Assignment-07-Clustering-Hierarchical (Airlines)-ver-01.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Assignment-07-Clustering-Hierarchical (Airlines)\n",
    "# Using Normalize Function"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Import Libraries\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "import scipy.cluster.hierarchy as sch\n",
    "from sklearn.cluster import AgglomerativeClustering\n",
    "from sklearn.preprocessing import normalize"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
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       "      <th>ID#</th>\n",
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       "      <td>1</td>\n",
       "      <td>1</td>\n",
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       "<p>3999 rows × 12 columns</p>\n",
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       "       ID#  Balance  Qual_miles  cc1_miles  cc2_miles  cc3_miles  Bonus_miles  \\\n",
       "0        1    28143           0          1          1          1          174   \n",
       "1        2    19244           0          1          1          1          215   \n",
       "2        3    41354           0          1          1          1         4123   \n",
       "3        4    14776           0          1          1          1          500   \n",
       "4        5    97752           0          4          1          1        43300   \n",
       "...    ...      ...         ...        ...        ...        ...          ...   \n",
       "3994  4017    18476           0          1          1          1         8525   \n",
       "3995  4018    64385           0          1          1          1          981   \n",
       "3996  4019    73597           0          3          1          1        25447   \n",
       "3997  4020    54899           0          1          1          1          500   \n",
       "3998  4021     3016           0          1          1          1            0   \n",
       "\n",
       "      Bonus_trans  Flight_miles_12mo  Flight_trans_12  Days_since_enroll  \\\n",
       "0               1                  0                0               7000   \n",
       "1               2                  0                0               6968   \n",
       "2               4                  0                0               7034   \n",
       "3               1                  0                0               6952   \n",
       "4              26               2077                4               6935   \n",
       "...           ...                ...              ...                ...   \n",
       "3994            4                200                1               1403   \n",
       "3995            5                  0                0               1395   \n",
       "3996            8                  0                0               1402   \n",
       "3997            1                500                1               1401   \n",
       "3998            0                  0                0               1398   \n",
       "\n",
       "      Award?  \n",
       "0          0  \n",
       "1          0  \n",
       "2          0  \n",
       "3          0  \n",
       "4          1  \n",
       "...      ...  \n",
       "3994       1  \n",
       "3995       1  \n",
       "3996       1  \n",
       "3997       0  \n",
       "3998       0  \n",
       "\n",
       "[3999 rows x 12 columns]"
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   "source": [
    "# Import Dataset\n",
    "airline=pd.read_csv('Database/EastWestAirlines.csv')\n",
    "airline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
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     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 3999 entries, 0 to 3998\n",
      "Data columns (total 12 columns):\n",
      " #   Column             Non-Null Count  Dtype\n",
      "---  ------             --------------  -----\n",
      " 0   ID#                3999 non-null   int64\n",
      " 1   Balance            3999 non-null   int64\n",
      " 2   Qual_miles         3999 non-null   int64\n",
      " 3   cc1_miles          3999 non-null   int64\n",
      " 4   cc2_miles          3999 non-null   int64\n",
      " 5   cc3_miles          3999 non-null   int64\n",
      " 6   Bonus_miles        3999 non-null   int64\n",
      " 7   Bonus_trans        3999 non-null   int64\n",
      " 8   Flight_miles_12mo  3999 non-null   int64\n",
      " 9   Flight_trans_12    3999 non-null   int64\n",
      " 10  Days_since_enroll  3999 non-null   int64\n",
      " 11  Award?             3999 non-null   int64\n",
      "dtypes: int64(12)\n",
      "memory usage: 375.0 KB\n"
     ]
    }
   ],
   "source": [
    "airline.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
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      "text/plain": [
