基于RFM的航空公司客户价值聚类分析.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 基于RFM的航空公司客户价值聚类分析"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 描述性统计分析"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "\n",
    "# 对数据进行基本的探索\n",
    "# 返回缺失值个数以及最大最小值\n",
    "\n",
    "import pandas as pd\n",
    "\n",
    "datafile= 'air_data.csv'  # 航空原始数据,第一行为属性标签\n",
    "resultfile = 'explore.csv'  # 数据探索结果表\n",
    "\n",
    "# 读取原始数据，指定UTF-8编码（需要用文本编辑器将数据装换为UTF-8编码）\n",
    "data = pd.read_csv(datafile, encoding = 'utf-8')\n",
    "\n",
    "# 包括对数据的基本描述，percentiles参数是指定计算多少的分位数表（如1/4分位数、中位数等）\n",
    "explore = data.describe(percentiles = [], include = 'all').T  # T是转置，转置后更方便查阅\n",
    "explore['null'] = len(data)-explore['count']  # describe()函数自动计算非空值数，需要手动计算空值数\n",
    "\n",
    "explore = explore[['null', 'max', 'min']]\n",
    "explore.columns = [u'空值数', u'最大值', u'最小值']  # 表头重命名\n",
    "'''\n",
    "这里只选取部分探索结果。\n",
    "describe()函数自动计算的字段有count（非空值数）、unique（唯一值数）、top（频数最高者）、\n",
    "freq（最高频数）、mean（平均值）、std（方差）、min（最小值）、50%（中位数）、max（最大值）\n",
    "'''\n",
    "\n",
    "explore.to_csv(resultfile)  # 导出结果"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
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       "    .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>空值数</th>\n",
       "      <th>最大值</th>\n",
       "      <th>最小值</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>MEMBER_NO</td>\n",
       "      <td>0</td>\n",
       "      <td>62988</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>FFP_DATE</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>FIRST_FLIGHT_DATE</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>GENDER</td>\n",
       "      <td>3</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>FFP_TIER</td>\n",
       "      <td>0</td>\n",
       "      <td>6</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                  空值数    最大值  最小值\n",
       "MEMBER_NO           0  62988    1\n",
       "FFP_DATE            0    NaN  NaN\n",
       "FIRST_FLIGHT_DATE   0    NaN  NaN\n",
       "GENDER              3    NaN  NaN\n",
