Clustering_Airline_data.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Performing clustering on Airline Data\n",
    "\n",
    "## Hierarchial Clustering"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "metadata": {},
   "outputs": [],
   "source": [
    "data=pd.read_csv(\"EastWestAirlines.csv\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>ID#</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>1</td>\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>2</td>\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>3</td>\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>4</td>\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>5</td>\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",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   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",
       "   Bonus_trans  Flight_miles_12mo  Flight_trans_12  Days_since_enroll  Award?  \n",
       "0            1                  0                0               7000       0  \n",
       "1            2                  0                0               6968       0  \n",
       "2            4                  0                0               7034       0  \n",
       "3            1                  0                0               6952       0  \n",
       "4           26               2077                4               6935       1  "
      ]
     },
     "execution_count": 59,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "metadata": {},
   "outputs": [],
   "source": [
    "data1 = data.set_index(['ID#'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "    .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",
       "    <tr>\n",
       "      <th>ID#</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1</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>2</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>3</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>4</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>5</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",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     Balance  Qual_miles  cc1_miles  cc2_miles  cc3_miles  Bonus_miles  \\\n",
       "ID#                                                                      \n",
       "1      28143           0          1          1          1          174   \n",
       "2      19244           0          1          1          1          215   \n",
       "3      41354           0          1          1          1         4123   \n",
       "4      14776           0          1          1          1          500   \n",
       "5      97752           0          4          1          1        43300   \n",
       "\n",
       "     Bonus_trans  Flight_miles_12mo  Flight_trans_12  Days_since_enroll  \\\n",
       "ID#                                                                       \n",
       "1              1                  0                0               7000   \n",
       "2              2                  0                0               6968   \n",
       "3              4                  0                0               7034   \n",
       "4              1                  0                0               6952   \n",
       "5             26               2077                4               6935   \n",
       "\n",
       "     Award?  \n",
       "ID#          \n",
       "1         0  \n",
       "2         0  \n",
       "3         0  \n",
       "4         0  \n",
       "5         1  "
      ]
     },
     "execution_count": 61,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data1.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "metadata": {},
   "outputs": [],
   "source": [
    "def norm_func(i):\n",
    "    x = (i-i.min())/(i.max()-i.min())\n",
    "    return (x)\n",
    "df_norm = norm_func(data1.iloc[:,1:])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
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\n",
      "text/plain": [
       "<Figure size 792x576 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "import scipy.cluster.hierarchy as sch\n",
    "from sklearn.cluster import AgglomerativeClustering\n",
    "plt.figure(figsize=(11,8))\n",
    "plt.title(\"Passenger Dendograms\")\n",
    "dendogram = sch.dendrogram(sch.linkage(df_norm,method='ward'))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "hc = AgglomerativeClustering(n_clusters=4, affinity = 'euclidean', linkage = 'ward')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "y_hc = hc.fit_predict(df_norm)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "clusters=pd.DataFrame(y_hc,columns=['cluster'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "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>cluster</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3994</th>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3995</th>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3996</th>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3997</th>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3998</th>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>3999 rows × 1 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "      cluster\n",