       "      Balance  Qual_miles  cc1_miles  cc2_miles  cc3_miles  Bonus_miles  \\\n",
       "0       28143           0          1          1          1          174   \n",
       "1       19244           0          1          1          1          215   \n",
       "2       41354           0          1          1          1         4123   \n",
       "3       14776           0          1          1          1          500   \n",
       "4       97752           0          4          1          1        43300   \n",
       "...       ...         ...        ...        ...        ...          ...   \n",
       "3994    18476           0          1          1          1         8525   \n",
       "3995    64385           0          1          1          1          981   \n",
       "3996    73597           0          3          1          1        25447   \n",
       "3997    54899           0          1          1          1          500   \n",
       "3998     3016           0          1          1          1            0   \n",
       "\n",
       "      Bonus_trans  Flight_miles_12mo  Flight_trans_12  Days_since_enroll  \\\n",
       "0               1                  0                0               7000   \n",
       "1               2                  0                0               6968   \n",
       "2               4                  0                0               7034   \n",
       "3               1                  0                0               6952   \n",
       "4              26               2077                4               6935   \n",
       "...           ...                ...              ...                ...   \n",
       "3994            4                200                1               1403   \n",
       "3995            5                  0                0               1395   \n",
       "3996            8                  0                0               1402   \n",
       "3997            1                500                1               1401   \n",
       "3998            0                  0                0               1398   \n",
       "\n",
       "      Award?  \n",
       "0          0  \n",
       "1          0  \n",
       "2          0  \n",
       "3          0  \n",
       "4          1  \n",
       "...      ...  \n",
       "3994       1  \n",
       "3995       1  \n",
       "3996       1  \n",
       "3997       0  \n",
       "3998       0  \n",
       "\n",
       "[3999 rows x 11 columns]"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "airline2=airline.drop(['ID#'],axis=1)\n",
    "airline2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
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       "      <th>Qual_miles</th>\n",
       "      <th>cc1_miles</th>\n",
       "      <th>cc2_miles</th>\n",
       "      <th>cc3_miles</th>\n",
       "      <th>Bonus_miles</th>\n",
       "      <th>Bonus_trans</th>\n",
       "      <th>Flight_miles_12mo</th>\n",
       "      <th>Flight_trans_12</th>\n",
       "      <th>Days_since_enroll</th>\n",
       "      <th>Award?</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0.970414</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000034</td>\n",
       "      <td>0.000034</td>\n",
       "      <td>0.000034</td>\n",
       "      <td>0.006000</td>\n",
       "      <td>0.000034</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.241371</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0.940209</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000049</td>\n",
       "      <td>0.000049</td>\n",
       "      <td>0.000049</td>\n",
       "      <td>0.010504</td>\n",
       "      <td>0.000098</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.340437</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0.981113</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000024</td>\n",
       "      <td>0.000024</td>\n",
       "      <td>0.000024</td>\n",
       "      <td>0.097817</td>\n",
       "      <td>0.000095</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.166880</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0.904428</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000061</td>\n",
       "      <td>0.000061</td>\n",
       "      <td>0.000061</td>\n",
       "      <td>0.030605</td>\n",
       "      <td>0.000061</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.425527</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0.912226</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000037</td>\n",
       "      <td>0.000009</td>\n",
       "      <td>0.000009</td>\n",
       "      <td>0.404078</td>\n",
       "      <td>0.000243</td>\n",
       "      <td>0.019383</td>\n",
       "      <td>0.000037</td>\n",
       "      <td>0.064718</td>\n",
       "      <td>0.000009</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3994</th>\n",