       "FFP_TIER            0      6    4"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "explore.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "下面进行分布分析，首先，进行客户基本信息分布分析：包含性别，入会时间，会员卡级别等"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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o4KAkhyQ5bZjafD+kJEl7qp057P4O4Ix5bP8t4HFJHgjsDzwSuLhfdyVwDLB+yJokSRrCgoI+ySvpJtB58zye9hm6e/JfCdxAdxX/Lf2624G1wKohazP1dFaSzUk2b926dR5tSZLUrnkFfZKH9OfLjwNOrKo75/H0NwC/VVVvBL4IPA/Yu1+3T9/L9iFr91FVG6pqoqom1qxZM4+2JElq16wX4/VX07+ILmj3BZ4MPAI4v6ouX8B7rQYOS7Kpf6230h2G30R3y96NdNPpDlOTJElDmOuq+5XAwcDddCPp/YEHAo+Y/LKbeb7XW4D30x2+vxb4U2BjkocDJwFH0k3OM0xNkiQNYdagr6ofAudMrSXZj+4c+8Ykv1xV3xz2jarqn+m+CGfq660Hng68varumE9NkiQNNmiu+3upqm3Am5J8ku6b7Y6vqu8s9M2r6tvcc0X9vGqSJGmweQX9pKr6bJK3A+fRXVQnSZpi5YpLBm5z512nj6AT7ekWFPQAVfXhJJ/flc1IkqRda+jb65KcPH1e+6ryCnhJknZjQwV9kp8E3ggsT/K+KfXTkiz4qIAkSVpcw47o3wL8L+Au+pnpkvwB8Gq62/AkSdJuaGDQJ/l14JvA8/p753+c5GzgQcCXqur7i9yjJElaoFmDPsmyJO8FDqqq3wd+2K8quolvXkX39bWSJGk3NdeIfjndofrJEfvkTHg/DZxXVTuA+y9ib5IkaSfNGvRV9aOqeimwvf/u+MkvlvkicH2SNwP3G0GPkiRpgQaeo6+qPwMeClyeJMBeVfWnwPeAxyxyf5IkaScMe2vc7wGfAi4BvtXX3oLfJCdJ0m5tqNvrqupuui+4+U5VvbAvPwW4dLEakyRJO2/omfGq6ur+G+1Isg64HPiFxWlLkiTtCkMH/aQkjwb+AXhzVX1s17ckSZJ2laGnr03yCOC3gZOA36uqv120riRJ0i4xzMx4a5P8JfB/geuAxxvykiSNhzmDPskhwD8BX6KbMOdInA1PkqSxMWhE/17g16rqzVV1FLAJ+EySUxe/NUmStLMGnaM/uaq+O/mgqj6U5BPApUkO6CfTkSRJu6k5g35qyE+pbU1yInB1kq1V9aFF607SLrdyxSUDt7nzrtNH0ImkURj6qvupqmp7kl8D7tzF/UiSpF1oQUEPUFX/tisbkSRJu968J8yRJEnjw6CXJKlhBr0kSQ0z6CVJatjIgz7JeUlO7pcvSHJtknOnrB+qJkmSBhtp0Cc5FnhoVV2R5DRgWT/j3kFJDhm2NsqeJUkaZyML+iQr6KbUvSnJKcB64OJ+9ZXAMfOoSZKkIYxyRP9C4AvA24GfA14G3NKvux1YC6wasnYfSc5KsjnJ5q1bty7KB5AkadyMMugfD2yoqluBDwJXA3v36/bpe9k+ZO0+qmpDVU1U1cSaNWsW5xNIkjRmFjwz3gJ8GTioX54A1tEdht8EHA7cCHx9yJq0W3DeeEm7u1EG/QXA+5I8F1hBd+798iQPB06i+677AjYOUZMkSUMY2aH7qvpuVZ1eVU+tqqOq6ma6sN8EHFdVd1TVtmFqo+pZkqRxN8oR/X1U1be554r6edUkSdJgSxr0kjQMr4WQFs4pcCVJaphBL0lSwwx6SZIaZtBLktQwg16SpIYZ9JIkNcyglySpYQa9JEkNM+glSWqYQS9JUsMMekmSGmbQS5LUMINekqSGGfSSJDXMoJckqWEGvSRJDTPoJUlqmEEvSVLDDHpJkhq2fKkbkCatXHHJwG3uvOv0EXQiSe1wRC9JUsMMekmSGmbQS5LUMINekqSGGfSSJDXMoJckqWEjD/oka5N8vl++IMm1Sc6dsn6omiRJGmwpRvTvAPZOchqwrKqOAg5KcsiwtSXoWZKksTTSoE9yPPA94FZgPXBxv+pK4Jh51GZ67bOSbE