East_West_Airlines.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "74a8585f",
   "metadata": {},
   "source": [
    "# Perform clustering (hierarchical,K means clustering and DBSCAN) for the airlines data to obtain optimum number of clusters. Draw the inferences from the clusters obtained"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f9789a16",
   "metadata": {},
   "source": [
    "## Data Description:"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "009766ab",
   "metadata": {},
   "source": [
    "The file EastWestAirlinescontains information on passengers who belong to an airline’s frequent flier program. For each passenger the data include information on their mileage history and on different ways they accrued or spent miles in the last year. The goal is to try to identify clusters of passengers that have similar characteristics for the purpose of targeting different segments for different types of mileage offers\n",
    "\n",
    "ID --Unique ID\n",
    "\n",
    "Balance--Number of miles eligible for award travel\n",
    "\n",
    "Qual_mile--Number of miles counted as qualifying for Topflight status\n",
    "\n",
    "cc1_miles -- Number of miles earned with freq. flyer credit card in the past 12 months:\n",
    "\n",
    "cc2_miles -- Number of miles earned with Rewards credit card in the past 12 months:\n",
    "\n",
    "cc3_miles -- Number of miles earned with Small Business credit card in the past 12 months:\n",
    "\n",
    "1 = under 5,000\n",
    "2 = 5,000 - 10,000\n",
    "3 = 10,001 - 25,000\n",
    "4 = 25,001 - 50,000\n",
    "5 = over 50,000\n",
    "\n",
    "Bonus_miles--Number of miles earned from non-flight bonus transactions in the past 12 months\n",
    "\n",
    "Bonus_trans--Number of non-flight bonus transactions in the past 12 months\n",
    "\n",
    "Flight_miles_12mo--Number of flight miles in the past 12 months\n",
    "\n",
    "Flight_trans_12--Number of flight transactions in the past 12 months\n",
    "\n",
    "Days_since_enrolled--Number of days since enrolled in flier program\n",
    "\n",
    "Award--whether that person had award flight (free flight) or not\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "7c8344cd",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2022-11-02T11:13:10.373978Z",
     "start_time": "2022-11-02T11:13:08.046943Z"
    }
   },
   "outputs": [],
   "source": [
    "#importing necessary libraries\n",
    "\n",
    "import scipy.cluster.hierarchy as sch \n",
    "from sklearn.cluster import AgglomerativeClustering, KMeans, DBSCAN\n",
    "from sklearn.metrics import silhouette_score as sil \n",
    "from sklearn.preprocessing import StandardScaler\n",
    "\n",
    "import pandas as pd \n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "\n",
    "import warnings\n",
    "warnings.filterwarnings('ignore') "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "86900368",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2022-11-02T11:13:11.508539Z",
     "start_time": "2022-11-02T11:13:10.373978Z"
    }
   },
   "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",
       "    <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  Award  \n",
       "Id                                                                             \n",
       "1             1                  0                0               7000      0  \n",
       "2             2                  0                0               6968      0  \n",
       "3             4                  0                0               7034      0  \n",
       "4             1                  0                0               6952      0  \n",
       "5            26               2077                4               6935      1  "
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "airlines = pd.read_excel('EastWestAirlines.xlsx', sheet_name='data') # open sheet with name 'data'\n",
    "airlines.rename(columns={'ID#':'Id', 'Award?':'Award'}, inplace=True)\n",
    "airlines.set_index('Id', inplace=True)\n",
    "\n",
    "airlines.head() "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "d5ece73c",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2022-11-02T11:13:11.524002Z",
     "start_time": "2022-11-02T11:13:11.508539Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(3999, 11)"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "airlines.shape"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "0af84aa4",
   "metadata": {},
   "source": [
    "## Descriptive Statistics"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "19b22b87",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2022-11-02T11:13:11.563991Z",
     "start_time": "2022-11-02T11:13:11.525016Z"
    }
   },
   "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>count</th>\n",
       "      <td>3.999000e+03</td>\n",
       "      <td>3999.000000</td>\n",
       "      <td>3999.000000</td>\n",
       "      <td>3999.000000</td>\n",
       "      <td>3999.000000</td>\n",
       "      <td>3999.000000</td>\n",
       "      <td>3999.00000</td>\n",
       "      <td>3999.000000</td>\n",
       "      <td>3999.000000</td>\n",
       "      <td>3999.00000</td>\n",
       "      <td>3999.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>7.360133e+04</td>\n",
       "      <td>144.114529</td>\n",
       "      <td>2.059515</td>\n",
       "      <td>1.014504</td>\n",
       "      <td>1.012253</td>\n",
       "      <td>17144.846212</td>\n",
       "      <td>11.60190</td>\n",
       "      <td>460.055764</td>\n",
       "      <td>1.373593</td>\n",
       "      <td>4118.55939</td>\n",
       "      <td>0.370343</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>1.007757e+05</td>\n",
       "      <td>773.663804</td>\n",
       "      <td>1.376919</td>\n",
       "      <td>0.147650</td>\n",
       "      <td>0.195241</td>\n",
       "      <td>24150.967826</td>\n",
       "      <td>9.60381</td>\n",
       "      <td>1400.209171</td>\n",
       "      <td>3.793172</td>\n",
       "      <td>2065.13454</td>\n",
       "      <td>0.482957</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.00000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>2.00000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>1.852750e+04</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1250.000000</td>\n",
       "      <td>3.00000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>2330.00000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>4.309700e+04</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>7171.000000</td>\n",
       "      <td>12.00000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>4096.00000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>9.240400e+04</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>23800.500000</td>\n",
       "      <td>17.00000</td>\n",
       "      <td>311.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>5790.50000</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>1.704838e+06</td>\n",
       "      <td>11148.000000</td>\n",
       "      <td>5.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>5.000000</td>\n",
       "      <td>263685.000000</td>\n",
       "      <td>86.00000</td>\n",
       "      <td>30817.000000</td>\n",
       "      <td>53.000000</td>\n",
       "      <td>8296.00000</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "            Balance    Qual_miles    cc1_miles    cc2_miles    cc3_miles  \\\n",
       "count  3.999000e+03   3999.000000  3999.000000  3999.000000  3999.000000   \n",
       "mean   7.360133e+04    144.114529     2.059515     1.014504     1.012253   \n",
       "std    1.007757e+05    773.663804     1.376919     0.147650     0.195241   \n",
       "min    0.000000e+00      0.000000     1.000000     1.000000     1.000000   \n",
       "25%    1.852750e+04      0.000000     1.000000     1.000000     1.000000   \n",
       "50%    4.309700e+04      0.000000     1.000000     1.000000     1.000000   \n",
       "75%    9.240400e+04      0.000000     3.000000     1.000000     1.000000   \n",
       "max    1.704838e+06  11148.000000     5.000000     3.000000     5.000000   \n",
       "\n",
       "         Bonus_miles  Bonus_trans  Flight_miles_12mo  Flight_trans_12  \\\n",
       "count    3999.000000   3999.00000        3999.000000      3999.000000   \n",
       "mean    17144.846212     11.60190         460.055764         1.373593   \n",
       "std     24150.967826      9.60381        1400.209171         3.793172   \n",
       "min         0.000000      0.00000           0.000000         0.000000   \n",
       "25%      1250.000000      3.00000           0.000000         0.000000   \n",
       "50%      7171.000000     12.00000           0.000000         0.000000   \n",
       "75%     23800.500000     17.00000         311.000000         1.000000   \n",
       "max    263685.000000     86.00000       30817.000000        53.000000   \n",
       "\n",
       "       Days_since_enroll        Award  \n",
       "count         3999.00000  3999.000000  \n",
       "mean          4118.55939     0.370343  \n",
       "std           2065.13454     0.482957  \n",
       "min              2.00000     0.000000  \n",
       "25%           2330.00000     0.000000  \n",
       "50%           4096.00000     0.000000  \n",
       "75%           5790.50000     1.000000  \n",
       "max           8296.00000     1.000000  "
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "airlines.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "a0aac17c",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2022-11-02T11:13:11.584197Z",
     "start_time": "2022-11-02T11:13:11.566149Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "Int64Index: 3999 entries, 1 to 4021\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: 374.9 KB\n"
     ]
    }
   ],
   "source": [
    "airlines.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "be48d168",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2022-11-02T11:13:11.603783Z",
     "start_time": "2022-11-02T11:13:11.584197Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "airlines.isna().sum().sum()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "fffd5942",
   "metadata": {},
   "source": [
    "No missing values "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "0ae8396e",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2022-11-02T11:13:11.614857Z",
     "start_time": "2022-11-02T11:13:11.603783Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "airlines.duplicated().sum() "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "63d854d7",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2022-11-02T11:13:11.631329Z",