       "      <td>0.905810</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000049</td>\n",
       "      <td>0.000049</td>\n",
       "      <td>0.000049</td>\n",
       "      <td>0.417949</td>\n",
       "      <td>0.000196</td>\n",
       "      <td>0.009805</td>\n",
       "      <td>0.000049</td>\n",
       "      <td>0.068784</td>\n",
       "      <td>0.000049</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3995</th>\n",
       "      <td>0.999649</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000016</td>\n",
       "      <td>0.000016</td>\n",
       "      <td>0.000016</td>\n",
       "      <td>0.015231</td>\n",
       "      <td>0.000078</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.021659</td>\n",
       "      <td>0.000016</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3996</th>\n",
       "      <td>0.944948</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000039</td>\n",
       "      <td>0.000013</td>\n",
       "      <td>0.000013</td>\n",
       "      <td>0.326726</td>\n",
       "      <td>0.000103</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.018001</td>\n",
       "      <td>0.000013</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3997</th>\n",
       "      <td>0.999592</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000018</td>\n",
       "      <td>0.000018</td>\n",
       "      <td>0.000018</td>\n",
       "      <td>0.009104</td>\n",
       "      <td>0.000018</td>\n",
       "      <td>0.009104</td>\n",
       "      <td>0.000018</td>\n",
       "      <td>0.025509</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3998</th>\n",
       "      <td>0.907271</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000301</td>\n",
       "      <td>0.000301</td>\n",
       "      <td>0.000301</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.420546</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>3999 rows × 11 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "       Balance  Qual_miles  cc1_miles  cc2_miles  cc3_miles  Bonus_miles  \\\n",
       "0     0.970414         0.0   0.000034   0.000034   0.000034     0.006000   \n",
       "1     0.940209         0.0   0.000049   0.000049   0.000049     0.010504   \n",
       "2     0.981113         0.0   0.000024   0.000024   0.000024     0.097817   \n",
       "3     0.904428         0.0   0.000061   0.000061   0.000061     0.030605   \n",
       "4     0.912226         0.0   0.000037   0.000009   0.000009     0.404078   \n",
       "...        ...         ...        ...        ...        ...          ...   \n",
       "3994  0.905810         0.0   0.000049   0.000049   0.000049     0.417949   \n",
       "3995  0.999649         0.0   0.000016   0.000016   0.000016     0.015231   \n",
       "3996  0.944948         0.0   0.000039   0.000013   0.000013     0.326726   \n",
       "3997  0.999592         0.0   0.000018   0.000018   0.000018     0.009104   \n",
       "3998  0.907271         0.0   0.000301   0.000301   0.000301     0.000000   \n",
       "\n",
       "      Bonus_trans  Flight_miles_12mo  Flight_trans_12  Days_since_enroll  \\\n",
       "0        0.000034           0.000000         0.000000           0.241371   \n",
       "1        0.000098           0.000000         0.000000           0.340437   \n",
       "2        0.000095           0.000000         0.000000           0.166880   \n",
       "3        0.000061           0.000000         0.000000           0.425527   \n",
       "4        0.000243           0.019383         0.000037           0.064718   \n",
       "...           ...                ...              ...                ...   \n",
       "3994     0.000196           0.009805         0.000049           0.068784   \n",
       "3995     0.000078           0.000000         0.000000           0.021659   \n",
       "3996     0.000103           0.000000         0.000000           0.018001   \n",
       "3997     0.000018           0.009104         0.000018           0.025509   \n",
       "3998     0.000000           0.000000         0.000000           0.420546   \n",
       "\n",
       "        Award?  \n",
       "0     0.000000  \n",
       "1     0.000000  \n",
       "2     0.000000  \n",
       "3     0.000000  \n",
       "4     0.000009  \n",
       "...        ...  \n",
       "3994  0.000049  \n",
       "3995  0.000016  \n",
       "3996  0.000013  \n",
       "3997  0.000000  \n",
       "3998  0.000000  \n",
       "\n",
       "[3999 rows x 11 columns]"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Normalize heterogenous numerical data \n",
    "airline2_norm=pd.DataFrame(normalize(airline2),columns=airline2.columns)\n",
    "airline2_norm"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x504 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Create Dendrograms\n",
    "plt.figure(figsize=(10, 7))  \n",
