBB6Ny5c9AbvoFzv/hSbwIBFUYAGUkWJF2QwlUtiYiIoiikYLN69Wo4HA4AwHfffVdt/FwvviQiIiKKlJBvHqZfGM0S3F4F5QUuGCUBJoPIqzZERERREFKwoV/Iooi1nx7Ch9uOwO0JoE0zB0YO6YCWqXYISlRfnE5ERNToNN47f+uBLAiYt/IbvLvxR7g9Z5+AOnzSiekLv0DeyTKYTFKUKyQiImpcGGzCUFTmxXeHCmsce33t9yj3cTqKiIhISww2ITKZDPi+llADACfOlMPnlzWsiIiIiBhsQqQoChLs5lrHJVGAJLG9REREWuKZN0SBgIIOmckwSDU/2t63cwZsvMeGiIhIUww2YTAbRUy87WKIYnC4aZEWhxHXtIcS4FQUERGRlvi4dzhkBe1aJuCliZdh+w/5KCrxoNOFTdCqaTxMAsC3KhAREWmLwSZcsgqrBFzVvSXi4iwoLXXD55PBJWyIiIi0x6moeuL/+QkohYmGiIgoahhs6kFcnBEBACdOuyAYeMMwERE1PpIkwCT4IJeXwiBF7x/5nIoKkywI+OZAEdZuPoQSlxcdMpNxff8sxNkkyF7eZENERPpnFz0InDyAkq/WQvG6Yc26GPFdrkC54NB8JoPBJgyyIODdjT/ig88PV207XuDCxq9/wuyxl6BFEwvKywPRK5CIiCjCrKIXzo3/D+69W6q2+c8ch2vXBqTfPhsuMQGqhtmGU1FhcHvloFBTyeuXsfDfu+F083FvIiLSL0EARHdRUKippHjKUfLZSphEbWcvGGxClJhowa4Dp2sd/+FwMbwBTkUREZF+GQwS3Pu21jru/mEbjKpHw4oYbMIi1rzocJXfGSYiImr4hHNEiXONRQiDTYhKSjzIuTC11vGOWSmw8JUKRESkY36/DFv7PrWO2zv0hV+waFgRg01YrGYJNw1qW227zWLA6D92xDnekUlERKQLiiUJcTlXVNsu2ROQcMnN8MraRg0+FRUGUVFxTa82yLkwFWs/y0NpmRcXZaXgyp6t4LAa4HbziSgiItK3CsUEe5+bYWvfB2VfrYPiLYctuxes7XqjHHHQ9JEoAIKqanzEGCDLCoqKyuvt+ywWIKBK8AcAu1mE2+2vt++mYAaDiKQkO4qLyxHgzdkRxV5rg33WBvsceaIowGxQYDVLKPcJ8Hrr98ng5GQ7JOn3r/5wKqoeeDwAZBVpyTb4fHzEm4iIGh9FUeFXJEgWO2Q5etdMGGzqicNx9uYoq5Wze0RERNHCs3C4BAEun4wP/rsPhaUV6JKdivatk+GwSqio4D02REREWmKwCYckYueBM3jpXzur7o36347jSE2yYuY9fWCVAIVTuURERJrhVFQYPH4FL7+zq9oN36eLK7D0/T1QRK5jQ0REpCUGmxDFxZmx+8cztb619Ms9p+DhjcRERESaYrAJkSgKKK+o/bFuRVEhy5yHIiIi0hKDTYicTg86ZTWpdbxV03i+UoGIiEhjDDZhSIwz4eL2adW2CwIw6vqOSLCbolAVERFR48WnosIgqSru/1NnbPzmJ7y/JQ+lLi/atU7CHdd1QFqCBS6XN9olEhERNSoMNmESFQVD+rZGv5wMAIAkCbAa6n8paSIiIvp9nIqqB+XlPlgNAjJS42ASwFBDREQUJVG/YlNSUoI5c+bg008/hcvlQrt27TBx4kR0794dAPDYY49h9erVQZ9p2rQpNm3aFI1yayUyIhIREUVd1IPNhAkTUFhYiDlz5iA5ORnLly/HqFGjsHr1amRlZWHfvn247777MGLEiKrPSFLsPG1ksRjgrAjgyAkXSvYVonWzeMRbjZAa30vTiYiIoi6qwebIkSPYsmULVqxYgW7dugEApk6dik2bNmHdunV48MEHcfDgQYwdOxapqanRLLVGVqsRRwrK8fTr2+D61Zo2XbJT8cBNOZD4PgUiIiJNRXUCJSkpCa+99ho6duxYtU0QBKiqitLSUhw+fBherxdZWVlRrLJ2JW4/Ziz6IijUAMDO/aexZuOPMFiifkGMiIioUYnqmdfhcGDAgAFB2/7zn//g6NGj6NevH/bv3w9BELB06VJs2rQJoihiwIABGD9+POLj48M6tsEQXqazWiXs2XMGFd6a3+C94aujGNIvE3Yjb76pT5