Points Model Exercise Part 3 Member behavioral segments_Susan Li.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "2019-10-17\n",
    "\n",
    "### Customer segmentation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "from datetime import datetime\n",
    "from sklearn.cluster import KMeans\n",
    "pd.set_option('display.max_columns', 100)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "df = pd.read_csv('Training Data Set.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "    }\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>tier</th>\n",
       "      <th>aag_yr1</th>\n",
       "      <th>cardholder</th>\n",
       "      <th>email_subscriber</th>\n",
       "      <th>enrollment_date</th>\n",
       "      <th>nonflt_use</th>\n",
       "      <th>awd_use</th>\n",
       "      <th>most_recent_flt</th>\n",
       "      <th>most_recent_awd</th>\n",
       "      <th>flt_segs_12mo</th>\n",
       "      <th>flt_segs_6mo</th>\n",
       "      <th>flt_segs_3mo</th>\n",
       "      <th>nonflt_earn_12mo</th>\n",
       "      <th>nonflt_earn_6mo</th>\n",
       "      <th>nonflt_earn_3mo</th>\n",
       "      <th>lifetime_aag_base_miles</th>\n",
       "      <th>affinity_spend_12mo</th>\n",
       "      <th>affinity_spend_6mo</th>\n",
       "      <th>affinity_spend_3mo</th>\n",
       "      <th>flt_base_12mo</th>\n",
       "      <th>flt_base_6mo</th>\n",
       "      <th>flt_base_3mo</th>\n",
       "      <th>flt_promo_12mo</th>\n",
       "      <th>flt_promo_6mo</th>\n",
       "      <th>flt_promo_3mo</th>\n",
       "      <th>ptnr_miles_earned_12mo</th>\n",
       "      <th>ptnr_miles_earned_6mo</th>\n",
       "      <th>ptnr_miles_earned_3mo</th>\n",
       "      <th>ptnr_miles_redeemed_12mo</th>\n",
       "      <th>ptnr_miles_redeemed_6mo</th>\n",
       "      <th>ptnr_miles_redeemed_3mo</th>\n",
       "      <th>partner_offer_opt_in</th>\n",
       "      <th>estatement_opt_in</th>\n",
       "      <th>target</th>\n",
       "      <th>ID</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>499922</td>\n",
       "      <td>3219710</td>\n",
       "      <td>Platinum</td>\n",
       "      <td>69052.0</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>1993-08-17T04:00:00.000Z</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>2019-05-10 00:00:00</td>\n",
       "      <td>2015-08-31 00:00:00</td>\n",
       "      <td>34</td>\n",
       "      <td>18</td>\n",
       "      <td>5</td>\n",
       "      <td>149463</td>\n",
       "      <td>99716</td>\n",
       "      <td>24701</td>\n",
       "      <td>727692</td>\n",
       "      <td>1455.41</td>\n",
       "      <td>964.44</td>\n",
       "      <td>245.51</td>\n",
       "      <td>47561</td>\n",
       "      <td>19040</td>\n",
       "      <td>8527</td>\n",
       "      <td>102463</td>\n",
       "      <td>38536</td>\n",
       "      <td>17357</td>\n",
       "      <td>187735</td>\n",
       "      <td>97344</td>\n",
       "      <td>24701</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>0</td>\n",
       "      <td>531021</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>499923</td>\n",
       "      <td>4211165</td>\n",
       "      <td>Regular</td>\n",
       "      <td>3872.0</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>1993-03-31T05:00:00.000Z</td>\n",
       "      <td>0</td>\n",
       "      <td>505000</td>\n",
       "      <td>2018-03-27 00:00:00</td>\n",
       "      <td>2019-03-22 00:00:00</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>166809</td>\n",
       "      <td>48760</td>\n",
       "      <td>24057</td>\n",
       "      <td>32034</td>\n",
       "      <td>1666.55</td>\n",
       "      <td>486.28</td>\n",
       "      <td>239.69</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>166809</td>\n",
       "      <td>48760</td>\n",
       "      <td>24057</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>0</td>\n",
       "      <td>575985</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>499924</td>\n",
       "      <td>6202340</td>\n",
       "      <td>Gold</td>\n",
       "      <td>55561.0</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>1984-09-12T04:00:00.000Z</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>2019-05-06 00:00:00</td>\n",
       "      <td>2009-08-29 00:00:00</td>\n",
       "      <td>68</td>\n",
       "      <td>29</td>\n",
       "      <td>17</td>\n",
       "      <td>243250</td>\n",
       "      <td>116941</td>\n",
       "      <td>80472</td>\n",
       "      <td>1226412</td>\n",
       "      <td>1856.72</td>\n",
       "      <td>938.01</td>\n",
       "      <td>657.84</td>\n",
       "      <td>38534</td>\n",
       "      <td>14369</td>\n",
       "      <td>9499</td>\n",
       "      <td>69588</td>\n",
       "      <td>30800</td>\n",
       "      <td>18761</td>\n",
       "      <td>235749</td>\n",
       "      <td>114440</td>\n",
       "      <td>80472</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>0</td>\n",
       "      <td>64434</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        balance       tier  aag_yr1  cardholder  email_subscriber  \\\n",
       "499922  3219710  Platinum   69052.0        True              True   \n",
       "499923  4211165    Regular   3872.0        True              True   \n",
       "499924  6202340       Gold  55561.0        True              True   \n",
       "\n",
       "                 enrollment_date  nonflt_use  awd_use      most_recent_flt  \\\n",
       "499922  1993-08-17T04:00:00.000Z           0        0  2019-05-10 00:00:00   \n",
       "499923  1993-03-31T05:00:00.000Z           0   505000  2018-03-27 00:00:00   \n",
       "499924  1984-09-12T04:00:00.000Z           0        0  2019-05-06 00:00:00   \n",
       "\n",
       "            most_recent_awd  flt_segs_12mo  flt_segs_6mo  flt_segs_3mo  \\\n",
       "499922  2015-08-31 00:00:00             34            18             5   \n",
       "499923  2019-03-22 00:00:00              0             0             0   \n",
       "499924  2009-08-29 00:00:00             68            29            17   \n",
       "\n",
       "        nonflt_earn_12mo  nonflt_earn_6mo  nonflt_earn_3mo  \\\n",
       "499922            149463            99716            24701   \n",
       "499923            166809            48760            24057   \n",
       "499924            243250           116941            80472   \n",
       "\n",
       "        lifetime_aag_base_miles  affinity_spend_12mo  affinity_spend_6mo  \\\n",
       "499922                   727692              1455.41              964.44   \n",
       "499923                    32034              1666.55              486.28   \n",
       "499924                  1226412              1856.72              938.01   \n",
       "\n",
       "        affinity_spend_3mo  flt_base_12mo  flt_base_6mo  flt_base_3mo  \\\n",
       "499922              245.51          47561         19040          8527   \n",
       "499923              239.69              0             0             0   \n",
       "499924              657.84          38534         14369          9499   \n",
       "\n",
       "        flt_promo_12mo  flt_promo_6mo  flt_promo_3mo  ptnr_miles_earned_12mo  \\\n",
       "499922          102463          38536          17357                  187735   \n",
       "499923               0              0              0                  166809   \n",
       "499924           69588          30800          18761                  235749   \n",
       "\n",
       "        ptnr_miles_earned_6mo  ptnr_miles_earned_3mo  \\\n",
       "499922                  97344                  24701   \n",
       "499923                  48760                  24057   \n",
       "499924                 114440                  80472   \n",
       "\n",
       "        ptnr_miles_redeemed_12mo  ptnr_miles_redeemed_6mo  \\\n",
       "499922                         0                        0   \n",
       "499923                         0                        0   \n",
       "499924                         0                        0   \n",
       "\n",
       "        ptnr_miles_redeemed_3mo  partner_offer_opt_in  estatement_opt_in  \\\n",
       "499922                        0                  True               True   \n",
       "499923                        0                  True               True   \n",
       "499924                        0                  True               True   \n",
       "\n",
       "        target      ID  \n",
       "499922       0  531021  \n",
       "499923       0  575985  \n",
       "499924       0   64434  "
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.tail(3)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Develop new behavioral segments based on member flight, miles earned and redemption behavior features. So I will use the above mentioned features only for this assignment.\n",
    "\n",
    "I don't see any particular feature for flight redemption, I will use \"ptnr_miles_earned\" and \"ptnr_miles_redeemed\". I made assumptions that partner miles are flight related.\n",
    "\n",
    "As we have learned earlier, features within each segment are highly correlated. So, I will choose one feature from each segment and I decided to use 3 months."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "df = df[['enrollment_date', 'most_recent_flt', 'aag_yr1', 'balance', 'lifetime_aag_base_miles', 'flt_base_3mo', 'flt_promo_3mo', 'ptnr_miles_earned_3mo', 'ptnr_miles_redeemed_3mo', 'ID']]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Fill missing \"most_recent_flt\" date with today's date, assume that that date has not come yet for that member."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "df['most_recent_flt'].fillna('2019-10-16', inplace=True)\n",