    "dendograms=sch.dendrogram(sch.linkage(airline2_norm,'complete'))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "AgglomerativeClustering(n_clusters=5)"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Create Clusters (y)\n",
    "hclusters=AgglomerativeClustering(n_clusters=5,affinity='euclidean',linkage='ward')\n",
    "hclusters"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "2    1547\n",
       "4    1191\n",
       "3     579\n",
       "1     453\n",
       "0     229\n",
       "Name: clustersid, dtype: int64"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y=pd.DataFrame(hclusters.fit_predict(airline2_norm),columns=['clustersid'])\n",
    "y['clustersid'].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Balance</th>\n",
       "      <th>Qual_miles</th>\n",
       "      <th>cc1_miles</th>\n",
       "      <th>cc2_miles</th>\n",
       "      <th>cc3_miles</th>\n",
       "      <th>Bonus_miles</th>\n",
       "      <th>Bonus_trans</th>\n",
       "      <th>Flight_miles_12mo</th>\n",
       "      <th>Flight_trans_12</th>\n",
       "      <th>Days_since_enroll</th>\n",
       "      <th>Award?</th>\n",
       "      <th>clustersid</th>\n",
       "    </tr>\n",
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       "      <th>0</th>\n",
       "      <td>28143</td>\n",
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       "      <td>174</td>\n",
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       "      <td>1</td>\n",
       "      <td>215</td>\n",
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       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>6968</td>\n",
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       "      <td>1</td>\n",
       "      <td>4123</td>\n",
       "      <td>4</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>7034</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
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       "      <th>3</th>\n",
       "      <td>14776</td>\n",
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       "      <td>1</td>\n",
       "      <td>1</td>\n",
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       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>6952</td>\n",
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       "      <td>2</td>\n",
       "    </tr>\n",
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       "      <th>4</th>\n",
       "      <td>97752</td>\n",
       "      <td>0</td>\n",
       "      <td>4</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>43300</td>\n",
       "      <td>26</td>\n",
       "      <td>2077</td>\n",
       "      <td>4</td>\n",
       "      <td>6935</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
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       "    <tr>\n",
       "      <th>...</th>\n",
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       "      <td>...</td>\n",
       "      <td>...</td>\n",
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       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3994</th>\n",
       "      <td>18476</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>8525</td>\n",
       "      <td>4</td>\n",
       "      <td>200</td>\n",
       "      <td>1</td>\n",
       "      <td>1403</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3995</th>\n",
       "      <td>64385</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>981</td>\n",
       "      <td>5</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1395</td>\n",
       "      <td>1</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3996</th>\n",
       "      <td>73597</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>25447</td>\n",
       "      <td>8</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1402</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
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       "      <th>3997</th>\n",
       "      <td>54899</td>\n",
       "      <td>0</td>\n",
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       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>500</td>\n",
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       "      <td>500</td>\n",
       "      <td>1</td>\n",
       "      <td>1401</td>\n",
       "      <td>0</td>\n",
       "      <td>4</td>\n",
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       "      <td>1</td>\n",
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       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1398</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>3999 rows × 12 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "      Balance  Qual_miles  cc1_miles  cc2_miles  cc3_miles  Bonus_miles  \\\n",
       "0       28143           0          1          1          1          174   \n",
       "1       19244           0          1          1          1          215   \n",