6yeevWrYvQvSRJ42dkM+MlWQm8HjgVuAxYBdzSr74deMI8avdRVRuADQATExO16z+BJEmD7W6zfI5yRP9a4Lyq+k7/eDuwd7+8T9/LsDVJkjSEUYbmCcDLklwFHAGczD2H4Q8HbgK2DFmTJElDGNmh+6p66uRyH/a/BGxM8nDgJOBIoIasSZKkISzJYfCqWl9V2+gutNsEHFdVdwxbW4qeJUkaR0v6NbVV9W3uuaJ+XjVJkjSYF7ZJktQwg16SpIYZ9JIkNcyglySpYQa9JEkNM+glSWqYQS9JUsMMekmSGmbQS5LUMINekqSGLekUuFo8u9v3IUuSloYjekmSGmbQS5LUMINekqSGGfSSJDXMoJckqWEGvSRJDTPoJUlqmEEvSVLDDHpJkhpm0EuS1DCDXpKkhhn0kiQ1zKCXJKlhBr0kSQ0z6CVJatjIgj7JA5J8PMmVSS5NsjLJBUmuTXLulO2GqkmSpMFGOaL/VeCdVfUM4FbgucCyqjoKOCjJIUlOG6Y2wp4lSRpry0f1RlV13pSHa4DnA+/qH18JHAM8Hrh4iNp/LHa/kiS1YOTn6JMcBawGvgbc0pdvB9YCq4aszfS6ZyXZnGTz1q1bF6l7SZLGy0iDPsmDgHcDZwLbgb37Vfv0vQxbu4+q2lBVE1U1sWbNmsX5AJIkjZlRXoy3ErgEeF1V3QxsoTsMD3A4cNM8apIkaQgjO0cP/DrwBOCcJOcA7wdekOThwEnAkUABG4eoSZKkIYxsRF9V76mq1VW1vv+5EFgPbAKOq6o7qmrbMLVR9SxJ0rgb5Yj+Pqrq29xzRf28apIkaTBnxpMkqWEGvSRJDTPoJUlqmEEvSVLDDHpJkhpm0EuS1DCDXpKkhhn0kiQ1zKCXJKlhBr0kSQ0z6CVJaphBL0lSwwx6SZIaZtBLktQwg16SpIYZ9JIkNcyglySpYQa9JEkNM+glSWqYQS9JUsMMekmSGmbQS5LUMINekqSGLV/qBsbByhWXDNzmzrtOH0EnkiTNjyN6SZIaZtBLktQwg16SpIaNTdAnuSDJtUnOXepeJEkaF2MR9ElOA5ZV1VHAQUkOWeqeJEkaB+Ny1f164OJ++UrgGOA/pm6Q5CzgrP7h9iQ37sL3fwhw21wbJLvw3UZkN+zZ/Twa7ufRaHI/w27Z99jt60Xo58DZVoxL0K8CbumXbweeMH2DqtoAbFiMN0+yuaomFuO1dQ/382i4n0fD/Tw67uu5jcWhe2A7sHe/vA/j07ckSUtqXAJzC93heoDDgZuWrhVJksbHuBy6vwzYmOThwEnAkSN+/0U5JaD7cD+Phvt5NNzPo+O+nkOqaql7GEqS1cDTgaur6tal7keSpHEwNkEvSZLmb1zO0UuSpAXYo4M+yQOSfDzJlUkuTbJyphn4ZpuVL8naJJ8ffefjZRfs5/OSnDz6zsfPQvd1ktVJPpZkc5Lzl+4TjId57Oe1STZOebwiyRVJrkly5tJ0Pz52Yj8fkOSqJJ9MsiHZ3e6iH609OuiBXwXeWVXPAG4Fnsu0GfgGzMr3Du657U+zW/B+TnIs8NCqumKpmh8zC93XLwAu6u9F3jeJ9yTPbZj9vBq4kG4ekEmvALZU1dHAs5PsO+rGx8xC9/PZwEuq6nhgf+CwEfe9W9mjg76qzquqT/QP1wDP574z8K2foUaS44Hv0f3n0xwWup+TrADeC9yU5JTRdTy+duL/9LeAxyV5IN0vxq+NqudxNOR+3gGcAWyb8tT1U7a7GvAPqjksdD9X1TlVdUP/8MEMmDWvdXt00E9KchSwmu6X29QZ+NZy31n51iZZCbweeO2IWx1r893PwAuBLwBvB34uyStG2vAYW8C+/gzdFJqvBG7o6xpgrv1cVduq6o5pT5lp32uABeznyeedAVxfVd8YTae7pz0+6JM8CHg3cCYzz8A3U+21wHlV9Z3Rdju+FrifHw9s6G+n/CBw3Ch7HlcL3NdvAH6rqt4IfBF48Sh7HkdD7OeZOMvnPC1wP5PkIOB3gFctdo+7uz36P1k/Mr8EeF1V3czMM/DNVDsBeFmSq4AjkvzF6LoePzuxn78MHNTXJoCbR9Px+NqJfb0aOCzJMuDJgPfdzmHI/TwTZ/mch4Xu5/68/V8BZ8422t+T7NH30Sd5CfBm4F/60vuBVwP/xD0z8BWwcWpt6