    "for i in ['enrollment_date', 'most_recent_flt']:\n",
    "    df[i] = pd.to_datetime(df[i])\n",
    "df['enrollment_date'] = df['enrollment_date'].dt.date\n",
    "df['most_recent_flt'] = df['most_recent_flt'].dt.date\n",
    "\n",
    "for i in ['enrollment_date', 'most_recent_flt']:\n",
    "    df[i] = pd.to_datetime(df[i])\n",
    "    \n",
    "df['years_since_enrollment'] = ((datetime.strptime('10-17-2019', '%m-%d-%Y') - df['enrollment_date']).dt.days)/365\n",
    "df['months_since_most_recent_flt'] = ((datetime.strptime('10-17-2019', '%m-%d-%Y') - df['most_recent_flt']).dt.days)/12"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "count    499925.000000\n",
       "mean          5.878132\n",
       "std           7.075616\n",
       "min           0.465753\n",
       "25%           1.723288\n",
       "50%           2.827397\n",
       "75%           6.736986\n",
       "max         106.835616\n",
       "Name: years_since_enrollment, dtype: float64"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.years_since_enrollment.describe()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Two members have been enrolled for over 100 years."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>enrollment_date</th>\n",
       "      <th>most_recent_flt</th>\n",
       "      <th>aag_yr1</th>\n",
       "      <th>balance</th>\n",
       "      <th>lifetime_aag_base_miles</th>\n",
       "      <th>flt_base_3mo</th>\n",
       "      <th>flt_promo_3mo</th>\n",
       "      <th>ptnr_miles_earned_3mo</th>\n",
       "      <th>ptnr_miles_redeemed_3mo</th>\n",
       "      <th>ID</th>\n",
       "      <th>years_since_enrollment</th>\n",
       "      <th>months_since_most_recent_flt</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>26659</td>\n",
       "      <td>1917-03-02</td>\n",
       "      <td>2019-10-16</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
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       "      <td>102.695890</td>\n",
       "      <td>0.083333</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>450940</td>\n",
       "      <td>1913-01-11</td>\n",
       "      <td>2015-01-27</td>\n",
       "      <td>0.0</td>\n",
       "      <td>21850</td>\n",
       "      <td>500</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>800</td>\n",
       "      <td>0</td>\n",
       "      <td>64378</td>\n",
       "      <td>106.835616</td>\n",
       "      <td>143.666667</td>\n",
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      ],
      "text/plain": [
       "       enrollment_date most_recent_flt  aag_yr1  balance  \\\n",
       "26659       1917-03-02      2019-10-16      0.0        0   \n",
       "450940      1913-01-11      2015-01-27      0.0    21850   \n",
       "\n",
       "        lifetime_aag_base_miles  flt_base_3mo  flt_promo_3mo  \\\n",
       "26659                         0             0              0   \n",
       "450940                      500             0              0   \n",
       "\n",
       "        ptnr_miles_earned_3mo  ptnr_miles_redeemed_3mo      ID  \\\n",
       "26659                       0                        0  119762   \n",
       "450940                    800                        0   64378   \n",
       "\n",
       "        years_since_enrollment  months_since_most_recent_flt  \n",
       "26659               102.695890                      0.083333  \n",
       "450940              106.835616                    143.666667  "
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.loc[df['years_since_enrollment'] > 100]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "df['aag_yr1'].fillna(0, inplace=True)\n",
    "df.drop(['enrollment_date', 'most_recent_flt'], axis=1, inplace=True)\n",
    "df.drop('ID', axis=1, inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>count</th>\n",
       "      <th>mean</th>\n",
       "      <th>std</th>\n",
       "      <th>min</th>\n",
       "      <th>25%</th>\n",
       "      <th>50%</th>\n",
       "      <th>75%</th>\n",
       "      <th>max</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>aag_yr1</td>\n",
       "      <td>499925.00</td>\n",
       "      <td>3121.02</td>\n",
       "      <td>7031.48</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>3912.00</td>\n",
       "      <td>185331.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>balance</td>\n",
       "      <td>499925.00</td>\n",
       "      <td>29389.24</td>\n",
       "      <td>77897.16</td>\n",
       "      <td>-5627.00</td>\n",
       "      <td>1671.00</td>\n",
       "      <td>8501.00</td>\n",
       "      <td>28551.00</td>\n",
       "      <td>9001052.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>lifetime_aag_base_miles</td>\n",
       "      <td>499925.00</td>\n",
       "      <td>21690.83</td>\n",
       "      <td>61924.44</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>4106.00</td>\n",
       "      <td>16374.00</td>\n",
       "      <td>2254169.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>flt_base_3mo</td>\n",
       "      <td>499925.00</td>\n",
       "      <td>742.32</td>\n",
       "      <td>2193.51</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>70987.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>flt_promo_3mo</td>\n",
       "      <td>499925.00</td>\n",
       "      <td>292.21</td>\n",
       "      <td>2176.72</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>188444.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>ptnr_miles_earned_3mo</td>\n",
       "      <td>499925.00</td>\n",
       "      <td>1818.77</td>\n",
       "      <td>7719.29</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>717139.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>ptnr_miles_redeemed_3mo</td>\n",
       "      <td>499925.00</td>\n",
       "      <td>488.88</td>\n",
       "      <td>7744.96</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>1020000.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>years_since_enrollment</td>\n",
       "      <td>499925.00</td>\n",
       "      <td>5.88</td>\n",
       "      <td>7.08</td>\n",
       "      <td>0.47</td>\n",
       "      <td>1.72</td>\n",
       "      <td>2.83</td>\n",
       "      <td>6.74</td>\n",
       "      <td>106.84</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>months_since_most_recent_flt</td>\n",
       "      <td>499925.00</td>\n",
       "      <td>34.23</td>\n",
       "      <td>48.94</td>\n",
       "      <td>0.08</td>\n",
       "      <td>0.08</td>\n",
       "      <td>21.17</td>\n",
       "      <td>42.92</td>\n",
       "      <td>476.17</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                 count     mean      std      min     25%  \\\n",
       "aag_yr1                      499925.00  3121.02  7031.48     0.00    0.00   \n",
       "balance                      499925.00 29389.24 77897.16 -5627.00 1671.00   \n",
       "lifetime_aag_base_miles      499925.00 21690.83 61924.44     0.00    0.00   \n",
       "flt_base_3mo                 499925.00   742.32  2193.51     0.00    0.00   \n",
       "flt_promo_3mo                499925.00   292.21  2176.72     0.00    0.00   \n",
       "ptnr_miles_earned_3mo        499925.00  1818.77  7719.29     0.00    0.00   \n",
       "ptnr_miles_redeemed_3mo      499925.00   488.88  7744.96     0.00    0.00   \n",
       "years_since_enrollment       499925.00     5.88     7.08     0.47    1.72   \n",
       "months_since_most_recent_flt 499925.00    34.23    48.94     0.08    0.08   \n",
       "\n",
       "                                 50%      75%        max  \n",
       "aag_yr1                         0.00  3912.00  185331.00  \n",
       "balance                      8501.00 28551.00 9001052.00  \n",
       "lifetime_aag_base_miles      4106.00 16374.00 2254169.00  \n",
       "flt_base_3mo                    0.00     0.00   70987.00  \n",
       "flt_promo_3mo                   0.00     0.00  188444.00  \n",
       "ptnr_miles_earned_3mo           0.00     0.00  717139.00  \n",
       "ptnr_miles_redeemed_3mo         0.00     0.00 1020000.00  \n",
       "years_since_enrollment          2.83     6.74     106.84  \n",
       "months_since_most_recent_flt   21.17    42.92     476.17  "
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pd.options.display.float_format = '{:.2f}'.format\n",
    "df.describe().T"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "From the summary statistics, we can see that most of the members do not have activities, means they are not actively collecting or redeem points. So, all the features are extremely right skewed."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.distplot(df['years_since_enrollment'])\n",
    "plt.title('Distribution of enrollment period')\n",
    "sns.despine();"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Very few members have enrolled for over 40 years, and majority of the members have enrolled for less than 10 years."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.distplot(df['months_since_most_recent_flt'])\n",
    "plt.title('Distribution of months since most recent flight')\n",