IkBv2XIoe91gb7rA32WRux0OeYuqSwY8cOPP7447j88ssxaNAgzJ8/H6Ioonnz5sjNzcWRI0fw3HPPYf/+/Vi6dCnEEO/YFUUBSUn2sOvNL3LXOub1yfAHFCSlhRfAqGYOhzXaJTQa7LU22GdtsM/aiGafYybYbNiwAZMmTUJOTg7mzJkDABg3bhxGjhwJh8MBAMjOzkZqaiqGDx+O3bt3IycnJ6RjKYoKp7P2UHI+JAnIbplY63iywwKTUUJxcXlYx6FgkiTC4bDC6azgu7gijL3WBvusDfZZG5Hss8NhPa8rQTERbJYtW4ZZs2bhyiuvxPPPPw+T6eyrCARBqAo1lbKzswEA+fn5IQcbAAgEwmu4qopommxDi7Q4HC9wVRv/y5XZaOIwwun0hXUcqpksK2H/GdL5Ya+1wT5rg33WRjT7HPXJxuXLl+Opp57Cbbfdhrlz51aFGgCYOHEiRo0aFbT/7t27AQBt27bVtM7fUlUVJgPw5N29cHH7NAjC2e3xNiNG/7EjumSn4kwpQw0REZGWonrFJi8vD7Nnz8aVV16Je++9F4WFhVVjFosFQ4YMwf33348FCxZg8ODByMvLw8yZMzFkyJCoPymlKCr8soBd+09h7I2dUeGT4fPLsJoNSLQZ8NYnP+KGfplQeMmTiIhIM1ENNh9++CH8fj/Wr1+P9evXB40NGzYMzz77LObNm4fc3Fzk5uYiPj4eQ4cOxfjx46NT8G+YDcAFLZPwyNxNcJb/cnWm50XpuOf6jgw1REREGhNUtfEtkSvLCoqK6uemXlEU4JVVFDo9KHP7kdHEDptJgtD42qoJg0FEUpIdxcXlnCePMPZaG+yzNthnbUSyz8nJ9oZz83BDZjYDAa+IOJsJRoMEg0GE2SzB56l5fRsiIiK9MRol2JQyqN5yeI77YbfGwW92wO3VvhYGmzDExRlQ6Axg/tvfYO/hIgCA2SRh2IAsXN6jFV+pQEREumc2SzBXFKBg7cvwncoDAIhmG5IG/AWOrB5wytquaRP1p6IasmKXjOmLvqgKNcDZhflWrt+PrbtPIi7OHMXqiIiIIs/iL8HJ5TOqQg0AKF43Cj96Hf6T+2E2a3sNhcEmRFargJ9Ou3CqltWHV/3vAIpcUbgGR0REpBGzWYLn2A9Q3M4ax4s3vw1zoFTTmhhsQmQ2m3HoRM1/kABQ6vLB5+dUFBER6ZfRaID3xIFax/2FP0FQtT0XMtiEKBAIoHlq7e+bspoNMIb5ok0iIqJYFggoMKZk1DouxScDELQrCAw2IXO5AsjMcCDeZqxx/Jo+beCwmWocIyIi0gOPxw9bVlcIhprPdwm9r4fXlKhpTQw2YUiyS5g+pg+SHZag7f1yMjC4bxt4K/hKBSIi0jefwYH04VMhWuN/2SiIiO96JWwX9oRH4+VPuEBfmKxWAaUVQEmZFy63D01Tfl6gj496RwQX2dIOe60N9lkb7HNkmY2ARS6DXFYExVcBY3I6FIMVZf76ezqYC/RppKJChQlARpIFSRek8P9oiIio0fH6AS/iYUhI+CVARukBGk5FERERkW4w2NQDq9UEt1/FsVNl8CqA1VrzDcVERER65bCosCul8J05DjtcMJmiMynEqagwyaKItz85gA+3HYHbE0CbZg7cObgDWqXZISiN7vYlIiJqZAwGIA7lKNm8Bq7dG6EGfDBnXIjky++AydEULr+2Twjzik0YZEHAvJXf4N2NP8L9813fh086MWPRF8g7WQa7nY97ExGRvtnVcuT/6zmUfbMeauDs08DeEwdwctk0oKwARiNfqdBgFJV58d2hwhrHXl/7PUrK/RpXREREpB2j0QD/mWPwnz5afVBVUPTJMtjUMk1rYrAJUVycGd/XEmoA4MSZcnj9soYVERERactqNcD949e1jnuP/wAo2q5jw2ATIllW4DjHVJMkCjCcx/P2REREDZWiqJBsjlrHBbNNw2rO4pk3RBUVfnTITIFBqvkdGH07Z8BmljSuioiISDsulxf29n1qHXfkXA7FUnvwiQQGmzBYzCIm3nYxRDE43LRIi8OIa9pD4VQUERHpnGq2I+WaMfjtyy7NzbLg6H4tytzaPiHMVyqEyyCgwqti+958FJZ60LltE7RKj4fVAPj9ja61Ecdl0bXDXmuDfdYG+xxZ8QYvBL8b7v1fQq4ogy2rGwyJaShT41BfbxjiKxW0ElBhlYDr+rSBxWKEy+VGRYUMPx+IIiKiRqIsYAYEM+K7/xEWiwllZeUo9/CVCg1axc9v8uZVGiIiaqwqKs7+qz6g7YNQQRhs6kF8vBEBACdOuyAYeMMwERE1PpIkwCT4IJeXwiBF7x/5nIoKkywI+Hp/EdZuPoQSlxcdMpNxff8sxNkkyF7O4xIRkf7ZRQ8CJw+g5Ku1ULxuWLMuRnyXK1AuOKBo/HohBpswyIKAdzf+iA8+P1y17XiBCxu//gmzx16ClqkWuFxRvB5HREQUYVbRC+fG/wf33i1V2/xnjsO1awPSb58Nl5gALR9T4lRUGNxeOSjUVPL6ZSz8926UlvNxbyIi0i9BAER3UVCoqaR4ylHy2UqYRG1nLxhsQpSYaMGuA6drHf/hcDG8fKSQiIh0zGCQ4N63tdZx9w/bYFQ9GlbEYBMWseZFh6v8zjAREVHDJ5wjSpxrLEIYbEJUUuJBzoWptY53zEqBxcQnpIiISL/8fhm2c7xSwd6hL/yCRcOKGGzCYjVLuGlQ22rbbRYDRv+xI+zmKBRFRESkIcWShLicK6ptl+wJSLjkZnhlbaMGn4oKg6iouKZXG+RcmIq1n+WhtMyLi7JScGXPVnBYDXC7+UQUERHpW4Vigr3PzbC174Oyr9ZB8ZbDlt0L1na9UY44aPpIFPiuqHphsQABVYI/ANjNItxuvk8hUvi+F+2w19pgn7XBPkeeKAowGxRYzRLKfQK83vp9Mvh83xXFqah64PEAkFWkJdvg8/ERbyIianwURYVfkSBZ7JDl6F0zYbCpJw7H2ZujrFbO7hERUeNktRoBAJIUvYdneBYOlyDA5ZPxwX/3obC0Al2yU9G+dTIcVgkVFbzHhoiI9C9eqoDgLYNr8yY4K8pgu7AHEpq2gQtxkDWeyGCwCYckYueBM3jpXzur7o36347jSE2yYuY9fWCVAIVTuUREpGPxBg8q9mxG8afLq7a5dm+EsUlLpN/8KEpg17QeTkWFweNX8PI7u6rd8H26uAJL398DReQ6NkREpG+C1xUUair5zxxDyba1iNN2GRsGm1DFxZmx+8cztb619Ms9p+DhjcRERKRjdrsJrr2f1zru2v0pJF+ZdgWBwSZkoiigvKL2x7oVRYUscx6KiIj0SxAEKJ7al09R/dq+JwpgsAmZ0+lBp6wmtY63ahrPVyoQEZGuVVT4Ybuwe63jltYdoUpGDStisAlLYpwJF7dPq7ZdEIBR13dEgt0UhaqIiIi0IcsKjMkZMDfLqj4oGpB82QiUKzZNa2KwCYOkqrj/T51x+7V/QLLDAkkU0CEzGc+M7YeWTexwubzRLpGIiCiiSmU70oZNRELfYRBtDkA0wHpBF2TcOQsBW5rmt2Xwce8wiYqCIX1bo19OBgBAkgRYDfW/lDQREVGsKgnYYOsxDPE5gwAIUCUjymUz5Cgs58YrNvWgvNwHq0FARmocTAIYaoiIqNFxVygoFxJgSm4Gt2LVfGG+SlEPNiUlJfjrX/+K/v37o1u3brjllluwffv2qvG9e/dixIgR6NKlCwYOHIjFixdHsdraCUK0KyAiIoouMeqpIgaCzYQJE7Br1y7MmTMH77zzDi666CKMGjUKP/74I4qLi3HXXXehTZs2WLVqFcaNG4d58+Zh1apV0S67iigK8KvA4YJybPrmOIrL/VCYcoiIqBExG1Q4xDIYTu1B2bf/g813GnGS9o96A1G+x+bIkSPYsmULVqxYgW7dugEApk6dik2bNmHdunWwWCwwmUyYPn06DAYDsrKycOTIESxcuBA33nhjNEsHcDbUFDi9mLl4G1y/WtOmW7tUjP1TDkSV69gQEZG+mQ0qpMIDOLF6TtC6NfY/9EXSZbejNGDVtJ6oXrFJSkrCa6+9ho4dO1ZtEwQBqqqitLQU27dvR48ePWAw/JK/evfujby8PBQWFkaj5CBeWcW0hV8EhRoA+HrfaazdcgiiFPULYkRERBFlkZ049a+/VVuMr3zv5yjfuwVms7bnwqhesXE4HBgwYEDQtv/85z84evQo+vXrhxdffBHZ2dlB42lpZ9eNOXHiBFJSUkI+tsEQXqMFQcD+I8Wo8NZ8y/d/vziC6/pmwhrmcSiY9HNYlBgaI4691gb7rA32OTKMRgnu3dsBpeZzofPLdUhv3weyIV6zmmLqce8dO3bg8ccfx+WXX45BgwbhmWeegckUvMid2WwGAHi9oa8RI4oCkpLCf9vo6V0nah3z+mSoQL0ch6pzOLS9tNmYsdfaYJ+1wT7Xv9PFp2odk8tLIELV9FwYM8Fmw4YNmDRpEnJycjBnzhwAgMVigc/nC9qvMtDYbKGvZKgoKpxOd+jF4uxTUBdkJNQ6nuywQBKA4uLa36FBdSdJIhwOK5zOCr6LK8LYa22wz9pgnyPDYBBhadkeZd98VOO4MbUVFEj1ci50OKzndcUtJoLNsmXLMGvWLFx55ZV4/vnnq67SpKeno6CgIGjfyt83bdo0rGMGAuH/xU5LtKJFWhyOF7iqjd12TXuYRKFejkPVybLC3mqEvdYG+6wN9rl+BQIKEppnQ4pPhlxWVG08eeCtqBDtCPi163nUJxuXL1+Op556Crfddhvmzp0bNPXUo0cP7NixA/KvVvnZunUrMjMzw7q/pr4YBODJu3uhZ4emVevYxNuMuG9YJ+RkpfBfBUREpHvlggPNbvkrLK06VG2T4hKROnQchCaZ8GsYagBAUFVV1fSIv5KXl4ehQ4di4MCBmDZtWtBY5TTUtddei0GDBmH06NH49ttvMX36dMyYMQPDhg0L+biyrKCoqH6miAQBUAQB3oACRQVMkgizAZADUWurrhkMIpKS7CguLue/uiKMvdYG+6wN9jmyRFGATfRA9LsB2Q+YbPAYHPD56q/Xycn22J+K+vDDD+H3+7F+/XqsX78+aGzYsGF49tlnsWjRIsyaNQvDhg1DamoqJk+eHFaoqW+qCgiqijiTxP+jISKiRklRVLgUMwxGK5LSfj4X1mOoqYuoXrGJlvq8YlOJ/xrQBvusHfZaG+yzNthnbUSyzw3iig0RERE1fBaLAWa/E6q3HJ7jftitcVBtDoT5AHJIGGyIiIgoZHa7CVLZSRSsfRm+U3kAANFsQ9KAvyAhqwdK5Ub0SgUiIiJq2AwVhTi5fEZVqAEAxetG4Uevw3dyP+x2s6b1MNgQERFRSOx2EzzHfoDidtY4Xrz5bRg81de3iSQGGyIiIgqJ0WiA98SBWsf9hT8BqrY3azPYEBERUUhkWYExJaPWcSk+GYCgXUFgsCEiIqIQlZV5YMvqCsFgqnE8off1kG1JmtbEYENEREQhU0wOpA+fCtEa/8tGQUR81ythv7AnXC6/pvXwcW8iIiIKWZlXgC2lNTLunA25rAiKrwLG5HTAYEWJX9snogAGGyIiIgqT2wMA8TAkJPyy8rDGL7+sxKkoIiIi0g0GGyIiIgpbvFmFXSmF78xx2FEOo1GKSh2ciiIiIqKQSRIQL5Sj5LM1cO3eCDXggznjQiRffgdMjnSU+42a1sMrNkRERBSyOJQj/1/Poeyb9VADPgCA98QBnFw2DULZKRgM2l65YbAhIiKikBgMEvxnjsF/+mj1QVVB0SfLYEO5pjUx2BAREVFILBYD3D9+Xeu49/gPEBRt17FhsCEiIqKQKIoKyeaodVww2zSs5iwGGyIiIgqJ2+2DvX2fWscdOZcjYK49+EQCgw0RERGFTDHFIeWaMfjtyy7NzbLg6H4tyitUTevh495EREQUMlfAjLgLuqP5mA5w7/8SckUZbFndYEhsCqei/VQUgw0RERGFxRUwA4IZ9m5DYbOZ4XSWo9zLVyoQERFRA+b1BgAAshy9GhhsiIiIKGxGowirUAF/SQHMRm3vq/k1TkURERFRWBySG75j+1C44z9QvG5YM3OQ0O1qlEuJCAS0nZJisCEiIqKQxUsVKP5kKdz7tlVt8585DtfuT9Hs9qfhkpKgaJhtOBVFREREIRFFQHUVBoWaSoqnHMWbVsIqBbStSdOjERERkW6YzUaU//B5rePu/V9Bkj0aVsRgQ0REROEQzhElzjUWIQw2REREFJKKCj/i/tC31nF7+96QJauGFTHYEBERUThsyYjrNLDaZsmegKRLb0a5X9K0HD4VRURERCErky1w9BsOe/s+cP78uLctqxvsHS5BuZgA8HFvIiIiakjKZCukJn9A4uC2MAoqPIIZpRUyNH3O+