       "2       41354           0          1          1          1         4123   \n",
       "3       14776           0          1          1          1          500   \n",
       "4       97752           0          4          1          1        43300   \n",
       "...       ...         ...        ...        ...        ...          ...   \n",
       "3994    18476           0          1          1          1         8525   \n",
       "3995    64385           0          1          1          1          981   \n",
       "3996    73597           0          3          1          1        25447   \n",
       "3997    54899           0          1          1          1          500   \n",
       "3998     3016           0          1          1          1            0   \n",
       "\n",
       "      Bonus_trans  Flight_miles_12mo  Flight_trans_12  Days_since_enroll  \\\n",
       "0               1                  0                0               7000   \n",
       "1               2                  0                0               6968   \n",
       "2               4                  0                0               7034   \n",
       "3               1                  0                0               6952   \n",
       "4              26               2077                4               6935   \n",
       "...           ...                ...              ...                ...   \n",
       "3994            4                200                1               1403   \n",
       "3995            5                  0                0               1395   \n",
       "3996            8                  0                0               1402   \n",
       "3997            1                500                1               1401   \n",
       "3998            0                  0                0               1398   \n",
       "\n",
       "      Award?  clustersid  \n",
       "0          0           4  \n",
       "1          0           2  \n",
       "2          0           2  \n",
       "3          0           2  \n",
       "4          1           3  \n",
       "...      ...         ...  \n",
       "3994       1           3  \n",
       "3995       1           4  \n",
       "3996       1           2  \n",
       "3997       0           4  \n",
       "3998       0           2  \n",
       "\n",
       "[3999 rows x 12 columns]"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Adding clusters to dataset\n",
    "airline2['clustersid']=hclusters.labels_\n",
    "airline2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
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       "\n",
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr>\n",
       "      <th></th>\n",
       "      <th>clustersid</th>\n",
       "      <th>Balance</th>\n",
       "      <th>Qual_miles</th>\n",
       "      <th>cc1_miles</th>\n",
       "      <th>cc2_miles</th>\n",
       "      <th>cc3_miles</th>\n",
       "      <th>Bonus_miles</th>\n",
       "      <th>Bonus_trans</th>\n",
       "      <th>Flight_miles_12mo</th>\n",
       "      <th>Flight_trans_12</th>\n",
       "      <th>Days_since_enroll</th>\n",
       "      <th>Award?</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th>mean</th>\n",
       "      <th>mean</th>\n",
       "      <th>mean</th>\n",
       "      <th>mean</th>\n",
       "      <th>mean</th>\n",
       "      <th>mean</th>\n",
       "      <th>mean</th>\n",
       "      <th>mean</th>\n",
       "      <th>mean</th>\n",
       "      <th>mean</th>\n",
       "      <th>mean</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0</td>\n",
       "      <td>5524.222707</td>\n",
       "      <td>8.755459</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>584.532751</td>\n",
       "      <td>2.401747</td>\n",
       "      <td>66.982533</td>\n",
       "      <td>0.209607</td>\n",
       "      <td>4875.301310</td>\n",
       "      <td>0.131004</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>31066.514349</td>\n",
       "      <td>111.415011</td>\n",
       "      <td>3.200883</td>\n",
       "      <td>1.026490</td>\n",
       "      <td>1.070640</td>\n",
       "      <td>40266.935982</td>\n",
       "      <td>17.289183</td>\n",
       "      <td>626.754967</td>\n",
       "      <td>1.812362</td>\n",
       "      <td>4205.624724</td>\n",
       "      <td>0.858720</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2</td>\n",
       "      <td>81201.080802</td>\n",
       "      <td>136.521008</td>\n",
       "      <td>2.115061</td>\n",
       "      <td>1.013575</td>\n",
       "      <td>1.000646</td>\n",
       "      <td>16350.149968</td>\n",
       "      <td>13.574014</td>\n",
       "      <td>488.550743</td>\n",
       "      <td>1.340659</td>\n",
       "      <td>4285.891403</td>\n",