    "sns.despine();"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Most of the members have taken a flight within the past 4 years or so."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 360x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.lmplot(x='years_since_enrollment',y='lifetime_aag_base_miles', data=df)\n",
    "plt.title('Years since enrollment vs. lifetime aag base miles');"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Members who have the most lifetime aag base miles are not the ones who have enrolled the longest. They are those who enrolled approximately less than 30 years."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 360x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.lmplot(x='years_since_enrollment',y='balance', data=df)\n",
    "plt.title('Years since enrollment vs. balance');"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Similar with the above discovery. Members who enrolled the longest are not the ones with the most balance. Members that have the most balance have enrolled for approx 30 years."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 360x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.lmplot(x='ptnr_miles_earned_3mo', y='ptnr_miles_redeemed_3mo', data=df)\n",
    "plt.title('partner miles earned 3 months vs. partner miles redeemed 3 months');"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "\"Earn more, redeem more\" does not always hold true, at least not in the same time period. Member who redeemed the most 3 months did not earn most in 3 months. Probably he earned all those points in the past?"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Visual examine outliers"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>aag_yr1</th>\n",
       "      <th>balance</th>\n",
       "      <th>lifetime_aag_base_miles</th>\n",
       "      <th>flt_base_3mo</th>\n",
       "      <th>flt_promo_3mo</th>\n",
       "      <th>ptnr_miles_earned_3mo</th>\n",
       "      <th>ptnr_miles_redeemed_3mo</th>\n",
       "      <th>years_since_enrollment</th>\n",
       "      <th>months_since_most_recent_flt</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>497642</td>\n",
       "      <td>75905.00</td>\n",
       "      <td>267248</td>\n",
       "      <td>647248</td>\n",
       "      <td>20453</td>\n",
       "      <td>29633</td>\n",
       "      <td>39040</td>\n",
       "      <td>1020000</td>\n",
       "      <td>30.10</td>\n",
       "      <td>15.25</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        aag_yr1  balance  lifetime_aag_base_miles  flt_base_3mo  \\\n",
       "497642 75905.00   267248                   647248         20453   \n",
       "\n",
       "        flt_promo_3mo  ptnr_miles_earned_3mo  ptnr_miles_redeemed_3mo  \\\n",
       "497642          29633                  39040                  1020000   \n",
       "\n",
       "        years_since_enrollment  months_since_most_recent_flt  \n",
       "497642                   30.10                         15.25  "
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.loc[df['ptnr_miles_redeemed_3mo'] > 1000000]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "On the other hand, member who earned the most in 3 months did not redeem at all in 3 months. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>aag_yr1</th>\n",
       "      <th>balance</th>\n",
       "      <th>lifetime_aag_base_miles</th>\n",
       "      <th>flt_base_3mo</th>\n",
       "      <th>flt_promo_3mo</th>\n",
       "      <th>ptnr_miles_earned_3mo</th>\n",
       "      <th>ptnr_miles_redeemed_3mo</th>\n",
       "      <th>years_since_enrollment</th>\n",
       "      <th>months_since_most_recent_flt</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>499882</td>\n",
       "      <td>35479.00</td>\n",
       "      <td>1531093</td>\n",
       "      <td>533207</td>\n",
       "      <td>12901</td>\n",
       "      <td>28787</td>\n",
       "      <td>717139</td>\n",
       "      <td>0</td>\n",
       "      <td>36.28</td>\n",
       "      <td>14.67</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        aag_yr1  balance  lifetime_aag_base_miles  flt_base_3mo  \\\n",
       "499882 35479.00  1531093                   533207         12901   \n",
       "\n",
       "        flt_promo_3mo  ptnr_miles_earned_3mo  ptnr_miles_redeemed_3mo  \\\n",
       "499882          28787                 717139                        0   \n",
       "\n",
       "        years_since_enrollment  months_since_most_recent_flt  \n",
       "499882                   36.28                         14.67  "
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.loc[df['ptnr_miles_earned_3mo'] > 700000]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>aag_yr1</th>\n",
       "      <th>balance</th>\n",
       "      <th>lifetime_aag_base_miles</th>\n",
       "      <th>flt_base_3mo</th>\n",
       "      <th>flt_promo_3mo</th>\n",
       "      <th>ptnr_miles_earned_3mo</th>\n",
       "      <th>ptnr_miles_redeemed_3mo</th>\n",
       "      <th>years_since_enrollment</th>\n",
       "      <th>months_since_most_recent_flt</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>359552</td>\n",
       "      <td>0.00</td>\n",
       "      <td>9001052</td>\n",
       "      <td>104807</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>18253</td>\n",
       "      <td>0</td>\n",
       "      <td>27.58</td>\n",
       "      <td>170.83</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        aag_yr1  balance  lifetime_aag_base_miles  flt_base_3mo  \\\n",
       "359552     0.00  9001052                   104807             0   \n",
       "\n",
       "        flt_promo_3mo  ptnr_miles_earned_3mo  ptnr_miles_redeemed_3mo  \\\n",
       "359552              0                  18253                        0   \n",
       "\n",
       "        years_since_enrollment  months_since_most_recent_flt  \n",
       "359552                   27.58                        170.83  "
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.loc[df['balance'] > 8000000]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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wAthisVhahBXAFovF0iKsALZYLJYWYQWwxWKxtAgrgC0Wi6VFWAFssVgsLcIKYIvFYmkRVgBbLBZLi7AC2GKxWFqEFcAWi8XSIqwAtlgslhZhBbDFYrG0CCuALRaLpUXYdJSWFc1yqY5rWZ1YAWxZsQTVcaOulFXHvQXKhLAV0pZWYU0QlhVLPdVxl1MJc8vKwwpgy4qlnuq4y6mEuWXlYU0QlhXLlt52BlNZ2mPTf+aV1XEPj6bpaYuWbVdPCXNrtrA0A6sBW1Ys9VTHnU8Jc2u2qM6e/YNcf/s+rrjtAa6/fd+qvx71YAWwZcVST3Xc+ZQwt2aLmdiX0vwQpVSr+7DkueSSS9TDDz/c6m5YFonAnHBkNM3mOswJV9z2AD1tUUQkbJvI5DkxkaMvGV+VJonrb983w9yTzhfpTyb4+rsva2HPlgxSrdHagC1NZ7nZRxstYV5pW57IFDg6liXizO7utpKZry19tWNNEJamshqGopVmi5OpLAAbuhOr1iQxH1u6xQpgS5NZDfbRStuyUrCpJ0EyMa0Brjbtbz62dIs1QViazEobiu7ZP8it9z7NgRHd/+3rOvjg1TvLzBaB/bOU1ab97drZzy3QkC3dYgXwquF02WXr8b1tFY1egz37B/mTu37CWLqAY6ZQnh2c5AN3/YS/vO7CcNsbr9zOzfc8RTpfpC3qkil4q1L7a9SWbrEmiBVHNV/M02mXXapD0flcg917B5jMFXFFcB3HfIRUtlhmUqnH3c1iqYbVgFcQtZLPdMTc0C4L0B6LkM5rIdKIkKhHg1yqQ9FS2zTUdw0Oj6bxfIVb4m4mAkXPn2FSsdqfZT5YAbyCqCVkBoan2NHfWbZuo3bZejOLwdIURvOxTW/pbWd4MofyteAFUAoijrMkTCqW5U/dJggRebWIdJjvbxOR/y0iZy5e1yyNUiv5DLBgF6Hl7t0wHzepG6/cTmc8gqcUnu+bjyKZiLTcpGJZGTRiA/4skBaRC4H3A88DX1mUXlnmRS0hc9ba9tAuO5HJ8+zJFAdHphidytVtB64ns9hS5sYrtzORKfDsyRRPHx/n2ZMpJjKFWQXprp39fPK6CzmnrwMRQUTY0d9ZNgFnsSyERkwQRaWUEpFrgb9RSv2diLxzsTpmaZxas/Efff15ANx2334OjqSJusLmnjYKviozI8xm410q3g0L8eZQgK8Ung+e71NM53niyNis25eaU4Jjf+TuJ9myd2nYti3Lm0Y04JSIfBj4beDfRMQBonNsYzmNzDYbv2tnPz3tMbatbWdHf5KutliZGWEuL4Gl4N2wEG+O3XsHiDiCAmKuQzziICJ8Zs/zdW2/GiL8LKefRjTg3wR+C/hdpdQJEdkK/OXidMsyX2abAJttImouL4Gl4N0wH0+GgMOjaVLZIg6CY5x6XYGC59e1/UKObbHUom4BbITuPwE7TNMw8M1F6ZVlUZjNjFCPl0CrvBuCof+PD54iEXFY1xmny/S1Xjv0lt52TozrhDkBSkHcderafqVF+FmWBo14QfwecBew2zRtAv5ljm2+ICKDIvJkSdvHReSoiDxuPq8rWfZhEXlORH4uIq8tab/atD0nIh8qaT9LRH5k2v9BRGKmPW5+P2eWb5vrGKuBUjNC5WRcZ8xdkslUSof+cVfIez7HxjNMZAoN9fHGK7fjOoKnFAqFrxRKQXd7tK7tbbIZy2LQiA34vcCrgQkApdSzwFzq0JeAq6u0f0op9TLz+RaAiJwHvAV4idnm/4qIKyIu8BngGuA84HqzLsBtZl/nAKNAMCn4TmDUtH/KrFfzGA1cg2VNYCOOuQ5HxrIghJNxI1N5xjOFJRfBVjr07+9KICat6vBkrmofa1Vl2LWzn/fuOhtHhILn4wqs7YwSdd26znE2G7itBGGZL40I4JxSKh/8EJEIemK5JkqpvcCpOvd/LXCnUiqnlDoAPAe80nyeU0oNmOPfCVwrOhv2a9BaOcCXgTeW7OvL5vtdwC+a9WsdY9VQazKuqy1KX2d8yYXTlrq/JRNRNvYkiDpCtujP6ONcE2U3XXUuu9/2cl65bS1rO+NsW9tZ9znWmuAE7OScZd40Mgn3HyLyp0CbiPwS8B7gX+d53D8UkbcDDwPvV0qNok0a+0rWOWLaAA5XtF8KrAXGlFLFKutvCrZRShVFZNysP9sxyhCRdwPvBti6des8TnHpUsueOZ4pcO8fXdmiXlWn0m6dTERxHalaaaGeibJadux6w6wr266/fZ+dnGsCyy2Jf7NoRAP+EDAE/BS4EfgW8JF5HPOzwNnAy4DjwP+axz4WHaXU7UqpS5RSl/T19bW6O01lOdkzG3F/m2+wyEJczJZ7gMpSYDW7+NUtgJVSvlLqb5VSv6GUus58b7ignFLqpFLKU0r5wN8ybQI4CmwpWXWzaavVPgL0GFNIaXvZvszybrN+rX2tKpaCT2+9NJJpbL4vloWEWS+nl9lSZbmHuS+EOU0QIvJTZrH1KqUuaOSAInKGUuq4+fkmIPCQuAf4moj8b2Aj2t3tx+hidjtE5Cy0sHwL8FsmKu97wHVou/ANwN0l+7oBeMgsf8CsX+sYLed0DsGWgk9vI9Tr/jbfvLwLcTGzuYAXzmp28avHBvwr8925iHwd2AWsE5EjwMeAXSLyMrRQP4g2Z6CUekpEvgH8DCgC71VKeWY/fwjcD7jAF5RST5lDfBC4U0T+DHgM+DvT/nfA34vIc+hJwLfMdYxW0kimsWaxFDOWLZRaLxbQttpaL7eFhFkvt5fZUmSphLm3AluWvg6aXZa+UtsdS+fJe37DJb1X68RFI5S+3Eo11GreE7OtY1k8Vsn1r1qWfk4bsIg8aP5PichEySclIhPN7uVKp9qEwzODkxQ9v2y9uYZgq3niohHqsS/aihatZTVf/zlNEEqpK8z/ycXvzsqnmqtU1BVOTuToaouF6801BKs3N8Fq15Kr2ReLns+jh0a54rYHyq7JarouS43Vev0bqgknIr0icoGIXBx8FqtjK5Vqbkvrk3EKvt+QV0I97k9WS57ppZDKFjg6lkWEVXtNLEuHugMxROQTwO8AA0AwXlboaDRLnSTjEZ4bnMRTipjr0JeME3EddvR10tsRr3sip56JC5vBa6aXwolxXT5+fTIRmiSaeU1W+4jD0hiNRMK9GTi7NBzZ0hh79g8yNJmj6CsckwrxyGiG3vZow1UW6nF/Ws3uPQGVXgoK2NSTCLOpQf3BGnMJ1lZ4s1iWN40I4CeBHsCO1ebJ7r0DdLdF6YhFGJ7Mkfd8Iq6wtiPW8ANaj/tTI+49K1lzK7UvXn/7PgZT2bLlc9nb6xWsiz3iWMn3aLXSiAD+C+Axk1oyFzQqpd7Q9F6tUAKNVGISamBKKcZNasVGmWviot4ggdWkudW6JpdvX1PTV7hewbqYI47VdI9WE40I4C+j0zr+lGkbsKUBTrfDeb1BAivdVvzp7zzD5x88wFTeoyPm8os7+zgxkQ+vyeXb13DXo0drCrd6Beti3t+Vfo9WK40I4LRS6tOL1pNVwGwa6WINL+tx71nJtuJPf+cZ/vqB53AEIo4WiPc8cYL3veYcbrpKB7nMldGsXsEa3N+hVJZUtkiu6OM6wrUXblzweazke7SaacQN7fsi8hcicrl1Q5sfSzWn7EpOKPP5Bw8Y4evgiGP+h8/9x0CYRP3RQ6OzBsLUm7xo185+rrt4E6PpAtmiR8wV1nREuevRowu+lyv5Hq1mGtGALzL/l8bGWje0BqmmkV79qf9gMJXF87Vr2rrOeBitdTqGlys5ocxU3iNSqWYoRbrghS+84cmc8Q0WkgmtZZYKt0byPTw0cIrNvW0zwsqDeznfkc5KvkermUaKcv7X2ZaLyA1KqS/Pto5lJnv2D/Ls0CSuCK4IRU9xdCyDg+LgSJrrb9+3IHNEvYnGl1NCmUaEWIepdVdSi5OCD44QCsn1yQRHxzKcGM/SGY9UFW71RmrNZipYyETacrtHlvpoWjIeEXlUKbUiTRLNTsZTyvW37+OxQ6MowBHB8xV5z0eA9pjLhu7EvBOTrMQkJ42eU6kN2BHwFRQ8RV9nlA3d08P3iUyeExM5+pPxBQm3wM2tWmIloOay2ZIuWVYE80vGs9ADrFbqLdR4eDTN+q44SoGvFEVf2yIVsK4zvqDk1Csx0XWj53TTVefyvtecQ1vUpehrbXRzT6Is7wZAxHW4eGsv3//ga/j6uy+b9wtqNnvxUq6eYQuLtoZmCmCb19LQSA6GLb3tRFyHjT0JIo7gK/0mi0ec0Fd4vg/pUn7g58t8zummq87l02+5iFduW0NXW5RkIrpoFaBny+y1VCfSbM6Q1tHIJNxcWA3YUOmzWfQUg6ksN97xCBdv7S0b3gaTK1FXOGtdB88NTlL0FRu6EuH+5vuQNtMvdalEYc3nnCptr5mChwBRRxjPFJpuT61lL16qE2nWx7h1NFMD/kET97WsKdXSJjIFjo1n8H2F5/sztItKjemsdR30tEeJuLJg7axZtd+WkoY0n3OqZrboaovS2xHnE9eeD8BH7n5y0YfeSzXv7UocKS0X6p6EE5H1wJ8DG5VS14jIecDlSqm/m2PTZU+jk3ClEzEDQ5MUPW1XiDjC9r7OOSdeAm2zGbPdzdjXbBNLrZg8avScrrjtAR0CLtODNKUUJ8YztMejK2qScj4stfu7QqlqIWjEBPEl4IvA/zC/nwH+gek6bBZD6VAz8GhACX3JODC3dtHM5NTBvgKh9ZG7n2TL3sYEcauisGqZPRq9PrXMFnlP0T2PofdSMcc0i6VqGlkNNGKCWKeU+gYmD4RSqgi0vKDlUqR0qOmI4IiwsSdR1cn/dLBQE0IrJo+aafaoZbaIRZyGh95LyRzTLJaqaWQ10IgAnhKRtRhvBxG5DBhflF6tAHbt7Ofr776M3W97Of1dCVxHmMjkeXYwxcGRNGPp/Gl7aBfqjtYsW/Lp7HMptQTMjv5kwy+WlejaB9N/rwt1w7M0RiMmiD8G7gHOFpEfAH3AdYvSqxVEEMF0671Pc3AkQ9Rx2NyTIO/5py2d4EJNCK2Iwmq22aOW2aLRobdNimNpJo2EIj8qIr8AvAhtUP65Ump+iWxXGbt29rN77wDbfFUzR8Bi0gx3tNNZNHHP/kEmMgVOjGeJR3RujC7jPtZMs8d8XiynO6WoZWXTSE243wDuU0o9JSIfAS4WkT9TSj26eN1bObRSc1pOkyyBjbUj7pLJe+Q9n2PjGXJFj1jEbXqfS18sZROVVYTxnv2DjKXzHBxJE3WF9aae31K9lpalTyMmiI8qpf5RRK4AfhH4JPBZ4NJF6dkKYyGa00Jn3ZdqIpdq5xXYWLvbEsQjLkOpHNmiRzrvceuvXbDgPte6lnMlyildvrknwcmJHEfGMuzo6+Sjrz+v5dfSsjxpxA/4MaXURSLyF8BPlVJfC9oWt4utpxnJeOabGGelJtS59d6neWZwEqV0kpyo65BMRPCV4ozuthk+u+OZAt//YH2ZT+sRspXXcvfegVl9Ya2vrGWBLNgP+KiI7AZ+CbhNROI0N5JuRTNfLbTRMNFW+6jOdfxACJ4Yz4R5L3wFnlKMpgvEXIdMwZu3jXU2TXa2azmXichOvlkWg0bL0l8NfFIpNSYiZwAfWJxurUxKizweHk2HrkuzCchGHvxWF26c7figz/vRQ6OIQN4zIy8BFHi+IuIIBc8PXd7mY6++9d6naya3n+1aVjMRDU/mSOc9rrjtAU5N5RmcyKKAmOvQl4zjOmIn3ywLom4NVimVVkr9MzAuIluBKLB/0Xq2ApmPE38jQRCt9lGtdfzb7tsfnrfn+/h+hdlLILCEOY7MOyggSG7v+ypMbn9sPEPR80MhW+taVvo6D6WyDE3m6Yi7uALZvI6cU0pR8HyOjGaYyBTs5JtlQdQtgEXkDSLyLHAA+A/z/72L1bGVyHwEZCNBEK1OqlLr+APDU+F5xyNuhX13Wvj6Cs5a2x4GBTSaKGf33gGijoOIICI4juAgnEzlqgrZ0mtZGayRznv0J2Os60wwPJknGnGImITuCoi4wtqO2Azzis2pa2mERmy4n0DXg3tGKXUWcBWwb1F6tUKZj4BsJEy01flmax0fCM+7L6mTz7tGBgDMzUMAACAASURBVAeiOOoKPe1RPnTNi4H5jRYqk9sH/2oJ2Zjr0B51QgEPhNFgXW1R1nbo3B15z0dEJ213HWHnhi7O6etkKj99risxRDnAvlgWj0YEcEEpNQI4IuIopb4HXLJI/VqRzFdA1hsm2oqQ4XqOf9ba6fNOJqJs7EkQdR2ijpBMRFjXGePlZ67hk9ddWGYnb3S0UJnc3vMVjgjn9neG+y3VrqfyHgVfVRWYpfcq5jqhph5z9SNTed9Ot/nndAnFlfxiWQo0Mgk3JiKdwPeBr4rIIDC1ON1aeezZP8joVI6DI1NEHYf1Xc134l9Mf9+FFPeE8pBf1xH6u2a37c7H66AyuX0wgffBq3fOOI9gMnB9MoHEZIZ3SWnwyrrOGEfHsgBs6IxXfbGdTi+J0znZapO1Ly6NCOBrgSzwR8BbgW4IJ7gts1DuxN/GyVSOI2NZzu3v5KOv39nUP+TFCBmu94GfTUifjpDf2V4A19++j2dOTjCZ81jTEcXzfRwRUw05g890hYxq+9rR34lSiqm8R38yMaP/pzNE+XQKRet+t7g0kgtiSkQ2AK8ETgH3G5OEZQ527x0gX/QYmdT5gWOuQ09nlJ72WEMPzOny8a08zuhULnzgU9lCGJ12052P8em3XFRXJFmjL4b5hk9XHqe0X9mCj68UI5MFIo6DpxRFX+GJrsFX8BWpbJE9+wcb7vPpDPc+nULR5r5YXBrJBfEu4GbgAfTcyf8RkVuUUl9YrM6tFJ45OcFEtoiDhO5RI1N5it5E3fto9rBzz/5BbrtvPwPDU3i+IuoKHfEI6zpiHJ/Ikiv4FH2f4VSOvOezpbeNVLbAsbGsnpByhHTemzPI4dZ7nw6FeTIeQSnFZN6b8wXSLHNKab/yno8rElaPLRpfZKVA6WLU9LZH56VJns5w79MpFJdTHpHlSCMmiA8AFwVar8kN/EPACuA5KJgH3XH0nL8I+L6aDkaog2YOO/fsH+QDd/2E0XQBpRSegqKvyBXyjKULFE1QRMRMPvkKjo9niboOIuCI4PsQj8isQQ5Fz+fgSIZtvsIVeHZwEoBNPYm6XiDNMKeU9ivmOhQ9hTg68MNxJPRJjrjCus4EyUSk6Skvm00gFIdSWVLZIrmij+sI1164senHWqp5RFYKjQjgESBV8jtl2ixVKB3GZ/IevlL4opAg6EBBLOLU3KZSQ2zmsHP33gFS2SKuIxQ8FQSjoUQLYtBuXIJjtF0dueYpj4ijha+PYl1nYtZIspOpHFHHCWvjuY6AguHJfFgbr1RDXgyzSmm/1nXGOTaeAV/be30FRWBTTxtd5tqm88VFG143y4S0a2c/1x0Z4zN7nqfo+8Rdh+72KHc9epQLNvc0XTiezlSkq405BbCI/LH5+hzwIxG5G/28Xgs8sYh9W7ZUmguGJ3P4RS3oPBOlJQJj6QIXfPx+YhGHvs44Q5M5utuiVU0M9Qw7633AD4+mKfp+qOEG1PruiBB1tcBP5z3iEa0tdrVFQ4FVa6i6uScBaF9a14wA8p4e7wcacn+2wHi6wPHxDI8eGuW9u87mpqvOXfiNYKa26PkKBSQiDmf2tjM0mQsrUC/m8LrZJqSHBk6xubetJfmlLc2jHg04af5/3nwC7m5+d1YGleaC9ckER8cy+ErhANmiFkCCFqKZvMdEpoCvoCMWmdMtqpotLnjA80WPVLbIifEsjx4a5XXnr+fERL5MKG/pbWc4lUMppjVyyr+DzkIWmCDO6evgQ9e8uCyb2IxIMsqHqjHXCYVtzHW0dl3iS3sylcMVYWSygJiMaJ6v+Mye5+vS5Op1jSvVFhMRrS0WTVhxtlAMTTGd8QjvuuKsRRFgzfZcsN4JK4M5BbBS6n/WsyMR+T9Kqf+28C4tfyofDq0pFhia1C5O4ZAfEARxIF/0ibrC8GQuHA6XPlBz2eJCT4upPA5CxBHyns83Hz/OmvYo2YIXapivO389yUSE0XRBZyMz/QxSQ/oKXEfwlMJ1hJ64jlCby80rEISfuPb8Ms+IWr60+aKvJ8BQOAIRx9GTlL6qqzJxvRplpbaoPTkyjGcKKPQ5g9ARdxdtGN9sgWm9E1YGjdiA5+LVTdzXsmZLbzsHRyaZyEy7nRU8n0TUCU0QuaKPAoq+X2YLDjRG0A9UR8wtE26zmRVSxtMimOwLGE0XiEWcUMP81pMnee+us7n3yRMMDE+B5+OZ8OB4xCERdZnKeyTjLjvWd5UdM7AHBtrnn9z1E1LZIr3tUdZ1xmcIwuuOjPH5Bw8wlfeIRxzWtEXwlbbBltmgFRSMmSIecTgymp5Vw21Eozw8msYVGBiaJO/5eL4+ZsHX2rjjCL5STGSKbOiOhPtopttfswWm9U5YGTRTAFsMl29fw48PnsIRrV3lTYrFvrYI6bxPruCFrlC+0rbQeEQP0UvtkeOZghEUqmaVhlvvfZoDI2mtTQJRBxzccN+gNe2cMXs4gHK0VnjvH10J0HCy8VLtM50rat/aqTzxiBvahYMQ3LsePUpfMs7WKgnQ12YKnJzIUWL1wPMVyUSEjphbVcO97sgYDw2c4scHTxF3dURdMjE9Ynh2MDXjhdUZc3luaApXtBtgocTOEuQFEnOfAq10oTbbSuF9+fY13PXo0aYJzKXundDqvNTLhborYsy5I5FHlVIXN2VnS4xGK2Jcf/s+DgxPkspOa8D5oofrOiTjEYYm8zO26W2PEHEc1nbEmMprzWgsnSfv+VUF441XbudP7voJY+mCMRsojIzVQthxQqFbScSBM7rbwgoTV9z2AD1t0ZpVKCofptJ+7T8xgesIyteuXNv7OsNtN1fR+oL+B0Pyw6fSjGeL4XIBtqxppyPmzjj34cksp6YKbO5t48R4VifJQdjYo4Vw6fJSIaeU4vh4FtfRk5+5gm/MP+UacMQRNnQn6E/qicMgr/CQ8YV2RYc4By+uWtSqvHHdxZt4aODUkhSYzWQlVnFpAguuiDGvA6xGDo+mWdcZp888yAATmTxHxrKkckUiDnhq2uaqvSPgU2++sOwPNBCMpQQa2u69A0zmirgm7aILKDw8Xw+t28uTrpXh+bC5tz0UrEOpHMOTOdYnE6H9ORgeV9MED46kQ++GYHJNRE8uDgxNki16dMQiZAopNnQlyo5d6rZ2cGSSTMHXrm1KhRq773k8M5iZoeGOpwt4prJ04FKmUAxOZMkWPE6mcghwYjwbVlJO54scGc2yqUenlcyb0UbB8/HRWq/y9D1ob4+EWulH7n4SV+D4eA4x9yhb9Hn6RIpr/movH7y6dgh5pXnE83UfP7PneS7e2hvayANWmrZo80fUTzMF8F83cV/Lmmr2vojrcG5/p7a5Au1Rl75knGQiGmqMUD6ZlYxHapbnOTyaDu3JAVHXAaXdy9Z2xkmPZnCYnmQLUGgzSSBYN3TFOTqW5ehYBlBlSYKqPUxRVzg5kaOrLUZfMs6xsSxF5eP7WqA5IrTHXEbTBaJujnWd00K4NAH6jXc8gkJrnkrpST/fVwynCyQi2oPi8Km0tl0rnVYy7goTmYJ27VOYBOmKTGHalJEr6krKAMlEJLz+2/s6w34cHU0zmimEk46OQDrv845XbdJuf3vbecwk7NH26emR4tMnUtx4xyNl7nKlQnQolWNDV5yJTIGTE1myRR9BmzlmK/ZZy8y03ISz9dCon0ZCkf8VqLRXjAMPA7uVUl9qYr+WNbUmSD76+p1Viz8GaQ9vvOMRPF9pe7Dna83SrFNpN9y9d4DhyRzKn7ZjKqWF8La17fR2xDkymkGhJ9eUWa6A9pjLvU+eYHAii6d06Z61HTHGMwVOTOS4eGtv+KD/yV0/IZ0rUjAlfvqScdYn4xwZy5DOF+mMR1jbGeX4eA7Q4b2xiEM84tLbDqemCrTHIjP6v2tnP51xl2zBD800eArH1Wkk13drM4WvwC/6GM81cp7ihVNpIo4QdYSCcW0LtFRlyhu5jvYoibhCX0eUI6OZ8NomExGm8h7rk3qUEgj0XNHn8w8e4ILNPdx45Xbe+ZX/1DZjT834w/fVtLscMMPv+/CpDI5JiQnmwVH6+gTRg7t29s+qLVbudzGyni2GgLceGvXTSD7gAWAS+FvzmUBHw51rflsMu3b28/Kt3RwcSfPksQkOjqR5+dbu0J83yJk7kcnz7MkUB4anODyaoeDp4XiQKyLi6KoL1RKIj6XzRI2rmOf75qOIucLxiSyPHRoF9IPvGQ0v4mrPg9ee188zg5P4xs2s6OuCmOu74vQn42HO4T37B0lltfAN1js2pm2vO/o6w371tMWMPVV7MKDg2HhGC7u4WzOZfF9nHK9kDqJghFXMdehqi+rQ5+Ac/Okk7qCFrNaYp9sijlO2PFf0Gc8UyHmKNR1RYq6QLXpGM4d1nVpLPTqWIZ33KPqKiWyRD9z1EwB29HXiOFImfAV9LV3RwnX33oEZuYDXJxPGw0WFtmaB8KVQWeyz6GnTzf4TEwwMTYYllBY7x/Bi5fptdV7q5UQjJohXKaVeUfL7X0XkP5VSrxCRp5rdseXMp7/zDPc8cUILvYgOeb3niROcte4ZbrrqXG4BbrtvPweMOSK0fQbFKV0HfEhli0Rdh/v++2UzhqqZgkci6tKdiDA0pc0XO/o6mMwVwwmneFTbOj0fij50JVzedcVZPDRwiqirJ84EPTHlozg5keOirb3heezeO0Bve5SRqXyoafsoTk0V+Is3XRAK0utv30c84mhhY6L88HWgxUVbevn6uy8LNa33/+Pj4XA+U/DwPO35UfB84xGiOKM7bq6H1haLviLqOjgi4WhBC+XpfA6B7zI4FH29r/aYS19nPJzMC0wh6XyRoVSOTMHj5EQ2DL8OGJrM89G7n+QT154fCqhswQ9HIxFHRxAG7nIKZvh9B9qvcTEm6jihR0ypNpiMR3h2UIdpBy+5o2NZdvR3LvpQfrFstUvdQ+N0onO++CSi1SdlGhHAnSKyVSl1CMAU5gyMajOn9Vcxn3/wAKC04AuGx6Lbb7rqXHbt7Oe2+/bjGNuBP0ML1P6wuaIfPqjVHhaA/mSCBz887Sr2oo/ca9zf9L5jrosnPiLCEx9/LQDfuO0B1ifjHBvPgh9EwCmKamaS8XWdceIRl+HJXGgqaIs6ZQ9TUAro+HgOH5PvAkXR09pQaZTehPF4KHg6QY/jSBgMEnMlrLemAu3c01UtQncxc8y48amOuQ7ZgqdNFb4O6HAdISrCp99yUTiZ9uzJVOh7DdM+z9kKT5FA4z48qm3It7zhJdx679PsPzlp3PyC/iqSiWh4fyqH3FFXm0g2dCfCDHLBiCPQBvfsH+TgyJROiWkSIAX3TSm16EP5xRTwqz1/hO8bL6Lnhvjajw5zZxV3TmhMAL8feFBEnkf/nZ4FvEdEOoAvL7jHK4jJXBFfEWo/QTjvZG7a3WpgeKps5r+UghleR10nFIjNfFiCB3tjd1soWF1H2L6mo+yh0f6zk6Gg29jdRsSV0E1rxv56EmUuW2f3dQBw052PMZUvGq8PIeo6FDztCx0RMZnI4mEZ+IMjaXxfkYg6dMRdcgVdSTmYyBIz/I86QldbhGzRpzvhUvBUmBnsvbvOZtfOfpL3RXjmZIrKxHOegmzBCwVuQOn3m+58jK62KFt623nTy5J868mTZoQiJBNRYhG3asWPTMGjMx4JzQ5ndMc5OZGjqBTb13SEde9uvucpCp4KvWIKviIRETZ1J5jKe3zomsUNtrC22ubj+YqxdJ4H9g/y9/te4Ikj47Ou30hC9m+JyA4gqO/yc6VU1nz/q2rbiMgXgF8BBpVS55u2NcA/ANuAg8CblVKjop1Q/xp4HZAGfkcp9ajZ5gbgI2a3f6aU+rJpfznwJaAN+BbwPqWUms8xmomYqfNg4qWs3VBL+AYoCIUI1P+wnLW2neeGphB/OvOar+CcddPr1SrdEwgG0PZBnbNY+9GmPY8XTqWJusIFm7rKvDWCIIPK/V1z/gYjQHQWtVxR6XwYokLvg8B97chYJpyw0pNo0N0WpegruhJ6Ek1E6WrHxrLaHo+wbW0n179iTU3/2iDdZjWyBW9G/otSJrJFtq7R1/3oWIb37jq75nEqh9wfff15UNJ2UcnEJmizTdTYzIOJuUBDjrgO/cnEog/lbTRd8/B8xampHPc/dZK/3/cCPz8xnTjyJRu7am7XqBvaDuBFQAK4UERQSn1llvW/BPwNULrOh4DvKqVuFZEPmd8fBK4x+98BXAp8FrjUCNOPoQuAKuAREblHKTVq1vk94EdoAXw1cG+jx2jwGsxJxNHmh0qijipZR8pcmyoRdLTaBaY6Q62H5fLta8qE4eteegZf2fcCk7li6A0Q5HIIqOfB3r13gK62KIK2iZYmMf/m48dxRGcU83yfo2OZqkEGgdkkEDKO6JdO0ddaqvLMRJpJ+lN67p6vs8Vt6m0j6gjrOmN6QtDzjVlHsa4jFvb7phrXcTLvhcK+koI/t/O6mAT6g6nafrzBNa0mGOeqeVeaIhNRZIt+mRBczKG8tdUunKLnc2oqz78+cYw79h0K53UALtzczdsuO5OrXlz7ejbihvYxYBdwHlrYXQM8SLlwLUMptVdEtlU0X2v2A9p0sQctHK8FvqJ0aN4+EekRkTPMut9WSp0y/fg2cLWI7AG6lFL7TPtXgDeiBXBDx1BKHa/3OtRDPBIhVyzOaI9Fpi93e8wlU6geqQbavFDpdlT6sHTEXPJFn79+4DmirrA+qfMw3PXoUd5+2ZlzRlzN9WAHAuLEeDaMFvN8vyyJvKdgZLLA2k79sqgMW/7I3U+WCZnAJ9lX4CpdfWIq71EajRmYbVDan1eHF0+yubeNjniEY2NZIsZmffBUek6f2S297drFLLjWZt/BEWfTgAEmMgWOjWe03VepprmCBSOaIPBFu8EpOmKR0xoxttpttfOl6PkMT+b450eP8rUfH+KImTMAeOVZa3jbZWdy2fY19LTFZuT9LqURDfg64ELgMaXUO0RkPXDHPPq+vkTgnQDWm++bgMMl6x0xbbO1H6nSPp9jNFUAB5RqV5XP+Lnru3j6+DhjmZmC2hV9gw+dSuOKcNt9+8tqlAWTWkOpnPbx9eHYuLbpRl2pKgwbJRAQ2aK2v+KVn4OvpvNLHB/PVdXmg32A9nfMm1Ucge72KDv6k9MBGaY+W6UwDLwe2qIuB4anwoocgRdEqVtW1dwRF2/i2cEUhWJemyJK9h/4R8/GoVPaxq6TBLlN8xQoHdEkExEiJsBkbUeMj9z9JFv2Wm10KVLwfE6MZ/nHR45w548PMZjKhcv+y451vPXSM7lkWy/dxo1yLhoRwBmllC8iRRHpAgaBLQ2fQQnGXtucZBRNPoaIvBt4N8DWrVsbPq7rmMAHY+esSFAWPoCJqMvoVJ68cfbXVSP0Rq7o2dRnBifDQpEw7RERpIsUBHytRZ21rmPes9ilGmQyHmFoIotf4ss6G+OZQlkfg3MszVeh7Zxa8/3L66bDrgO7tSNCUU2rpxFHe0tsN3bl0qTuSmE8MtwZPrMw7VL10MApPnndhdx679M8PzxF0dPeBlHXIRF1OJUuzHpeoenFV6yN6303w1OgckTTGY+gmJl4KUg+tBQj4ZZjlN58yRd9jo2nufPHh/nGw0c4NaUdvxyB1+zs562XnsmFW3robouGf6P10IgAflhEetBBF4+ggzIeamD7gJPBsN+YGAKv76OUC/TNpu0o0+aEoH2Pad9cZf35HGMGSqnbgdtBJ+Np5ASjrnYnCtynlNJuS7GSSILSBzDqpsPkOweGp1BMuyMJEHUp07gC80BpHoYgm9d8Z7Gr+RlnTAhtPSdf8FRZleTgHPs640xmi2HEXV8yjutI2fl86JoX8yd3/YTJXNFE7GlN+Jz+JB+8Ws/53nzPU7gioTdEUSnwFPtPpGiPuaTzE5zR3VbWp0BQVhtmf/o7z/BX33121nNySswTEVeYzBXpp3meAqX9uv72fWXJh9pjEYaM3Xlzb9uiRcLNl2ZX+Fiq5Is+h0enuOOhQ/zTo0dCN8qII7z2JRt466VbOW9jF12J6Iw0sPXQiBfEe8zXz4nIfWj763xKEt0D3ADcav6/u6T9D0XkTvTE2LgRoPcDfy4iQYTALwMfVkqdEpEJEbkMPQn3duD/zOcY8ziHWQnMCxPZYphnoCsRYcf66rOhgYC75vwN/NV3nw1DhgXtJ7s5mSjTuIKhfZCHwUeFfrPzncWupkH6StEWc0MXsWzRDz0VqjGVK854CFO5Iuf0d87ItFZ6Prt29vPJ6y6cdTIoCF55ZnASV/T5+qJ9cjviLqemdDhxaQKk2Uo2jUzmZrX9BhU6ip72oRYgV/RmjepaiEZYzc0wlS1S9P2mB0o0g5WecCdX9Dg4NMWXHzrI3Y8fYyqvTWHxiMPrLziDt166lXP6k3QlImV/243SkBeEEYI70F4QiMiVSqm9s6z/dbT2uk5EjqC9GW4FviEi7wReAN5sVv8W2j3sObSL2DsAjKD9BPCfZr1bggk54D1Mu6Hdaz40eoxmE+QD1gEB2l6aynlcvn1NuE41DeLv970wQ91USvu2nrVuOpFMqRtZqY9pf3uMjpg7LxtiNQEQd3VKy662aDhZ9NOjtf0aPQWT2QJ/8NVHwsAITymOjWZCQd5ltOtKDXKuyaBS+3fgVxxo1EG2tFNTBTriM/NOwMzrXTppUo3AfBQxtiRxBFESpgKt7OtCNcJqboa5oi64WcpSSWqzUhPu5Ioez5xM8cUfHOTfnjgeznO0x1yufdlGrn/FVs7q69B+3gsQvAGNeEG8C3gfetj+OHAZ2gTxmlrbKKWur7HoF6usq4D31tjPF4AvVGl/GDi/SvtIo8doJg8NnKI/GSuriNHVFuGhgVOhu1Q1DeLoaIaIqzMH+MY9y1c6pPe3Xjlth660H160tTf0xc17ftMEQHd7lFNThTLXt7lMEoFNNeJAaZBZpuBxbDxDruiVBTA0yq6d/XS1Rdm6pp1UVocUHx3LEHMdYiZIpFbJpqhJ9FPqKjQbec8nas4jJtqd7qlj47z/Hx/n3IpKIQvVCKu5GbqO0N1eLuTmY/6wCXfmJlvweOroBF/84QHuf+pEOKmcTET49Ys38Zuv3MrW3nY64s2tYdHI3t4HvALYp5T6ryKyE/jzpvZmhXB4NM3ajnhZGsbKYXc1DaLo6+HumvYog6np9IpKwWf26HqoQfrDSo0xcOxvpgCIui6vO38N390/xFRel0e67KxefnRwdNYgEn0uM9t8pUjnPX7rlVvZvXdAa+omkCOYaErGIyilmMx7NYXFlt52DgxPhvXvXJEwDeZsJZsq8/vWZds259ERc0lliyCQKcCB4cmyF1yjGmE1oRhUCgleINdeuHHBVTQWy1a7UoI4sgWPxw6N8oUfHOS7T58M/65726P8xiVbePMlm9nU005bbJYE2wugEQGcVUpldbIViSul9ovIixalV8ucerSDqjmDHQdEhyy7FRpkrujzqe/oSaNqJdvnEgDVSuRUzq5XCoBAq+5Lxun1fE6mcvz4hTHWtEcZnprde6AS1xFetD7JoVPpsEJxRITjYxkeGhjB1afOEXPOEaFmmfrAdQ1AjLeJIKzpiNZ84Wzpnc7v6xibbiMzq6lcEcfkavB9RSpbXj+uEY2wplB8w0tmuA9esLlnQYESNuFOdTJ5jx8dGOELPzjA958ZDv8W+jrjvOWVW7ju5ZtZ35WomUSnWTQigI8YL4h/Ab4tIqNo+6qlghuv3M4H7voJR0czWtA4OgdtEJ4arFOpQSQT2hVpNJ2foWEGAqNWyfbZBEDlA39wZJIfHzxFX2esvJBmhQAItOqipzg2nkX5Oqy3UeEL2mVsZCpHKlcM8/aWVoOrjBwsKoiZShmV57xrZz/JRGRGnuLOeKSmxlma37fCHbguCp4iETWeKVJePy7Yf70aYSNCcaGBEjbhTjmZvMfeZ4f4woMH+NGBU2H7pp42rr90K2+6aCN9nYlZgyeaSSNeEG8yXz8uIt8DuoH7guUi0mvCgy3oyahsUT/mRd9HsuVCq5oGEQjom+58LHR3AZNwXQUhuorb7ts/Y/g6mwCofOAnMkUc0bPsfclEKAAq9/vsoC4pdGB8CuUrigvw2E4mIpyaKlDi5jsnRSNcgzL1mOt1eFQXIe1uj5aZedL5Yk0b5K6d/ezo6+TgKV1JZD6nEvh0B0mFnhuaRCn9orp8+xraow4HRtJ4vk8s4tIec8N+V2aPO10TWCvNVjtfpnIFvvv0IF/4wUEePzwWtm9b285bLzuTX73wDNZ2xOsKnmgmtihnHTRalPOKW7/LkbHsjPbNPQke/NCMucEZ7Nk/yI13PEKu6JcFQJTmjN22tn1GwUOoPiQMastN5vSk1VTeC13czjtDu8YFNetK93tkNMOajijDk/lZ81aUUmto32722chfm6A154grtEUd2uPRsNDj8GSOock8/ckYazvidRV+LB0JHBiemtOOXdmXiCng5ysV5keOmPvhKehpi9DdFuWoufebehJheafSfjVahXohrPYCmZPZAv/20+N8+Ycv8LPjE2H7ues7+e3LzuSal55Bb3usoeCJeVL1ALYo5yJwtIrwna29kl07+3nvrrP51HeenZEq0fd9oq5TdfgaVLKoJCiAOTJZCKPyAjexiUyBrrYoJydyM4bFvcYLIvAvrhfX0T60QXkgHWbsz5l3oRpB3t103iNS9BiZnPYsibvCYCrPYCpPR0wnm5/LlS0YdTw/VJ8nROk5FUoktmvSYiJCoE+PZ4om6GQ6p/CW3vayEkSweBNYtbwdlrOtdr6MZwrc/fhRvvLDF3huaDJsf+mmbt5+uU6Q090Wm1fwRDNppgBe1JDi5UStC6HQlY7rcQW6YHMPybhLKueFQRnKVxSBjT3xsnXnGr6WFsB0TJSdr3QEXVA3reD7bO4pjyRb1xmn6PmsD8HOGwAAIABJREFU64yz/+Rkjb1P46A76vkg6Pprgraf5j1FRGYWCJ2xj9LMZaK9D2IRl6lcsczrIVfwKCotCF98RheZgsddjx6tah8vJbBbXvDx+8vMPLPRHnXIe4pgIjzvaY03IgpHnPCloqDMTOMrXZppY3diRuBJs4ViadL7VLbIifFs2QTmShe4AaPpPHc9fISvPHQwTKoPcMmZvdzwqm1ceW7fgoMnmklzndosczI6lafo+XO6Au3eO0BfV4I1vmIopQtGBlFoJ1M5QGaUkK/Frp3lBTDjEYc1MZfJXJFs0ac/mZgucFlCpuCxY30XX3/3ZbzoI/eGTum18CF8+xR9xeZknKNjmTA5fTTi4he8UAjrMGud0+KMrgQoxfFUjpipOlFUikzB53dfvclUGSHUWIJ9KAjrpTUyuz/bJEsi4rBjfTIsXRQkmJ8ushnY9kGYqdkHj7YIOEhYmqmUahNYC/HX3b13gHzRC19SEVMvsNak7UpCKcXIZI6v//gwX/3RIU5MTI80X332Wm541TYuP3tt04Inmok1QSwCMVfK0jaWEhTcXNsRC4XFnv2D3Hrv0xwY0VrS9nUdDE3m2NCVCPP6BnXLHNH7qFZCfjbOXd81w+6YLLE7BhpUrWFxZ9yl6PtV8xzX4tBIWpcXAqJG3kVcXW4eCL0RzjDRbAPDU0Qdhw3diTC6LUioE4s4ZPJeqLkH7wqn5IGqNhKoJdR29CfJ5keZzM88ofVd8TDkOBZxdFCEqcJRqLgABV+VVbYttYFHHSkrzTQb8/HXLT23IZOVy0HCl5QrOnvXSgkPrkQpxclUljseOsTX//MQI5PTCXJ+4dw+3vHqs3j5mb1ND55oJo2GIl8B7FBKfVFE+oBOpdQBs3ju2aVVQlebnriqRiB8RtMFoqNp9uwf5L99/VFSOS9c5+kTKQTI5ovkPSj400lxlIK1ndVLyM/GXHbHuYbF567v4okjo0xVEVi1UGiB6ysPZGY+YQE8T3F8IseJlE767gocG8uysQeSiWgoVHf0Jzk4MhlGFzomAVG8RJOtlvuh0v3uxjse0dWRCx7V0jEn4w6+0rX2Lt++hs8/eICCpyhS3u+y16tAb1uEsbQ2aSRMnzylwtJMc92fRv11K89tOKVzdUQdwWE6Y1zcdZZ9eHAlSmkF5Ms/PMg3Hj7CeEZ7GLmO8Mvnrecdr97GSzf1LFrwRDNpNCH7JeiKGF8Eouh8wK8GnbNhMTq4HFnXEWN0SvvyVurBwe9c0ef4eIY/+OojVROzK2Ay75dl5AoYS+fZsT7JeKZQ96x5PXbH0nwLYaSaySmxoSvGQw0I3+Ac0nmv5Fc5EUenn9SpJYV80cdHF/Q8dCrN1jXtuI6Efb35nqfY0B0p84LoatORc9UmskqFWipbYGSyQMHzyc1i+s0WFZ+4Vke333zPU7QbU02lx4Qj+j5P5orkPMXODd1lpZlKX3JBRrfZaNQ1rVJgb+hO8MJImoKvcF0V5rLo7oiuGJczpRQvjKT5wg8O8E+PHmHKKC1RV3jdS8/gHa/exs4NXYsePNFMGtGA3wRcBDwKoJQ6JiLJRenVMsdECxIrKbdejaKvfYRno3JbBeQ8ZXL/dlbdBmoPvefSxGoNhU+Oz568Zj6U2pwr7cu+goMjugbdtRduLHuBPDuYIl/0aYsKUzmPgpcNk7vX8rcdSuXwlT/nTHHBm/Y5jrpCd1uCTN5jvHLCTumacQXfpyMWCY8938i1Rv11KwV2MhFlXWeUIfOSibsO3R1Rou78824sFXxf8fzQJH/7/QHuefxYWMk6EXW49sJN3PCqMzmnP3nagieaSSMCOF+a3NxUQ7ZUIZUr0tseYWSq0JCvaSOMTOW59deqP1gLif+vNRTONeCGthAqh/e+r2Z4N9x8z1N0t8kMU0qt6MCip3NQ1HsGD79wCl9pU0JHzGUiW5zZL3Qx0agruA7ceMcjdMZdzl3fZcK8tZCsFohRjUZd06oJ7K427RPd2xFfES5nvq94+sQEt+8d4Fs/PR66QnbGI7zpIi14t63t0BnrlimNCOBviMhuoEdEfg/4XXRydksFnTGX42OZhn1eG0LN7kEx3/j/WkPh00XlJfMUpLL5sO+NnFtpRY5GKHiKuKsT/KQnjRtglQgTAVCK0fS0dvz08XGd28LYpz1/bo8XaNw1rZbA/ujrz1u2AjfA9xVPHBnjc3sH+PbPTobePz1tUX7jks389uXb2NTTdjqCJxadRkKRPykivwRMoO3ANyulvr1oPVvGTOW9mqXQm0EQxVZK5Yz4hq7GfIUDSjWriYxOcp7Je3Nut1BmS44zli7y7EkdxdSIrTSoyDGRKdDIGbiO1nCL3nTIcvAyLXXXU1A2kZcr+qEpxVPaXzgoWlrPy6+R3Aq7dvZz3ZExPv/ggTBT3VyBKEsdz1f858FT7P6P59nz86Hw2q/rjHH9K7fyW5duZX0y0fLgiWbSkBeEUurbIvKjYDsRWWMn32ZyfB720kAAOSbCarbKEwpdRy1gxoz4ZI6jY1lEJHTnGp7Mkc57XHHbA3TGXESEVK44w9800KyGUllGTN2roF+LZU6B2tWJg+uSzvtcf/s+hlI5hidzrE8m6vKDHjKVL2IRZ1Z7fCm+Dx664GdpBKADuO5Mf+laeL4CB8bTBY449Xki1OsLvGf/YJipbqvRgOsJRFmKeL7iB88Ns/s/nucHz4+E7Wd0J3jbpWfy5ldsYV1nbMn58DaDRrwgbgT+J5BFKwjBs7G8LfyLwBzxClVZ1xljbUeMgq8oeooXTtV+YCOOrqMWUDksX59McHQsw4nxLJ3xSFneBFfgOROGu6knMcM+HAyFb7rzMXylSERcfOU3HkPcAIJ2IfIrhg2lj1u26DGYyrKhK87RsWzdftB5HS0RegXUQxB5GHEclNIBMI5Je1npBzwXvlLkPEVHzOX62/fNKlgbsd2vhJJAhaLH934+xO17B3j4hek8XlvXtPP2y8/k1y/eRE/7yhS8AY1owH8CnK+UGl6szqxGHOBFG6aLTwY2S1eoacYo+lpAvuuKs7jpqnPDYXlgMgiqBxd9n/FMgXTeoz8ZY11ngoGhSVyTkXx4Ms/2vs4ZD25p1QkRYWBoMqyJ9f+3d+5xctX13X9/z5nr7s7uZpPdJCSEXAiGiyAYEVqElFIL1IJSqlBbEC9oiw/YvvQRX/r0gj59Qe1Tqy2PEC0itV6xKo8V1KoRRO4RBCWSZBPIbbOb7H13ruf8nj9+58yemczMzuzO7Oxmf+/XazKzZ87ld05mvvM939/3+/nO6lxFT2xNFqXdiehb+7Ctix6Cpb3aOFOgfyEi9I2kqsqDDtvCeFp3Fykm+LXOl3t7f4csHYsUge62qXSzc9d28dLhUcZTuXwn63L4edsh0ZOmxR2Piw1rLUZ1IbcEyuYcvvdCH597pJcXDkwJ5Jzc3cY7fnstV551AomiczteqcUA70b3UTPMAkH/widiIUaSWR75cGFHp6kuwvo/x4WC22BbdIfeZNbh0z/exZ4j44wmsxwcSubb2vtlqCJCa8Tm4EgKx1VE7GxBY800Uz3eXhmcLGgrH4wFL2uLMlHBI68W24uftkRsMjldEu33iTsynmJwIkunV9AQNGyOC10tUxOBiViYtqi+fr7cZqnuGicu0T8glSIGCp2P7EtUhiywLQvHM76WgqFkFluEU3ra+MqN53Ht1sfZc2ScvtF0yX0K5Kv1RGBlR5xIqLSA0kxlKheizGQ66/DtZw/y+Z/1sjOgLXLaynbedcFaLjtjJS3zuGqtEdRyth8Bfu7FgPOfPKXUzeU3WZxU7LbguV3BmGzwlrRcF+FfHRwlZIvumoH2JjM5h289e6jgeLmAtVFKsXdwMj+jf2A4WTa2nHUUH7r/OT559VmALvbw83AT0fpkQfg/JO1Ri0zOYSLjMDk4iTCl7ZB1XGIhIZVT+eyDkMDQZI7WaDYf005m9cTTdELz5SoSIejt6pLh1kiIz1xzNgAfuv85hiazeAqUZB2Xnf1jbP7ED+luizJRppojWLEowJvPWslTLw8fk0lSyrDWYlQXUkugZCbHN57Zz7/9bA8vH50653PWdPKuC9Zx8aYe4t45N6J/3Xymaj1gEXkS+BnwPAFRK6XUFxsztPlDrXrAa2/9r6rWa4vo23HfU3rzWSvpG83kv4RjqSwDY2lSOSevweB7VyFrSlOhFMUTSNO14NExzymthrBl0R4LMZzMltW1mA1+hd90exYoUIVrjdgkYiEiIZvWiE3GcfPX6pXBSVyl963vMsIVuzj72AJdrRH+7LyT8t7z0fE0jqfz6wYE3KO2sLqrhVcGJ/PX1/L+P3KOm/9ilBunTyn930ravcAxhslfNl9zfsdTWb785Ct84dG9HBqZEsg5f/1S3n3hOt5wcndB8cRxrl1cMpBdiwH+hVLq7LoOaYHQKANcire8ZiU/23WUkYDhC2YgTGdI68VcHada/Ji4bWljd9OWDXz9mf15ofmDw6mCHyRBx24HxjNVGflVHVEs285/+Xf0jaG8MI7jlUv712R5Iuop0vmKbha2JaRzDq7Sxnd9t65SnMzkCFvCZNatyrD4HmDQqAILyjANT2b498de5r7HXmZgfOo6XXhKNzdeuJ7Xr+sqWTwxl0L1TWDWguwPisiNwP+jMARh0tBmQT7S4HmD3/9VP7ZFgdfpBr78c2UU55PxBW18gzKRj/UO5m/Z+0ZSx2QnKGCgQviheN2DI2nWdbfmv/zRkOV1Dply0/1r4htff1nGcbGcqdvCjOOy8/AYjlKELaElGuIfrz6rKm+1VC7wbDtel6Pet/tHx9Pc8+gevvzEKwx5xS+WwO+dupwbL9rAa07srFg8sZAnFmdKLQb4Wu/5I4FlJg1tlhTfgExmHUoVVs43g9gMcq5iLJVFKcX2V4Zoi+oy4XLdOvylHVGbkXTlLA5deOHSOzCeb3EPfv+36VPYfONroWPdWZTWAvE6KAMz9uIaYZjq2a6+byTJ5x7Zw9ee2se4FxsP28JlZ6zkvReu59SV7VUVTyzEicXZUksl3LpGDsQwxXSZpvMtPDB3KPpGUuRcRcgWVnbEGZoYnXartKMIW5SUnwxyYDiFLbrjRtDgVluA0uOFPHyyjh7nkpZwRVnJSl7oth39jCaz9I2kCrJGZmuY6pFHvH9okrt+2ss3n9lPMqt/4KIhiyvOOoH3XLiejUWTydOxkCYW68W0BlhELlZK/VhErir1vlLqP+s/LEMlFqfx1WGIXM7FFlAu/PrQaMUfKz9enKmyAg7wZjnBmUalrpiIBcs74hyZyOisDu94J3TEScRCJb3V6bxQ//3WqE0y45BxXA6OJEnnHCKh2amczcar7h0Y57PbdvOd5w7qQhd03Puq167m3Res46SlM9PpWoz966rxgC8Cfgz8YYn3FGAMsGFO8L1St5w6ThGWCDFbKur/BrEtwXHcfFPNWnAUjCazxMO2TgVUOl+7PR5mMpMr6a1O54X673fEY0RDdj4jZjLjcPtVZ87KMM3kdn/HoVHu3LaLB5/vy6c7tsdCXHPuGm74rbWsLOopOBNq0cM4HpjWACul/sZ7eVug+wUAImLCEnNENKTTnOZIFXJeEpivrKq02IJpY79B/OtrUZsB1qmBwuGxFMsT0Xz36xVtU62NSnmr03mhwfcTsTCJWBilFCPJ7KyNVC23+8/tH+Zff7yLH714OO/Zd7VGePvr13D9+WtZloges42hOmqZhPsmcE7RsvuB19ZvOIZyOF5bIt/xC/ZFWyyELd11uFpNhlo0jGMhK69kVquUh0KnyaWyLn2jaVZ2xGiN2ExkHHoSsbK30dN5oY2clKrmdv/JPUf515/s5uGXBvLLlrdHuf78tbz99WvoaInMehyLnWpiwJuA04GOojhwOxBr1MAMU3S3hRlLO6SyLhFbsL1qOH/iY7GQcXWsMZV1SrZ7mg3L22P5SsGZ7DfnKizRyl79Y2kuP2M5faOZiqLs03mhjZ6UKne7//BLA9z5k108sWcqw/TEJXFu+O21vO11a+Z1k8uFRjVX8lXAm4BOCuPAY8B7GjEowxQCDCdz+dS0jKOISWFhAJSXc5xP1CN7w3H1uUfDWgWtkmxnLcc7OJLMd1yeyXV0PNGdkCVkXZdvPXuIFe3RfEl0qRSv6bzQuZyUUkrxg18f5v/+ZBfP7Z+qHlzf3cp73rCeq85eRXQB9VpbKNRSCXe+UuqxBo9nXjKXlXCliNi6x1xx37R6p6M1Or0tZEmBVsVM8NNJl8RDHJ2sPLvmlwhXKtn2iYb02GpUmywgMk1F3Hys6HIcl+8+f4i7tu3mxb6x/PJTVya48cIN/OGZKxd0y595xKwr4Y6KyI+A5UqpM0TkTOAKpdQn6jI8Q1myjiL4HahXVdxJXS0cHEmCgpwnzNAasVnWFqFvNH2MwZ8tOVdhB1TCim1xNT8AroIlLSGyLlo5LVneCLtVxooBMjk167sJfSwrX7kYNPyVUryaIUCTc1y+uX0/dz/cS6+nDw1w9omdvO+iDVxyag+2MbwNpxYD/DngQ8DdAEqpX4rIlwFjgBuMolDlLGRV35WhEkfG00Rsi5zXAy3nTVrtH04CQmc8RNZRddEC9nEUdMRCJLMuWdctMHTVntFkxiWTcwnZlZP8a/HoVWB9v/ptuktcvH+FNsKC3j4SMGDlJs/qWZFWDZmcw1ee3MfnHull/9BU55ZNyxMg0D+W4t6f7yUashZVOlizqMUAtyilniyqbKkyw9JQT+phfEF7aEviYS1Y49lYlXOwRBvjiYxDZzxcVwMMMJLKEbKEcJXhgWLSOTd/P1fJyCZiIUaL28lXIpBhYounX+z9KPl9+ILKaKWOq4C2qE0y65KIhVBKkcw6jCSzRGzrGPnRmVSkzcRjTmVy3PfYy9zz6F76RqeUyS46pZvz1y/ly0++QtgWlrREGv4jYJiiFgN8REQ24H3uRORq4FBDRmWYEwQYmswW3HK7LtiW8rw5VbWgTa1oj/5YExYSLYmZmib84Y+vErV2svGvQUj0/jtjITJeS3u/DPjIeJpU9timq+L9o71n4ZaLT+ax3kH2D03SGrHzIYliL7fWirRaPeaxZJYv/Hwv9/58L4Nejz9L4JLTlnPT75zMWas76yb2s9i0fOtBLQb4JmArsElEDgB7gLc3ZFSGOSHjKIIaKb43malv6Lcmcl658WwJWTBaIT4MXrsj0ccM4ni5xkcnpn6cklmHI+NpwraQygld8RCDgXb3tgWWWKzsiOIquPmSU/A7FVy79XGvE8ixBq5Srm8pg3bHQzvoH03hKEXEtuhORAnbcoyxHJ7IsPWRXv7jiZcZ8a5DyBIuf/VK3n/xyZyyPJFftx5iP3MdSjleqCYP+Bal1KeBlUqpS0SkFbCUUmPTbWuY/7hKGyv/9UKjVAgibAm2rXviVaIlYhMNWQWGFAqvQ74/nYJU1iHnWlxx5gr6RjM8vXcQx1shGrLpTkSxLaEnUZgeX8nAffzKM0rm+p6/vusYg/ah+5/jyHiGsC1ezz/FweEUKzuieWM5MJrisz/dzVef2sekFzqKhCze8poT+PMtG1i7rO2Y61CPgo/joUloM6hmmvMG7/lfAJRSE8b4NpeTulryrXLqQc5duMa3VJhBRMhVEVuOhy1dQFHl8Ryluyx/9/k+AP7HxSezekkL65a1sm5Zaz5mXFwoceKSlmOKZo5OpBlJZvnYd16gJWwRsS1Gkll6EjFuu+J0HusdzBs0Ed2z7ognLp91FK6rQx0icHg0TXdblI/85/Nc8A8/4Z5H9zKZcYiHba47/yS2fXALd1x9VknjC7rgI+soJjM5lFIVy6fLsW9osqq2S4ZCqglBvCgiO4ETROSXgeUCKKXUmY0ZmqEcrwxOLlpFtCBhW0uXFU/kVTuxNziZrfmHxzeAe4+Oc2A4ydXnrMrHeksVSmzb0c/QRJq9RycIWxbL26NkHJfDo2ls8UIbltDmibb7237sOy/kveaxVJaDw6mCyb+M4xJWglKKnILn9o+wfd8woCcf/+TcNbznDetYlpi+WLUeBR+LUcu3HlQjxnOtiKwAvg9c0fghGabDGF+N7tlW/mpYVNZ1mI3XPzSRZdWSEI/1DpYtrgjGRVd3xjk8lmb/cIqQFzexLEvHmF0Ynsxy+4MvluxKPTCWRgJ3PH4aYjAbxlGKrpYw152/lhsuWEdHjW3dZ6tCthi1fOtBVZNwSqk+4KwGj8VgqIlySRB+/7hGziWmci45x614i10cF22PR5jM5Og9MkHYlnyXCG2EFXsCHYODBi3tpQZalg5FFFcTdsbDvO+iDVx3/klNa+u+GLV860E1k3BfV0q9VUSep9D5MiEIw7yk0fFs3xM9PJrm7DVL8suLsxZeOjzKyo5Cjdyco4tPMo4Cx/G6UcsxsexCg5bEVbpTdVAsSIA/ef0a/vpNp5XVaag2NaweKWSLTcu3HlTzc3mL9/ymRg7EYJgJpbIgFNOHH2rF96r9/YcsyLpu/ha7OA1rz5FxBiezDE1qkfZlbVozd99gsmC/Cl1YYwGvWlE4SXbRq7qxLOHj3/01O/vHyXpnFLaFRCzEJ//oLH73tOVlx1xtalipsb/3S8+QiIXY2JMwnmwDqSYGfMh7frnxwzEYaqOcsxuyddVacY7vTLGkMORhibB+WWveMAXDDaPJLEcnMp48JflWQiiV1w4uDp/YtvDhSzfpc1KKH/zqMP/yk528cGCq511LxCYWsjhleYL3XbRhWqNYbWpYqbEDTKZzJp+3wVQTghij9OfcD0G0131UBsMMsbwyYlfprg0DXurWbMm6et9hW3CVNqCXv3pl/v1gru+R8TQWOhdZ0AUTKU8hzbJE5ykryLlTveriYZu7frqbv/r6sySzbkHa2lmrO3j/xSdzyanLa2pyWW2BRamxi0W+eMTk8zaOajzgxHTrGAyNpFpRnYgttMdCLGuLcnQiw3AyWxfjawFi6TLtrKOIh23a4zoDwq92C2YtZBw331k5GrJY392GUoqd/eP5rAfbEmzLxlUK13WZSOd4+uWhggm2Vy1v43+96TQu2Ng9o3FXmxpWbuy+mJDJ520cRm/OMO9R6JZB0ZBF2DrWA7QtWNEeZUVHnH/849fw0F9exJ+dd5Ke6JolHVEb2xbCti6WiIe1xm/Ettj+yhAX3PFjrt36OOev78oXM0RsC0dp4fhur19aMuuwflkrbdEQjlI4rkvOccjkXLKu1/HZM75t0RAndETpao3O2PhC9QUWwfXKjd3k8zYG01vEsCDwFdCCGQAK7aW5StE3mkaAG+59ik0rEiilqpKUrMRJXXHa4xF6B8bJubpbRsZxGU1mOTCcwhJd+rt/KMljvUdZ2hKipz2er7Drag3TFg3lDd//+gMd4/377/2a3QMTx8SB22MhehJR4hGtouZ7nTPNUKg2NSy43shkpuTYTT5vY6i6I8ZiptkdMRY7xZNWvg9crhLOX2e2n+wN3a20REKBSjSV9xCznmCQozjmh2HTigSXnbHimAq5c9Yu4Qs/28MXHt3LcFLrT9iWzjyIRyyWtEx1F/Y7aPj5wGFbl1cfHkuTdRSn9LTx4Us3NSQu6xt8k89bV0oG740BrgJjgJuPoA1uPcIK1RANWSRiIcbTORxXIUDOUVrvwfs7SNAAR0MWPe1a02HLph4GJzJsfbiXLz3+MuNprUwWsS3ecs4q3r/lZHYPjOeNbLCK7LYrTufuh3vpH0uRcxQHR5JYSH7yzrJgY3cbt152qjGQ8x9jgGeKMcDNJ2JbOK5btvqtEdgW+R5xtqUV0XoSUcZSubxIfUk1NltY09VCZzzCq1d38LWn9uWzGuJhmz/evJqbtmxgeaBIo5zXecEdP6YzHmbn4TEyTmHHZtvTLRaRunvERtu37sy6J1xdEZG96M7KDpBTSm0WkS7ga8BaYC/wVqXUkOjcm08DlwOTwDuUUtu9/VwPfMzb7SeUUl/0lr8WuBeIA98DblFKqXLHaPDpGmaJq9ScGl/Qxjfklf/6hnhoMsuK9hiTniBS8ZDE64w8OJFh98AEz7yiP1pt0RB/et4a3nvhBpa0Ro45VrkqshOXtLDnyDjpEiefX6RU3ouuR76u0fadO5qdBfE7SqnXKKU2e3/fCvxIKbUR+JH3N8BlwEbvcSPwWQDPmP4N8HrgXOBvRMSvDf0s8J7AdpdOcwzDPGa23ZTrddx0ziWZyeU1lIvR4u0uQ57GcGdLmL+8ZCOPfeRibr3s1JLGtxLvvXB9fl/lMoB1tw1F1nG4++Hegve27ejn2q2P57M1tu3on/aYwcIMEf3si74b6kuzDXAxVwJf9F5/EXhzYPl9SvM40CkiK4HfB36olBr0vNgfApd677UrpR5XOsZyX9G+Sh3DYChJsenvH8+Q9TziSmURPYkId1z1as5a3cmN9z1TkxH02bKph0QsRNSWstVQ/vPIZLYgX9f3ZPvHUgWe7HTHN9q+c0cz09AU8AMRUcDdSqmt6Jb3fp+5PsAvdF8F7Atsu99bVmn5/hLLqXCMAkTkRrS3zZo1a2o+OcPioJRRDAmc0BkjHLL56LdfQID2eLjACF69f5jHegerirFu7EnQP5bCcRV9I6mS/fLCtpB23IJ83Zl2qTDavnNHMz3gC5RS56DDCzeJyIXBNz3PtaH3nZWOoZTaqpTarJTa3N0982R4w+IhbAmCFgHaP5yibyTFyGSGsVSu4HY+k3O4c9vuqj1Tv1DCtoSTe9qI2IV+d8gSTyDIKsjXnaknW48OGYbqaJoBVkod8J77gW+hY7iHvfAB3rP/iTwAnBjYfLW3rNLy1SWWU+EYBsOsyHpSka7Sj3TWIevqNkZBxlI5cq5bdYx1y6YebrvidHoSMUaSWTZ0t7GsLcKK9igtEVurv4lw05ZCgZ5SrZCq8WSLj+e3STKwmAXyAAAZ20lEQVQTcPWnKSGIYGNP7/UbgduAB4Drgdu95+94mzwAvF9EvoqecBtRSh0Ske8Dfx+YeHsj8BGl1KCIjIrIecATwHV4Pe0qHMNgqCt5s1sUKE7nXKJ2oe8znWdanCVRTbHEbLpUGG3fuaFZMeDlwLc8ZacQ8GWl1EMi8hTwdRF5F/Ay8FZv/e+hU9B2odPQbgDwDO3Hgae89W5TSg16r/+CqTS0B70HaMNb6hgGw7T4trSa2JirpnJ1g0bQtoSOlkKVslpjrNUYyFq6VJi83+ZgCjGqwBRiGHws0almCoiFLVLZyrLvtkA0bLNmSZyJjDay56/v4v7tB0pWvs3G6M3UiAbzfus5HkMBJRNm5lsamsEw7wimekVsi5AthG1hbVdlj9USHZvtiIeYzLr88Wv1tMTXn9lPa8QmbEndYqyf+e+XeO+XnuGpvYMMTWTYc2S8qpQzMHm/zcSooRkMFQi6LWHbQkSwBbJeaVzUlpJVaj5drRGiIZsDQ5P803/vJBaaak2fdRQfv/KMulSu3bltd35MExmHyYxDRzxUlZB6tcLthvpjDLDBUIFi0+q4iqyrG2vuODxe9hYybOm280cnMjAxpSmhgEMjaZ0n7HmZMzXA23b0c8dDO9jRN1ayL95IMsfOw6OlNi3A5P02DxOCMByXlBJuny0ZxyXjdTX2cQG7qE2QAJZYWOLpSHi94PRy3QF5YCw9Ky9z245+PnT/c+zsHz9Wj4IpkaBq1ONM3m/zMAbYcFySnUPtCKdoIlsBaccl67hasMfTDLY9YZ+s4zKRcdjVP05bdGY3oXc/3MtYKodtCcW/NcHRRMqJVgQweb/Nw4QgDIYqCHs6wLXgeOtbAktbIwxOZvPi8YIW+hkYT7NtR3/Nxm5n/xgpr0tIObpawmzsqa6lo8n7bQ7GAzYYqmAmamx+F+Wu1gjt8TAhmZrUi4YsVnXG6YiHa8422Lajn7FUruz7AqzoiNIWC5swwjzHGGCDoQpmEtBwPKWR6847iZ5EjJyCeNhi7dIWNi5P0B4PzygOfPfDvSxpCec1IIJjC1mwtC3C2qVtJoywADAhCIOhgdiWcP/2A9x2xekA02YbVFNMsW9okmVtUaIhm8OjKdKe1oRtweeve50xugsIY4ANhgbSHpsqaiinzXD++i6u3fo4O/vHGEvlWNISZllbtGwnCj9trD0ept3L3/WbeG7Z1GPKihcQJgRhMNSARW0pbhMZJx9mKJVtcPU5q7h/+wH6x1JMpnO4SnF0YkrCslRFWqW0sZmKsBuag9GCqAKjBWEIUkvLe79Bp99i/u6He3np8ChZRxEJWWRyLi0Rm+5EjB19o9iWoFwI2cL67jaUUvSNJNm4vL3Ao4XSIjvXbn38mDDHwFiKyYxDezw8K4/YeNazYn415TQYjnf83F8/zPDXD/yKTM5h1MtgSGYccq5iLJWjfyyd384WyaerHRlPM5Z2jvFob7vidL5y43nHHLO4rHg0meXoRAZXKdZ0tcy4weZibtTZyB8eE4IwGGokbFtEQ9YxnSmKEYG1XS3cdsXpPNY7SNgWxlI5LISQZYEnVZn3phVkHUUq5+K4ioGxFEOTWbpaw1UL5RSLsB8Z14Y9FrJnJbRTq2DPTJqBzkcaHdIxBthgqJGM45LOufkyX0Gnl4UsbXTDlnDqigT3XP86HvrLi9iyqSffHijjuPiVy04gt9hVAQF3b59Dk1miIYulrdGC41dKXSuOD6dzLijoTkSr2r4ctbQ3Op7i0I1WijMhCINhlih0l4t7ri+fAuZnLkRsi5yjENFGtxyOAuW4oBTJrENLJMRYKsvAWJpUzqE1EipZQVcswt4SsWmN2iRiU2GJmQjt1CLYM9NmoPORRivFGQ/YYKgDroL3/PvTbP7ED0vecvueaSSkuxcX92ort8+0ozgynubIeIoDQ0kyjoslQkvELutVbtnUw1duPI9HPnwxn7nmbMK2PWuhnVoEe46ntvYz7atXLcYAGwx1Iusojoxn+MUrQ9zytV9w6ad+mo+BAlx9zipGU6UNb3E0WQJly5mcy0TaQaEF4U/oiNOdiFV1K1wvoZ1a9tNoozWXNFopzqShVYFJQzNUi5/5kHMVEVs4ZXkiX3Dhui77h1PasHqtjSrtB3RpsYuwsiNGZzyMBKQvlVKMJLM88uGLG3hGtXO8tTiqpgFqFZg0NIOh0SimJtcyjmLPkQkyjottSUH/uGLjG7aFnKPyGREiELIsFIrWsJ2Pwea8kIS/z+naIjWDWpqBLgQaqRRnDLDBUGeCtjXnKGwR3MCMW0nHV8GqjigHR9N5jV9XKVwF775gHWeu7uSD9z/H8GQWywtP5BxdNTcTOctGY+Qtq8PEgA2GGVBtMbJlCSLeo8J6G7pb+dlHLuEDv7uReNgm5+qJq1suPpmbLzmFLZt66G6L5hXQwrbF6iVx2mcgZ2mYPxgP2GCYAdXOnCiUbmOvoLstQv945ph1utvCTGT0pNXNl5zCzZecUnJfY+kcJ/e0HRMHXojZBZVYTCXPxgM2GOqABfnQQLBFnOMqQpZwQmeMRDxMS8QmFrII20JrxGbt0hba45GqMgSOp+yCchxPRRzVYAywwTALfGE0F91/zbYANdUYc1lbhGVtEfpGUuw9OkFXS5i2WIg1XS2sW9aa14qoJq3p/PVd7B9K8utDI+zuH+PIeOq4a57Z6Mqz+YYxwAbDLLDQGQwAqayLV/mLAJ0tIY6MZ9g3lMRVipAIfaNpRpJZ0pkcI8ksYUt7wh/7zgsVNRO27ejn/u0HWNISJhayyTiKwYksr13Twd0P9y54zQWf46mIoxpMDNhgmAUugKMK0sgEPfnWHouQzKRA/OVCyNJdlAcmsty0ZQP3bz9QlcKY7xl2xGN0e302j4yn+N4Lh1m9JF6w/dX7h3msd3BBxlBrKXk+HjAesMEwC1wFSiARaC/vx4CPjKfJudortpB8RoQu1HD5/M/25A33niMTvDI4Sf9YitsffPGY45TyDEcmsziuKrhdz+Qc7ty2e8HGUBtdeTbfMAbYYJgFIvoWeSyVA28STindRTmVc71iisKJOaUgaltMZBxyjsu+oUkmM7paLJ11+c3h8WMMZvEE3Ggyy2RWy1b2DowzmswCMJbKkXPdaWOo81Uusl6l0wsFE4IwGGZBPGx7ko+KsGWRdYLVbopELIyTzJJ1XBTaYxZgSUsYxOXlwWTB/vz0ttsffLHA6AT7yeUclwPDKUBPAuYcxcERvZ90ziVqF/pVxTHUUuLqH7r/OZa2RhjPOE0PWyymIg7jARsMM8S2IOuJpztK6wQHBdYtgU9efRZvevUKHDVlfC0LhpNZXNctud+QBXuOFk46BT3DvtE0IUtYnohie8LuAhweS2FbQkdLoXxicQy1ONPAcRVDk1n2Dk4uyLDFQsYYYIOhRvwUs4htY3llw6WIhGx+uX+YH+0Y0IZX9ORcLGRjFycMB/ZtlVgeLE4AWN4epac9xgmdMUKW4Cpd8HHTlg3Tyk8Wx5MHxtJYonOWF0Pq13zChCAMhhpRgN+NSCqomqWzDp/+8S6U0jrAILoiLhFl/9AkjquIhizSOTdfpuyHKTZ2t+b3UxwyODKe5sBwChEhEQuTiIXzbelvvuQUzlzdWVEIpzjTIOO43g/KlD92PKd+zSeMATYYZoCjIJV18uEGW/Qy0J6uLZBz8dLOAKWzIFwUA2NpQpaF47o4nvUO2nAR2D80yZl/+33txWZdlFLEwzbdiSjLEzEODCfpG0nRFg3l5R59L3e6GGownhwP29ii5TOXtU21LTqeU7/mEyYEYTDMkKDR9I2voGUkLUt/taYq5ZSniKZI5RwitpBztZEuJmpr6cqJTI6xtIPjalW0dM7l4HAKEVjVGUPBjDIFijMN1i1rpbMlTMiWRZH6NZ8wHrDBUEcUeG2DAIF0TmEJdMbDTGYc0jlFayTEqs44uwfGyfqNPb1QhgImsy5h28JxCmMbrlLYljAwlmZFR4xz1iwp2Zq+Goq95DqJjhtqxBhgg2GWRG0tnJ4JGExX6SwJf9FwMsvS1ghLQza3XXE6H/vOC7hKEQ1b+EKVfp6vq0rHlrWksM4vrreHuphSv+YTJgRhMMwS8cTTLdHtiHz8lGBX6X5xA+MZrj5nFVs29XDikhZdpBEwsv6Wlmd8/WQISyAkeJkK0BoJHdfFCYsJY4ANhlkg6JBDztXpY5W+UK6ruH/7Abbt6Oe9F64nEQvpHGLXxXHdfHqbiI73