       "0           0\n",
       "1           0\n",
       "2           0\n",
       "3           0\n",
       "4           1\n",
       "...       ...\n",
       "3994        3\n",
       "3995        3\n",
       "3996        1\n",
       "3997        0\n",
       "3998        0\n",
       "\n",
       "[3999 rows x 1 columns]"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "clusters"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# KMeans Clustering"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Elbow Curve for optimal number of clusters"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.cluster import KMeans\n",
    "wcss=[]\n",
    "for i in range (1,11):\n",
    "    kmeans=KMeans(n_clusters=i,random_state=2)\n",
    "    kmeans.fit(df_norm)\n",
    "    wcss.append(kmeans.inertia_)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "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": [
    "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": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [],
   "source": [
    "# As per the curve,the number of clusters is 4"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "KMeans(n_clusters=4, random_state=30)"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "clust4=KMeans(4,random_state=30).fit(df_norm)\n",
    "clust4"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([2, 2, 2, ..., 3, 2, 2])"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "clust4.labels_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [],
   "source": [
    "data4=data.copy()\n",
    "data4['clust4id']=clust4.labels_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[ 6.55837114e-03,  6.44122383e-01,  8.05152979e-04,\n",
       "         5.63607085e-03,  1.18636504e-01,  2.00595439e-01,\n",
       "         7.31260853e-03,  1.19405706e-02,  5.34640411e-01,\n",
       "        -3.88578059e-16],\n",
       "       [ 2.60542873e-02,  3.90044577e-02,  1.63447251e-02,\n",
       "         2.22882615e-03,  3.35642727e-02,  1.21825219e-01,\n",
       "         3.34267751e-02,  5.94073285e-02,  5.22892182e-01,\n",
       "         1.00000000e+00],\n",
       "       [ 8.28362120e-03,  2.31945177e-02,  8.96151819e-03,\n",
       "         1.05429626e-03,  1.26482465e-02,  7.54496083e-02,\n",
       "         7.35308092e-03,  1.24327389e-02,  4.36111859e-01,\n",
       "         3.27515792e-15],\n",
       "       [ 1.77912301e-02,  7.28960396e-01,  6.18811881e-04,\n",
       "         6.49752475e-03,  1.72970238e-01,  2.34903868e-01,\n",
       "         2.31602349e-02,  4.04212591e-02,  5.86139300e-01,\n",
       "         1.00000000e+00]])"
      ]
     },
     "execution_count": 43,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "clust4.cluster_centers_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "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>clust4id</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>118032.061192</td>\n",
       "      <td>73.112721</td>\n",
       "      <td>3.576490</td>\n",
       "      <td>1.001610</td>\n",
       "      <td>1.022544</td>\n",
       "      <td>31282.666667</td>\n",
       "      <td>17.251208</td>\n",
       "      <td>225.352657</td>\n",
       "      <td>0.632850</td>\n",
       "      <td>4436.307568</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>83529.153046</td>\n",
       "      <td>290.453195</td>\n",
       "      <td>1.156018</td>\n",
       "      <td>1.032689</td>\n",
       "      <td>1.008915</td>\n",
       "      <td>8850.395245</td>\n",
       "      <td>10.476969</td>\n",
       "      <td>1030.112927</td>\n",
       "      <td>3.148588</td>\n",
       "      <td>4338.867756</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2</td>\n",
       "      <td>40747.617290</td>\n",
       "      <td>92.345809</td>\n",
       "      <td>1.092778</td>\n",
       "      <td>1.017923</td>\n",
       "      <td>1.004217</td>\n",
       "      <td>3335.152873</td>\n",
       "      <td>6.488666</td>\n",
       "      <td>226.599895</td>\n",
       "      <td>0.658935</td>\n",
       "      <td>3619.111755</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>3</td>\n",
       "      <td>108317.387376</td>\n",
       "      <td>198.336634</td>\n",
       "      <td>3.915842</td>\n",
       "      <td>1.001238</td>\n",
       "      <td>1.025990</td>\n",
       "      <td>45609.657178</td>\n",
       "      <td>20.201733</td>\n",
       "      <td>713.728960</td>\n",
       "      <td>2.142327</td>\n",
       "      <td>4863.439356</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  clust4id        Balance  Qual_miles cc1_miles cc2_miles cc3_miles  \\\n",
       "                     mean        mean      mean      mean      mean   \n",
       "0        0  118032.061192   73.112721  3.576490  1.001610  1.022544   \n",
       "1        1   83529.153046  290.453195  1.156018  1.032689  1.008915   \n",
       "2        2   40747.617290   92.345809  1.092778  1.017923  1.004217   \n",
       "3        3  108317.387376  198.336634  3.915842  1.001238  1.025990   \n",
