Assignment-07-Clustering-Q1-(Airline).ipynb

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   "source": [
    "# Assignment-07-Clustering-Q1-(Airline)\n"
   ]
  },
  {
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   "source": [
    "## Hierarchical Clustering"
   ]
  },
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   "source": [
    "## Using Normalize Function"
   ]
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   "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": 6,
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       "      <th>cc1_miles</th>\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",
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       "3996       1  \n",
       "3997       0  \n",
       "3998       0  \n",
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   "source": [
    "# Import Dataset\n",
    "airline=pd.read_csv('C:/Users/Asus/OneDrive/Desktop/My DATA/DATA SCIENCE/Assignments/ASSIGNMENT MATERIAL/Clustering/EastWestAirlines.csv')\n",
    "airline"
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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"
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   "source": [
    "airline.info()"
   ]
  },
  {
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   "execution_count": 8,
   "id": "d9fe8ec1",
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       "      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": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "airline2=airline.drop(['ID#'],axis=1)\n",
    "airline2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "8452560d",
   "metadata": {},
   "outputs": [
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       "    .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>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",
       "  </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": 9,
     "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": 10,
   "id": "e79fb333",
   "metadata": {},
   "outputs": [
    {
     "data": {
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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": 11,
   "id": "346e7335",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "AgglomerativeClustering(n_clusters=5)"
      ]
     },
     "execution_count": 11,
     "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": 12,
   "id": "f6c1d126",
   "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": 12,
     "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": 13,
   "id": "d846e343",
   "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",
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       "      <th>Award?</th>\n",
       "      <th>clustersid</th>\n",
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       "      <th>0</th>\n",
       "      <td>28143</td>\n",
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       "      <td>215</td>\n",
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       "      <td>0</td>\n",
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       "      <td>4123</td>\n",
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       "      <td>0</td>\n",
       "      <td>7034</td>\n",
       "      <td>0</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",
       "      <td>1</td>\n",
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       "      <td>0</td>\n",
       "      <td>0</td>\n",
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       "      <td>2</td>\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",
       "    </tr>\n",
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       "      <th>...</th>\n",
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       "      <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>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",
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       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>500</td>\n",
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       "      <td>500</td>\n",
       "      <td>1</td>\n",
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       "      <th>3998</th>\n",
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       "      <td>0</td>\n",
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       "      <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": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Adding clusters to dataset\n",
    "airline2['clustersid']=hclusters.labels_\n",
    "airline2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "ef3f36a2",
   "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": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "airline2.groupby('clustersid').agg(['mean']).reset_index()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "351ced7a",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.collections.PathCollection at 0x11c1da80490>"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "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": [
    "# Plot Clusters\n",
    "plt.figure(figsize=(10, 7))  \n",
    "plt.scatter(airline2['clustersid'],airline2['Balance'], c=hclusters.labels_) "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "69a97b84",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "id": "0500e215",
   "metadata": {},
   "source": [
    "## K-Means Clustering"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "7594860b",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Import Libraries\n",
    "from sklearn.cluster import KMeans\n",
    "from sklearn.preprocessing import StandardScaler"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "214dc882",
   "metadata": {},
   "outputs": [],
   "source": [
    "# EastWestAirlines dataset is already imported"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "b537b9d6",