n+cJFdV1foRtj12FrqfgR8D76M7vLkCeHZV3YJmtRP7+jH9tgcC1wKnVtX2kTY/RobZz5O/J6b+jkhyIPAx4B+Bp/Tb7Rht9+NjJ/bz2+hO/U1+i+kbqurTI2x9t7JHB/1MZpqBz1n5dj338+i4r0dj2H2abirvY4B/cLQ5f/7fnT+DXpKkhu3R5+glSWqdQS9JUsMMeklDS3K/JAcvdR+ShmfQS5qP59Hd0zyrJG9JMpFkryRPS3Jwkt8YUX+SpvFiPElDSbIGuA74T7rpRh8G/BfdgGHvqjouyU8An6Cb6vVI4EXAS4GP9vOVSxqx5UvdgKTdX/+9AxcB766qt/a1TVX1zGmbng18uqp29PdAv62q7kpyY5KnVNVnR9y6tMdzRC9poCTr6KYg3Q94VF8+mm5yneXA5cDfAp8FLgA+Dby4ql7YP//BwN8Az6yq746yd2lPZ9BLGlqSTwLPqKq7+xH9kVPWPQ94CHAw3TS6twDH081qtj9wPd0hfL/vXhohD91LGqifAx9mmQM/yV7AR4CjgAfRBfydwOVV9YtJXgdsnvKVo5JGxKCXNIyzgFOAHwCXJQF4TJK/69cvAy6l+1phqur7SZ4E/Hu//n7A90fasSTAoJc0hKp6D/CeqbUkn5t+MV6SpwJ79d869j/pviYUYA3gvO7SEjDoJS3UPjPU7gesBP4E+FBV3ZDkL+m+BvdLI+xNUs+L8STtcklS/nKRdgsGvSRJDXMKXEmSGmbQS5LUMINekqSGGfSSJDXMoJckqWH/H0q4hNSNKBtYAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 576x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "<function matplotlib.pyplot.close(fig=None)>"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 对数据的分布分析\n",
    "\n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt \n",
    "\n",
    "datafile= 'air_data.csv'  # 航空原始数据,第一行为属性标签\n",
    "\n",
    "# 读取原始数据，指定UTF-8编码（需要用文本编辑器将数据装换为UTF-8编码）\n",
    "data = pd.read_csv(datafile, encoding = 'utf-8')\n",
    "\n",
    "# 客户信息类别\n",
    "# 提取会员入会年份\n",
    "from datetime import datetime\n",
    "ffp = data['FFP_DATE'].apply(lambda x:datetime.strptime(x,'%Y/%m/%d'))\n",
    "ffp_year = ffp.map(lambda x : x.year)\n",
    "# 绘制各年份会员入会人数直方图\n",
    "fig = plt.figure(figsize = (8 ,5))  # 设置画布大小\n",
    "plt.rcParams['font.sans-serif'] = 'SimHei'  # 设置中文显示\n",
    "plt.rcParams['axes.unicode_minus'] = False\n",
    "plt.hist(ffp_year, bins='auto', color='#0504aa')\n",
    "plt.xlabel('年份')\n",
    "plt.ylabel('入会人数')\n",
    "plt.title('各年份会员入会人数')\n",
    "plt.show()\n",
    "plt.close\n",
    "\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 504x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "<function matplotlib.pyplot.close(fig=None)>"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 提取会员不同性别人数\n",
    "male = pd.value_counts(data['GENDER'])['男']\n",
    "female = pd.value_counts(data['GENDER'])['女']\n",
    "# 绘制会员性别比例饼图\n",
    "fig = plt.figure(figsize = (7 ,4))  # 设置画布大小\n",
    "plt.pie([ male, female], labels=['男','女'], colors=['lightskyblue', 'lightcoral'],\n",
    "       autopct='%1.1f%%')\n",
    "plt.title('会员性别比例')\n",
    "plt.show()\n",
    "plt.close\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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JkiY3ra/X9TfMORk4ZYJNHgi8vKr+HvgP4AhgXlUdCuyVZO8kT5hu23RqliRpWzStr9cBVNVHklwwwbr/BEhyGF2vfldgVb/6HGAZ3f3zp9v2o/HHTLICWAGwdOnS6Z6WJElNGfg+9UnuOEF76G6o81u6J+Bd0a9aDywBFg7QdhtVtbKqRqpqZPHixYOckiRJzdhs0Ce5d5KlSe7Zv39lkpcmeUWSpcAZdI+rvY3qvIjuOfYPAXboVy3qj3vtAG2SJGkKJgvNrwHvohsyB3gu3cNtbqR7st13quri8T+U5Pgkx/ZvdwbeSjfkDt1jbi8D1g7QJkmSpmCya/QXVdXjk3y+f/+zqnoPQJJPAn89wc+tBFYleR7wPeAs4LwkuwPHAIfQDeevnmabJEmagsmCvsb9S5LHAP8DeEVV/XiTP1T1W+CosW1JlvdtJ1fV1YO2SZKkyU1n1v0NdHfMe0mSp29q6H5T+vBfNVNtkiRpcrd3YltV1eer6iTgccA/DKEmSZI0Qybr0R+U5GvA3fr390zymn75o8BVSe5dVZcOrUJJkjRtmw36qtppXNPbgJuAABvovjp3GGDQS5K0FZryNfokjwZOr6pbx7SdCUz4YBtJkjS7pnSNPsldgDcA85OcPmbVIXS9e0mStBWa6mS8twD/RDdsf1eAJK8HXk733HpJkrQVmjTokzyXbnj+6VVVwC1Jnk/3oJofVtX1Q65RkiRN04RBn2RekvcBe1XVa+huewvdzXPeD7wMGD9ZT5IkbUU216OfTzdUP9pjH7073p8Cp1TVLcCdhlibJEka0IRBX1W/r6oXAtcmeTUbnyB3MXBRkjcDd9gCNUqSpGma9Bp9Vb2LbgLe2f0z5rerqn8ErgPuO+T6JEnSAKb6PfpXA1+muxveb/q2twCXDKMoSZI0M6b09bqquhk4Ebiqqo7t226tqo8NszhJkjSYKd8Zr6rOG2YhkiRp5t3ep9dJkqQ5xKCXJKlhBr0kSQ0z6CVJaphBL0lSwwx6SZIaZtBLktQwg16SpIYZ9JIkNcyglySpYQa9JEkNM+glSWqYQS9JUsMMekmSGmbQS5LUMINekqSGGfSSJDXMoJckqWEGvSRJDTPoJUlqmEEvSVLDhhL0SXZK8tkk5yT5RJIFSU5Lcn6Sk8ZsN+02SZI0uWH16P8X8I6qegTwS+CpwLyqOhTYK8neSZ4w3bYh1SxJUnPmD2OnVXXKmLeLgWcA7+zfnwMsAw4AVk2z7Ufjj5lkBbACYOnSpTN0JpIkzW1DvUaf5FBgF+CnwBV983pgCbBwgLbbqKqVVTVSVSOLFy+e4TORJGluGlrQJ9kV+GfgOcC1wA79qkX9cQdpkyRJUzCsyXgLgI8Cf1NVlwNr6YbcAfYDLhuwTZIkTcFQrtEDzwUOBE5MciLwfuCZSXYHjgEOAQpYPc02SZI0BUPp0VfVe6tql6pa3r/OAJYDXwcOr6qrq+qa6bYNo2ZJklo0rB79bVTVb9k4e37gNkmSNDkntkmS1DCDXpKkhhn0kiQ1zKCXJKlhBr0kSQ0z6CVJaphBL0lSwwx6SZIaZtBLktQwg16SpIYZ9JIkNcyglySpYQa9JEkNM+glSWqYQS9JUsMMekmSGmbQS5LUMINekqSGGfSSJDXMoJckqWEGvSRJDTPoJUlqmEEvSVLDDHpJkhpm0EuS1DCDXpKkhhn0kiQ1zKCXJKlhBr0kSQ0z6CVJaphBL0lSwwx6SZIaZtBLktQwg16SpIYZ9JIkNcyglySpYUMN+iRLkqzul7dP8qkkX03ynEHbJEnS5IYW9El2Ac4AFvZNLwHWVtVDgScl2XHANkmSNIlh9uhvAZ4CXNO/Xw6s6pfPA0YGbPsjSVYkWZNkzbp162buLCRJmsOGFvRVdU1VXT2maSFwRb+8HlgyYNv4462sqpGqGlm8ePFMnookSXPWlpyMdy2wQ7+8qD/2IG2SJGkSWzIw1wLL+uX9gMsGbJMkSZOYvwWPdQbwmSQPA+4HfINuOH66bZIkaRJD79FX1fL+38uBo4CvAkdW1S2DtA27bkmSWrAle/RU1c/ZOHt+4DZJkrR5TmqTJKlhBr0kSQ0z6CVJaphBL0lSwwx6SZIaZtBLktQwg16SpIYZ9JIkNcyglySpYQa9JEkNM+glSWqYQS9JUsMMekmSGmbQS5LUMINekqSGGfSSJDXMoJckqWEGvSRJDTPoJUlqmEEvSVLDDHpJkhpm0EuS1LD5s12ApMF94OKrZruErcpx++w82yVIWw179JIkNcyglySpYQa9JEkNM+glSWqYQS9JUsMMekmSGmbQS5LUMINekqSGecMcSdIW4Y2dNtqSN3WyRy9JUsMMekmSGmbQS5LUMINekqSGzZmgT3JakvOTnDTbtUiSNFfMiaBP8gRgXlUdCuyVZO/ZrkmSpLkgVTXbNUwqyT8Bn6uqzyR5KrBDVb1/3DYrgBX92/sCl2zhMreE3YArZ7sIaTP8G9XWrtW/0T2qavGmVsyV79EvBK7ol9cDB47foKpWAiu3ZFFbWpI1VTUy23VIE/FvVFu7bfFvdE4M3QPXAjv0y4uYO3VLkjSr5kpgrgWW9cv7AZfNXimSJM0dc2Xo/ixgdZLdgWOAQ2a5ntnS9KUJNcG/UW3ttrm/0TkxGQ8gyS7AUcB5VfXL2a5HkqS5YM4EvSRJuv3myjV6SZI0DQb9HJJkSZILZrsOabwk85P8d5Jz+9e+s12TtClJTkny6NmuY0uaK5Px1Hk7G79mKG1NHgj8W1UdP9uFSBNJ8jDgrlX1qdmuZUuyRz9HJDkCuA5wIqK2RocAj0ry//rnUtiJ0FYlyfbA+4DLkjx2tuvZkgz6OSDJAuC1wAmzXYs0gW8CR1bVwcD2wCNnuR5pvGOB7wMnAwcnecks17PFGPRzwwnAKVV11WwXIk3gwqr6Rb+8BvDBU9raHACs7L+e/UHg8FmuZ4sx6OeGI4EXJTkX2D/Jv85yPdJ4ZybZL8k84HHAd2a7IGmcS4G9+uUR4PJZrGWL8nv0c0ySc6tq+WzXIY2V5AHAh4EAZ1fVibNckvRHkuwInA4sobu89KSqumLzP9UGg16SpIY5dC9JUsMMekmSGmbQS5LUMINe0h9JcsdBbniTZPckzxzX9rx+Rv74bfec7nEkTY2T8ST9kSTvBRZU1XPHtQe4GvjWuB95APCQqvphv93/Bp4A/JSuM/FuumeAf4VuVv4Xq+odSXYDVtE9fvrLwM3ABrpbc7++qlYP5wylbYs9ekl/kORlwM+BXyf5y7HrqusVfL+qlo990QX4hv7nl9F9R/lBdM9leArwZOBgujD/EnBqv8uXAK/p1/858OuqOhr4NXDZEE9T2qYY9JJIsn2SfwTuWFVvpAvgvZK8P8mSfpsAd0vyhbEvYF9gdFj+W8DrgKcB5wDvAG4AjgDuBKyvquuSHAQ8GNgFGB052ND/u3tV/XTY5yxtK3zwhLSNS/Jw4M10w+iP6Xvl0PXIPwyck+R1VfVJYI/N7auqrk/yXWA9cAtdj34DcC7d/zejt8ndju7ueW+j+1BwJ2BB/6Fi3ySfBh5TVbfO2IlK2yiv0UvbuCR3AnYGfjk+WJNsByygu37+bOB4bjusfg/g9Ko6OcnrgUOB0f0s7pevA4ou/L9YVW9JcjRwYFW9ue/hn0t3i9L1wMeq6vEzfa7Stsigl0SSM4Hd6IJ4H+DiftU84FdVdVyS44B7VNWbkjwCuFNVnZXkxcD8qnpnv687ABtqzH8u/bX/q4APVdVNSe4LrAW+QDfcfznwULoPFGcBB1TV24Z+4tI2wKF7SVTVMwGSvBxYB7wAeHZVvXfMZhmz/HDgx2Pebz9m+TRg9yQF3NS37UH3IeLYJEcBPwH2B3YF7tYf7+l0jxB9K93kPEkzwMl4kkgyL8mJdEPtz6EL6PsnecOYzb4MrEqyFPifwOokZ9MNy68