BtvWiyX0JKKc0BnnM9ecbYzvcYIxwAbDLPDTxmwLBEXWLZOThjasfn7tlk09fPLqs9joCayLCCd0RElEbcK2lW9LBBAL2XTEQqxb1jannu98LVc+njAxYINhFsS8PF7HhdIN56dwVWF+bXHc9dqtjx+jBObn9850sm2mlCpXLqfUZpg5xgM2GGaIAB3xcNXtiaByfm0pxbOc47L9laE590IXmzB6szAG2GCYIbYljKdz+ao4mL5ZZ6nsBf9Wf2Asza7+ccZSWtlsNJnVFW8w5xoNi00YvVkYA2wwzAABoiGLjONiW4LI9MbXtuSYGG6wB9qK9ig5V7F/KMloMsPhMa14tqIjNude6GLoPzcfMAbYYJgBCmiN6DJeV8Gy1gghW4jYVoEhDlt64s22hFsuPrlsp4uWSIj2eIRVnXFCtm5dpJSueEvEptTN5soLXWzC6M3CTMIZDDMgYgsjqRzrl7UyMJ6m3et4fHgsjaWEJfEQkxmXtOPSGrZ59wXrSrab3zekJSB92uNhErEQI8ksqz3RnCBz5YVu2dTDbWCq4xqMMcAGA16KmCUF1WzliIYsNva0MZLM8uAHLiwo4z37xCU1GapKPdCKRXOKRXcajamOazzGABuOWwSqylCwPOProtPKXKV1cdctbeHQaIrRZK6g2eaK9liBJzobQ1XJyBov9PjHGGDDcYklWt82k5vqUuFXmvkCZGFLWNGhjWlHPFxgAP3Jsm07+rnjoR281D9O2NZdKEK21M0Tnc7IGi/0+MYYYMNxSSRk5WUaAe54aAe9RyYAWLe0hVsvOzVv2CopgfkGMLhOTyJWV0/UGNnFi6hycv6GPJs3b1ZPP/101euvvfW/GjgaQzVsWt5WYGQNhiZTMktxUXrAInIp8GnABj6vlLq9yUMyzILWiAUI6ZzOyb1py4aSGQcGw3xj0RlgEbGBO4HfA/YDT4nIA0qpXzd3ZIublrBNS9QmHrY5MJScavVjCa6rsG3BEqG7LUIiFmY8nWP1khbOX9/FY72DZpLKsCBZdAYYOBfYpZTqBRCRrwJXAnUxwKnsdJIsi5tgZoIAb37NSj51zTkF69TaneHmho3WYGgsi9EArwL2Bf7eD7y+eCURuRG4EWDNmjXT7nQinePLT7zC1keMWImPoCfD1i9r5bIzVvC95w+x56iu4lq/rJUPX7qppGE1k1KGxcJiNMBVoZTaCmwFPQlXbr2RZJb7fr6Xex7dw9Bkds7G1wx871UEuuJhetpjDIynyeRcIrawcXl7RW/VxGUNhkIWowE+AJwY+Hu1t6wmjo6nuefRPdz385cZS+cAXSF17blruPfne+sy0GZx6opEWe/UYDDUj8VogJ8CNorIOrThvQb4k2o37htJsfXhXr7y5Ct5tajWiM2fnb+Wd12wju5ElL+94vS6paJZQO/tfzDtett29POxbz/PgeFU2eovAV5l0rMMhnnDoswDFpHLgX9Gp6Hdo5T635XW37x5s/rWDx7msz/dzf1P7yfjdVvsiIe54bfX8o7fWktnS6TxAzcYDAuVknnAi9IA18qKDaer1rd9Esfr97WsLcK737CePz3vJNqii/EmwmAw1IgpxJgpQ5MZYq5iZYcuQX3b69YQj9jTb2gwGAwVMAa4CiK2xe1XvZqrzllNJGQ07A0GQ30wBrgKTlmR4Jpzp88FNhgMhlow7lwVTNfry2AwGGaCMcAGg8HQJIwBNhgMhiZhDLDBYDA0CWOADQaDoUkYA2wwGAxNwhhgg8FgaBLGABsMBkOTMAbYYDAYmoQxwAaDwdAkjAE2GAyGJmEMsMFgMDQJowdcBSIyALw8w82XAUfqOBwzhoV5fDOG+XH8Zo3hiFLq0uKFxgA3GBF5Wim12YyhuWNo9vHNGObH8efLGHxMCMJgMBiahDHABoPB0CSMAW48W5s9AMwY5sPxwYxhPhwf5scYABMDNhgMhqZhPGCDwWBoEsYAGwwGQ7NQSplHgx7ApcBvgF3ArbPc14nAT4BfA78CbvGW/y1wAHjWe1we2OYj3rF/A/z+dOMC1gFPeMu/BkRKjGMv8Lx3rKe9ZV3AD4Gd3vMSb7kAn/H290vgnMB+rvfW3wlcH1j+Wm//u7xtJfDeqwLn+SwwCnyg0dcAuAfoB14IrNfwcy46xgFgoGgMnwR2eMf5FtDpLV8LJAPX464aj1XufAa9R3AMDb323vKo9/cokAV+E9jma4Fj7wWebfA1KPg/nbWNaLaROl4fgA3sBtajv8TPAafNYn8r/f94IAG8BJzmfQE+WGL907xjRr0P9m5vTGXHBXwduMZ7fRfw5yX2uxdYVrTsH/wvEnArcIf3+nLgQe8DfB7whLe8C+j1npd4r/0P+5PeuuJte1mF69sHnNToawBcCJxDoeFp+DkXHeMu4N6iMbwRCHmv7wiMYW1wvaJrUs2xyp3P+9CGq9gAN/TzB/yF9/eF3thGypzb/wH+usHXIP9/Wo+HCUE0jnOBXUqpXqVUBvgqcOVMd6aUOqSU2u69HgNeBFZV2ORK4KtKqbRSag/61/vccuMSEQEuBu73tv8i8OYqh3elt37xdlcC9ynN40CniKwEfh/4oVJqUCk1hPY2LvXea1dKPa70J/++CmP4XWC3UqpShWJdroFS6mG05zfX5xw8xt8BbwgOQCn1A6VUzvvzcWB1hWtBDccqdz53AS1AqNJxAtvV6/N3JfBF7//hG0Crt37w3AR4K/CVBl+D4P/prDEGuHGsAvYF/t5PZYNZNSKyFjgbfbsG8H4R+aWI3CMiS6Y5frnlS4HhwBe63HgV8AMReUZEbvSWLVdKHfJe9wHLZziGVd7r4uWluIbCL9tcXgOYm3MuPkZ3mbEAvBPtpfmsE5FfiMhPRcQ33LUcq9z59HGsAW70tQ9u43iPpUVjeANwWCm1cw6uQd2+y8YALzBEpA34JvABpdQo8FlgA/Aa4BD6NqyRXKCUOge4DLhJRC4Mvul5FQ3NbRSRCHAF2huCub8GBczFOVc6hoh8FMgB/+EtOgSsUUqdDfwV8GURaa/HsUrQ1Gsf4FoKf5Dn8hrMGGOAG8cB9MSZz2pv2YwRkTDa+P6HUuo/AZRSh5VSjlLKBT6HvsWrdPxyy4+ib61CRcsLUEod8J770RM/5wKH/Vsy77l/hmM4QOFtdLlrdhmwXSl1uBnXwGMuzrn4GMcIyIjIO4A3AW/3jAbebf9R7/Uz6JjrKTUeq9z5rEAbe7z9z8W1D27jx5GPBq5BCLgKPSHnj6uR12DW32UfY4Abx1PARhFZ53ls1wAPzHRnXozr34AXlVL/FFgejEW9BXjBe/0AcI2IREVkHbARPflQclzel/cnwNXe9tcD3ykaQ6uIJPzX6EmgF7xjXV9iuweA60RzHnry5BDwfeCNIrLEu2V9I/B9771RETnPO9/risfgUeDtzOU1CDAX51x8jB8GByAilwL/E7hCKTUZWN4tIrb3er133r01Hqvc+YwRMMBzdO2DY7scmPB/bDwuAXYopfKhhQZfA///dPZMN0tnHjN/eB+Wl9C/vh+d5b4uQN8S/ZJAyg/w7+iZ6V96H5SVgW0+6h37NwSyCcqNCz0z/SR6wuQbQLRoDOvRs9bPoVPhPuotXwr8CJ2+899Al7dcgDu94zwPbA7s653ecXYBNwSWb0Z/iXcD/0ogDc17vxXt/XQEljX0GqCN/SF0CtR+4F1zcc5Fx+jzHsEx7ELHJgtSrYA/8v5/ngW2A39Y47HKnc8w2gsPjqHhnz8g5v09BmSCx/fevxd4X9HnpFHXoOD/dLYPU4psMBgMTcKEIAwGg6FJGANsMBgMTcIYYIPBYGgSxgAbDAZDkzAG2GAwGJqEMcAGg8HQJIwBNhiahIhsEpHHRCQtIh9s9ngMc081qkYGg6HOeOWzg8DNVK86ZzjOMB6wYdEiIt/2VN1+5Su7ichnReRpb9nfBda9XER2eOt/RkS+W2aflojsFJHuwN+7vNLYe0XkLhF5AvgHpVS/UuopdGWXYRFiPGDDYuadSqlBEYkDT4nIN9GlsYOejsCPRORMdNns3cCFSqk9IlJWc1Yp5YrIl4C3A/+M1il4Tik1oKUHWA38llLKafC5GRYAxgM2LGZuFpHn0GLmJ6IFW94qItuBXwCnozs7bEILuezxtqso+o1uYXSd9/qdwBcC733DGF+Dj/GADYsSEdmC9k7PV0pNisg24FTgg8DrlFJDInIvWgimJpRS+0TksIhcjJZnfHvg7YnZjt1w/GA8YMNipQMY8ozvJnSvr3a0gRwRkeVo3WHQal7rRXciAXhbFfv/PPAljMdrqIDxgA2LlYeA94nIi2gD+zhaZvMX6E7D+4BHAZRSSRH5C+AhEZlAa9pOxwPo0MMXyq0gIiuAp9GG3xWRD6AbVI7O+KwMCwojR2kwVIGItCmlxj0R7zuBnUqpT1VYfzPwKaXUG8qtYzCYEITBUB3vEZFn0SLfHeisiJKIyK3o1lEfmaOxGRYoxgM2GGaIiNwA3FK0+FGl1E3NGI9h4WEMsMFgMDQJE4IwGAyGJmEMsMFgMDQJY4ANBoOhSRgDbDAYDE3i/wMNU/Eoahhm+gAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 360x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.lmplot(x='aag_yr1',y='lifetime_aag_base_miles', data=df)\n",
    "plt.title('Aag yr1 vs. lifetime aag base miles');"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "aag_yr1 and lifetime aag base miles are somewhat positive correlated. This makes sense."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x576 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(10, 8))\n",
    "sns.heatmap(df.corr(), annot=True)\n",
    "plt.suptitle(\"Pearson Correlation Heatmap\")\n",
    "plt.show();"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We can see that these attributes do not have very high correlation among them."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## KMeans Clustering \n",
    "\n",
    "K-means clustering is one of the simplest and popular unsupervised machine learning algorithms. So, this is what I am going to use."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Method 1, Clustering members based on partner miles earned 3 months and partner miles redeemed 3 months."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "x = df.iloc[:, [5, 6]].values\n",
    "\n",
    "distortions = []\n",
    "for i in range(1, 11):\n",
    "    km = KMeans(\n",
    "        n_clusters=i, init='random',\n",
    "        n_init=10, max_iter=300,\n",
    "        tol=1e-04, random_state=0\n",
    "    )\n",
    "    km.fit(x)\n",
    "    distortions.append(km.inertia_)\n",
    "    \n",
    "plt.plot(range(1, 11), distortions, marker='o')\n",
    "plt.xlabel('Number of clusters')\n",
    "plt.ylabel('Distortion')\n",
    "plt.show();"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The elbow is located either k=6 or k=7. I will go with k=7."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "km = KMeans(n_clusters = 7, init = 'k-means++', max_iter = 300, n_init = 10, random_state = 0)\n",
    "y_means = km.fit_predict(x)\n",
    "\n",
    "plt.scatter(x[y_means == 0, 0], x[y_means == 0, 1], s = 100, c = 'pink', label = 'invisible')\n",
    "plt.scatter(x[y_means == 1, 0], x[y_means == 1, 1], s = 100, c = 'yellow', label = 'saver')\n",
    "plt.scatter(x[y_means == 2, 0], x[y_means == 2, 1], s = 100, c = 'cyan', label = 'target')\n",
    "plt.scatter(x[y_means == 3, 0], x[y_means == 3, 1], s = 100, c = 'magenta', label = 'dreamer')\n",
    "plt.scatter(x[y_means == 4, 0], x[y_means == 4, 1], s = 100, c = 'orange', label = 'general')\n",
    "plt.scatter(x[y_means == 5, 0], x[y_means == 5, 1], s = 100, c = 'brown', label = 'greedy')\n",
    "plt.scatter(x[y_means == 6, 0], x[y_means == 6, 1], s = 100, c = 'green', label = 'inactive')\n",
    "plt.scatter(km.cluster_centers_[:,0], km.cluster_centers_[:, 1], s = 50, c = 'blue' , label = 'centeroid')\n",
    "\n",
    "plt.title('K Means Clustering')\n",
    "plt.xlabel('partner miles earned 3mo')\n",
    "plt.ylabel('partner miles redeemed 3mo')\n",
    "plt.legend()\n",
    "plt.grid()\n",
    "plt.show();"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The reason I think the cluster \"target\" could be a good campaign target is that, they have redeemed significant amount of miles recently while have not earned equivalent amount of miles in the same time period. I think some of them may look forward to receive promotions of buying miles."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Summary statistics for the target cluster, and there are 174 members in this cluster."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead 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>ptnr_miles_earned_3mo</th>\n",
       "      <th>ptnr_miles_redeemed_3mo</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>count</td>\n",
       "      <td>174.00</td>\n",
       "      <td>174.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>mean</td>\n",
       "      <td>32550.64</td>\n",
       "      <td>273842.53</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>std</td>\n",
       "      <td>48740.66</td>\n",
       "      <td>97858.28</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>min</td>\n",
       "      <td>0.00</td>\n",
       "      <td>192500.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>25%</td>\n",
       "      <td>4958.75</td>\n",
       "      <td>220000.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>50%</td>\n",
       "      <td>16602.00</td>\n",
       "      <td>240000.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>75%</td>\n",
       "      <td>40566.50</td>\n",
       "      <td>300000.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>max</td>\n",
       "      <td>300000.00</td>\n",
       "      <td>1020000.00</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       ptnr_miles_earned_3mo  ptnr_miles_redeemed_3mo\n",
       "count                 174.00                   174.00\n",
       "mean                32550.64                273842.53\n",