     "start_time": "2022-11-02T11:13:11.614857Z"
    }
   },
   "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",
       "    <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>3899</th>\n",
       "      <td>1000</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>1597</td>\n",
       "      <td>0</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",
       "3899     1000           0          1          1          1            0   \n",
       "\n",
       "      Bonus_trans  Flight_miles_12mo  Flight_trans_12  Days_since_enroll  \\\n",
       "Id                                                                         \n",
       "3899            0                  0                0               1597   \n",
       "\n",
       "      Award  \n",
       "Id           \n",
       "3899      0  "
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "airlines[airlines.duplicated()] "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "16dad3ea",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2022-11-02T11:13:11.644080Z",
     "start_time": "2022-11-02T11:13:11.633843Z"
    }
   },
   "outputs": [],
   "source": [
    "airlines=airlines.drop_duplicates() "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "baa8687e",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2022-11-02T11:13:11.664134Z",
     "start_time": "2022-11-02T11:13:11.647634Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(3998, 11)"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "airlines.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "19ce4d02",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2022-11-02T11:13:11.680631Z",
     "start_time": "2022-11-02T11:13:11.664134Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "airlines.duplicated().sum()  "
   ]
  },
  {
   "cell_type": "markdown",
   "id": "9aab4afb",
   "metadata": {},
   "source": [
    "One duplicate entry was present. We removed it "
   ]
  },
  {
   "cell_type": "markdown",
   "id": "8c6bcc79",
   "metadata": {},
   "source": [
    "## Data Visualisation"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "32b828c4",
   "metadata": {},
   "source": [
    "### Plotting histogram to see distribution & Plotting boxplot for Outlier Detection"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "ac916a38",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2022-11-02T11:13:14.584731Z",
     "start_time": "2022-11-02T11:13:11.680631Z"
    },
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 1440x576 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 1440x576 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 1440x576 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 1440x576 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 1440x576 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 1440x576 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 1440x576 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 1440x576 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 1440x576 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 1440x576 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 1440x576 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "for feature in airlines.columns:\n",
    "    \n",
    "    plt.figure(figsize=(20,8))\n",
    "    \n",
    "    plt.subplot(1,2,1)\n",
    "    airlines[feature].hist(bins=25)\n",
    "    plt.ylabel('Count')\n",
    "    plt.title(feature)\n",
    "    \n",
    "    plt.subplot(1,2,2)\n",
    "    plt.boxplot(airlines[feature])\n",
    "    plt.title(feature)\n",
    "    \n",
    "    plt.show() "
   ]
  },
  {
   "cell_type": "markdown",
   "id": "2d084b0b",
   "metadata": {},
   "source": [
    "### Correlations"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "5f02a9fc",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2022-11-02T11:13:15.127720Z",
     "start_time": "2022-11-02T11:13:14.584731Z"
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 1440x576 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(20,8))\n",
    "sns.heatmap(airlines.corr(), annot=True, fmt='0.1f', linewidths =.5) \n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b432920b",
   "metadata": {},
   "source": [
    "Positive correlation is found betweeen: \n",
    "\n",
    "a. Number of flight transactions in the past 12 months AND Number of flight miles in the past 12 months\n",
    "\n",
    "b. -Miles earned from non-flight bonus transactions AND  Miles earned with freq. flyer credit card in the past 12 months\n",
    "\n",
    "c. Non-flight bonus transactions AND  Miles earned with freq. flyer credit card\n",
    "\n",
    "d. Non-flight bonus transactions AND Miles earned from non-flight bonus transactions"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "853be5f4",
   "metadata": {},
   "source": [
    "# Data Preprocessing"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "5de94c4b",
   "metadata": {},
   "source": [
    "### Normalisation of data (use it for hierarchical clustering)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "6d051af6",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2022-11-02T11:13:15.142674Z",
     "start_time": "2022-11-02T11:13:15.130222Z"
    }
   },
   "outputs": [],
   "source": [
    "def norm_func(i):\n",
    "    x = (i - i.min()) / (i.max() - i.min())\n",
    "    return(x)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "b47cd00d",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2022-11-02T11:13:15.169137Z",
     "start_time": "2022-11-02T11:13:15.143685Z"
    }
   },
   "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",
       "    <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>0.016508</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000660</td>\n",
       "      <td>0.011628</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.843742</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0.011288</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000815</td>\n",
       "      <td>0.023256</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.839884</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0.024257</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.015636</td>\n",
       "      <td>0.046512</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.847842</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0.008667</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.001896</td>\n",
       "      <td>0.011628</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.837955</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>0.057338</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.75</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.164211</td>\n",
       "      <td>0.302326</td>\n",
       "      <td>0.067398</td>\n",
       "      <td>0.075472</td>\n",
       "      <td>0.835905</td>\n",
       "      <td>1.0</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   0.016508         0.0       0.00        0.0        0.0     0.000660   \n",
       "2   0.011288         0.0       0.00        0.0        0.0     0.000815   \n",
       "3   0.024257         0.0       0.00        0.0        0.0     0.015636   \n",
       "4   0.008667         0.0       0.00        0.0        0.0     0.001896   \n",
       "5   0.057338         0.0       0.75        0.0        0.0     0.164211   \n",
       "\n",
       "    Bonus_trans  Flight_miles_12mo  Flight_trans_12  Days_since_enroll  Award  \n",
       "Id                                                                             \n",
       "1      0.011628           0.000000         0.000000           0.843742    0.0  \n",
       "2      0.023256           0.000000         0.000000           0.839884    0.0  \n",
       "3      0.046512           0.000000         0.000000           0.847842    0.0  \n",
       "4      0.011628           0.000000         0.000000           0.837955    0.0  \n",
       "5      0.302326           0.067398         0.075472           0.835905    1.0  "
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Normalized data frame (considering the numerical part of data)\n",
    "airlines_norm = norm_func(airlines)\n",
    "airlines_norm.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "96e57d51",
   "metadata": {},
   "source": [
    "### Standardisation of data (use it for K means and DBSCAN)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "018351dd",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2022-11-02T11:13:15.185236Z",
     "start_time": "2022-11-02T11:13:15.169137Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[-4.51293870e-01, -1.86322793e-01, -7.69731660e-01, ...,\n",
       "        -3.62219103e-01,  1.39523468e+00, -7.67071631e-01],\n",
       "       [-5.39604653e-01, -1.86322793e-01, -7.69731660e-01, ...,\n",
       "        -3.62219103e-01,  1.37973643e+00, -7.67071631e-01],\n",
       "       [-3.20192202e-01, -1.86322793e-01, -7.69731660e-01, ...,\n",
       "        -3.62219103e-01,  1.41170157e+00, -7.67071631e-01],\n",
       "       ...,\n",
       "       [-2.23153612e-04, -1.86322793e-01,  6.82893866e-01, ...,\n",
       "        -3.62219103e-01, -1.31599028e+00,  1.30365921e+00],\n",
       "       [-1.85776026e-01, -1.86322793e-01, -7.69731660e-01, ...,\n",
       "        -9.85832074e-02, -1.31647460e+00, -7.67071631e-01],\n",
       "       [-7.00646048e-01, -1.86322793e-01, -7.69731660e-01, ...,\n",
       "        -3.62219103e-01, -1.31792757e+00, -7.67071631e-01]])"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "scaler = StandardScaler()\n",
    "airlines_std = scaler.fit_transform(airlines) \n",
    "airlines_std"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a6ba86ca",
   "metadata": {},
   "source": [
    "# Clustering: "
   ]
  },
  {
   "cell_type": "markdown",
   "id": "4ed6a844",
   "metadata": {},
   "source": [
    "Clustering is the task of dividing the population or data points into a number of groups such that data points in the same groups are more similar to other data points in the same group and dissimilar to the data points in other groups. It is basically a collection of objects on the basis of similarity and dissimilarity between them. "
   ]
  },
  {
   "cell_type": "markdown",
   "id": "2284e020",
   "metadata": {},
   "source": [
    "# 1) Hierarchical Clustering "
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e9b107ee",
   "metadata": {},