a3aiqngH8C3BJVR3dz6RfCby9qo6oqluAg4D3AY8AHkU3bH8jsJBuqH+fYZ6vtC2xRy8Juu+9vxi4BPgs3Uz5q4EDk1wInA88FXgg3Uz5p1fVD/sRgFcDL0jyrKr6AUBVrUpyA0CS5wPPA54/erCqOh84PMkuwCvohvE/DfwdcCHwwSR3raqPDv3MpcZ5jV7ShJIsops1fytwHHDe6I1xxm13P+Cyqrp+E+t2BW6oqhs2c5z5wK1jJwMmmdf3/iUNwKCXJKlhXqOXJKlhBr0kSQ0z6CVJaphBL0lSwwx6SZIa9v8BQhm76oFpzuEAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 576x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# 提取不同级别会员的人数\n",
    "lv_four = pd.value_counts(data['FFP_TIER'])[4]\n",
    "lv_five = pd.value_counts(data['FFP_TIER'])[5]\n",
    "lv_six = pd.value_counts(data['FFP_TIER'])[6]\n",
    "# 绘制会员各级别人数条形图\n",
    "fig = plt.figure(figsize = (8 ,5))  # 设置画布大小\n",
    "plt.bar(range(3), height=[lv_four,lv_five,lv_six], width=0.4, alpha=0.8, color='skyblue')\n",
    "plt.xticks([index for index in range(3)], ['4','5','6'])\n",
    "plt.xlabel('会员等级')\n",
    "plt.ylabel('会员人数')\n",
    "plt.title('会员各级别人数')\n",
    "plt.show()\n",
    "plt.close()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 360x720 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "<function matplotlib.pyplot.close(fig=None)>"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 提取会员年龄\n",
    "age = data['AGE'].dropna()\n",
    "age = age.astype('int64')\n",
    "# 绘制会员年龄分布箱型图\n",
    "fig = plt.figure(figsize = (5 ,10))\n",
    "plt.boxplot(age, \n",
    "            patch_artist=True,\n",
    "            labels = ['会员年龄'],  # 设置x轴标题\n",
    "            boxprops = {'facecolor':'lightblue'})  # 设置填充颜色\n",
    "plt.title('会员年龄分布箱线图')\n",
    "# 显示y坐标轴的底线\n",
    "plt.grid(axis='y')\n",
    "plt.show()\n",
    "plt.close"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "客户的乘机信息分布分析："
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 360x576 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "<function matplotlib.pyplot.close(fig=None)>"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 乘机信息类别\n",
    "lte = data['LAST_TO_END']\n",
    "fc = data['FLIGHT_COUNT']\n",
    "sks = data['SEG_KM_SUM']\n",
    "\n",
    "# 绘制最后乘机至结束时长箱线图\n",
    "fig = plt.figure(figsize = (5 ,8))\n",
    "plt.boxplot(lte, \n",
    "            patch_artist=True,\n",