   "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>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",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>28143</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>174</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>7000</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>19244</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>215</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>6968</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>41354</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <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",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>14776</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>500</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>6952</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <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",
       "    </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>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",
       "    </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",
       "    </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",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3997</th>\n",
       "      <td>54899</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>500</td>\n",
       "      <td>1</td>\n",
       "      <td>500</td>\n",
       "      <td>1</td>\n",
       "      <td>1401</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3998</th>\n",
       "      <td>3016</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1398</td>\n",
       "      <td>0</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       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": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "airline2=airline.drop(['ID#'],axis=1)\n",
    "airline2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "79fc2367",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 3999 entries, 0 to 3998\n",
      "Data columns (total 11 columns):\n",
      " #   Column             Non-Null Count  Dtype\n",
      "---  ------             --------------  -----\n",
      " 0   Balance            3999 non-null   int64\n",
      " 1   Qual_miles         3999 non-null   int64\n",
      " 2   cc1_miles          3999 non-null   int64\n",
      " 3   cc2_miles          3999 non-null   int64\n",
      " 4   cc3_miles          3999 non-null   int64\n",
      " 5   Bonus_miles        3999 non-null   int64\n",
      " 6   Bonus_trans        3999 non-null   int64\n",
      " 7   Flight_miles_12mo  3999 non-null   int64\n",
      " 8   Flight_trans_12    3999 non-null   int64\n",
      " 9   Days_since_enroll  3999 non-null   int64\n",
      " 10  Award?             3999 non-null   int64\n",
      "dtypes: int64(11)\n",
      "memory usage: 343.8 KB\n"
     ]
    }
   ],
   "source": [
    "airline2.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "7296cd61",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Normalize heterogenous numerical data by using Standard Scaler\n",
    "airline2_norm=StandardScaler().fit_transform(airline2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "1465b921",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Use Elbow Graph to find optimum number of  clusters (K value) from K values range\n",
    "# The K-means algorithm aims to choose centroids that minimise the inertia, or within-clusster sum-of-squares criterion WCSS \n",
    "# random state can be anything from 0 to 42, but the same number to be used everytime,so that the results don't change. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "342784fa",
   "metadata": {},
   "outputs": [],
   "source": [
    "# within-cluster sum-of-squares criterion \n",
    "wcss=[]\n",
    "for i in range (1,11):\n",
    "    kmeans=KMeans(n_clusters=i,random_state=2)\n",
    "    kmeans.fit(airline2_norm)\n",
    "    wcss.append(kmeans.inertia_)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "de2a9deb",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Plot K values range vs WCSS to get Elbow graph for choosing K (no. of clusters)\n",
    "plt.plot(range(1,11),wcss)\n",
    "plt.title('Elbow Graph')\n",
    "plt.xlabel('Number of clusters')\n",
    "plt.ylabel('WCSS')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "0abfee7d",
   "metadata": {},
   "source": [
    "## Build Cluster Algorithm using K=4 "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "67d803cd",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "KMeans(n_clusters=4, random_state=30)"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Cluster algorithm using K=4\n",
    "clusters4=KMeans(4,random_state=30).fit(airline2_norm)\n",
    "clusters4"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "id": "a17c4c1d",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([0, 0, 0, ..., 3, 0, 0])"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "clusters4.labels_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "id": "8aad9cd9",
   "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>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>clusters4id</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>28143</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>174</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>7000</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>19244</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>215</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>6968</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>41354</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <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>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>14776</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>500</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>6952</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <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",
       "    </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",
       "    </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>0</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>0</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>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3997</th>\n",