   "source": [
    "Hierarchy of clusters is in the form of a tree, and this tree-shaped structure is known as the dendrogram. There is no requirement to predetermine the number of clusters as in the K-Means algorithm.\n",
    "\n",
    "Linkage methods are ways to calculate the distance between two clusters.\n",
    "\n",
    "Single Linkage: It is the Shortest Distance between the closest points of the clusters\n",
    "\n",
    "Complete Linkage: It is the farthest distance between the two points of two different clusters\n",
    "\n",
    "Average Linkage: It is the linkage method in which the distance between each pair of datasets is added up and then divided by the total number of datasets to calculate the average distance between two clusters.\n",
    "\n",
    "Centroid Linkage: It is the linkage method in which the distance between the centroid of the clusters is calculated. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "3bfd0cc6",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2022-11-02T11:14:13.303809Z",
     "start_time": "2022-11-02T11:13:15.185236Z"
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 1440x576 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(20,8))\n",
    "dendrogram = sch.dendrogram(sch.linkage(airlines_norm, method= 'complete'))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "61194a2b",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2022-11-02T11:15:05.571482Z",
     "start_time": "2022-11-02T11:14:13.305371Z"
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 1440x576 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(20,8))\n",
    "dendrogram = sch.dendrogram(sch.linkage(airlines_norm, method= 'single'))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "bb5f5f2b",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2022-11-02T11:15:59.297019Z",
     "start_time": "2022-11-02T11:15:05.571482Z"
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 1440x576 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(20,8))\n",
    "dendrogram = sch.dendrogram(sch.linkage(airlines_norm, method= 'average'))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "739cf557",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2022-11-02T11:16:49.899636Z",
     "start_time": "2022-11-02T11:15:59.297019Z"
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 1440x576 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(20,8))\n",
    "dendrogram = sch.dendrogram(sch.linkage(airlines_norm, method= 'centroid'))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "db8034c4",
   "metadata": {},
   "source": [
    "### Silhouette Score "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "90e7da12",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2022-11-02T11:17:22.469164Z",
     "start_time": "2022-11-02T11:16:49.899636Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "For n= 2 and linkage = complete Silhouette score is 0.4704\n",
      "For n= 2 and linkage = single Silhouette score is 0.5359\n",
      "For n= 2 and linkage = ward Silhouette score is 0.5359\n",
      "For n= 2 and linkage = average Silhouette score is 0.4704\n",
      "For n= 3 and linkage = complete Silhouette score is 0.5318\n",
      "For n= 3 and linkage = single Silhouette score is 0.5351\n",
      "For n= 3 and linkage = ward Silhouette score is 0.4355\n",
      "For n= 3 and linkage = average Silhouette score is 0.3961\n",
      "For n= 4 and linkage = complete Silhouette score is 0.4645\n",
      "For n= 4 and linkage = single Silhouette score is 0.4812\n",
      "For n= 4 and linkage = ward Silhouette score is 0.4249\n",
      "For n= 4 and linkage = average Silhouette score is 0.5286\n",
      "For n= 5 and linkage = complete Silhouette score is 0.4167\n",
      "For n= 5 and linkage = single Silhouette score is 0.48\n",
      "For n= 5 and linkage = ward Silhouette score is 0.394\n",
      "For n= 5 and linkage = average Silhouette score is 0.5082\n",
      "For n= 6 and linkage = complete Silhouette score is 0.4172\n",
      "For n= 6 and linkage = single Silhouette score is 0.4328\n",
      "For n= 6 and linkage = ward Silhouette score is 0.3522\n",
      "For n= 6 and linkage = average Silhouette score is 0.4872\n",
      "For n= 7 and linkage = complete Silhouette score is 0.4161\n",
      "For n= 7 and linkage = single Silhouette score is 0.4313\n",
      "For n= 7 and linkage = ward Silhouette score is 0.3482\n",
      "For n= 7 and linkage = average Silhouette score is 0.4387\n",
      "For n= 8 and linkage = complete Silhouette score is 0.3912\n",
      "For n= 8 and linkage = single Silhouette score is 0.3913\n",
      "For n= 8 and linkage = ward Silhouette score is 0.3514\n",
      "For n= 8 and linkage = average Silhouette score is 0.4086\n",
      "For n= 9 and linkage = complete Silhouette score is 0.3518\n",
      "For n= 9 and linkage = single Silhouette score is 0.3906\n",
      "For n= 9 and linkage = ward Silhouette score is 0.3334\n",
      "For n= 9 and linkage = average Silhouette score is 0.4008\n",
      "For n= 10 and linkage = complete Silhouette score is 0.3522\n",
      "For n= 10 and linkage = single Silhouette score is 0.3916\n",
      "For n= 10 and linkage = ward Silhouette score is 0.3293\n",
      "For n= 10 and linkage = average Silhouette score is 0.3571\n",
      "For n= 11 and linkage = complete Silhouette score is 0.3539\n",
      "For n= 11 and linkage = single Silhouette score is 0.3864\n",
      "For n= 11 and linkage = ward Silhouette score is 0.3245\n",
      "For n= 11 and linkage = average Silhouette score is 0.3569\n",
      "For n= 12 and linkage = complete Silhouette score is 0.3315\n",
      "For n= 12 and linkage = single Silhouette score is 0.3135\n",
      "For n= 12 and linkage = ward Silhouette score is 0.3259\n",
      "For n= 12 and linkage = average Silhouette score is 0.3373\n",
      "For n= 13 and linkage = complete Silhouette score is 0.3317\n",
      "For n= 13 and linkage = single Silhouette score is 0.3157\n",
      "For n= 13 and linkage = ward Silhouette score is 0.3291\n",
      "For n= 13 and linkage = average Silhouette score is 0.3367\n",
      "For n= 14 and linkage = complete Silhouette score is 0.334\n",
      "For n= 14 and linkage = single Silhouette score is 0.3103\n",
      "For n= 14 and linkage = ward Silhouette score is 0.3283\n",
      "For n= 14 and linkage = average Silhouette score is 0.3005\n",
      "For n= 15 and linkage = complete Silhouette score is 0.3338\n",
      "For n= 15 and linkage = single Silhouette score is 0.1902\n",
      "For n= 15 and linkage = ward Silhouette score is 0.3294\n",
      "For n= 15 and linkage = average Silhouette score is 0.3488\n"
     ]
    }
   ],
   "source": [
    "\n",
    "linkages = ['complete' , 'single' , 'ward' , 'average']\n",
    "\n",
    "for i in range (2,16):\n",
    "    for x in linkages:\n",
    "        hc=AgglomerativeClustering(n_clusters= i, linkage=x)\n",
    "        y_hc=hc.fit_predict(airlines_norm)\n",
    "        sil_score = sil(airlines_norm, y_hc)\n",
    "        print('For n=',i,'and linkage =', x, 'Silhouette score is', np.round(sil_score,4))\n",
    " "
   ]
  },
  {
   "cell_type": "markdown",
   "id": "abe1049c",
   "metadata": {},
   "source": [
    "### Silhouette score is maximum for \n",
    "\n",
    "For n= 2 and linkage = single Silhouette score is 0.5359\n",
    "\n",
    "For n= 2 and linkage = ward Silhouette score is 0.5359 "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "5812a3d9",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2022-11-02T11:17:22.481481Z",
     "start_time": "2022-11-02T11:17:22.469164Z"
    }
   },
   "outputs": [],
   "source": [
    "# create clusters with single linkage and n_clusters = 2\n",
    "hc = AgglomerativeClustering(n_clusters=2, affinity='euclidean', linkage= 'single')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "4d6fa91d",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2022-11-02T11:17:22.683928Z",
     "start_time": "2022-11-02T11:17:22.483994Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([1, 1, 1, ..., 0, 1, 1], dtype=int64)"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y_hc = hc.fit_predict(airlines_norm)\n",
    "y_hc "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "3f56b934",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2022-11-02T11:17:22.713759Z",
     "start_time": "2022-11-02T11:17:22.683928Z"
    }
   },
   "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>ClusterId_hc</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",
       "      <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",
       "      <td>1</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",
       "      <td>1</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",
       "      <td>1</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",
       "      <td>1</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",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4017</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>4018</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>4019</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>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4020</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>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4021</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>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>3998 rows × 12 columns</p>\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",
       "4017    18476           0          1          1          1         8525   \n",
       "4018    64385           0          1          1          1          981   \n",
       "4019    73597           0          3          1          1        25447   \n",
       "4020    54899           0          1          1          1          500   \n",
       "4021     3016           0          1          1          1            0   \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",
       "4017            4                200                1               1403   \n",
       "4018            5                  0                0               1395   \n",
       "4019            8                  0                0               1402   \n",
       "4020            1                500                1               1401   \n",
       "4021            0                  0                0               1398   \n",
       "\n",
       "      Award  ClusterId_hc  \n",
       "Id                         \n",