       "\n",
       "    Bonus_miles Bonus_trans Flight_miles_12mo Flight_trans_12  \\\n",
       "           mean        mean              mean            mean   \n",
       "0  31282.666667   17.251208        225.352657        0.632850   \n",
       "1   8850.395245   10.476969       1030.112927        3.148588   \n",
       "2   3335.152873    6.488666        226.599895        0.658935   \n",
       "3  45609.657178   20.201733        713.728960        2.142327   \n",
       "\n",
       "  Days_since_enroll Award?  \n",
       "               mean   mean  \n",
       "0       4436.307568      0  \n",
       "1       4338.867756      1  \n",
       "2       3619.111755      0  \n",
       "3       4863.439356      1  "
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data4.groupby('clust4id').agg(['mean']).reset_index()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.collections.PathCollection at 0x26e5a85b2e0>"
      ]
     },
     "execution_count": 22,
     "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": [
    "plt.figure(figsize=(10, 7))  \n",
    "plt.scatter(data4['clust4id'],data4['Balance'], c=clust4.labels_)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "KMeans(n_clusters=5, random_state=30)"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Cluster algorithm using K=5\n",
    "clust5=KMeans(5,random_state=30).fit(df_norm)\n",
    "clust5"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([0, 0, 0, ..., 1, 4, 4])"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "clust5.labels_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Assign clusters to the data set\n",
    "data5=data.copy()\n",
    "data5['clust5id']=clust5.labels_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[ 8.06451613e-03,  3.05299539e-02,  9.79262673e-03,\n",
       "         2.88018433e-04,  1.31485480e-02,  8.03906334e-02,\n",
       "         7.91232441e-03,  1.37379358e-02,  6.71078504e-01,\n",
       "         1.11022302e-15],\n",
       "       [ 1.77912301e-02,  7.28960396e-01,  6.18811881e-04,\n",
       "         6.49752475e-03,  1.72970238e-01,  2.34903868e-01,\n",
       "         2.31602349e-02,  4.04212591e-02,  5.86139300e-01,\n",
       "         1.00000000e+00],\n",
       "       [ 6.59020789e-03,  6.46035599e-01,  8.09061489e-04,\n",
       "         5.66343042e-03,  1.19022293e-01,  2.00383834e-01,\n",
       "         7.27197078e-03,  1.18458814e-02,  5.32620376e-01,\n",
       "        -4.44089210e-16],\n",
       "       [ 2.60542873e-02,  3.90044577e-02,  1.63447251e-02,\n",
       "         2.22882615e-03,  3.35642727e-02,  1.21825219e-01,\n",
       "         3.34267751e-02,  5.94073285e-02,  5.22892182e-01,\n",
       "         1.00000000e+00],\n",
       "       [ 8.44382696e-03,  1.76841085e-02,  8.23643411e-03,\n",
       "         1.69573643e-03,  1.23045313e-02,  7.17842978e-02,\n",
       "         6.90692719e-03,  1.13902296e-02,  2.39980966e-01,\n",
       "         3.94129174e-15]])"
      ]
     },
     "execution_count": 52,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "clust5.cluster_centers_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "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>clust5id</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>49921.633641</td>\n",
       "      <td>89.903226</td>\n",
       "      <td>1.122120</td>\n",
       "      <td>1.019585</td>\n",
       "      <td>1.001152</td>\n",
       "      <td>3467.074885</td>\n",
       "      <td>6.913594</td>\n",
       "      <td>243.834101</td>\n",
       "      <td>0.728111</td>\n",
       "      <td>5567.925115</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>108317.387376</td>\n",
       "      <td>198.336634</td>\n",
       "      <td>3.915842</td>\n",
       "      <td>1.001238</td>\n",
       "      <td>1.025990</td>\n",
       "      <td>45609.657178</td>\n",
       "      <td>20.201733</td>\n",
       "      <td>713.728960</td>\n",
       "      <td>2.142327</td>\n",
       "      <td>4863.439356</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2</td>\n",
       "      <td>118297.325243</td>\n",
       "      <td>73.467638</td>\n",
       "      <td>3.584142</td>\n",
       "      <td>1.001618</td>\n",
       "      <td>1.022654</td>\n",
       "      <td>31384.393204</td>\n",
       "      <td>17.233010</td>\n",
       "      <td>224.100324</td>\n",
       "      <td>0.627832</td>\n",
       "      <td>4419.553398</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>3</td>\n",
       "      <td>83529.153046</td>\n",
       "      <td>290.453195</td>\n",
       "      <td>1.156018</td>\n",
       "      <td>1.032689</td>\n",
       "      <td>1.008915</td>\n",
       "      <td>8850.395245</td>\n",
       "      <td>10.476969</td>\n",
       "      <td>1030.112927</td>\n",
       "      <td>3.148588</td>\n",
       "      <td>4338.867756</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>4</td>\n",
       "      <td>33097.301357</td>\n",
       "      <td>94.131783</td>\n",
       "      <td>1.070736</td>\n",
       "      <td>1.016473</td>\n",