       "std                 48740.66                 97858.28\n",
       "min                     0.00                192500.00\n",
       "25%                  4958.75                220000.00\n",
       "50%                 16602.00                240000.00\n",
       "75%                 40566.50                300000.00\n",
       "max                300000.00               1020000.00"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ptnr_miles_earned_3mo = x[y_means == 2, 0]\n",
    "ptnr_miles_redeemed_3mo = x[y_means == 2, 1] \n",
    "pd.DataFrame({'ptnr_miles_earned_3mo': ptnr_miles_earned_3mo, 'ptnr_miles_redeemed_3mo': list(ptnr_miles_redeemed_3mo)}, columns=['ptnr_miles_earned_3mo', 'ptnr_miles_redeemed_3mo']).describe()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Method 2, Clustering members based on balance and flight promo 3 months. \n",
    "\n",
    "I assume that flight promo 3 months is the points collected by promotion in the past 3 months."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "x = df.iloc[:, [1, 4]].values\n",
    "distortions = []\n",
    "for i in range(1, 11):\n",
    "    km = KMeans(\n",
    "        n_clusters=i, init='random',\n",
    "        n_init=10, max_iter=300,\n",
    "        tol=1e-04, random_state=0\n",
    "    )\n",
    "    km.fit(x)\n",
    "    distortions.append(km.inertia_)\n",
    "    \n",
    "plt.plot(range(1, 11), distortions, marker='o')\n",
    "plt.xlabel('Number of clusters')\n",
    "plt.ylabel('Distortion')\n",
    "plt.show();"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "This time I go with k=6."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "km = KMeans(n_clusters = 6, init = 'k-means++', max_iter = 300, n_init = 10, random_state = 0)\n",
    "y_means = km.fit_predict(x)\n",
    "\n",
    "plt.scatter(x[y_means == 0, 0], x[y_means == 0, 1], s = 100, c = 'pink', label = 'sad')\n",
    "plt.scatter(x[y_means == 1, 0], x[y_means == 1, 1], s = 100, c = 'yellow', label = 'dreamer')\n",
    "plt.scatter(x[y_means == 2, 0], x[y_means == 2, 1], s = 100, c = 'cyan', label = 'target')\n",
    "plt.scatter(x[y_means == 3, 0], x[y_means == 3, 1], s = 100, c = 'magenta', label = 'slow')\n",
    "plt.scatter(x[y_means == 4, 0], x[y_means == 4, 1], s = 100, c = 'orange', label = 'greedy')\n",
    "plt.scatter(x[y_means == 5, 0], x[y_means == 5, 1], s = 100, c = 'brown', label = 'far')\n",
    "plt.scatter(km.cluster_centers_[:,0], km.cluster_centers_[:, 1], s = 50, c = 'blue' , label = 'centeroid')\n",
    "\n",
    "plt.title('K Means Clustering')\n",
    "plt.xlabel('balance')\n",
    "plt.ylabel('flight promo 3 months')\n",
    "plt.legend()\n",
    "plt.grid()\n",
    "plt.show();"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The reason I think cluster \"target\" can be a potential target is that they have some points but may not enough for a dream trip, and some of them have earned points by promotion recently, seems thay are inclined to promotions. Have you heard “Stuck in the Middle: The Psychophysics of Goal Pursuit”? They are not too far to accomplish their goals. They have the tendency to buy if it does not cost too much."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Summary statistics for target cluster. And there are 838 members in this cluster."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>balance</th>\n",
       "      <th>flt_pro_3mo</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>count</td>\n",
       "      <td>838.00</td>\n",
       "      <td>838.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>mean</td>\n",
       "      <td>992630.70</td>\n",
       "      <td>9097.18</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>std</td>\n",
       "      <td>252921.17</td>\n",
       "      <td>14759.11</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>min</td>\n",
       "      <td>718497.00</td>\n",
       "      <td>0.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>25%</td>\n",
       "      <td>797307.50</td>\n",
       "      <td>0.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>50%</td>\n",
       "      <td>914990.50</td>\n",
       "      <td>2542.50</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>75%</td>\n",
       "      <td>1109698.75</td>\n",
       "      <td>13126.75</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>max</td>\n",
       "      <td>1801951.00</td>\n",
       "      <td>159430.00</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "         balance  flt_pro_3mo\n",
       "count     838.00       838.00\n",
       "mean   992630.70      9097.18\n",
       "std    252921.17     14759.11\n",
       "min    718497.00         0.00\n",
       "25%    797307.50         0.00\n",
       "50%    914990.50      2542.50\n",
       "75%   1109698.75     13126.75\n",
       "max   1801951.00    159430.00"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "balance = x[y_means == 2, 0]\n",
    "flt_pro_3mo = x[y_means == 2, 1]\n",
    "pd.DataFrame({'balance': balance, 'flt_pro_3mo': list(flt_pro_3mo)}, columns=['balance', 'flt_pro_3mo']).describe()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Method 3, Clustering members based on balance and partner miles redeemed 3 months."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "x = df.iloc[:, [1, 6]].values\n",
    "distortions = []\n",
    "for i in range(1, 11):\n",
    "    km = KMeans(\n",
    "        n_clusters=i, init='random',\n",
    "        n_init=10, max_iter=300,\n",
    "        tol=1e-04, random_state=0\n",
    "    )\n",
    "    km.fit(x)\n",
    "    distortions.append(km.inertia_)\n",
    "    \n",
    "plt.plot(range(1, 11), distortions, marker='o')\n",
    "plt.xlabel('Number of clusters')\n",
    "plt.ylabel('Distortion')\n",
    "plt.show();"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "k=5 this time."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "km = KMeans(n_clusters = 5, init = 'k-means++', max_iter = 300, n_init = 10, random_state = 0)\n",
    "y_means = km.fit_predict(x)\n",
    "\n",
    "plt.scatter(x[y_means == 0, 0], x[y_means == 0, 1], s = 100, c = 'pink', label = 'sad')\n",
    "plt.scatter(x[y_means == 1, 0], x[y_means == 1, 1], s = 100, c = 'yellow', label = 'target')\n",
    "plt.scatter(x[y_means == 2, 0], x[y_means == 2, 1], s = 100, c = 'cyan', label = 'poor')\n",
    "plt.scatter(x[y_means == 3, 0], x[y_means == 3, 1], s = 100, c = 'magenta', label = 'burning')\n",
    "plt.scatter(x[y_means == 4, 0], x[y_means == 4, 1], s = 100, c = 'orange', label = 'greedy')\n",
    "#plt.scatter(x[y_means == 5, 0], x[y_means == 5, 1], s = 100, c = 'brown', label = 'greedy')\n",
    "plt.scatter(km.cluster_centers_[:,0], km.cluster_centers_[:, 1], s = 50, c = 'blue' , label = 'centeroid')\n",
    "\n",
    "plt.title('K Means Clustering')\n",
    "plt.xlabel('balance')\n",
    "plt.ylabel('partner miles redeemed 3 months')\n",
    "plt.legend()\n",
    "plt.grid()\n",
    "plt.show();"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Again, the reason I think the cluster target could be a good target is that they do have some points, and most of them have not redeemed recently, they maybe pursue a vacation goal, maybe a little short for a vacation they desire. They may need a little push."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Summary statiatics for this target cluster, and there are 1,972 members in this cluster."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>balance</th>\n",
       "      <th>partner_miles_redeemed_3mo</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>count</td>\n",
       "      <td>1972.00</td>\n",
       "      <td>1972.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>mean</td>\n",
       "      <td>713676.88</td>\n",
       "      <td>7682.56</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>std</td>\n",
       "      <td>202454.23</td>\n",
       "      <td>41036.54</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>min</td>\n",
       "      <td>490402.00</td>\n",
       "      <td>0.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>25%</td>\n",
       "      <td>551870.50</td>\n",
       "      <td>0.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>50%</td>\n",
       "      <td>653472.00</td>\n",
       "      <td>0.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>75%</td>\n",
       "      <td>822299.25</td>\n",
       "      <td>0.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>max</td>\n",
       "      <td>1380329.00</td>\n",
       "      <td>605000.00</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "         balance  partner_miles_redeemed_3mo\n",
       "count    1972.00                     1972.00\n",
       "mean   713676.88                     7682.56\n",
       "std    202454.23                    41036.54\n",
       "min    490402.00                        0.00\n",
       "25%    551870.50                        0.00\n",
       "50%    653472.00                        0.00\n",
       "75%    822299.25                        0.00\n",
       "max   1380329.00                   605000.00"
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "balance = x[y_means == 1, 0]\n",
    "partner_miles_redeemed_3mo = x[y_means == 1, 1]\n",
    "pd.DataFrame({'balance': balance, 'partner_miles_redeemed_3mo': list(partner_miles_redeemed_3mo)}, columns=['balance', 'partner_miles_redeemed_3mo']).describe()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The end."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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