2eciiIiIqKwybICj2yEIT4Jfn/0Vh5msKknBsPZVoqi8Dt7EhER6ZPZfHYiSBSjFy84FRUuQYDbL+Ojzw/jdFEFLv5DGto2T4RJBBQleomViIhIK3bJA9HrRPmWzXBVOGG7sCccTdvApdo1n41isAmHIOC7w0V4ceU3UH/OMB9vP4bUJCueuqcPzCKqthMREemRXfLCu3czij99s2qba/dGGJu0RNObH0Up7JrWw6moMHhlFXPf2lktvJwursA/P9gLVeC0FBER6ZvkKwsKNZX8Z46hdNta2Eza1sNgEyJJErD3cFGt001ffJ8Pb4CXa4iISL8sFiNce2t/pYJr96cwBFzaFQQGmzAIKPf4ax1VFJX32BARka4JwtmXXdZG9XsAaHsuZLAJkSwr6JCZXOt4q6bxMBnZXiIi0i+vV4btwh61jltad4QiajsXxTNvGBJsJvTs0LTadkEA7rmhE0zsLhER6ZiiKDAkN4O5WVb1QdGA5MtGwK1o+64oPhUVBlFVcc8NnXDRBSn496ZDKHV50b5NEu68rgOaxJshy5yKIiIifXPKdqQOm4iynRtQtnMDFI8b1jYdkTTgFvgsqVBkvlKhQREVBQO7ZKBvp2YwGKSzy0crKlQ+501ERI1EacAGy8U3oFnOIIiCAFk0ojxggiJrXwsnS+qBHFBglgQ0SbRCEsBQQ0REjY7Hq8AtOGBMSkeFYonGa6IAxFiweeWVV3D77bcHbXvsscfQrl27oF/9+/ePUoVERERUmyi+SaFKzExFvfHGG5g/fz569Ai+u3rfvn247777MGLEiKptkiRpXR4RERHVwmxQYVZcCJz6CWVHXLA1zYRitMMlWzSvJerB5tSpU5g6dSp27NiBzMzMoDFZlnHw4EGMHTsWqampUaqQiIiIamM2qJAKD+DE6jk/r1tzlv0PfZF02e0oDWj7VFTULxp9//33SEhIwHvvvYecnJygscOHD8Pr9SIrq4bHyIiIiCjqLLITp/71t6BQAwDlez9H+d4tMJu1jRpRv2IzaNAgDBo0qMax/fv3QxAELF26FJs2bYIoihgwYADGjx+P+Pj4sI5rMNRvoyVJDPovRQb7rB32WhvsszbY58gwGiW4d28HlECN484v1yG9fR/IhvDO2XUR9WBzLgcOHIAoimjevDlyc3Nx5MgRPPfcc9i/fz+WLl0KMcS7lERRQFJSZN426nBoe8mtsWKftcNea4N91gb7XP9OF5+qdUwuL4EINWLn3JrEdLAZN24cRo4cCYfDAQDIzs5Gamoqhg8fjt27d1ebujpfiqLC6XTXZ6mQJBEOhxVOZwVkjRcjakzYZ+2w19pgn7XBPkeGwSDC0rI9yr75qMZxY2orKJBQXFz7+6TOl8NhPa8rbjEdbARBqAo1lbKzswEA+fn5IQcbAAgEIvMXW5aViH03/YJ91g57rQ32WRvsc/0KBBQkNM+GFJ8Muayo2njywFtRIdoR8GvX85iebJw4cSJGjRoVtG337t0AgLZt20ajJCIiIvqVcsGBZrf8FZZWHaq2SXGJSB06DkKTTPg1DDVAjF+xGTJkCO6//34sWLAAgwcPRl5eHmbOnIkhQ4bwSSkiIqIYEAgoKBOTkDTkYYh+NyD7AZMNHoMDHp/2V8diOthcdtllmDdvHnJzc5Gbm4v4+HgMHToU48ePj3ZpRERE9DNFUeFSzDAYrUhKs6O4uByBKIQaABDURvhiI1lWUFQU/o1Mv2YwiEhK+vkPk/O3EcM+a4e91gb7rA32WRuR7HNysv28bh6O6XtsiIiIiOqCwYaIiIh0g8GGiIiIdIPBhoiIiHSDwYaIiIh0g8GGiIiIdIPBhoiIiHSDwYaIiIh0g8GGiIiIdKNRrjysqioUpf5/bEkSIctc0TLS2GftsNfaYJ+1wT5rI1J9FkUBgiD87n6NMtgQERGRPnEqioiIiHSDwYaIiIh0g8GGiIiIdIPBhoiIiHSDwYaIiIh0g8GGiIiIdIPBhoiIiHSDwYaIiIh0g8GGiIiIdIPBhoiIiHSDwYaIiIh0g8GGiIiIdIPBhoiIiHSDweY8KYqC+fPn49JLL0VOTg7uvvtuHDlypNb9i4uLMXHiRPTo0QM9evTAk08+CbfbrWHFDVNd+3zgwAHcc