       "      <td>1.006783</td>\n",
       "      <td>3244.520349</td>\n",
       "      <td>6.173450</td>\n",
       "      <td>212.850775</td>\n",
       "      <td>0.603682</td>\n",
       "      <td>1992.402132</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  clust5id        Balance  Qual_miles cc1_miles cc2_miles cc3_miles  \\\n",
       "                     mean        mean      mean      mean      mean   \n",
       "0        0   49921.633641   89.903226  1.122120  1.019585  1.001152   \n",
       "1        1  108317.387376  198.336634  3.915842  1.001238  1.025990   \n",
       "2        2  118297.325243   73.467638  3.584142  1.001618  1.022654   \n",
       "3        3   83529.153046  290.453195  1.156018  1.032689  1.008915   \n",
       "4        4   33097.301357   94.131783  1.070736  1.016473  1.006783   \n",
       "\n",
       "    Bonus_miles Bonus_trans Flight_miles_12mo Flight_trans_12  \\\n",
       "           mean        mean              mean            mean   \n",
       "0   3467.074885    6.913594        243.834101        0.728111   \n",
       "1  45609.657178   20.201733        713.728960        2.142327   \n",
       "2  31384.393204   17.233010        224.100324        0.627832   \n",
       "3   8850.395245   10.476969       1030.112927        3.148588   \n",
       "4   3244.520349    6.173450        212.850775        0.603682   \n",
       "\n",
       "  Days_since_enroll Award?  \n",
       "               mean   mean  \n",
       "0       5567.925115      0  \n",
       "1       4863.439356      1  \n",
       "2       4419.553398      0  \n",
       "3       4338.867756      1  \n",
       "4       1992.402132      0  "
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data5.groupby('clust5id').agg(['mean']).reset_index()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.collections.PathCollection at 0x26e5ac66400>"
      ]
     },
     "execution_count": 27,
     "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": [
    "plt.figure(figsize=(10, 7))  \n",
    "plt.scatter(data5['clust5id'],data5['Balance'], c=clust5.labels_)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# DBSCAN Clustering"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 83,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.cluster import DBSCAN\n",
    "from sklearn.preprocessing import StandardScaler"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 86,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
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       "      <th>ID#</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",
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       "      <th>0</th>\n",
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       "      <td>4123</td>\n",
       "      <td>4</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>7034</td>\n",
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       "      <td>6952</td>\n",
       "      <td>0</td>\n",
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       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5</td>\n",
       "      <td>97752</td>\n",
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       "  </tbody>\n",
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       "</div>"
      ],
      "text/plain": [
       "   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",
       "   Bonus_trans  Flight_miles_12mo  Flight_trans_12  Days_since_enroll  Award?  \n",
       "0            1                  0                0               7000       0  \n",
       "1            2                  0                0               6968       0  \n",
       "2            4                  0                0               7034       0  \n",
       "3            1                  0                0               6952       0  \n",
       "4           26               2077                4               6935       1  "
      ]
     },
     "execution_count": 86,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 87,
   "metadata": {},
   "outputs": [],
   "source": [
    "data2=data.drop(['ID#'],axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 88,
   "metadata": {},
   "outputs": [
    {
     "data": {
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      ],
      "text/plain": [
       "   Balance  Qual_miles  cc1_miles  cc2_miles  cc3_miles  Bonus_miles  \\\n",
       "0    28143           0          1          1          1          174   \n",
       "1    19244           0          1          1          1          215   \n",
       "2    41354           0          1          1          1         4123   \n",
       "3    14776           0          1          1          1          500   \n",
       "4    97752           0          4          1          1        43300   \n",
       "\n",
       "   Bonus_trans  Flight_miles_12mo  Flight_trans_12  Days_since_enroll  Award?  \n",
       "0            1                  0                0               7000       0  \n",
       "1            2                  0                0               6968       0  \n",