       "1         0             1  \n",
       "2         0             1  \n",
       "3         0             1  \n",
       "4         0             1  \n",
       "5         1             0  \n",
       "...     ...           ...  \n",
       "4017      1             0  \n",
       "4018      1             0  \n",
       "4019      1             0  \n",
       "4020      0             1  \n",
       "4021      0             1  \n",
       "\n",
       "[3998 rows x 12 columns]"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "airlines['ClusterId_hc'] = y_hc\n",
    "airlines "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "id": "5e05afed",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2022-11-02T11:17:22.730270Z",
     "start_time": "2022-11-02T11:17:22.713759Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1    2517\n",
       "0    1481\n",
       "Name: ClusterId_hc, dtype: int64"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "airlines['ClusterId_hc'].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "id": "de82ec00",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2022-11-02T11:17:22.769957Z",
     "start_time": "2022-11-02T11:17:22.730270Z"
    }
   },
   "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",
       "\n",
       "    .dataframe thead tr:last-of-type th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr>\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></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",
       "    <tr>\n",
       "      <th>ClusterId_hc</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>97053.051317</td>\n",
       "      <td>240.196489</td>\n",
       "      <td>2.661715</td>\n",
       "      <td>1.015530</td>\n",
       "      <td>1.018231</td>\n",
       "      <td>28905.414585</td>\n",
       "      <td>15.782579</td>\n",
       "      <td>857.501013</td>\n",
       "      <td>2.599595</td>\n",
       "      <td>4625.062120</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>59831.203814</td>\n",
       "      <td>87.637267</td>\n",
       "      <td>1.705602</td>\n",
       "      <td>1.013905</td>\n",
       "      <td>1.008741</td>\n",
       "      <td>10231.752483</td>\n",
       "      <td>9.146603</td>\n",
       "      <td>226.382201</td>\n",
       "      <td>0.652761</td>\n",
       "      <td>3821.535558</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                   Balance  Qual_miles cc1_miles cc2_miles cc3_miles  \\\n",
       "                      mean        mean      mean      mean      mean   \n",
       "ClusterId_hc                                                           \n",
       "0             97053.051317  240.196489  2.661715  1.015530  1.018231   \n",
       "1             59831.203814   87.637267  1.705602  1.013905  1.008741   \n",
       "\n",
       "               Bonus_miles Bonus_trans Flight_miles_12mo Flight_trans_12  \\\n",
       "                      mean        mean              mean            mean   \n",
       "ClusterId_hc                                                               \n",
       "0             28905.414585   15.782579        857.501013        2.599595   \n",
       "1             10231.752483    9.146603        226.382201        0.652761   \n",
       "\n",
       "             Days_since_enroll Award  \n",
       "                          mean  mean  \n",
       "ClusterId_hc                          \n",
       "0                  4625.062120   1.0  \n",
       "1                  3821.535558   0.0  "
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "airlines.groupby('ClusterId_hc').agg(['mean'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "id": "db98d339",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2022-11-02T11:17:22.785757Z",
     "start_time": "2022-11-02T11:17:22.769957Z"
    }
   },
   "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>ClusterId_hc</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",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\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",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>20856</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>5250</td>\n",
       "      <td>4</td>\n",
       "      <td>250</td>\n",
       "      <td>1</td>\n",
       "      <td>6938</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>443003</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>1753</td>\n",
       "      <td>43</td>\n",
       "      <td>3850</td>\n",
       "      <td>12</td>\n",
       "      <td>6948</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>104860</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>28426</td>\n",
       "      <td>28</td>\n",
       "      <td>1150</td>\n",
       "      <td>3</td>\n",
       "      <td>6931</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>96522</td>\n",
       "      <td>0</td>\n",
       "      <td>5</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>61105</td>\n",
       "      <td>19</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>6924</td>\n",
       "      <td>1</td>\n",
       "      <td>0</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",
       "5     97752           0          4          1          1        43300   \n",
       "8     20856           0          1          1          1         5250   \n",
       "9    443003           0          3          2          1         1753   \n",
       "10   104860           0          3          1          1        28426   \n",
       "12    96522           0          5          1          1        61105   \n",
       "\n",
       "    Bonus_trans  Flight_miles_12mo  Flight_trans_12  Days_since_enroll  Award  \\\n",
       "Id                                                                              \n",
       "5            26               2077                4               6935      1   \n",
       "8             4                250                1               6938      1   \n",
       "9            43               3850               12               6948      1   \n",
       "10           28               1150                3               6931      1   \n",
       "12           19                  0                0               6924      1   \n",
       "\n",
       "    ClusterId_hc  \n",
       "Id                \n",
       "5              0  \n",
       "8              0  \n",
       "9              0  \n",
       "10             0  \n",
       "12             0  "
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "airlines[airlines.ClusterId_hc==0].head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "id": "c4a2bfef",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2022-11-02T11:17:22.817384Z",
     "start_time": "2022-11-02T11:17:22.785757Z"
    }
   },
   "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>ClusterId_hc</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",
       "      <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",
       "      <td>1</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",
       "      <td>1</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",
       "      <td>1</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",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>16420</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>6942</td>\n",
       "      <td>0</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",
       "6     16420           0          1          1          1            0   \n",
       "\n",
       "    Bonus_trans  Flight_miles_12mo  Flight_trans_12  Days_since_enroll  Award  \\\n",
       "Id                                                                              \n",
       "1             1                  0                0               7000      0   \n",
       "2             2                  0                0               6968      0   \n",
       "3             4                  0                0               7034      0   \n",
       "4             1                  0                0               6952      0   \n",
       "6             0                  0                0               6942      0   \n",
       "\n",
       "    ClusterId_hc  \n",
       "Id                \n",
       "1              1  \n",
       "2              1  \n",
       "3              1  \n",
       "4              1  \n",
       "6              1  "
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "airlines[airlines.ClusterId_hc==1].head()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "dfae66ce",
   "metadata": {},
   "source": [
    "Here, Clustering is very precisely done because mean(Award) of clusterid 0 is 1 \n",
    "\n",
    "And mean(Award) of clusterid 1 is 0 "
   ]
  },
  {
   "cell_type": "markdown",
   "id": "8cadb19a",
   "metadata": {},
   "source": [
    "Optimum no. of clusters is 2 \n",
    "\n",
    "Cluster0 indicates the group of fliers that earn the most out of the card and are frequent fliers\n",
    "\n",
    "Cluster1 indicates the group of fliers that earn the least and are the least frequent fliers"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f64d2d49",
   "metadata": {},
   "source": [
    "# 2) KMeans Clustering "
   ]
  },
  {
   "cell_type": "markdown",
   "id": "cba7c68a",
   "metadata": {},
   "source": [
    "K-means clustering is an unsupervised algorithm. In an unsupervised algorithm, we are not interested in making predictions (since we don’t have a target/output variable). The objective is to discover interesting patterns in the data, e.g., are there any subgroups or ‘clusters’ in dataset\n",
    "\n",
    "K-means is a centroid-based algorithm, where we calculate the distances to assign a point to a cluster. In K-Means, each cluster is associated with a centroid.\n",
    "\n",
    "The main objective of the K-Means algorithm is to minimize the sum of distances between the points and their respective cluster centroid. The first step in k-means is to pick the number of clusters, k. Then keep the iterations running until one of the below satisifes:\n",
    "\n",
    "Centroids of newly formed clusters do not change\n",
    "\n",
    "Points remain in the same cluster\n",
    "\n",
    "Maximum number of iterations are reached\n",
    "\n",
    "In K Means clustering we need to first assign the K number of clusters which can be taken up using the thumb rule of \"Sqrt(n)/2\" or plotting the Elbow curve. "
   ]
  },
  {
   "cell_type": "markdown",
   "id": "50302471",
   "metadata": {},