       "      <td>54899</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>500</td>\n",
       "      <td>1</td>\n",
       "      <td>500</td>\n",
       "      <td>1</td>\n",
       "      <td>1401</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3998</th>\n",
       "      <td>3016</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1398</td>\n",
       "      <td>0</td>\n",
       "      <td>0</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?  clusters4id  \n",
       "0          0            0  \n",
       "1          0            0  \n",
       "2          0            0  \n",
       "3          0            0  \n",
       "4          1            3  \n",
       "...      ...          ...  \n",
       "3994       1            0  \n",
       "3995       1            0  \n",
       "3996       1            3  \n",
       "3997       0            0  \n",
       "3998       0            0  \n",
       "\n",
       "[3999 rows x 12 columns]"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Assign clusters to the data set\n",
    "airline4=airline2.copy()\n",
    "airline4['clusters4id']=clusters4.labels_\n",
    "airline4"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "id": "4306dc32",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[-0.29584258, -0.0603903 , -0.60911906,  0.03222463, -0.06075301,\n",
       "        -0.51562007, -0.48778976, -0.18468409, -0.19727389, -0.2065444 ,\n",
       "        -0.34933297],\n",
       "       [ 0.63971926, -0.08443292,  1.0220844 , -0.09824189, 15.64629931,\n",
       "         3.17969131,  1.71461374,  0.03329269,  0.05969539,  0.23987261,\n",
       "         0.33752735],\n",
       "       [ 1.19916278,  0.8413837 ,  0.07934291,  0.15576844, -0.06276658,\n",
       "         0.61091878,  1.63802866,  3.57547132,  3.86140846,  0.28565421,\n",
       "         0.91563614],\n",
       "       [ 0.43059508,  0.0158422 ,  1.18872871, -0.08236624, -0.05476264,\n",
       "         0.91116803,  0.74463543, -0.08026444, -0.09129375,  0.37198921,\n",
       "         0.57588937]])"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Compute the centroids for K=4 clusters with 11 variables\n",
    "clusters4.cluster_centers_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "id": "9a2cf006",
   "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 tr th {\n",
       "        text-align: left;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr>\n",
       "      <th></th>\n",
       "      <th>clusters4id</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>43828.396152</td>\n",
       "      <td>97.283863</td>\n",
       "      <td>1.223007</td>\n",
       "      <td>1.019238</td>\n",
       "      <td>1.000393</td>\n",
       "      <td>4707.805654</td>\n",
       "      <td>6.926973</td>\n",
       "      <td>201.254417</td>\n",
       "      <td>0.624656</td>\n",
       "      <td>3693.855124</td>\n",
       "      <td>0.201413</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>138061.400000</td>\n",
       "      <td>78.800000</td>\n",
       "      <td>3.466667</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>4.066667</td>\n",
       "      <td>93927.866667</td>\n",
       "      <td>28.066667</td>\n",
       "      <td>506.666667</td>\n",
       "      <td>1.600000</td>\n",
       "      <td>4613.866667</td>\n",
       "      <td>0.533333</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2</td>\n",
       "      <td>194432.643750</td>\n",
       "      <td>794.981250</td>\n",
       "      <td>2.168750</td>\n",
       "      <td>1.037500</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>31897.281250</td>\n",
       "      <td>27.331250</td>\n",
       "      <td>5465.837500</td>\n",
       "      <td>16.018750</td>\n",
       "      <td>4708.400000</td>\n",
       "      <td>0.812500</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>3</td>\n",
       "      <td>117087.423649</td>\n",
       "      <td>156.736883</td>\n",
       "      <td>3.697729</td>\n",
       "      <td>1.002349</td>\n",
       "      <td>1.001566</td>\n",
       "      <td>39200.451057</td>\n",
       "      <td>18.761942</td>\n",
       "      <td>348.499608</td>\n",
       "      <td>1.029757</td>\n",
       "      <td>4885.918559</td>\n",
       "      <td>0.649961</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  clusters4id        Balance  Qual_miles cc1_miles cc2_miles cc3_miles  \\\n",
       "                        mean        mean      mean      mean      mean   \n",
       "0           0   43828.396152   97.283863  1.223007  1.019238  1.000393   \n",
       "1           1  138061.400000   78.800000  3.466667  1.000000  4.066667   \n",
       "2           2  194432.643750  794.981250  2.168750  1.037500  1.000000   \n",
       "3           3  117087.423649  156.736883  3.697729  1.002349  1.001566   \n",
       "\n",
       "    Bonus_miles Bonus_trans Flight_miles_12mo Flight_trans_12  \\\n",
       "           mean        mean              mean            mean   \n",
       "0   4707.805654    6.926973        201.254417        0.624656   \n",
       "1  93927.866667   28.066667        506.666667        1.600000   \n",
       "2  31897.281250   27.331250       5465.837500       16.018750   \n",
       "3  39200.451057   18.761942        348.499608        1.029757   \n",
       "\n",
       "  Days_since_enroll    Award?  \n",
       "               mean      mean  \n",
       "0       3693.855124  0.201413  \n",
       "1       4613.866667  0.533333  \n",
       "2       4708.400000  0.812500  \n",
       "3       4885.918559  0.649961  "
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Group data by Clusters (K=4)\n",
    "airline4.groupby('clusters4id').agg(['mean']).reset_index()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "id": "53184eb6",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.collections.PathCollection at 0x11c1e059790>"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "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": [