8896NWrF/r06YOHHnoIJ06c0LDihquuvf61tWvXol27djh+/HiEq2z46tpnv9+PF154AZdeeim6dOmCESNGYO/evRpW3DDVtc+nT5/GhAkT0KtXL/Tq1QsPP/ww8vPzNay44XvllVdw++23n3OfaJwLGWzO0yuvvIKVK1fi6aefxltvvQVBEDBmzBj4fL4a93/ooYdw7NgxvPHGG5g/fz62bNmCGTNmaFx1w1OXPhcXF+Ouu+6C3W7HsmXLsHDhQhQXF2P06NHwer1RqL5hqevf6Uo//fQT/y7XQV37PH36dLzzzjt46qmnsGrVKiQmJmLMmDEoKyvTuPKGpa59fuSRR3Dy5EksWbIES5YsQX5+PsaOHatx1Q1X5bnt90TlXKjS7/J6vWrXrl3V5cuXV20rLS1VO3furK5bt67a/l9//bWanZ2tHjx4sGrb5s2b1Xbt2qn5+fma1NwQ1bXPb7/9ttqtWzfV4/FUbTt58qSanZ2tfv7555rU3FDVtdeVZFlWb7nlFvWOO+5Qs7Oz1WPHjmlRboNV1z4fPXpUzc7OVv/3v/8F7X/ZZZfx7/Q51LXPpaWlanZ2tvrxxx9XbduwYYOanZ2tFhUVaVJzQ5Wfn6+OGjVK7dKli3rNNdeoI0aMqHXfaJ0LecXmPPzwww8oLy9H7969q7Y5HA506NABX331VbX9t2/fjtTUVGRlZVVt69mzJwRBwI4dOzSpuSGqa5/79OmDf/zjHzCbzdXGSktLI1prQ1fXXlfKzc2F3+/Hvffeq0WZDV5d+/zZZ5/B4XCgf//+Qft/8skn6NOnjyY1N0R17bPZbIbNZsOaNWvgcrngcrnw73//G23atEFCQoKWpTc433//PRISEvDee+8hJyfnnPtG61xoiNg360jlvGuzZs2CtqelpeHkyZPV9j916lS1fU0mExITE2vcn86qa59btGiBFi1aBG179dVXYTab0aNHj8gVqgN17TUAfPvtt3j99dfxzjvv4NSpUxGvUQ/q2ufDhw+jZcuW+Oijj/Daa6/h1KlT6NChAx599NGgkwMFq2ufzWYzZs2ahZkzZ6J79+4QBAGpqalYtmwZRJH/3j+XQYMGYdCgQee1b7TOhfwTPA8VFRUAzv6B/JrZbK7xXo6Kiopq+55rfzqrrn3+rX/+859Yvnw5JkyYgJSUlIjUqBd17bXb7cakSZMwadIktGnTRosSdaGufXa5XDh69CheeeUVTJgwAQsWLIDBYMCtt96KwsJCTWpuiOraZ1VVsW/fPnTt2hVvvvkmli5diubNm+OBBx6Ay+XSpObGIFrnQgab82CxWACg2k1oXq8XVqu1xv1rumHN6/XCZrNFpkgdqGufK6mqirlz52LWrFm49957MXLkyEiWqQt17fXTTz+NNm3a4C9/+Ysm9elFXftsNBpRVlaGF198Ef369UPnzp3x4osvAgDefffdyBfcQNW1z++//z6WL1+Ov//977j44ovRs2dP5Obm4qeffsKqVas0qbkxiNa5kMHmPFReSisoKAjaXlBQgPT09Gr7p6enV9vX5/OhpKQETZs2jVyhDVxd+wycfTT2//7v/5Cbm4vJkydjwoQJEa9TD+ra61WrVmHr1q3o2rUrunbtijFjxgAAhgwZgr/+9a+RL7iBCuX/dxgMhqBpJ4vFgpYtW/LR+nOoa5937NiBzMxMxMXFVW1LSEhAZmYmDh8+HNFaG5NonQsZbM5D+/btERcXh23btlVtczqd2LNnD7p3715t/x49eiA/Pz9oDYXKz3br1i3yBTdQde0zAEyePBn//e9/8cILL2DUqFFaldrg1bXXH330EdatW4c1a9ZgzZo1ePrppwEAr732Gh5++GHN6m5o6trn7t27IxAIYPfu3VXbPB4Pjh07htatW2tSc0NU1z43a9YMR44cCZoOqaiowPHjx9nnehStcyFvHj4PJpMJI0aMwPPPP4/k5GQ0b94cf//735Geno4rr7wSsiyjqKgI8fHxsFgsyMnJQbdu3fDII49g+vTpcLvdmDZtGm644QZesTmHuvZ59erV+OCDDzB58mT07NkTp0+frvquyn2oZnXt9W//n33lzZoZGRm8n+kc6trn7t27o2/fvpgyZQpmzpyJxMREzJ8/H5Ik