   "source": [
    "How to find optimum number of cluster?\n",
    "\n",
    "The K-means algorithm aims to choose centroids that minimise the inertia, or within-cluster sum-of-squares criterion"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "id": "17bb6740",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2022-11-02T11:17:25.100500Z",
     "start_time": "2022-11-02T11:17:22.817384Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[43978.00000000003,\n",
       " 35402.34837621176,\n",
       " 30893.11168165174,\n",
       " 27148.665385482644,\n",
       " 23696.281421752734,\n",
       " 21086.32585789828,\n",
       " 19877.921691970343,\n",
       " 17315.915710341415,\n",
       " 15961.460885732235,\n",
       " 14725.18561100447,\n",
       " 13704.877019618045,\n",
       " 12945.293037319609,\n",
       " 12426.457990719557,\n",
       " 11974.043140119276,\n",
       " 11562.614067481605]"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "wcss = []\n",
    "\n",
    "for i in range (1, 16):\n",
    "    kmeans = KMeans(n_clusters=i, random_state=0)\n",
    "    kmeans.fit(airlines_std)\n",
    "    wcss.append(kmeans.inertia_)\n",
    "    \n",
    "wcss "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "id": "67e4be65",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2022-11-02T11:17:25.212759Z",
     "start_time": "2022-11-02T11:17:25.100500Z"
    }
   },
   "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, 16), wcss)\n",
    "plt.title('Elbow Method')\n",
    "plt.xlabel('Number of clusters')\n",
    "plt.ylabel('WCSS')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "59417e4b",
   "metadata": {},
   "source": [
    "Elbow is not clearly visible, so lets take help of silhouette score to find out optimum no. of clusters."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "id": "b7eb126a",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2022-11-02T11:17:30.697025Z",
     "start_time": "2022-11-02T11:17:25.213764Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "For cluster = 2 The silhouette score is 0.3257060145329871\n",
      "For cluster = 3 The silhouette score is 0.3111038346820003\n",
      "For cluster = 4 The silhouette score is 0.32048584058774454\n",
      "For cluster = 5 The silhouette score is 0.32375619142546985\n",
      "For cluster = 6 The silhouette score is 0.3327949137024568\n",
      "For cluster = 7 The silhouette score is 0.26577652217528075\n",
      "For cluster = 8 The silhouette score is 0.23669847546322711\n",
      "For cluster = 9 The silhouette score is 0.29892167080233395\n",
      "For cluster = 10 The silhouette score is 0.26152289188446176\n",
      "For cluster = 11 The silhouette score is 0.2677620338174949\n",
      "For cluster = 12 The silhouette score is 0.2747091046689006\n",
      "For cluster = 13 The silhouette score is 0.2691925979169026\n",
      "For cluster = 14 The silhouette score is 0.27041305382843694\n",
      "For cluster = 15 The silhouette score is 0.27265461657652895\n"
     ]
    }
   ],
   "source": [
    "for i in range (2,16):\n",
    "        clusters = KMeans(n_clusters=i, random_state= 1)\n",
    "        clusters.fit(airlines_std) \n",
    "        sil_score =sil(airlines_std , clusters.labels_ )\n",
    "        print('For cluster =', i , 'The silhouette score is', sil_score)  "
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ba62ab70",
   "metadata": {},
   "source": [
    "### Silhouette score is maximum for n_clusters = 2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "id": "da9a31e7",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2022-11-02T11:17:30.753751Z",
     "start_time": "2022-11-02T11:17:30.697025Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "KMeans(n_clusters=2, random_state=1)"
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "clusters = KMeans(n_clusters=2, random_state= 1)\n",
    "clusters.fit(airlines_std)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "id": "0976a58f",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2022-11-02T11:17:30.760287Z",
     "start_time": "2022-11-02T11:17:30.753751Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([1, 1, 1, ..., 1, 1, 1])"
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "clusters.labels_ "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "id": "47fada31",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2022-11-02T11:17:30.792129Z",
     "start_time": "2022-11-02T11:17:30.760287Z"
    }
   },
   "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>ClusterId_hc</th>\n",
       "      <th>Clusterid_kmeans</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",
       "      <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",
       "      <td>1</td>\n",
       "      <td>1</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",
       "      <td>1</td>\n",
       "      <td>1</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",
       "      <td>1</td>\n",
       "      <td>1</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",
       "      <td>1</td>\n",
       "      <td>1</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",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4017</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",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4018</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",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4019</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>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4020</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>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4021</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>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>3998 rows × 13 columns</p>\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",
       "4017    18476           0          1          1          1         8525   \n",
       "4018    64385           0          1          1          1          981   \n",
       "4019    73597           0          3          1          1        25447   \n",
       "4020    54899           0          1          1          1          500   \n",
       "4021     3016           0          1          1          1            0   \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",
       "4017            4                200                1               1403   \n",
       "4018            5                  0                0               1395   \n",
       "4019            8                  0                0               1402   \n",
       "4020            1                500                1               1401   \n",
       "4021            0                  0                0               1398   \n",
       "\n",
       "      Award  ClusterId_hc  Clusterid_kmeans  \n",
       "Id                                           \n",
       "1         0             1                 1  \n",
       "2         0             1                 1  \n",
       "3         0             1                 1  \n",
       "4         0             1                 1  \n",
       "5         1             0                 0  \n",
       "...     ...           ...               ...  \n",
       "4017      1             0                 1  \n",
       "4018      1             0                 1  \n",
       "4019      1             0                 1  \n",
       "4020      0             1                 1  \n",
       "4021      0             1                 1  \n",
       "\n",
       "[3998 rows x 13 columns]"
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "airlines['Clusterid_kmeans'] = clusters.labels_ \n",
    "airlines "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "id": "2367d5c5",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2022-11-02T11:17:30.808122Z",
     "start_time": "2022-11-02T11:17:30.792129Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1    2699\n",
       "0    1299\n",
       "Name: Clusterid_kmeans, dtype: int64"
      ]
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "airlines['Clusterid_kmeans'].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "id": "1238fead",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2022-11-02T11:17:30.839716Z",
     "start_time": "2022-11-02T11:17:30.808122Z"
    }
   },
   "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",
       "\n",
       "    .dataframe thead tr:last-of-type th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr>\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>ClusterId_hc</th>\n",
       "    </tr>\n",
       "    <tr>\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",
       "      <th>mean</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Clusterid_kmeans</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>132849.454965</td>\n",
       "      <td>258.394149</td>\n",
       "      <td>3.612009</td>\n",
       "      <td>1.010008</td>\n",
       "      <td>1.036952</td>\n",
       "      <td>41481.426482</td>\n",
       "      <td>20.393380</td>\n",
       "      <td>969.467283</td>\n",
       "      <td>2.909161</td>\n",
       "      <td>4941.665127</td>\n",
       "      <td>0.709777</td>\n",
       "      <td>0.290223</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>45112.733235</td>\n",
       "      <td>89.166358</td>\n",
       "      <td>1.312708</td>\n",
       "      <td>1.016673</td>\n",
       "      <td>1.000371</td>\n",
       "      <td>5438.261208</td>\n",
       "      <td>7.374954</td>\n",
       "      <td>215.051871</td>\n",
       "      <td>0.635050</td>\n",
       "      <td>3723.341608</td>\n",
       "      <td>0.207114</td>\n",
       "      <td>0.792886</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                        Balance  Qual_miles cc1_miles cc2_miles cc3_miles  \\\n",
       "                           mean        mean      mean      mean      mean   \n",
       "Clusterid_kmeans                                                            \n",
       "0                 132849.454965  258.394149  3.612009  1.010008  1.036952   \n",
       "1                  45112.733235   89.166358  1.312708  1.016673  1.000371   \n",
       "\n",
       "                   Bonus_miles Bonus_trans Flight_miles_12mo Flight_trans_12  \\\n",
       "                          mean        mean              mean            mean   \n",
       "Clusterid_kmeans                                                               \n",
       "0                 41481.426482   20.393380        969.467283        2.909161   \n",
       "1                  5438.261208    7.374954        215.051871        0.635050   \n",
       "\n",
       "                 Days_since_enroll     Award ClusterId_hc  \n",
       "                              mean      mean         mean  \n",
       "Clusterid_kmeans                                           \n",
       "0                      4941.665127  0.709777     0.290223  \n",
       "1                      3723.341608  0.207114     0.792886  "
      ]
     },
     "execution_count": 36,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "airlines.groupby('Clusterid_kmeans').agg(['mean'])"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "3bf27f7a",
   "metadata": {},
   "source": [
    "Optimum no. of clusters is 2\n",
    "\n",
    "Cluster0 indicates the group of fliers that earn the most out of the card and are frequent fliers\n",
    "\n",
    "Cluster1 indicates the group of fliers that earn the least and are the least frequent fliers"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a45c7a12",
   "metadata": {},
   "source": [
    "# 3)DB SCAN "
   ]
  },
  {
   "cell_type": "markdown",
   "id": "860abb3b",
   "metadata": {},
   "source": [
    "K-means, hierarchical clustering work for finding spherical-shaped clusters or convex clusters. They are suitable only for compact and well-separated clusters. Moreover, they are also severely affected by the presence of noise and outliers in the data.\n",
    "\n",
    "In real life, Clusters can be of arbitrary shape and may contain noise.\n",
    "\n",
    "Hence, to overcome these shortcomings we use DB SCAN"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "8866b96d",
   "metadata": {},
   "source": [
    "### The DBSCAN algorithm uses two parameters: "
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b99efb0d",
   "metadata": {},
   "source": [
    "minPts: The minimum number of points clustered together for a region to be considered dense.\n",
    "\n",
    "eps (ε): A distance that will be used to locate the points in the neighborhood of any point. "
   ]
  },
  {
   "cell_type": "markdown",
   "id": "5f43f846",
   "metadata": {},
   "source": [
    "### There are three types of points after the DBSCAN clustering is complete:"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "7745aa67",
   "metadata": {},
   "source": [
    "Core — This is a point that has at least m points within distance n from itself.\n",
    "\n",
    "Border — This is a point that has at least one Core point at a distance n.\n",
    "\n",
    "Noise — This is a point that is neither a Core nor a Border. And it has less than m points within distance n from itself."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d228a439",
   "metadata": {},
   "source": [
    "#### Silhouette score"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "id": "76e1acb1",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2022-11-02T11:17:30.855784Z",
     "start_time": "2022-11-02T11:17:30.839716Z"
    }
   },
   "outputs": [],
   "source": [
    "df=pd.DataFrame()\n",
    "epss=[]\n",
    "min_sample=[]\n",
    "sil_scores=[]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "id": "3a6d8e5f",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2022-11-02T11:21:23.526330Z",
     "start_time": "2022-11-02T11:17:30.855784Z"
    }
   },
   "outputs": [],
   "source": [
    "eps = [0.25,0.5,0.75,1.00,1.25,1.5,1.75,2.0,2.25,2.5,2.75,3.0]\n",
    "min_samples = [2,3,4,5,6,7,8,9,10,11,12,13,14,15,16,17,18,19,20,21,22,23,24]\n",
    "\n",
    "for i in range (len(eps)):\n",
    "    for j in range (len(min_samples)):\n",
    "        dbscan = DBSCAN(eps=eps[i] , min_samples=min_samples[j])\n",
    "        dbscan.fit(airlines_std)\n",
    "        sil_score=sil(airlines_std, dbscan.labels_)\n",
    "        epss.append(eps[i])\n",
    "        min_sample.append(min_samples[j])\n",
    "        sil_scores.append(sil_score) "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "id": "4f2e0dd3",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2022-11-02T11:21:23.542166Z",
     "start_time": "2022-11-02T11:21:23.526330Z"
    }
   },
   "outputs": [],
   "source": [
    "df['eps']=epss\n",
    "df['min_sample']=min_sample\n",
    "df['sil_scores']=sil_scores"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "id": "dc93859b",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2022-11-02T11:21:23.558035Z",
     "start_time": "2022-11-02T11:21:23.543676Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.6454683428134459"
      ]
     },
     "execution_count": 40,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['sil_scores'].max() "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "id": "622158f9",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2022-11-02T11:21:23.573966Z",
     "start_time": "2022-11-02T11:21:23.558035Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "252"
      ]
     },
     "execution_count": 41,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['sil_scores'].idxmax()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "id": "1468a3fc",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2022-11-02T11:21:23.589962Z",
     "start_time": "2022-11-02T11:21:23.573966Z"
    }
   },
   "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>eps</th>\n",
       "      <th>min_sample</th>\n",
       "      <th>sil_scores</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>252</th>\n",
       "      <td>2.75</td>\n",
       "      <td>24</td>\n",
       "      <td>0.645468</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      eps  min_sample  sil_scores\n",
       "252  2.75          24    0.645468"
      ]
     },
     "execution_count": 42,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.iloc[252:253, :]"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ae93a69a",
   "metadata": {},
   "source": [
    "### Silhouette score is maximum for eps = 2.75 and min_sample = 24"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "id": "9e7b7ce4",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2022-11-02T11:21:24.487409Z",
     "start_time": "2022-11-02T11:21:23.593472Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "DBSCAN(eps=2.75, min_samples=24)"
      ]
     },
     "execution_count": 43,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dbscan = DBSCAN(eps= 2.75, min_samples=24)\n",
    "dbscan.fit(airlines_std) "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "id": "c2846167",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2022-11-02T11:21:24.503705Z",
     "start_time": "2022-11-02T11:21:24.487409Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([0, 0, 0, ..., 0, 0, 0], dtype=int64)"
      ]
     },
     "execution_count": 44,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Noisy samples are given the label -1.\n",
    "dbscan.labels_ "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "id": "73d4209d",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2022-11-02T11:21:24.519683Z",
     "start_time": "2022-11-02T11:21:24.503705Z"
    }
   },
   "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>ClusterId_hc</th>\n",
       "      <th>Clusterid_kmeans</th>\n",
       "      <th>Noise</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",
       "      <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",
       "      <td>1</td>\n",
       "      <td>1</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",
       "      <td>1</td>\n",
       "      <td>1</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",
       "      <td>1</td>\n",
       "      <td>1</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",
       "      <td>1</td>\n",
       "      <td>1</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",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</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  Award  \\\n",
       "Id                                                                              \n",
       "1             1                  0                0               7000      0   \n",
       "2             2                  0                0               6968      0   \n",
       "3             4                  0                0               7034      0   \n",
       "4             1                  0                0               6952      0   \n",
       "5            26               2077                4               6935      1   \n",
       "\n",
       "    ClusterId_hc  Clusterid_kmeans  Noise  \n",
       "Id                                         \n",
       "1              1                 1      0  \n",
       "2              1                 1      0  \n",
       "3              1                 1      0  \n",
       "4              1                 1      0  \n",
       "5              0                 0      0  "
      ]
     },
     "execution_count": 45,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "airlines['Noise'] = dbscan.labels_ \n",
    "airlines.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "id": "1b717ae7",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2022-11-02T11:21:24.535747Z",
     "start_time": "2022-11-02T11:21:24.519683Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       " 0    3814\n",
       "-1     184\n",
       "Name: Noise, dtype: int64"
      ]
     },
     "execution_count": 46,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "airlines.Noise.value_counts()\n",
    "#184 data points are categorised as noise"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "id": "1f0b1992",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2022-11-02T11:21:24.567652Z",
     "start_time": "2022-11-02T11:21:24.535747Z"
    }
   },
   "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>ClusterId_hc</th>\n",
       "      <th>Clusterid_kmeans</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Noise</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>-1</th>\n",
       "      <td>220448.108696</td>\n",
       "      <td>1568.804348</td>\n",
       "      <td>2.418478</td>\n",
       "      <td>1.315217</td>\n",
       "      <td>1.266304</td>\n",
       "      <td>45862.885870</td>\n",
       "      <td>24.472826</td>\n",
       "      <td>3278.190217</td>\n",
       "      <td>9.228261</td>\n",
       "      <td>4669.641304</td>\n",
       "      <td>0.679348</td>\n",
       "      <td>0.320652</td>\n",
       "      <td>0.250000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>66535.987677</td>\n",