       "2            4                  0                0               7034       0  \n",
       "3            1                  0                0               6952       0  \n",
       "4           26               2077                4               6935       1  "
      ]
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     "execution_count": 88,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data2.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 89,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[28143,     0,     1, ...,     0,  7000,     0],\n",
       "       [19244,     0,     1, ...,     0,  6968,     0],\n",
       "       [41354,     0,     1, ...,     0,  7034,     0],\n",
       "       ...,\n",
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       "       [54899,     0,     1, ...,     1,  1401,     0],\n",
       "       [ 3016,     0,     1, ...,     0,  1398,     0]], dtype=int64)"
      ]
     },
     "execution_count": 89,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "array=data2.values\n",
    "array"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 90,
   "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": 90,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "stscaler=StandardScaler().fit(array)\n",
    "X=stscaler.transform(array)\n",
    "X"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 91,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "DBSCAN(eps=0.8, min_samples=6)"
      ]
     },
     "execution_count": 91,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dbscan=DBSCAN(eps=0.8,min_samples=6)\n",
    "dbscan.fit(X)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 92,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([0, 0, 0, ..., 1, 0, 0], dtype=int64)"
      ]
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     "execution_count": 92,
     "metadata": {},
     "output_type": "execute_result"
    }
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   "source": [
    "dbscan.labels_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 93,
   "metadata": {},
   "outputs": [
    {
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       "      cluster\n",
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       "4          -1\n",
       "...       ...\n",
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     },
     "execution_count": 93,
     "metadata": {},
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   "source": [
    "c1=pd.DataFrame(dbscan.labels_,columns=['cluster'])\n",
    "c1"
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  {
   "cell_type": "code",
   "execution_count": 94,
   "metadata": {},
   "outputs": [
    {
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       "      <th>3995</th>\n",
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       "      <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>1</td>\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>0</td>\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>0</td>\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 × 12 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "      cluster  Balance  Qual_miles  cc1_miles  cc2_miles  cc3_miles  \\\n",
       "0           0    28143           0          1          1          1   \n",
       "1           0    19244           0          1          1          1   \n",
       "2           0    41354           0          1          1          1   \n",
       "3           0    14776           0          1          1          1   \n",
       "4          -1    97752           0          4          1          1   \n",
       "...       ...      ...         ...        ...        ...        ...   \n",
       "3994        1    18476           0          1          1          1   \n",
       "3995        1    64385           0          1          1          1   \n",
       "3996        1    73597           0          3          1          1   \n",
       "3997        0    54899           0          1          1          1   \n",
       "3998        0     3016           0          1          1          1   \n",
       "\n",
       "      Bonus_miles  Bonus_trans  Flight_miles_12mo  Flight_trans_12  \\\n",
       "0             174            1                  0                0   \n",
       "1             215            2                  0                0   \n",
       "2            4123            4                  0                0   \n",
       "3             500            1                  0                0   \n",
       "4           43300           26               2077                4   \n",
       "...           ...          ...                ...              ...   \n",
       "3994         8525            4                200                1   \n",
       "3995          981            5                  0                0   \n",
       "3996        25447            8                  0                0   \n",
       "3997          500            1                500                1   \n",
       "3998            0            0                  0                0   \n",
       "\n",