       "      <td>0.312217</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>3</td>\n",
       "      <td>69569.894646</td>\n",
       "      <td>97.257340</td>\n",
       "      <td>3.326425</td>\n",
       "      <td>1.032815</td>\n",
       "      <td>1.022453</td>\n",
       "      <td>35743.675302</td>\n",
       "      <td>17.784111</td>\n",
       "      <td>406.804836</td>\n",
       "      <td>1.274611</td>\n",
       "      <td>4090.832470</td>\n",
       "      <td>0.525043</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>4</td>\n",
       "      <td>94957.590260</td>\n",
       "      <td>215.220823</td>\n",
       "      <td>1.141058</td>\n",
       "      <td>1.005038</td>\n",
       "      <td>1.002519</td>\n",
       "      <td>3524.928631</td>\n",
       "      <td>5.640638</td>\n",
       "      <td>461.104954</td>\n",
       "      <td>1.521411</td>\n",
       "      <td>3736.071369</td>\n",
       "      <td>0.230898</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  clustersid       Balance  Qual_miles cc1_miles cc2_miles cc3_miles  \\\n",
       "                      mean        mean      mean      mean      mean   \n",
       "0          0   5524.222707    8.755459  1.000000  1.000000  1.000000   \n",
       "1          1  31066.514349  111.415011  3.200883  1.026490  1.070640   \n",
       "2          2  81201.080802  136.521008  2.115061  1.013575  1.000646   \n",
       "3          3  69569.894646   97.257340  3.326425  1.032815  1.022453   \n",
       "4          4  94957.590260  215.220823  1.141058  1.005038  1.002519   \n",
       "\n",
       "    Bonus_miles Bonus_trans Flight_miles_12mo Flight_trans_12  \\\n",
       "           mean        mean              mean            mean   \n",
       "0    584.532751    2.401747         66.982533        0.209607   \n",
       "1  40266.935982   17.289183        626.754967        1.812362   \n",
       "2  16350.149968   13.574014        488.550743        1.340659   \n",
       "3  35743.675302   17.784111        406.804836        1.274611   \n",
       "4   3524.928631    5.640638        461.104954        1.521411   \n",
       "\n",
       "  Days_since_enroll    Award?  \n",
       "               mean      mean  \n",
       "0       4875.301310  0.131004  \n",
       "1       4205.624724  0.858720  \n",
       "2       4285.891403  0.312217  \n",
       "3       4090.832470  0.525043  \n",
       "4       3736.071369  0.230898  "
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "airline2.groupby('clustersid').agg(['mean']).reset_index()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.collections.PathCollection at 0x29593e2e0d0>"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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r2R07tD2XizkNgEZDyQIQrD1aWyouT5upNcPVEgCiRckCEKwPLu7crUy1ZDJ626GHK5tOJ5QKQKOgZAEI1rv+5ki9e9FRak6nNb2pSU3ptF574MH6l+NPTDoagAZg9XjxZ2dnp3d1dSUdA0Agtg0P66mtWzR32nTNaWtLOg6AwJjZSnfvHLucI1kAglZy133PbtDt657SXc90K1csJh0JQIPgyk8AwdqRy+m0n1+uJ7duUa5QUHMmo2lNzfr5O07XvtNnJB0PQOA4kgUgWP/+x9u0pq9PA/m8Cu7qz+e1caBfn/7NjUlHA9AAKFkAgvWL1Y8oV9r19GDRXXc9063BfD6hVAAaBSULQLBc1W/sqb9bfgCEhpIFIFinHPxiZVO7vs2lzHTUPnPVls0mlApAo6BkAQjWJ1/xKs2bMfP5QtWWzWpWS4u++pqlCScD0Ai4uxBAsGa2tOiGM9+j3zzxuB7p7dF+M2fplENewlEsALGgZAEIWjad1rKDX6xlB7846SgAGgynCwEAACJAyQIAAMFxd7kn+4QHShYAAAiG+5BKW/9V/twR8ucWqrTxzfL8A4lkoWQBAIBg+JaPSYNXShqW5FLhIXnfu+WFp2PPQskCAABB8MLT0vDtGilYowdy8v4fxJ6HuwsBBO3h3h79xx23a1XPc9p/5ix99JiX67j5+ycdC0AUik9J1iT5mJKlglR4JPY4HMkCEKwHnntWb//ZZfqfJx7Xc/07dNf6bn3wl9fo+j+tTjoagChkDqpQsCQpK2WPiD0OJQtAsL7yh5s1WCjs8pzCoUJBX7zl93Ln6YVAaCz9IqnltZJaRi+VrFnW/p7Y81CyAARrVU9PxeUbBwe0PZeLOQ2AONjMr0rt75NslqQmqelY2Z5XyNJzY8/CNVkAgtXR3qb+LbuXqaZUmkfrAIEyy8qmf1ya/vGko3AkC0C4PtL5crVmdv1dsjWT0ZlHLFImxdsfgGhxJAtAsN5y6EJtHOzXt+66Q+5S0V1vX3i4PvmKVyUdDUADoGQBCJaZ6e+PXqKzFy1WT3+/9mxr4zQhgNhQsgAErzmT0fyZM5OOAaDBcFECAABABChZAAAAEeB0IQAACEqp2CPtuEAq9Umtp8qaXy0ziz0HJQsAAASjNPBTadvn/7xg+EZ5+kBpzgqZxXsCj9OFAAAgCKXSsLTtvN0Himvl2/8j9jyULAAAEIahKyVVeS7p4M9ijSLVWLLMbKmZrTazNWZ2boXxT5rZfeWPVWZWNLPZ5bEnzezB8ljXZG8AAACAJMmHxxksxRZjpwlLlpmlJV0gaZmkhZJON7OFo9dx96+5+5HufqSkz0i62d37Rq1yUnm8cxKzAwAA/FnrW6uPtZwcX46yWo5kLZG0xt3XuntO0uWSTh1n/dMlXTYZ4QAAAGpl1U4VSpLmxJZjp1pK1r6S1o36uru8bDdm1iZpqaQrRy12STeZ2UozW17tm5jZcjPrMrOu3t7eGmIBAAD8mQ/fWn1w8Kr4gpTVUrIqTSxRrSq+UdJtY04VHufuizVyuvEjZnZ8pRe6+8Xu3ununR0dHTXEAgAAGCX/p3EGN8cWY6daSla3pPmjvp4naX2VdU/TmFOF7r6+/GePpKs1cvoRAABgcvnQOIPxT0ZaS8m6W9IhZnaAmTVppEhdO3YlM5sp6QRJvxi1rN3Mpu/8XNLrJK2ajOAA8Jco+XjXagAIQvM4x3FSe8WXo2zCGd/dvWBm50i6UVJa0iXu/pCZfag8flF51TdLusnd+0e9fG9JV5enss9IutTdb5jMDQCA8dzwp8f05T/crO7t2zS7tVXnvOzles+ioxJ5xAaAaFnzSXK1SKpwRGv6P8efx+vwt7vOzk7v6mJKLQAvzP88sVbnXP9LDRUKzy9rzWT0sWNeoeVHvyzBZACiUso/Im06TdJgeYlJrWcoNfP8yL6nma2sNE0VM74DCNbX//iHXQqWJA0WCrrw7jtVLMU/MSGA6NnzJ9SaNXICrknSgJI4qETJAhCsJzb3VVy+Izes/nwu5jQAouZekG/+Xxo5ijUsqTjy5+CN0vBNseehZAEIVipV+S3OJbVnm+INAyB6+Xsl5SsMDMgHfh53GkoWgHBVu6PQzNSfr/RGDGBqc1