4frrr4/2jxOz6trnG264AYsXL8b48eOrgvncuXNhMpnwpz/9Kco/TcMVM+fCiD1IrjOBQED929/+pvbu3Vvt0qWLOmbMmKo1PI4dO6ZmZ2erq1atqtr/zJkz6rhx49QuXbqovXr1UqdNmxa03grVrC59vuuuu9Ts7Owaf/36z4JqVte/07/2xRdfcB2b81TXPpeVlanTpk1Te/Xqpebk5Kh33XWXeuDAgWiV32DUtc8HDx5U7733XrVnz55q79691QcffJB/n+toypQpQevYxMq5UFBVVY1cbCIiIiLSDu+xISIiIt1gsCEiIiLdYLAhIiIi3WCwISIiIt1gsCEiIiLdYLAhIiIi3WCwIaKYxJUoiCgUDDZEFHM+/vhjTJkyJdplEFEDxFcqEFHMeeONN6JdAhE1ULxiQ0RERLrBVyoQUUy5/fbb8eWXX1b9PjU1Fb169cILL7wQtN+1116LLl264JlnnsGgQYMwdOhQeDwevPvuuwCAAQMG4PHHH0dSUlLVZ7Zv3465c+di9+7dMJvNuOyyyzBlyhQkJydr88MRUcTxig0RxZRp06ahQ4cO6NChA9566y0MHjwYGzZsgMvlqtpn165dOHToUNCbmJcvX44dO3Zg9uzZmDRpEjZt2oTRo0dDURQAwFdffYWRI0fCYrFg7ty5ePzxx/Hll1/ijjvugMfj0fznJKLI4D02RBRT2rZti7i4OABAly5dkJCQgDfeeAMffvghbrzxRgDAu+++i1atWqF79+5VnxMEAUuWLEF8fDwAIDk5GQ888AA2bdqEgQMH4oUXXkBmZiZeffVVSJIEAMjJycHgwYOxatUq3HbbbRr/pEQUCbxiQ0QxLTMzExdffDH+/e9/AwB8Ph8++OAD3HDDDRAEoWq/yy67rCrUAMCgQYNgNBqxfft2VFRUYNeuXRgwYABUVUUgEEAgEEDLli2RlZWFLVu2aP5zEVFk8IoNEcW8m266CY8//jhOnDiBXbt2wel0YtiwYUH7pKWlBf1eFEUkJibC6XTC6XRCURQsXLgQCxcurPb9ZrM5ovUTkXYYbIgo5l1zzTV4+umn8eGHH+Kbb75Bnz59kJGREbRPSUlJ0O9lWUZxcTGSk5Nht9shCAJGjhyJwYMHV/t+q9UayfKJSEMMNkQUc0RRrLrpFwBsNhuuu+46rFu3DocOHcKMGTOqfWbz5s3w+XwwmUwAzi7yFwgE0KdPH8TFxaFDhw44dOgQOnXqVPUZj8eDhx9+GP3790fbtm0j/4MRUcTxHhsiijkOhwN5eXnYunUrSktLAZydjvruu+8giiKuuuqqap/Jz8/H/fffj40bN2LlypV44okn0K9fP/Tq1QsAMGHCBHz22WeYOHEiNm7ciE8++QSjR4/G559/josuukjTn4+IIofBhohizm233Qaj0YgxY8Zg06ZNAM4+IZWUlITBgwfDYrFU+8zgwYPRqlUrjB8/Hi+99BKGDRuGf/zjH1Xj/fr1w+LFi5Gfn4+HHnoIkydPhiRJWLJkCbp06aLVj0ZEEcYF+oioQfj2229x8803Y9WqVejYsWPQ2KBBg9CzZ088++yzUaqOiGIF77Ehopi2bds2bNu2DWvWrEHv3r2rhRoiol/jVBQRxbTi4mIsWbIEKSkpeOaZZ6JdDhHFOE5FERERkW7wig0RERHpBoMNERER6QaDDREREekGgw0RERHpBoMNERER6QaDDREREekGgw0RERHpBoMNERER6QaDDREREenG/wf7Cihf0iYEygAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#visualize cluster on base of type & mileage\n",
    "\n",
    "sns.scatterplot(x=cars['type'],y=cars['mpg'],hue=cars['cluster'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "id": "e184ff85",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='sales', ylabel='mpg'>"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#lets visualise the cluster based on sales and mileage\n",