       "      Days_since_enroll  Award?  \n",
       "0                  7000       0  \n",
       "1                  6968       0  \n",
       "2                  7034       0  \n",
       "3                  6952       0  \n",
       "4                  6935       1  \n",
       "...                 ...     ...  \n",
       "3994               1403       1  \n",
       "3995               1395       1  \n",
       "3996               1402       1  \n",
       "3997               1401       0  \n",
       "3998               1398       0  \n",
       "\n",
       "[3999 rows x 12 columns]"
      ]
     },
     "execution_count": 94,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data3=pd.concat([c1,data2],axis=1)\n",
    "data3"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 95,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       " 0    2216\n",
       " 1     944\n",
       "-1     820\n",
       " 2      12\n",
       " 3       7\n",
       "Name: cluster, dtype: int64"
      ]
     },
     "execution_count": 95,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data3.cluster.value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 96,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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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",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>cluster</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
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       "  </thead>\n",
       "  <tbody>\n",
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       "      <th>-1</th>\n",
       "      <td>158564.875610</td>\n",
       "      <td>698.553659</td>\n",
       "      <td>2.670732</td>\n",
       "      <td>1.056098</td>\n",
       "      <td>1.059756</td>\n",
       "      <td>35956.408537</td>\n",
       "      <td>20.157317</td>\n",
       "      <td>1805.067073</td>\n",
       "      <td>5.232927</td>\n",
       "      <td>4647.563415</td>\n",
       "      <td>0.654878</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>50589.121390</td>\n",
       "      <td>0.676895</td>\n",
       "      <td>1.629964</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>8534.324458</td>\n",
       "      <td>8.112365</td>\n",
       "      <td>85.902978</td>\n",
       "      <td>0.291065</td>\n",
       "      <td>3790.477888</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>54510.457627</td>\n",
       "      <td>2.118644</td>\n",
       "      <td>2.543432</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>21126.111229</td>\n",
       "      <td>12.194915</td>\n",
       "      <td>179.287076</td>\n",
       "      <td>0.590042</td>\n",
       "      <td>4458.901483</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
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       "      <td>1.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>8048.333333</td>\n",
       "      <td>11.916667</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>3407.416667</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>47279.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>18041.857143</td>\n",
       "      <td>33.571429</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1332.285714</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "               Balance  Qual_miles  cc1_miles  cc2_miles  cc3_miles  \\\n",
       "cluster                                                               \n",
       "-1       158564.875610  698.553659   2.670732   1.056098   1.059756   \n",
       " 0        50589.121390    0.676895   1.629964   1.000000   1.000000   \n",
       " 1        54510.457627    2.118644   2.543432   1.000000   1.000000   \n",
       " 2        34516.083333    0.000000   1.000000   2.000000   1.000000   \n",
       " 3        47279.000000    0.000000   3.000000   1.000000   1.000000   \n",
       "\n",
       "          Bonus_miles  Bonus_trans  Flight_miles_12mo  Flight_trans_12  \\\n",
       "cluster                                                                  \n",
       "-1       35956.408537    20.157317        1805.067073         5.232927   \n",
       " 0        8534.324458     8.112365          85.902978         0.291065   \n",
       " 1       21126.111229    12.194915         179.287076         0.590042   \n",
       " 2        8048.333333    11.916667           0.000000         0.000000   \n",
       " 3       18041.857143    33.571429           0.000000         0.000000   \n",
       "\n",
       "         Days_since_enroll    Award?  \n",
       "cluster                               \n",
       "-1             4647.563415  0.654878  \n",
       " 0             3790.477888  0.000000  \n",
       " 1             4458.901483  1.000000  \n",
       " 2             3407.416667  0.000000  \n",
       " 3             1332.285714  0.000000  "
      ]
     },
     "execution_count": 96,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data4=data3.groupby(data3.cluster).mean()\n",
    "data4"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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