    "# Plot Clusters\n",
    "plt.figure(figsize=(10, 7))  \n",
    "plt.scatter(airline4['clusters4id'],airline4['Balance'], c=clusters4.labels_) "
   ]
  },
  {
   "cell_type": "markdown",
   "id": "8643a235",
   "metadata": {},
   "source": [
    "## Build Cluster Algorithm Using K=5"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "id": "865ca82d",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "KMeans(n_clusters=5, random_state=30)"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Cluster algorithm using K=5\n",
    "clusters5=KMeans(5,random_state=30).fit(airline2_norm)\n",
    "clusters5"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "id": "580eda82",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([0, 0, 0, ..., 3, 0, 0])"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "clusters5.labels_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "id": "e16767b9",
   "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>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>clusters5id</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>28143</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>174</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>7000</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>19244</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>215</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>6968</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>41354</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <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>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>14776</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>500</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>6952</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <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",
       "    </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",
       "    </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>0</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>0</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>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3997</th>\n",
       "      <td>54899</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>500</td>\n",
       "      <td>1</td>\n",
       "      <td>500</td>\n",
       "      <td>1</td>\n",
       "      <td>1401</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3998</th>\n",
       "      <td>3016</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1398</td>\n",
       "      <td>0</td>\n",
       "      <td>0</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?  clusters5id  \n",
       "0          0            0  \n",
       "1          0            0  \n",
       "2          0            0  \n",
       "3          0            0  \n",
       "4          1            3  \n",
       "...      ...          ...  \n",
       "3994       1            0  \n",
       "3995       1            0  \n",
       "3996       1            3  \n",
       "3997       0            0  \n",
       "3998       0            0  \n",
       "\n",
       "[3999 rows x 12 columns]"
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Assign clusters to the data set\n",
    "airline5=airline2.copy()\n",
    "airline5['clusters5id']=clusters5.labels_\n",
    "airline5"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "id": "6ee149d7",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[-2.97441370e-01, -6.26013958e-02, -6.09944285e-01,\n",
       "        -9.82418871e-02, -6.07159307e-02, -5.21330789e-01,\n",
       "        -5.01454441e-01, -1.87180704e-01, -1.99831577e-01,\n",
       "        -2.10475283e-01, -3.52420208e-01],\n",
       "       [ 1.14970340e+00,  1.06407580e+00,  1.02900923e-01,\n",
       "        -9.82418871e-02, -6.27665798e-02,  5.78252911e-01,\n",
       "         1.52034538e+00,  3.44900129e+00,  3.66768718e+00,\n",
       "         2.64756357e-01,  8.99553448e-01],\n",
       "       [ 6.39719256e-01, -8.44329231e-02,  1.02208440e+00,\n",
       "        -9.82418871e-02,  1.56462993e+01,  3.17969131e+00,\n",
       "         1.71461374e+00,  3.32926913e-02,  5.96953922e-02,\n",
       "         2.39872612e-01,  3.37527346e-01],\n",
       "       [ 4.24748143e-01, -1.21396375e-02,  1.19282031e+00,\n",
       "        -9.82418871e-02, -5.47249449e-02,  9.11488076e-01,\n",
       "         7.40508649e-01, -9.38523027e-02, -1.02849450e-01,\n",
       "         3.77190968e-01,  5.65958814e-01],\n",
       "       [-4.68896637e-02, -1.56235600e-01, -6.68227273e-01,\n",
       "         9.03825361e+00, -6.27665798e-02, -1.01665326e-01,\n",
       "         6.17851143e-01,  8.75493989e-02,  2.20346809e-01,\n",
       "        -7.24639805e-02,  5.17838824e-02]])"
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Compute the centroids for K=5 clusters with 11 variables\n",
    "clusters5.cluster_centers_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "id": "98eb1a0f",
   "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 tr th {\n",
       "        text-align: left;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr>\n",
       "      <th></th>\n",
       "      <th>clusters5id</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>43669.876000</td>\n",
       "      <td>95.611600</td>\n",
       "      <td>1.221200</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000400</td>\n",
       "      <td>4568.151200</td>\n",
       "      <td>6.791600</td>\n",
       "      <td>197.838000</td>\n",
       "      <td>0.615200</td>\n",
       "      <td>3684.750400</td>\n",
       "      <td>0.200000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>189448.964497</td>\n",
       "      <td>967.248521</td>\n",
       "      <td>2.201183</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>31108.467456</td>\n",
       "      <td>26.201183</td>\n",
       "      <td>5288.775148</td>\n",
       "      <td>15.284024</td>\n",
       "      <td>4665.248521</td>\n",
       "      <td>0.804734</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2</td>\n",
       "      <td>138061.400000</td>\n",
       "      <td>78.800000</td>\n",
       "      <td>3.466667</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>4.066667</td>\n",
       "      <td>93927.866667</td>\n",
       "      <td>28.066667</td>\n",
       "      <td>506.666667</td>\n",
       "      <td>1.600000</td>\n",
       "      <td>4613.866667</td>\n",
       "      <td>0.533333</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>3</td>\n",
       "      <td>116436.737421</td>\n",
       "      <td>134.935535</td>\n",
       "      <td>3.702830</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.001572</td>\n",
       "      <td>39185.495283</td>\n",
       "      <td>18.721698</td>\n",
       "      <td>329.176101</td>\n",
       "      <td>0.985063</td>\n",
       "      <td>4897.754717</td>\n",
       "      <td>0.644654</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>4</td>\n",
       "      <td>68876.581395</td>\n",
       "      <td>23.255814</td>\n",
       "      <td>1.139535</td>\n",
       "      <td>2.348837</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>14689.837209</td>\n",
       "      <td>17.534884</td>\n",
       "      <td>582.627907</td>\n",
       "      <td>2.209302</td>\n",
       "      <td>3968.930233</td>\n",
       "      <td>0.395349</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  clusters5id        Balance  Qual_miles cc1_miles cc2_miles cc3_miles  \\\n",
       "                        mean        mean      mean      mean      mean   \n",
       "0           0   43669.876000   95.611600  1.221200  1.000000  1.000400   \n",
       "1           1  189448.964497  967.248521  2.201183  1.000000  1.000000   \n",
       "2           2  138061.400000   78.800000  3.466667  1.000000  4.066667   \n",
       "3           3  116436.737421  134.935535  3.702830  1.000000  1.001572   \n",
       "4           4   68876.581395   23.255814  1.139535  2.348837  1.000000   \n",
       "\n",
       "    Bonus_miles Bonus_trans Flight_miles_12mo Flight_trans_12  \\\n",
       "           mean        mean              mean            mean   \n",
       "0   4568.151200    6.791600        197.838000        0.615200   \n",
       "1  31108.467456   26.201183       5288.775148       15.284024   \n",
       "2  93927.866667   28.066667        506.666667        1.600000   \n",
       "3  39185.495283   18.721698        329.176101        0.985063   \n",
       "4  14689.837209   17.534884        582.627907        2.209302   \n",
       "\n",
       "  Days_since_enroll    Award?  \n",
       "               mean      mean  \n",
       "0       3684.750400  0.200000  \n",
       "1       4665.248521  0.804734  \n",
       "2       4613.866667  0.533333  \n",
       "3       4897.754717  0.644654  \n",
       "4       3968.930233  0.395349  "
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Group data by Clusters (K=5)\n",
    "airline5.groupby('clusters5id').agg(['mean']).reset_index()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "id": "e4a45ad7",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.collections.PathCollection at 0x11c1e0e0f70>"
      ]
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "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": [
    "# Plot Clusters\n",
    "plt.figure(figsize=(10, 7))  \n",
    "plt.scatter(airline5['clusters5id'],airline5['Balance'], c=clusters5.labels_) "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "d8373b8b",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "id": "9dcfacb8",
   "metadata": {},
   "source": [
    "## DBSCAN Clustering "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "id": "c5f42645",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Import Libraries\n",
    "from sklearn.cluster import DBSCAN\n",
    "from sklearn.preprocessing import StandardScaler"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "0ff8920f",
   "metadata": {},
   "outputs": [],
   "source": [
    "# EastWestAirlines dataset is already imported"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "id": "f57ad872",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "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": 38,
   "id": "8385e1da",
   "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>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",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>28143</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>174</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>7000</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>19244</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>215</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>6968</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>41354</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <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",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>14776</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>500</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>6952</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <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",
       "    </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>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",
       "    </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",
       "    </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",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3997</th>\n",
       "      <td>54899</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>500</td>\n",
       "      <td>1</td>\n",
       "      <td>500</td>\n",
       "      <td>1</td>\n",
       "      <td>1401</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3998</th>\n",
       "      <td>3016</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1398</td>\n",