WfqqEYZxBJlCwAAZs7bXrF5S3pjNqy2ZjTAIhc9khVLFnWKmt9c+xxKFkAgnXOy45Ra2bXmWpaMxmdtehIZaqcSgQwdZk1yWZ9Q7JWjVzwrpHPm46TWk6JPc+E82QBwFT15kMPU9/goP7zrj+qWCrJJZ1++BH6xLGvTDoagIhY86ukOb+Rhq6Tl7bIml8pZTsTmRuPebIABC9XLKp3oF+zW1rVymlCAJOs2jxZHMkCELymdFr7Tp+RdAwADYaLEgAAACJAyQIAAIgAJQsAACAClCwAABAMd1dp4Kcq9Zyo0nMvU2nzP8qLPYlk4cJ3AEG779kN+uptt+rRTb2aN32GPv7y43TyAQcmHQtARHzrJ6ShX/55wfCv5L2/lXf8Tqn0nrFm4UgWgGDds2G9zrjqCt3xzDptGRrSqt4effT6X+qaRx9OOhqACJSKPbsWrOcNSdu+FHseShaAYH3lDzdrqFDYZdlgoaCv/OFm1eMcgQBeoKFfVR/L/SG+HGWULADBenRjb8Xlm4eGtD2XizkNgMjZOPPhWfwTEVOyAARrnyoPiG5Kp3lANBAga32Dqlab1jNizSJRsgAE7B+OObbiA6Lfe+RiHhANBMisWbL9Kg+2vD7eMKJkAQjYG1/8Up173PGa0dys5nRGrZmM3r3oKH38mFckHQ1ABEr5JyV/svLgti/GGUUSUzgACNxZi47SWw49TI9u7NWBe8zWHq2tSUcCEJXhG6qP5e+NL0cZJQtAsNxdF3bdqQvvvkspkwqlkt566GE6/4STlU2nk44HYLKl9xlnMP7KQ8kCEKyfP/KQLrz7Tg2OmsbhqkcfVmsmq88df2JywQBEw/aqPpaeHV+OMq7JAhCssQVLkoYKBV266n4VSqWEUgGIiqXaJbVUHswcHGsWiZIFIGAbBwYqLs+XShrI52NOAyBy2SOk1GxJNmagVdZ2ZuxxKFkAgnXE3ntXXD6nrU3Tm5piTgMgamYmm/1dKbWXZO2STZPULE37sKz52NjzcE0WgGB95pUn6J0/v1xDhYJ2PkSnJZPRecefJLOxv+kCCIFlDpI6bpbyXVJpq9R0tCwV//VYEiULQMAO32tvXfmOM/R/b7tFDz73nPafNVOfesXxOmbe/KSjAYiQWUpqWpJ0DE4XAghXsVTS9+67R3d0r1O+VNTDvRt1Yded6ue5hQBiQMkCEKzv3rtS1z32qIaLRW3P5TRcLOjOZ7p13u9/m3Q0AA2AkgUgWD+4/57dpnDIFYv61Z9WK1csJpQKQKPgmiwAwdpe5bRgyV3DhYKamPUdCJLnH5QPXCqV+mTNr5Fa3zTy8OiYcSQLQLBeMW8/pSrcRbj/zFma3hz/Gy6A6JUGfirfdKY0eLU0/Dv5tv8t3/ROuQ/FnoWSBSBY577yeE1ralI2NfJWlzZTayajL5382oSTAYiCl/qlbV+SNCRp51MdBqXCE/KBq2LPw+lCAMFaMGsP3Xjm2frve7p01zPr9JI5Hfpw5xIduEcyc+YAiFj+Pskyen5ivOcNSkPXS+1nxBqHkgUgWO6uKx5+UJetul8pM/2pb5OyqZS+cOKrleV6LCA8qen68xGssWOzYo0iUbIABOyKh1fpoq67drnD8JrVj6g1m9Xnjz8pwWQAIpH5G8mrVJuWd8abRVyTBSBgYwuWJA0VCrps1QPKM4UDEJyRi9u3VR7M/yHWLBIlC0DANg4MVFxeKJV2K18AAjB45Thj18SXo4ySBSBYi/bep+LyjrZ2TW9qijkNgMh5fpyx+B+nRckCEKzPvPL456dv2CltpvNPOElWYf4sAFNc9rDqY6n47yqmZAEI1m3rnla+tOudRkV3XffY6oQSAYiSKS+pylHq9N6xZpFqLFlmttTMVpvZGjM7t8L4iWa21czuK3+cV+trASAq/3nn7RWX/+pPq1UoVbnNG8DUlV2kytWmRWpZGneaiUuWmaUlXSBpmaSFkk43s4UVVr3V3Y8sf/zbX/haAJh01S5ud0nP7tgebxgAkbPUNGn6pyS1Stp5SUCLlJkva31r7HlqmSdriaQ17r5WkszsckmnSno44tcCwAuSTaV2O124015t7TGnARCHVPu75NmF8oEfS6VNUvNrZW1vlVlr7FlqKVn7Slo36utuScdUWO9YM7tf0npJn3D3h/6C18rMlktaLkn77bdfDbEAYHx7tU/TM9t3nzOnKZVmxncgYNa0WNa0OOkYNV2TVekWnLFPBbpH0v7uvkjSNyXtnIyilteOLHS/2N073b2zo6OjhlgAML4tQ4MVlxe9pO25+G/nBhAPL/XLB38h7/+hPP+nxHLUciSrW9L8UV/P08jRque5+7ZRn68wswvNbE4trwWAqKRTlX+PdEmtGZ4qBoSoNHyPtPk9kvIaeY5hWt7yetnMr8U+dUstR7LulnSImR1gZk2STpN07egVzGwfKyc3syXlv3dTLa8FgKgUq1yP5e4qecWD6gCmMPeCtPlsScP684Oii9LQtfKhX8SeZ8Jf5dy9YGbnSLpRUlrSJe7+kJl9qDx+kaS3SfqwmRUkDUo6zd1dUsXXRrQtALCL8YpUoVRSc4xZAETPh2+RNFR5cMeFUuvfxZqnpuPl7r5C0ooxyy4a9fm3JH2r1tcCQByyqbQGtfs0DiapNZuNPxCAaBXWVh8rbowvRxkzvgMI1kC+8sXtLmlHbjjeMACil15QfcxaYouxEyULQLCqnSx0SU1pLnwHwjPOkxy8GF+MMkoWgGAVx7kmK1+M/w0XQMQKj40zuCO2GDtRsgA0pGqP3AEwhfl489/Ff0cxJQtAsMabEaeZGd+BAI336Jz4HwpPyQIQrKP2mVtx+ZzWNs1sif8iWAARKz053mBcKZ5HyQIQrO++6S2a2bzrbFhNqbQufes7EkoEIFLpg8YbjC3GTpQsAMGa2dKiK99xhhbvM1ft2awO2mO2Ln3L23Xw7D2TjgYgCqnx/m9TsgBg0jy6sVdLf/ID3fPsBvXn83p8c5/eeeVP9evH1yQdDUAU8neMNxhbjJ0oWQCC9dHrr1NhzPMLi+76x5tWyHl2IRAemzPOYPyVh5IFIFiPb+6ruHwgn9fmwcGY0wCIXPOrq49ZR3w5yihZABpSrsg8WUBozAZVdfKWNCULAGKRSTFPFhCc9DxJlR7+blJ2YdxpKFkAGtMz27cmHQHAJLPMAap8F6FLzSfEHYeSBaAxLZg1O+kIACZZKf+YpCrXWw78JNYsksRj6AEEKy2p2mOgZ4yZpBThGSgM6I6+u9Qz1KMDpx2gxbOOUibFj72g5f44zti98eUo418bgGBVK1iStGlgQHPa22PLgnh1DzyjLz/yf5T3gnKlnJpTzZrddI0+v/Bzas+0JR0PUck/Pc7gQGwxduJ0IYCGNFwcr4Jhqrt47XfUXxxQrpSTJA2XhtUz1KtrnvlFwskQqeHbkk6wC0oWgIY0p42jGaHaUdihdQPduy0vqqjbN403IzimvlzSAXZByQLQkIYLzJMVKnNTSaWKY8PF4ZjTIFbZxUkn2AUlC0BDWreVKRxCVVBBVm1CSoSt5TXjDFaaPytalCwADWnfGTOSjoCItKRaqpasmdmZMadBrHK3jjPIA6IBIBZt2fh/q0U8mlJNyljlm+enpbmjNGheX5cBULIANKT1O7YnHQER2VbYpqJXvnu0N7cx5jSIV32dJqZkAWhILWmmCQxVS6pFZpV/2E7PTo85DWKV2mOcwfifV0rJAtCQmtI8IDpUzelmvWx2p7JjThk2pZq0bJ+/TSgVYtHyyupjqX3jy1HGr3IAGtJgIf6LYBGf9y54twaLA3po6yPKpDIqlAp6zV4n64SOVyUdDRGypk55tcHWt8cZRRIlC0CDKhYrz6OEMDSnm/WPL/6Y+nJ96hvu09zWuWrPcNF78Irrq4/l74kvRxklC0BDGmQy0oYwu2m2ZjfNTjoGYuJDv6s+mLs9viBlXJMFoCFVuzAawBSWXzXOYPyP3KFkAQ88MVIAAA+iSURBVGhI6RQlCwhOet44g/H/n+d0IYCGtO90ZnwP3Y7CDt2+8Y96dqhHB087SC+bfbSyKSahDZqN9/86/spDyQLQkDYO9mtedlbSMRCRdQPd+uLDX1KulJfL9bue3+vn3Vfq3w4/X9My05KOh6ik96o+ZuPNoRUNThcCaEjbhoaSjoAI/cdj/6nhUk5evqG/pJI25fr0oyd/knAyRKppvHmyOuLLsfNbxv4dAaAONDHje7C25bdrU66v4tjdm1fGnAax6v9R9bHiI/HlKKNkAWhMVWcsxFS3o7Cj6li1ZxoiELnbxhmMf99TsgA0pHSat79QNaeako6ApBQHkk6wC95l0DAGh3K6/5FuPdW9KekoqAMp4+0vVONd2D49zQOiw1Zf11pyUQIawpUr7tG3f3yL0umUisWS5r9otr722bdozmzuMmpUO4aHk46AiDSnm7V41mLds2XXx6iYTH83740JpUIszOvqUgB+lUPw7nnwaX37x7doaLig/oGchoYLevypXn3yy1clHQ0JYjLSsH3wwPfqJdNfrKxl1JxqVlppndhxvE7e66SkoyFK6YOTTrALjmQheFf8aqWGhnd9Tl2p5Hr6mT492b1JC+btmVAyJGlmS0vSERChtkybPnvop7Vh8FltzG3U/NZ5mtXEvGjBq7PLAChZCF7flsoXQmYyKW3dPhhzGtSLoUI+6QiIwdzWfTS3dZ+kYyAu+dVJJ9hFTZXPzJaa2WozW2Nm51YYP9PMHih/3G5mi0aNPWlmD5rZfWbWNZnhgVocd/SBasqmd1teLJZ0yIJxZgdG0NK2+78JhOfZwWf14NZV2pLbmnQUxCHVnHSCXUx4JMvM0pIukPRaSd2S7jaza9394VGrPSHpBHffbGbLJF0s6ZhR4ye5+8ZJzA3U7C2nHKVf/vZB9W3pVy4/Mk9KS3NGH37X8Wpr5VbvRvX0ti3abxanj0I1WBzUNx77ptb2P6G0pVUoFfSqjuN01v5ncmdpyGy+pMeTTvG8Wk4XLpG0xt3XSpKZXS7pVEnPlyx3v33U+ndIGu8x2ECspre36Htff7euWnGvblv5uPaY2a53vuFoLf6b/ZKOhgRx2XvYvrP2e1qz43EV/M/XY/5h4+3at/VFes3er04wGSKVflESc45WVUvJ2lfSulFfd2vXo1RjvV/S9aO+dkk3mZlL+n/ufnGlF5nZcknLJWm//fjhh8k1vb1F73n7sXrP249NOgrqRAuP1QnWcHFY926+T8UxP21zpZxu3PBrSlbIig8knWAXtbzLVPqFr+IsFGZ2kkZK1ugnNB7n7uvNbC9JvzazR939lt3+wpHydbEkdXZ21tEsFwBCtHmwvmaGxuQZLg3vVrB22prn2qygFZ9JOsEuajkx3S1p/qiv50laP3YlMztC0nckneruz0+p7e7ry3/2SLpaI6cfASBRz23bnnQERKTJql9rWaqnmSoRgcLEq8SolpJ1t6RDzOwAM2uSdJqka0evYGb7SbpK0lnu/tio5e1mNn3n55JeJ2nVZIUHgL9WkZ+1wRoY5/l1PCA6dPX12KQJTxe6e8HMzpF0o6S0pEvc/SEz+1B5/CJJ50naU9KFZiZJBXfvlLS3pKvLyzKSLnX3GyLZEgD4C7iVko6AiNg4xw9KYr8HzfaUfLeTbYmp6cpPd18hacWYZReN+vwDkj5Q4XVrJS0auxwAktbeVF/z6WDyVLseCw3AtySdYBdMFgKgQXG+MFS5IrP5Ny5KFgAkbt3WbUlHQET2aa3+JIfxLopHCIaSDrALShaAhpRN8fYXqu356neO5j0XYxLEb+rdXQgAwdl/Jo/UCdVgsfqD3zlJHLr62sOULAAN6fbup5OOgIikxMO/UR8oWQAaUqnIrfyh6i/2Jx0BkETJAtCgpre0JB0BEWmybNIRkJj6evQ7JQtAQ2rL8oM4VDOaZiQdAYmpr1rDY+jREDb27dAPfn6H7rj3Cc2a0arT3tSpk1/xEpWfRoAG1J/jLrNQ9Y/zWB2Err4uA6BkIXibt/br7H/+gbb3D6tYLGlDz1Z95YIb9VT3Jr3vncclHQ8JmdPWlnQERGTttieSjoDEZCTVz2S09XVcDYjAT3+5Uv0DORVHXeg8NJzXj6++S9v762viOsQnk+IOtFBtGNyQdAQkpn4KlkTJQgNY+eDTyhd2f5ZZNpvW2qc2JpAI9WD1xp6kIyAi89r3TToCIImShQYwd6/KF8EWCiXNmT0t5jSoFxmuxwtWW4pTwagPlCwE7+2vP1qp1O4/UOfPnaV992HW70a1cZC5lEJ1/5YHko4ASKJkoQE88+yWijOn9Gzasct1Wmgsm7btSDoCIlIs1dejVdC4KFkI3uXXdlV8093eP6zHnngugUSoC5wtDNZggSkcUB8oWQje+ue2VFzu7lr/3NaY06BeOAc7gvX0wLqkIwCSKFloAIPD1W/pHRqqr9t9EZ+teSYjDZVzGQDqBCULwRvviMV9D/Ebb6Pao7k16QiIyOy22UlHACRRstDgZs3kB22j2jTIdTuhanH+X6M+ULIQvKZs9Zm9jz36wBiToJ60ZXiqWKh2+LakIwCSKFloAIUKs73v9PT6zTEmQT0Z8ur/LjC17d++X9IRAEmULDSA8abMeWYDJatRbRzgdGGoeoc3JR0BkETJQoN7+hlKVqPiZGG41u/gAdGoD5QsNLRqc2gBmLoGnNn8UR8oWWhoHXtOTzoCEjKUdABEZlDDSUcAJFGy0OCy49x5iLAxDW24jGcmoU5QstDQHvkT1240qmzSARCZvJjNH/WBkoWGtr2fk0aNije/cPFYStQL3mfQ0PIF3o4b1WDSAQAEj5IFAAhKlpPBqBOULABAUPLc1oA6QckCAACIACULAAAgApQsAACACFCyAAAAIkDJAgAAiEDDPYj+iQef0g+/cIWyLVl94Cvv0l7z5yQdCQAABKihStZZB5+jZ9c+9/zXv7v0Ni055Sh96brPJpgKAACEqGFOF/7wiz/dpWDtdNeKe7XmvrUJJAIAACFrmJL1o/N/XnXsw4s/HWMSAADQCGoqWWa21MxWm9kaMzu3wriZ2X+Vxx8ws8W1vhYAACBEE5YsM0tLukDSMkkLJZ1uZgvHrLZM0iHlj+WSvv0XvBYAACA4tRzJWiJpjbuvdfecpMslnTpmnVMl/dBH3CFplpnNrfG1AABMmmmalnQEQFJtJWtfSetGfd1dXlbLOrW8FgBi966XHJp0BERk2T5/m3QEQFJtJcsqLPMa16nltSN/gdlyM+sys67e3t4aYgEv3LS2hrn3A2N84XXLko6AiLxhv1OSjoCkZD+YdIJd1PITplvS/FFfz5O0vsZ1anmtJMndL3b3Tnfv7OjoqCHWX+aoZYdVHfvxhm9N+vdD/RjvH/kNP/qn2HIgfh8/eknF5QdNmy6zSr8DIhQnzT6x4vLzXvq5eIMgVqk9P1l9sDX+OTFrKVl3SzrEzA4wsyZJp0m6dsw610p6d/kuw5dL2uruG2p8bSy++qt/lbXuvvyo1y3U3nvvHX8gxOaWKz9RcflPvvGemJMgbv9w3Kt0+Zvfscuyr7/6dfr1+5YnlAhxOfvgs/SFhedpRnqGTKb5LfP0rSO/oYNmHJh0NESt42FJTbsua/2UUjPPjj2KuVc8e7frSmanSPqGpLSkS9z9S2b2IUly94ts5FfCb0laKmlA0nvdvavaayf6fp2dnd7V1fVXbhJQnbtzBAMAMKnMbKW7d+62vJaSFTdKFgAAmCqqlSyu+gUAAIgAJQsAACAClCwAAIAIULIAAAAiQMkCAACIACULAAAgApQsAACACFCyAAAAIkDJAgAAiAAlCwAAIAKULAAAgAhQsgAAACJQlw+INrNeSU9F/G3mSNoY8feoV4287VJjb38jb7vU2NvPtjeuRt7+uLZ9f3fvGLuwLktWHMysq9ITsxtBI2+71Njb38jbLjX29rPtjbntUmNvf9LbzulCAACACFCyAAAAItDIJevipAMkqJG3XWrs7W/kbZcae/vZ9sbVyNuf6LY37DVZAAAAUWrkI1kAAACRoWQBAABEIPiSZWZLzWy1ma0xs3MrjJuZ/Vd5/AEzW5xEzijUsO0nmtlWM7uv/HFeEjmjYGaXmFmPma2qMh7yfp9o20Pe7/PN7Hdm9oiZPWRmH6uwTsj7vpbtD3L/m1mLmd1lZveXt/0LFdYJed/Xsv1B7vudzCxtZvea2XUVxpLZ9+4e7IektKTHJR0oqUnS/ZIWjlnnFEnXSzJJL5d0Z9K5Y9z2EyVdl3TWiLb/eEmLJa2qMh7kfq9x20Pe73MlLS5/Pl3SY43yf/4v2P4g9395f04rf56VdKeklzfQvq9l+4Pc96O2758kXVppG5Pa96EfyVoiaY27r3X3nKTLJZ06Zp1TJf3QR9whaZaZzY07aARq2fZgufstkvrGWSXU/V7LtgfL3Te4+z3lz7dLekTSvmNWC3nf17L9QSrvzx3lL7Plj7F3doW872vZ/mCZ2TxJr5f0nSqrJLLvQy9Z+0paN+rrbu3+hlPLOlNRrdt1bPnw8vVmdlg80epCqPu9VsHvdzNbIOkojfxGP1pD7Ptxtl8KdP+XTxfdJ6lH0q/dvaH2fQ3bLwW67yV9Q9KnJJWqjCey70MvWVZh2dhmX8s6U1Et23WPRp63tEjSNyVdE3mq+hHqfq9F8PvdzKZJulLSx91929jhCi8Jat9PsP3B7n93L7r7kZLmSVpiZoePWSXofV/D9ge5783sDZJ63H3leKtVWBb5vg+9ZHVLmj/q63mS1v8V60xFE26Xu2/beXjZ3VdIyprZnPgiJirU/T6h0Pe7mWU1UjB+4u5XVVgl6H0/0faHvv8lyd23SPq9pKVjhoLe9ztV2/6A9/1xkt5kZk9q5NKYk83sx2PWSWTfh16y7pZ0iJkdYGZNkk6TdO2Yda6V9O7ynQcvl7TV3TfEHTQCE267me1jZlb+fIlG/j1sij1pMkLd7xMKeb+Xt+u7kh5x93+vslqw+76W7Q91/5tZh5nNKn/eKuk1kh4ds1rI+37C7Q9137v7Z9x9nrsv0MjPuv9x93eNWS2RfZ+J+hskyd0LZnaOpBs1crfdJe7+kJl9qDx+kaQVGrnrYI2kAUnvTSrvZKpx298m6cNmVpA0KOk0L9+GMdWZ2WUauZNmjpl1SzpfIxeCBr3fpZq2Pdj9rpHfaM+S9GD52hRJ+qyk/aTw971q2/5Q9/9cST8ws7RGysMV7n5dI7zfl9Wy/aHu+4rqYd/zWB0AAIAIhH66EAAAIBGULAAAgAhQsgAAACJAyQIAAIgAJQsAACAClCwAAIAIULIAAAAi8P8BZO4+Zv6hr5wAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 720x504 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Plot Clusters\n",
    "plt.figure(figsize=(10, 7))  \n",
    "plt.scatter(airline2['clustersid'],airline2['Balance'], c=hclusters.labels_) "
   ]
  },
  {
   "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",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.8.3"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}