       "      <td>75.420556</td>\n",
       "      <td>2.042475</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>15763.888044</td>\n",
       "      <td>10.984006</td>\n",
       "      <td>324.220241</td>\n",
       "      <td>0.995018</td>\n",
       "      <td>4092.634504</td>\n",
       "      <td>0.355532</td>\n",
       "      <td>0.644468</td>\n",
       "      <td>0.695595</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "             Balance   Qual_miles  cc1_miles  cc2_miles  cc3_miles  \\\n",
       "Noise                                                                \n",
       "-1     220448.108696  1568.804348   2.418478   1.315217   1.266304   \n",
       " 0      66535.987677    75.420556   2.042475   1.000000   1.000000   \n",
       "\n",
       "        Bonus_miles  Bonus_trans  Flight_miles_12mo  Flight_trans_12  \\\n",
       "Noise                                                                  \n",
       "-1     45862.885870    24.472826        3278.190217         9.228261   \n",
       " 0     15763.888044    10.984006         324.220241         0.995018   \n",
       "\n",
       "       Days_since_enroll     Award  ClusterId_hc  Clusterid_kmeans  \n",
       "Noise                                                               \n",
       "-1           4669.641304  0.679348      0.320652          0.250000  \n",
       " 0           4092.634504  0.355532      0.644468          0.695595  "
      ]
     },
     "execution_count": 47,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df=airlines.groupby('Noise').agg('mean')\n",
    "df "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "id": "272abe6e",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2022-11-02T11:21:24.836830Z",
     "start_time": "2022-11-02T11:21:24.567652Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='Noise', ylabel='cc2_miles'>"
      ]
     },
     "execution_count": 48,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 1440x576 with 4 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(20,8))\n",
    "\n",
    "plt.subplot(2,2,1)\n",
    "sns.barplot(x=df.index , y=df.Balance)\n",
    "\n",
    "plt.subplot(2,2,2)\n",
    "sns.barplot(x=df.index , y=df.Qual_miles)\n",
    "\n",
    "plt.subplot(2,2,3)\n",
    "sns.barplot(x=df.index , y=df.cc1_miles)\n",
    "\n",
    "plt.subplot(2,2,4)\n",
    "sns.barplot(x=df.index , y=df.cc2_miles)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "id": "5106467e",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2022-11-02T11:21:25.153824Z",
     "start_time": "2022-11-02T11:21:24.836830Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='Noise', ylabel='Flight_miles_12mo'>"
      ]
     },
     "execution_count": 49,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 1440x576 with 4 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(20,8))\n",
    "\n",
    "plt.subplot(2,2,1)\n",
    "sns.barplot(x=df.index , y=df.cc3_miles)\n",
    "\n",
    "plt.subplot(2,2,2)\n",
    "sns.barplot(x=df.index , y=df.Bonus_miles)\n",
    "\n",
    "plt.subplot(2,2,3)\n",
    "sns.barplot(x=df.index , y=df.Bonus_trans)\n",
    "\n",
    "plt.subplot(2,2,4)\n",
    "sns.barplot(x=df.index , y=df.Flight_miles_12mo)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "id": "77339a19",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2022-11-02T11:21:25.413833Z",
     "start_time": "2022-11-02T11:21:25.153824Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='Noise', ylabel='Award'>"
      ]
     },
     "execution_count": 50,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAABI8AAAHgCAYAAAAsb00qAAAAOXRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjUuMSwgaHR0cHM6Ly9tYXRwbG90bGliLm9yZy/YYfK9AAAACXBIWXMAAAsTAAALEwEAmpwYAAAxJUlEQVR4nO3dfbxlZ1kf/N/FhEB4iYAZFDOJCTY+GBAEhhDFF15KG/Al+IaDhESkzzQWTKAFDW0FXx6qFlEEQuKUBgiiKS0oIwZTG0WkgmQCaSCJ0WlQMiaaAaQJ4JOQ5OofZ41uzux15kzY++xzZr7fz2d/9lr3utda1/njTFZ+577vVd0dAAAAAJjmXosuAAAAAID1S3gEAAAAwCjhEQAAAACjhEcAAAAAjBIeAQAAADBKeAQAAADAqCMWXcDBOuaYY/qEE05YdBkAwJxceeWVn+ruzYuugy/lGQwADm0rPYNtuPDohBNOyK5duxZdBgAwJ1X1V4uugf15BgOAQ9tKz2CmrQEAAAAwSngEAAAAwCjhEQAAAACjhEcAAAAAjBIeAQAAADBKeAQAAADAKOERAAAAAKOOWHQB69XjX3bxokuAde/KV5+56BIAOIR4/oLV8QwGrDUjjwAAAAAYJTwCAAAAYJTwCAAAAIBRwiMAAAAARgmPAAAAABglPAIAAABglPAIAAAAgFHCIwAAAABGCY8AAAAAGCU8AgAAAGCU8AgAAACAUcIjAAAAAEYJjwAAAAAYJTwCAAAAYJTwCAAAAIBRwiMAAAAARgmPAAAAABglPAIAAABglPAIAAAAgFHCIwAAAABGCY8AAAAAGLXw8KiqXlJV11TVx6vqN6vqvouuCQAAAIAlCw2PqurYJOck2drdj0qyKcm2RdYEAAAAwD86YtEFZKmGo6rqi0nul+SmBdcDALBuVdWmJLuS/HV3f1dVPSTJf0lyQpK/TPLs7v67oe/Lk7wgyV1Jzunuy4b2xyd5S5Kjklya5Nzu7rX9SQCST/7sNy66BNgQjn/FxxZ6/4WOPOruv07yS0k+meTmJP+nu//78n5Vtb2qdlXVrr179651mQAA68m5Sa6b2D8vyeXdfVKSy4f9VNXJWRrR/cgkpyV54xA8JckFSbYnOWn4nLY2pQMAG9Gip609OMnpSU5M8jVJ7l9VZyzv1907untrd2/dvHnzWpcJALAuVNWWJN+Z5E0Tzacneeuw/dYkz5pov6S7b+/uTyTZneSUqnpYkqO7+4PDaKOLJ84BANjPohfM/qdJPtHde7v7i0neleRbFlwTAMB69dokP5Hk7om2r+rum5Nk+H7o0H5skhsn+u0Z2o4dtpe378fobwAgWXx49Mkkp1bV/aqqkjwtXzoMGwCAJFX1XUlu6e4rV3vKlLZeoX3/RqO/AYAseMHs7v7TqvpvST6S5M4kH02yY5E1AQCsU09K8j1V9cwk901ydFX9epK/raqHdffNw5S0W4b+e5IcN3H+liy9mGTPsL28HQBgqkWPPEp3v7K7H9Hdj+ru53X37YuuCQBgvenul3f3lu4+IUsLYf9Bd5+RZGeSs4ZuZyV597C9M8m2qrpPVZ2YpYWxPzxMbbutqk4dRn6fOXEOAMB+FjryCACAL9svJHlHVb0gS0sC/GCSdPc1VfWOJNdmaYT3C7v7ruGcH0vyliRHJXnv8AEAmEp4BACwwXT3+5K8b9j+dJbWjZzW71VJXjWlfVeSR82vQgDgULLwaWsAAAAArF/CIwAAAABGCY8AAAAAGCU8AgAAAGCU8AgAAACAUcIjAAAAAEYJjwAAAAAYJTwCAAAAYJTwCAAAAIBRwiMAAAAARgmPAAAAABglPAIAAABglPAIAAAAgFHCIwAAAABGCY8AAAAAGHXEogsAADhcVNX3rXS8u9+1VrUAAKyW8AgAYO189wrHOonwCABYd4RHAABrpLufv+gaAAAOlvAIAGCNVNW/Xul4d//yWtUCALBawiMAgLXzwEUXAABwsIRHAABrpLt/ZtE1AAAcrHvN4iJVtamq/mVV/VxVPWnZsX8/i3sAABwqqmpLVf1WVd1SVX9bVe+sqi2LrgsAYJqZhEdJfi3JdyT5dJLXVdXkfP0VX0kLAHAYenOSnUm+JsmxSX5naAMAWHdmFR6d0t0/3N2vTfLEJA+oqndV1X2S1IzuAQBwqNjc3W/u7juHz1uSbF50UQAA08wqPDpy38bwALQ9yVVJ/iDJA2Z0DwCAQ8WnquqMYer/pqo6I0sjuAEA1p1ZhUe7quq0yYbu/tksDb8+YUb3AAA4VPxokmcn+ZskNyf5gaENAGDdmcnb1rr7jJH2NyV50yzuAQBwKKiqTUn+Q3d/z6JrAQBYjVmNPBpVVU+f9z0AADaK7r4ryeaqOvKAnQEA1oGZjDw6gP+c5Pg1uA8AwEbxl0n+Z1XtTPL5fY3d/cujZwAALMhMwqPhwWfqoSRfuYrzH5Sl6W2PStJJfrS7PziL2gAA1qGbhs+9kjxwwbUAAKxoViOPvi3JGUk+t6y9kpyyivN/NcnvdfcPDEO47zejugAA1p3u/plF1wAAsFqzCo8+lOQL3f1Hyw9U1fUrnVhVRyf59iQ/kiTdfUeSO2ZUFwDAulNVX5/kpVl6K+0/PI9191NXOOe+Sd6f5D7DOf+tu19ZVQ9J8l+Ga/1lkmd3998N57w8yQuS3JXknO6+bGh/fJK3JDkqyaVJzu3unuXPCAAcOmb1trVnrHDs2w9w+sOT7E3y5qp6TJIrs/QA8/mVTwMA2LD+a5ILszRt/65VnnN7kqd29+eq6t5JPlBV703yfUku7+5fqKrzkpyX5Cer6uQk25I8MsnXJPkfVfX1w4LdFyTZnqU/AF6a5LQk753djwcAHErm/ra1SVU1bR2jI5I8LskF3f3YLC0aed6y87ZX1a6q2rV37941qBQAYK7u7O4LuvvD3X3lvs9KJ/SSfUsE3Hv4dJLTk7x1aH9rkmcN26cnuaS7b+/uTyTZneSUqnpYkqO7+4PDaKOLJ84BANjPmoZHSe47pW1Pkj3d/afD/n/LUpj0D7p7R3dv7e6tmzdvnneNAADz9jtV9a+q6mFV9ZB9nwOdVFWbquqqJLck+f3h+emruvvmJBm+Hzp0PzbJjROn7xnajh22l7cDAEw1qzWPVmu/ufTd/TdVdWNV/T/dfX2SpyW5do3rAgBYS2cN3y+baOssTecfNUw5+6bhTbW/VVWPWqF7TbvECu37X6Bqe5amt+X4449fqTQA4BC21uHRmB9P8vbhTWs3JHn+gusBAJib7j7xyzz/s1X1viytVfS3VfWw7r55mJJ2y9BtT5LjJk7bkuSmoX3LlPZp99mRZEeSbN261YLaAHCYWutpa9P+0pXuvmqYlvbo7n7WvjeEAAAciqrqflX176tqx7B/UlV91wHO2TyMOEpVHZXknyb5syQ7848jmc5K8u5he2eSbVV1n6o6MclJST48TG27rapOrapKcubEOQAA+5npyKOqun+Sv+/uu4dX0D4iyXu7+4tDl+fN8n4AABvUm7P0htlvGfb3ZOkNbO9Z4ZyHJXlrVW3K0h8A39Hd7xleSPKOqnpBkk8m+cEk6e5rquodWVoO4M4kLxymvSXJjyV5S5KjsvSWNW9aAwBGzXra2vuTfFtVPTjJ5Ul2JfmhJM9Nku7++IzvBwCwEX1dd/9QVT0nSbr774dRQKO6++okj53S/uksrRk57ZxXJXnVlPZdSVZaLwkA4B/MetpadfcXknxfktd39/cmOXnG9wAA2OjuGKaedZJU1dcluX2xJQEATDfrkUdVVd+cpZFGL5jTPQAANrpXJvm9JMdV1duTPCnJjyy0IgCAEbMOds5N8vIkvzXMs394kj+c8T0AADa07v79qvpIklOz9EKRc7v7U/uOV9Uju/uahRUIADBhpuFRd78/S+se7du/Ick5s7wHAMChYFir6HdHDr8tyePWsBwAgFGzftva1yd5aZITJq/d3U+d5X0AAA5xKy6eDQCwlmY9be2/JrkwyZuS3HWAvgAATNeLLgAAYJ9Zh0d3dvcFM74mAAAAAAtyrxlf73eq6l9V1cOq6iH7PjO+BwDAoe6ORRcAALDPrEcenTV8v2yirZM8fMb3AQDYsKqqkjw3ycO7+2er6vgkX93dH06S7j51oQUCAEyY9dvWTpzl9QAADlFvTHJ3kqcm+dkktyV5Z5InLLIoAIBpZj3yKFX1qCQnJ7nvvrbuvnjW9wEA2MCe2N2Pq6qPJkl3/11VHbnoogAApplpeFRVr0zy5CyFR5cmeUaSDyQRHgEA/KMvVtWmDG9Vq6rNWRqJBACw7sx6wewfSPK0JH/T3c9P8pgk95nxPQAANrrXJfmtJA+tqldl6Y9t/2GxJQEATDfraWt/3913V9WdVXV0kltisWwAgC/R3W+vqiuz9Ee3SvKs7r5uwWUBAEw16/BoV1U9KMl/SnJlks8l+fCM7wEAsKFV1alJrunu84f9B1bVE7v7TxdcGgDAfmYWHg2vnP357v5skgur6veSHN3dV8/qHgAAh4gLkjxuYv/zU9oAANaFma151N2d5Lcn9v9ScAQAMFUNz05Jku6+O3N4Cy4AwCzMesHsD1XVE2Z8TQCAQ80NVXVOVd17+Jyb5IZFFwUAMM2sw6OnJPlgVf3vqrq6qj5WVUYfAQB8qbOTfEuSv06yJ8kTk2xfaEUAACNmPTz6GTO+HgDAIae7b0mybdF1AACsxqzDo/+vu5832VBVb0vyvJH+AACHnaranOT/TXJCJp7HuvtHF1UTAMCYWYdHj5zcqapNSR4/43sAAGx0707yx0n+R5K7FlwLAMCKZhIeVdXLk/zbJEdV1a37mpPckWTHLO4BAHAIuV93/+SiiwAAWI2ZLJjd3T/f3Q9M8uruPnr4PLC7v7K7X76vX1U9coXLAAAcLt5TVc9cdBEAAKsx07etTQZFI942y/sBAGxQ52YpQPr7qrq1qm6bGL0NALCuzHrNowOpNb4fAMC6M4zYBgDYENY6POo1vh8AwLpRVY/o7j+rqsdNO97dH1nrmgAADmStwyMAgMPZv06yPclrphzrJE9d23IAAA5srcOjO9b4fgAA60Z3bx++n7LoWgAAVmumC2ZX1eUrtXX3qbO8HwDARlRVP1hVDxy2/31VvauqHrvougAApplJeFRV962qhyQ5pqoeXFUPGT4nJPmaVZy/qao+WlXvmUU9AADr3E91921V9a1J/nmStya5cME1AQBMNauRR/8yyZVJHjF87/u8O8n5qzj/3CTXzagWAID17q7h+zuTXNDd705y5EonVNVxVfWHVXVdVV1TVecO7Q+pqt+vqr8Yvh88cc7Lq2p3VV1fVf98ov3xVfWx4djrqsobcQGAUTMJj7r7V7v7xCQv7e6Hd/eJw+cx3f2Glc6tqi1ZenB60yxqAQDYAP66qn4tybOTXFpV98mBn8vuTPJvuvsbkpya5IVVdXKS85Jc3t0nJbl82M9wbFuSRyY5Lckbq2rTcK0LsrRw90nD57RZ/nAAwKFlpgtmd/frq+pbkpwwee3uvniF016b5CeSPHCWtQAArGPPzlJg80vd/dmqeliSl+07WFUP7u6/mzyhu29OcvOwfVtVXZfk2CSnJ3ny0O2tSd6X5CeH9ku6+/Ykn6iq3UlOqaq/THJ0d39wuNfFSZ6V5L1z+UkBgA1vpuFRVb0tydcluSr/OBy7k0wNj6rqu5Lc0t1XVtWTV7ju9iz9dSzHH3/87AoGAFiA7v5CkndN7P9DMDS4PMnjxs4f1pV8bJI/TfJVw/np7pur6qFDt2OTfGjitD1D2xeH7eXtAABTzTQ8SrI1ycnd3avs/6Qk31NVz0xy3yRHV9Wvd/cZk526e0eSHUmydevW1V4bAGCjGl2DqKoekOSdSV7c3beusFzRtAO9Qvu0e/kDHgAwswWz9/l4kq9ebefufnl3b+nuE7I0J/8PlgdHAACHobEw595ZCo7e3t37Ri797TDtLcP3LUP7niTHTZy+JclNQ/uWKe37F9G9o7u3dvfWzZs339OfBQDY4GYSHlXV71TVziTHJLm2qi6rqp37PrO4BwDA4Wx4I9p/TnJdd//yxKGdSc4ats/K0ttu97Vvq6r7VNWJWVoY+8PDFLfbqurU4ZpnTpwDALCfWU1b+6Uv9wLd/b4sLfAIAHC4mza17ElJnpfkY1V11dD2b5P8QpJ3VNULknwyyQ8mSXdfU1XvSHJtlt7U9sLu3rcm5Y8leUuSo7K0ULbFsgGAUTMJj7r7j2ZxHQCAw0FVfV2SPd19+/DSkEcnubi7Pzt0edryc7r7AxlfC2m//sM5r0ryqintu5I86qALBwAOSzNd86iqbquqW5d9bqyq36qqh8/yXgAAG9g7k9xVVf8kS1PRTkzyG/sOdvdnFlUYAMBys37b2i9nacHF38jSX8a2ZWkB7euTXJTkyTO+HwDARnR3d99ZVd+b5LXd/fqq+uiiiwIAmGbWb1s7rbt/rbtv6+5bu3tHkmd2939J8uAZ3wsAYKP6YlU9J0sLXL9naLv3AusBABg16/Do7qp6dlXda/g8e+LY1FfOAgAchp6f5JuTvKq7PzG8De3XF1wTAMBUs5629twkv5rkjVkKiz6U5IyqOirJi2Z8LwCAjerhSV7c3XcnSXd/IktvTQMAWHdmGh519w1Jvnvk8AdmeS8AgA1sW5Jfrap3Jnlzd1+36IIAAMbMJDyqqp/o7v9YVa/PlOlp3X3OLO4DAHAo6O4zquroJM9J8uaq6iRvTvKb3X3bYqsDAPhSsxp5tO+vZbtmdD0AgENad986jDw6KsmLk3xvkpdV1eu6+/ULLQ4AYMJMwqPu/p3h+62zuB4AwKGsqr47yY8m+bokb0tySnffUlX3y9If5YRHAMC6Matpa7+TFd6m1t3fM4v7AAAcIn4wya909/snG7v7C1X1owuqCQBgqllNW/ulGV0HAOCQ191nrnDs8rWsBQDgQGYVHn2iuz85o2sBABzSqurULE1N+4YkRybZlOTz3X30QgsDAJjiXjO6zm/v2xgWfgQAYNwbsvSmtb/I0oLZ/yLWOQIA1qlZjTyqie2Hz+iaAACHrO7eXVWbuvuuJG+uqj9ZdE0AANPMKjzqkW0AAPb3hao6MslVVfUfk9yc5P4LrgkAYKpZTVt7TFXdWlW3JXn0sH1rVd1WVbfO6B4AAIeK52XpOexFST6f5Lgk37/QigAARsxk5FF3b5rFdQAADgfd/VdVtXnY/plF1wMAsJJZjTwCAOAAaslPV9WnkvxZkj+vqr1V9YpF1wYAMEZ4BACwdl6c5ElJntDdX9ndD07yxCRPqqqXLLQyAIARwiMAgLVzZpLndPcn9jV09w1JzhiOAQCsO8IjAIC1c+/u/tTyxu7em+TeC6gHAOCAhEcAAGvnjnt4DABgYWbytjUAAFblMVV165T2SnLftS4GAGA1hEcAAGukuzctugYAgINl2hoAAAAAo4RHAAAAAIwSHgEAAAAwSngEAAAAwCjhEQAAAACjhEcAAAAAjBIeAQAAADBq4eFRVR1XVX9YVddV1TVVde6iawIAAABgycLDoyR3Jvk33f0NSU5N8sKqOnnBNQEArDtVdVFV3VJVH59oe0hV/X5V/cXw/eCJYy+vqt1VdX1V/fOJ9sdX1ceGY6+rqlrrnwUA2DgWHh51983d/ZFh+7Yk1yU5drFVAQCsS29JctqytvOSXN7dJyW5fNjP8Me4bUkeOZzzxqraNJxzQZLtSU4aPsuvCQDwDxYeHk2qqhOSPDbJny64FACAdae735/kM8uaT0/y1mH7rUmeNdF+SXff3t2fSLI7ySlV9bAkR3f3B7u7k1w8cQ4AwH7WTXhUVQ9I8s4kL+7uW5cd215Vu6pq1969exdTIADA+vRV3X1zsjSiO8lDh/Zjk9w40W/P0HbssL28fT+ewQCAZJ2ER1V17ywFR2/v7nctP97dO7p7a3dv3bx589oXCACw8Uxbx6hXaN+/0TMYAJB1EB4NCzT+5yTXdfcvL7oeAIAN5m+HqWgZvm8Z2vckOW6i35YkNw3tW6a0AwBMtfDwKMmTkjwvyVOr6qrh88xFFwUAsEHsTHLWsH1WkndPtG+rqvtU1YlZWhj7w8PUttuq6tThj3hnTpwDALCfIxZdQHd/INOHTwMAMKGqfjPJk5McU1V7krwyyS8keUdVvSDJJ5P8YJJ09zVV9Y4k1ya5M8kLu/uu4VI/lqU3tx2V5L3DBwBgqoWHRwAArE53P2fk0NNG+r8qyaumtO9K8qgZlgYAHMLWw7Q1AAAAANYp4REAAAAAo4RHAAAAAIwSHgEAAAAwSngEAAAAwCjhEQAAAACjhEcAAAAAjBIeAQAAADBKeAQAAADAKOERAAAAAKOERwAAAACMEh4BAAAAMEp4BAAAAMAo4REAAAAAo45YdAEA68Enf/YbF10CbAjHv+Jjiy4BAIA1ZuQRAAAAAKOERwAAAACMEh4BAAAAMEp4BAAAAMAo4REAAAAAo4RHAAAAAIwSHgEAAAAwSngEAAAAwCjhEQAAAACjhEcAAAAAjBIeAQAAADBKeAQAAADAKOERAAAAAKOERwAAAACMEh4BAAAAMEp4BAAAAMCohYdHVXVaVV1fVbur6rxF1wMAcDjwDAYArNZCw6Oq2pTk/CTPSHJykudU1cmLrAkA4FDnGQwAOBiLHnl0SpLd3X1Dd9+R5JIkpy+4JgCAQ51nMABg1RYdHh2b5MaJ/T1DGwAA8+MZDABYtSMWfP+a0tb7daranmT7sPu5qrp+rlWxXh2T5FOLLoJ/VL901qJL4NDn9369eeW0/3TP3NeuxU0Oc57BOBj+LV5nPIMxZ37n16MFP4MtOjzak+S4if0tSW5a3qm7dyTZsVZFsT5V1a7u3rroOoC14/ce5sYzGKvm32I4vPidZ5pFT1u7IslJVXViVR2ZZFuSnQuuCQDgUOcZDABYtYWOPOruO6vqRUkuS7IpyUXdfc0iawIAONR5BgMADsaip62luy9Ncumi62BDMGweDj9+72FOPINxEPxbDIcXv/Psp7r3WxsRAAAAAJIsfs0jAAAAANYx4REbQlU9oqo+WFW3V9VLF10PMF9VdVpVXV9Vu6vqvEXXA3A48vwFhx/PYIxZ+JpHsEqfSXJOkmctuA5gzqpqU5Lzkzw9S68Tv6Kqdnb3tYutDOCw4/kLDiOewViJkUdsCN19S3dfkeSLi64FmLtTkuzu7hu6+44klyQ5fcE1ARx2PH/BYcczGKOERwCsN8cmuXFif8/QBgDA/HgGY5TwCID1pqa0eTUoAMB8eQZjlPCIdauqXlhVVw2fr1l0PcCa2ZPkuIn9LUluWlAtAIcVz19wWPMMxijhEetWd5/f3d80fPyjBYePK5KcVFUnVtWRSbYl2bngmgAOC56/4LDmGYxR1W0UGutfVX11kl1Jjk5yd5LPJTm5u29daGHAXFTVM5O8NsmmJBd196sWWxHA4cfzFxx+PIMxRngEAAAAwCjT1gAAAAAYJTwCAAAAYJTwCAAAAIBRwiMAAAAARgmPAAAAABglPALWVFV1Vb1mYv+lVfXTBzjn7Ko6c+7FAQAcojyDAV8O4RGw1m5P8n1VdcxqT+juC7v74jnWBABwqPMMBtxjwiNgrd2ZZEeSlyw/UFVfW1WXV9XVw/fxQ/tPV9VLh+1zquraoc8lQ9v9q+qiqrqiqj5aVaev5Q8EALABeAYD7jHhEbAI5yd5blV9xbL2NyS5uLsfneTtSV435dzzkjx26HP20PbvkvxBdz8hyVOSvLqq7j+f0gEANizPYMA9IjwC1lx335rk4iTnLDv0zUl+Y9h+W5JvnXL61UneXlVnZOkvaEnyz5KcV1VXJXlfkvsmOX62VQMAbGyewYB76ohFFwActl6b5CNJ3rxCn57S9p1Jvj3J9yT5qap6ZJJK8v3dff2siwQAOMS8Np7BgINk5BGwEN39mSTvSPKCieY/SbJt2H5ukg9MnlNV90pyXHf/YZKfSPKgJA9IclmSH6+qGvo9dq7FAwBsUJ7BgHtCeAQs0muSTL7x45wkz6+qq5M8L8m5y/pvSvLrVfWxJB9N8ivd/dkkP5fk3kmurqqPD/sAAEznGQw4KNU9bUQiAAAAABh5BAAAAMAKhEcAAAAAjBIeAQAAADBKeAQAAADAKOERAAAAAKOERwAAAACMEh4BAAAAMEp4BAAAAMAo4REAAAAAo46Y58Wr6rQkv5pkU5I3dfcvLDv+siTPnajlG5Js7u7PjF3zmGOO6RNOOGE+BQMAC3fllVd+qrs3L7oOAACWVHfP58JVm5L8eZKnJ9mT5Iokz+nua0f6f3eSl3T3U1e67tatW3vXrl2zLhcAWCeq6sru3rroOgAAWDLPaWunJNnd3Td09x1JLkly+gr9n5PkN+dYDwAAAAAHaZ7h0bFJbpzY3zO07aeq7pfktCTvnGM9AAAAABykeYZHNaVtbI7cdyf5n2NrHVXV9qraVVW79u7dO7MCAQAAAFjZPMOjPUmOm9jfkuSmkb7bssKUte7e0d1bu3vr5s3WzwQAAABYK/MMj65IclJVnVhVR2YpINq5vFNVfUWS70jy7jnWAgAAAMA9cMS8Ltzdd1bVi5JclmRTkou6+5qqOns4fuHQ9XuT/Pfu/vy8agEAAADgnqnusWWI1qetW7f2rl27Fl0GADAnVXVld29ddB0AACyZ57Q1AAAAADa4uU1b2+ge/7KLF10CrHtXvvrMRZcAAADAnBl5BAAAAMAo4REAAAAAo4RHAAAAAIwSHgEAAAAwSngEAAAAwCjhEQAAAACjhEcAAAAAjBIeAQAAADBKeAQAAADAKOERAAAAAKOERwAAAACMEh4BAAAAMEp4BAAAAMAo4REAAAAAo4RHAAAAAIwSHgEAAAAwSngEAAAAwKi5hkdVdVpVXV9Vu6vqvJE+T66qq6rqmqr6o3nWAwAAAMDBOWJeF66qTUnOT/L0JHuSXFFVO7v72ok+D0ryxiSndfcnq+qh86oHAAAAgIM3z5FHpyTZ3d03dPcdSS5JcvqyPj+c5F3d/ckk6e5b5lgPAAAAAAdpnuHRsUlunNjfM7RN+vokD66q91XVlVV15hzrAQAAAOAgzW3aWpKa0tZT7v/4JE9LclSSD1bVh7r7z7/kQlXbk2xPkuOPP34OpQIAAAAwzTxHHu1JctzE/pYkN03p83vd/fnu/lSS9yd5zPILdfeO7t7a3Vs3b948t4IBAAAA+FLzDI+uSHJSVZ1YVUcm2ZZk57I+707ybVV1RFXdL8kTk1w3x5oAAAAAOAhzm7bW3XdW1YuSXJZkU5KLuvuaqjp7OH5hd19XVb+X5Ookdyd5U3d/fF41AQAAAHBw5rnmUbr70iSXLmu7cNn+q5O8ep51AAAAAHDPzHPaGgAAAAAbnPAIAAAAgFHCIwAAAABGCY8AAAAAGCU8AgAAAGCU8AgAAACAUcIjAAAAAEYJjwAAAAAYJTwCAAAAYJTwCAAAAIBRwiMAAAAARgmPAAAAABglPAIAAABglPAIAAAAgFHCIwAAAABGCY8AAAAAGCU8AgAAAGCU8AgAAACAUcIjAAAAAEbNNTyqqtOq6vqq2l1V5005/uSq+j9VddXwecU86wEAAADg4BwxrwtX1aYk5yd5epI9Sa6oqp3dfe2yrn/c3d81rzoAAAAAuOfmOfLolCS7u/uG7r4jySVJTp/j/QAAAACYsXmGR8cmuXFif8/Qttw3V9X/qqr3VtUjp12oqrZX1a6q2rV379551AoAAADAFPMMj2pKWy/b/0iSr+3uxyR5fZLfnnah7t7R3Vu7e+vmzZtnWyUAAAAAo+YZHu1JctzE/pYkN0126O5bu/tzw/alSe5dVcfMsSYAAAAADsI8w6MrkpxUVSdW1ZFJtiXZOdmhqr66qmrYPmWo59NzrAkAAACAgzC3t611951V9aIklyXZlOSi7r6mqs4ejl+Y5AeS/FhV3Znk75Ns6+7lU9sAAAAAWJC5hUfJP0xFu3RZ24UT229I8oZ51gAAAADAPTfPaWsAAAAAbHDCIwAAAABGCY8AAAAAGCU8AgAAAGCU8AgAAACAUcIjAAAAAEYJjwAAAAAYJTwCAAAAYJTwCAAAAIBRwiMAAAAARgmPAAAAABglPAIAAABglPAIAAAAgFHCIwAAAABGCY8AAAAAGCU8AgAAAGCU8AgAAACAUcIjAAAAAEbNNTyqqtOq6vqq2l1V563Q7wlVdVdV/cA86wEAAADg4MwtPKqqTUnOT/KMJCcneU5VnTzS7xeTXDavWgAAAAC4Z+Y58uiUJLu7+4buviPJJUlOn9Lvx5O8M8ktc6wFAAAAgHvgiLEDVfW4lU7s7o8c4NrHJrlxYn9Pkicuu8exSb43yVOTPOEA1wMAAABgjY2GR0leM3zfN8nWJP8rSSV5dJI/TfKtB7h2TWnrZfuvTfKT3X1X1bTuw4WqtifZniTHH3/8AW4LAAAAwKyMTlvr7qd091OS/FWSx3X31u5+fJLHJtm9imvvSXLcxP6WJDct67M1ySVV9ZdJfiDJG6vqWVNq2THcf+vmzZtXcWsAAAAAZmGlkUf7PKK7P7Zvp7s/XlXftIrzrkhyUlWdmOSvk2xL8sOTHbr7xH3bVfWWJO/p7t9exbUBAAAAWAOrCY/+rKrelOTXszTt7Iwk1x3opO6+s6pelKW3qG1KclF3X1NVZw/HL7znZQMAAACwFlYTHv1Ikh9Lcu6w//4kF6zm4t19aZJLl7VNDY26+0dWc00AAAAA1s6K4VFVbcrSVLJ/muRX1qYkAAAAANaL0QWzk6S770ryhar6ijWqBwAAAIB1ZDXT1v7/JB+rqt9P8vl9jd19ztyqAgAAAGBdWE149LvDBwAAAIDDzAHDo+5+61oUAgAAAMD6c8DwqKpOSvLzSU5Oct997d398DnWBQAAAMA6sOKC2YM3J7kgyZ1JnpLk4iRvm2dRAAAAAKwPq1nz6Kjuvryqqrv/KslPV9UfJ3nlnGsDWDOf/NlvXHQJsCEc/4qPLboEAADW2KretlZV90ryF1X1oiR/neSh8y0LAAAAgPVgNdPWXpzkfknOSfL4JGckOWuONQEAAACwTqxm5NGnu/tzST6X5PlzrgcAAACAdWQ14dFbqurYJFckeX+SP+5uCx4AAAAAHAYOGB5197dX1ZFJnpDkyUl+t6oe0N0PmXdxAAAAACzWAcOjqvrWJN82fB6U5D1J/ni+ZQEAAACwHqxm2tofJdmV5OeTXNrdd8y3JAAAAADWi9WER1+Z5ElJvj3JOVV1d5IPdvdPzbUyAAAAABZuNWsefbaqbkhyXJItSb4lyb3nXRgAAAAAi3evA3Woqv+d5DVJHpzkwiRnJblmznUBAAAAsA6sZtraSUkek+Q5Sd6W5BNJ3jnPogAAAABYH0ZHHlXV11fVK7I0yuj1SW5MUt39lO5+w2ouXlWnVdX1VbW7qs6bcvz0qrq6qq6qql3Dm90AAAAAWCdWGnn0Z0n+OMl3d/fuJKmql6z2wlW1Kcn5SZ6eZE+SK6pqZ3dfO9Ht8iQ7u7ur6tFJ3pHkEQf5MwAAAAAwJyutefT9Sf4myR9W1X+qqqclqYO49ilJdnf3Dd19R5JLkpw+2aG7P9fdPezeP0kHAAAAgHVjNDzq7t/q7h/K0kig9yV5SZKvqqoLquqfreLax2Zpqts+e4a2L1FV31tVf5bkd5P86EHUDgAAAMCcHfBta939+e5+e3d/V5ItSa5Kst/6RVNMG6W038iiIaR6RJJnJfm5qReq2j6sibRr7969q7g1AAAAALNwwPBoUnd/prt/rbufuorue5IcN7G/JclNK1z7/Um+rqqOmXJsR3dv7e6tmzdvPpiSAQAAAPgyHFR4dJCuSHJSVZ1YVUcm2ZZk52SHqvonVVXD9uOSHJnk03OsCQAAAICDsNLb1r4s3X1nVb0oyWVJNiW5qLuvqaqzh+MXZmlR7jOr6otJ/j7JD00soA0AAADAgs0tPEqS7r40yaXL2i6c2P7FJL84zxoAAAAAuOfmOW0NAAAAgA1OeAQAAADAKOERAAAAAKOERwAAAACMEh4BAAAAMEp4BAAAAMAo4REAAAAAo4RHAAAAAIwSHgEAAAAwSngEAAAAwCjhEQAAAACjhEcAAAAAjBIeAQAAADBKeAQAAADAKOERAAAAAKOERwAAAACMEh4BAAAAMEp4BAAAAMAo4REAAAAAo+YaHlXVaVV1fVXtrqrzphx/blVdPXz+pKoeM896AAAAADg4cwuPqmpTkvOTPCPJyUmeU1UnL+v2iSTf0d2PTvJzSXbMqx4AAAAADt48Rx6dkmR3d9/Q3XckuSTJ6ZMduvtPuvvvht0PJdkyx3oAAAAAOEjzDI+OTXLjxP6eoW3MC5K8d9qBqtpeVbuqatfevXtnWCIAAAAAK5lneFRT2npqx6qnZCk8+slpx7t7R3dv7e6tmzdvnmGJAAAAAKzkiDlee0+S4yb2tyS5aXmnqnp0kjcleUZ3f3qO9QAAAABwkOY58uiKJCdV1YlVdWSSbUl2TnaoquOTvCvJ87r7z+dYCwAAAAD3wNxGHnX3nVX1oiSXJdmU5KLuvqaqzh6OX5jkFUm+MskbqypJ7uzurfOqCQAAAICDM89pa+nuS5Ncuqztwontf5HkX8yzBgAAAADuuXlOWwMAAABggxMeAQAAADBKeAQAAADAKOERAAAAAKOERwAAAACMEh4BAAAAMEp4BAAAAMAo4REAAAAAo4RHAAAAAIwSHgEAAAAwSngEAAAAwCjhEQAAAACjhEcAAAAAjBIeAQAAADBKeAQAAADAKOERAAAAAKOERwAAAACMEh4BAAAAMGqu4VFVnVZV11fV7qo6b8rxR1TVB6vq9qp66TxrAQAAAODgHTGvC1fVpiTnJ3l6kj1Jrqiqnd197US3zyQ5J8mz5lUHAAAAAPfcPEcenZJkd3ff0N13JLkkyemTHbr7lu6+IskX51gHAAAAAPfQPMOjY5PcOLG/Z2gDAAAAYIOYZ3hUU9r6Hl2oantV7aqqXXv37v0yywIAAABgteYZHu1JctzE/pYkN92TC3X3ju7e2t1bN2/ePJPiAAAAADiweYZHVyQ5qapOrKojk2xLsnOO9wMAAABgxub2trXuvrOqXpTksiSbklzU3ddU1dnD8Qur6quT7EpydJK7q+rFSU7u7lvnVRcAAAAAqze38ChJuvvSJJcua7twYvtvsjSdDQAAAIB1aJ7T1gAAAADY4IRHAAAAAIwSHgEAAAAwSngEAAAAwCjhEQAAAACjhEcAAAAAjBIeAQAAADBKeAQAAADAKOERAAAAAKOERwAAAACMEh4BAAAAMEp4BAAAAMAo4REAAAAAo4RHAAAAAIwSHgEAAAAwSngEAAAAwCjhEQAAAACjhEcAAAAAjBIeAQAAADBqruFRVZ1WVddX1e6qOm/K8aqq1w3Hr66qx82zHgAAAAAOztzCo6ralOT8JM9IcnKS51TVycu6PSPJScNne5IL5lUPAAAAAAdvniOPTkmyu7tv6O47klyS5PRlfU5PcnEv+VCSB1XVw+ZYEwAAAAAHYZ7h0bFJbpzY3zO0HWwfAAAAABbkiDleu6a09T3ok6ranqVpbUnyuaq6/susjY3pmCSfWnQR/KP6pbMWXQKHPr/3680rp/2ne+a+di1uAgDA6swzPNqT5LiJ/S1JbroHfdLdO5LsmHWBbCxVtau7ty66DmDt+L0HAIDFm+e0tSuSnFRVJ1bVkUm2Jdm5rM/OJGcOb107Ncn/6e6b51gTAAAAAAdhbiOPuvvOqnpRksuSbEpyUXdfU1VnD8cvTHJpkmcm2Z3kC0meP696AAAAADh41b3fEkOwLlXV9mEKI3CY8HsPAACLJzwCAAAAYNQ81zwCAAAAYIMTHrEhVNUjquqDVXV7Vb100fUA81VVp1XV9VW1u6rOW3Q9AABwOJvbgtkwY59Jck6SZy24DmDOqmpTkvOTPD3JniRXVNXO7r52sZUBAMDhycgjNoTuvqW7r0jyxUXXAszdKUl2d/cN3X1HkkuSnL7gmgAA4LAlPAJgvTk2yY0T+3uGNgAAYAGERwCsNzWlzatBAQBgQYRHrFtV9cKqumr4fM2i6wHWzJ4kx03sb0ly04JqAQCAw57wiHWru8/v7m8aPv7HEQ4fVyQ5qapOrKojk2xLsnPBNQEAwGGrus0EYP2rqq9OsivJ0UnuTvK5JCd3960LLQyYi6p6ZpLXJtmU5KLuftViKwIAgMOX8AgAAACAUaatAQAAADBKeAQAAADAKOERAAAAAKOERwAAAACMEh4BAAAAMEp4BKypquqqes3E/kur6qcPcM7ZVXXm3IsDAABgP8IjYK3dnuT7quqY1Z7Q3Rd298VzrAkAAIARwiNgrd2ZZEeSlyw/UFVfW1WXV9XVw/fxQ/tPV9VLh+1zquraoc8lQ9v9q+qiqrqiqj5aVaev5Q8EAABwKBMeAYtwfpLnVtVXLGt/Q5KLu/vRSd6e5HVTzj0vyWOHPmcPbf8uyR909xOSPCXJq6vq/vMpHQAA4PAiPALWXHffmuTiJOcsO/TNSX5j2H5bkm+dcvrVSd5eVWdkaRRTkvyzJOdV1VVJ3pfkvkmOn23VAAAAh6cjFl0AcNh6bZKPJHnzCn16Stt3Jvn2JN+T5Keq6pFJKsn3d/f1sy4SAADgcGfkEbAQ3f2ZJO9I8oKJ5j9Jsm3Yfm6SD0yeU1X3SnJcd/9hkp9I8qAkD0hyWZIfr6oa+j12rsUDAAAcRoRHwCK9JsnkW9fOSfL8qro6yfOSnLus/6Ykv15VH0vy0SS/0t2fTfJzSe6d5Oqq+viwDwAAwAxU97RZIQAAAABg5BEAAAAAKxAeAQAAADBKeAQAAADAKOERAAAAAKOERwAAAACMEh4BAAAAMEp4BAAAAMAo4REAAAAAo/4vP1CfPBIeV1sAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 1440x576 with 3 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(20,8))\n",
    "\n",
    "plt.subplot(2,2,1)\n",
    "sns.barplot(x=df.index , y=df.Flight_trans_12)\n",
    "\n",
    "plt.subplot(2,2,2)\n",
    "sns.barplot(x=df.index , y=df.Days_since_enroll)\n",
    "\n",
    "plt.subplot(2,2,3)\n",
    "sns.barplot(x=df.index , y=df.Award)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "061bd199",
   "metadata": {},
   "source": [
    "# Thank You"
   ]
  }
 ],
 "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
}