       "      <td>0</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       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": 38,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "airline.drop(['ID#'],axis=1,inplace=True)\n",
    "airline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "id": "4540368c",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[-4.51140783e-01, -1.86298687e-01, -7.69578406e-01, ...,\n",
       "        -3.62167870e-01,  1.39545434e+00, -7.66919299e-01],\n",
       "       [-5.39456874e-01, -1.86298687e-01, -7.69578406e-01, ...,\n",
       "        -3.62167870e-01,  1.37995704e+00, -7.66919299e-01],\n",
       "       [-3.20031232e-01, -1.86298687e-01, -7.69578406e-01, ...,\n",
       "        -3.62167870e-01,  1.41192021e+00, -7.66919299e-01],\n",
       "       ...,\n",
       "       [-4.29480975e-05, -1.86298687e-01,  6.83121167e-01, ...,\n",
       "        -3.62167870e-01, -1.31560393e+00,  1.30391816e+00],\n",
       "       [-1.85606976e-01, -1.86298687e-01, -7.69578406e-01, ...,\n",
       "        -9.85033311e-02, -1.31608822e+00, -7.66919299e-01],\n",
       "       [-7.00507951e-01, -1.86298687e-01, -7.69578406e-01, ...,\n",
       "        -3.62167870e-01, -1.31754109e+00, -7.66919299e-01]])"
      ]
     },
     "execution_count": 39,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Normalize heterogenous numerical data using standard scalar fit transform to dataset\n",
    "airline_norm=StandardScaler().fit_transform(airline)\n",
    "airline_norm"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "id": "65a7150c",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "DBSCAN(eps=1, min_samples=4)"
      ]
     },
     "execution_count": 40,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# DBSCAN Clustering\n",
    "dbscan=DBSCAN(eps=1,min_samples=4)\n",
    "dbscan.fit(airline_norm)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "id": "fee4eb5b",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([0, 0, 0, ..., 1, 0, 0], dtype=int64)"
      ]
     },
     "execution_count": 41,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Noisy samples are given the label -1.\n",
    "dbscan.labels_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "id": "c1f12bb9",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>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>clusters</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
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       "      <th>0</th>\n",
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       "      <td>1</td>\n",
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       "      <td>0</td>\n",
       "      <td>6968</td>\n",
       "      <td>0</td>\n",
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       "      <th>2</th>\n",
       "      <td>41354</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <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>0</td>\n",
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       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>14776</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>500</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
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       "      <td>6952</td>\n",
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       "      <td>43300</td>\n",
       "      <td>26</td>\n",
       "      <td>2077</td>\n",
       "      <td>4</td>\n",
       "      <td>6935</td>\n",
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       "      <td>...</td>\n",
       "      <td>...</td>\n",
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       "    <tr>\n",
       "      <th>3994</th>\n",
       "      <td>18476</td>\n",
       "      <td>0</td>\n",
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       "      <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>1</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>1</td>\n",
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       "    <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>1</td>\n",
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       "    <tr>\n",
       "      <th>3997</th>\n",
       "      <td>54899</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>500</td>\n",
       "      <td>1</td>\n",
       "      <td>500</td>\n",
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       "      <td>0</td>\n",
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       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
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       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1398</td>\n",
       "      <td>0</td>\n",
       "      <td>0</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?  clusters  \n",
       "0          0         0  \n",
       "1          0         0  \n",
       "2          0         0  \n",
       "3          0         0  \n",
       "4          1         1  \n",
       "...      ...       ...  \n",
       "3994       1         1  \n",
       "3995       1         1  \n",
       "3996       1         1  \n",
       "3997       0         0  \n",
       "3998       0         0  \n",
       "\n",
       "[3999 rows x 12 columns]"
      ]
     },
     "execution_count": 42,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Adding clusters to dataset\n",
    "airline['clusters']=dbscan.labels_\n",
    "airline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "id": "441d815b",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr>\n",
       "      <th></th>\n",
       "      <th>clusters</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>-1</td>\n",
       "      <td>184694.319471</td>\n",
       "      <td>977.113422</td>\n",
       "      <td>2.763705</td>\n",
       "      <td>1.058601</td>\n",
       "      <td>1.092628</td>\n",
       "      <td>40964.655955</td>\n",
       "      <td>22.765595</td>\n",
       "      <td>2248.875236</td>\n",
       "      <td>6.516068</td>\n",
       "      <td>4754.215501</td>\n",