    "\n",
    " \n",
    "\n",
    "sns.scatterplot(x=cars[\"sales\"],y=cars[\"mpg\"],hue=cars[\"cluster\"])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "id": "932db19e",
   "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>manufact</th>\n",
       "      <th>model</th>\n",
       "      <th>sales</th>\n",
       "      <th>resale</th>\n",
       "      <th>type</th>\n",
       "      <th>price</th>\n",
       "      <th>engine_s</th>\n",
       "      <th>horsepow</th>\n",
       "      <th>wheelbas</th>\n",
       "      <th>width</th>\n",
       "      <th>length</th>\n",
       "      <th>curb_wgt</th>\n",
       "      <th>fuel_cap</th>\n",
       "      <th>mpg</th>\n",
       "      <th>lnsales</th>\n",
       "      <th>partition</th>\n",
       "      <th>cluster</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>46</th>\n",
       "      <td>Ford</td>\n",
       "      <td>F-Series</td>\n",
       "      <td>540.561</td>\n",
       "      <td>15.075</td>\n",
       "      <td>1</td>\n",
       "      <td>26.935</td>\n",
       "      <td>4.6</td>\n",
       "      <td>220</td>\n",
       "      <td>138.5</td>\n",
       "      <td>79.1</td>\n",
       "      <td>224.5</td>\n",
       "      <td>4.241</td>\n",
       "      <td>25.1</td>\n",
       "      <td>18.0</td>\n",
       "      <td>6.293</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   manufact     model    sales  resale  type   price  engine_s  horsepow  \\\n",
       "46     Ford  F-Series  540.561  15.075     1  26.935       4.6       220   \n",
       "\n",
       "    wheelbas  width  length  curb_wgt  fuel_cap   mpg  lnsales  partition  \\\n",
       "46     138.5   79.1   224.5     4.241      25.1  18.0    6.293          0   \n",
       "\n",
       "    cluster  \n",
       "46        1  "
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cars[cars[\"sales\"]>500]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "4faafcbf",
   "metadata": {},
   "outputs": [],
   "source": []
  }
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