       "      <td>0.706994</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0</td>\n",
       "      <td>53978.079794</td>\n",
       "      <td>5.088374</td>\n",
       "      <td>1.658945</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>9064.591163</td>\n",
       "      <td>8.487773</td>\n",
       "      <td>138.320463</td>\n",
       "      <td>0.434148</td>\n",
       "      <td>3802.836122</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1</td>\n",
       "      <td>62092.632969</td>\n",
       "      <td>9.785974</td>\n",
       "      <td>2.591985</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>22952.648452</td>\n",
       "      <td>12.862477</td>\n",
       "      <td>288.490893</td>\n",
       "      <td>0.913479</td>\n",
       "      <td>4511.039162</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2</td>\n",
       "      <td>34806.538462</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>8389.769231</td>\n",
       "      <td>12.000000</td>\n",
       "      <td>19.230769</td>\n",
       "      <td>0.153846</td>\n",
       "      <td>3186.153846</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>3</td>\n",
       "      <td>202750.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>4.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>43155.666667</td>\n",
       "      <td>20.333333</td>\n",
       "      <td>2456.666667</td>\n",
       "      <td>4.666667</td>\n",
       "      <td>4447.333333</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>4</td>\n",
       "      <td>36413.428571</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>14341.142857</td>\n",
       "      <td>13.857143</td>\n",
       "      <td>57.142857</td>\n",
       "      <td>0.285714</td>\n",
       "      <td>3537.285714</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>5</td>\n",
       "      <td>60932.000000</td>\n",
       "      <td>1794.500000</td>\n",
       "      <td>3.750000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>39889.750000</td>\n",
       "      <td>16.250000</td>\n",
       "      <td>250.000000</td>\n",
       "      <td>0.750000</td>\n",
       "      <td>5029.000000</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>6</td>\n",
       "      <td>164883.400000</td>\n",
       "      <td>1471.600000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>8472.800000</td>\n",
       "      <td>5.000000</td>\n",
       "      <td>300.000000</td>\n",
       "      <td>1.400000</td>\n",
       "      <td>4173.400000</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>7</td>\n",
       "      <td>27113.888889</td>\n",
       "      <td>2475.444444</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>2169.111111</td>\n",
       "      <td>4.555556</td>\n",
       "      <td>44.444444</td>\n",
       "      <td>0.333333</td>\n",
       "      <td>1900.111111</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  clusters        Balance   Qual_miles cc1_miles cc2_miles cc3_miles  \\\n",
       "                     mean         mean      mean      mean      mean   \n",
       "0       -1  184694.319471   977.113422  2.763705  1.058601  1.092628   \n",
       "1        0   53978.079794     5.088374  1.658945  1.000000  1.000000   \n",
       "2        1   62092.632969     9.785974  2.591985  1.000000  1.000000   \n",
       "3        2   34806.538462     0.000000  1.000000  2.000000  1.000000   \n",
       "4        3  202750.000000     0.000000  4.000000  1.000000  1.000000   \n",
       "5        4   36413.428571     0.000000  1.000000  3.000000  1.000000   \n",
       "6        5   60932.000000  1794.500000  3.750000  1.000000  1.000000   \n",
       "7        6  164883.400000  1471.600000  1.000000  1.000000  1.000000   \n",
       "8        7   27113.888889  2475.444444  1.000000  1.000000  1.000000   \n",
       "\n",
       "    Bonus_miles Bonus_trans Flight_miles_12mo Flight_trans_12  \\\n",
       "           mean        mean              mean            mean   \n",
       "0  40964.655955   22.765595       2248.875236        6.516068   \n",
       "1   9064.591163    8.487773        138.320463        0.434148   \n",
       "2  22952.648452   12.862477        288.490893        0.913479   \n",
       "3   8389.769231   12.000000         19.230769        0.153846   \n",
       "4  43155.666667   20.333333       2456.666667        4.666667   \n",
       "5  14341.142857   13.857143         57.142857        0.285714   \n",
       "6  39889.750000   16.250000        250.000000        0.750000   \n",
       "7   8472.800000    5.000000        300.000000        1.400000   \n",
       "8   2169.111111    4.555556         44.444444        0.333333   \n",
       "\n",
       "  Days_since_enroll    Award?  \n",
       "               mean      mean  \n",
       "0       4754.215501  0.706994  \n",
       "1       3802.836122  0.000000  \n",
       "2       4511.039162  1.000000  \n",
       "3       3186.153846  0.000000  \n",
       "4       4447.333333  0.000000  \n",
       "5       3537.285714  0.000000  \n",
       "6       5029.000000  1.000000  \n",
       "7       4173.400000  1.000000  \n",
       "8       1900.111111  0.000000  "
      ]
     },
     "execution_count": 43,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "airline.groupby('clusters').agg(['mean']).reset_index()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "id": "a5fa7de6",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.collections.PathCollection at 0x11c208ff820>"
      ]
     },
     "execution_count": 45,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "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": [
    "# Plot Clusters\n",
    "plt.figure(figsize=(10, 7))  \n",
    "plt.scatter(airline['clusters'],airline['Bonus_miles'], c=dbscan.labels_) "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "f3e033d9",
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "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": "3.9.12"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}