    "            labels = ['时长'],  # 设置x轴标题\n",
    "            boxprops = {'facecolor':'lightblue'})  # 设置填充颜色\n",
    "plt.title('会员最后乘机至结束时长分布箱线图')\n",
    "# 显示y坐标轴的底线\n",
    "plt.grid(axis='y')\n",
    "plt.show()\n",
    "plt.close\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 360x576 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "<function matplotlib.pyplot.close(fig=None)>"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 绘制客户飞行次数箱线图\n",
    "fig = plt.figure(figsize = (5 ,8))\n",
    "plt.boxplot(fc, \n",
    "            patch_artist=True,\n",
    "            labels = ['飞行次数'],  # 设置x轴标题\n",
    "            boxprops = {'facecolor':'lightblue'})  # 设置填充颜色\n",
    "plt.title('会员飞行次数分布箱线图')\n",
    "# 显示y坐标轴的底线\n",
    "plt.grid(axis='y')\n",
    "plt.show()\n",
    "plt.close"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 360x720 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "<function matplotlib.pyplot.close(fig=None)>"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 绘制客户总飞行公里数箱线图\n",
    "fig = plt.figure(figsize = (5 ,10))\n",
    "plt.boxplot(sks, \n",
    "            patch_artist=True,\n",
    "            labels = ['总飞行公里数'],  # 设置x轴标题\n",
    "            boxprops = {'facecolor':'lightblue'})  # 设置填充颜色\n",
    "plt.title('客户总飞行公里数箱线图')\n",
    "# 显示y坐标轴的底线\n",
    "plt.grid(axis='y')\n",
    "plt.show()\n",
    "plt.close"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "客户的积分信息分布分析："
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 576x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "<function matplotlib.pyplot.close(fig=None)>"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 积分信息类别\n",
    "# 提取会员积分兑换次数\n",
    "ec = data['EXCHANGE_COUNT']\n",
    "# 绘制会员兑换积分次数直方图\n",
    "fig = plt.figure(figsize = (8 ,5))  # 设置画布大小\n",
    "plt.hist(ec, bins=5, color='#0504aa')\n",
    "plt.xlabel('兑换次数')\n",
    "plt.ylabel('会员人数')\n",
    "plt.title('会员兑换积分次数分布直方图')\n",
    "plt.show()\n",
    "plt.close"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 360x576 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "<function matplotlib.pyplot.close(fig=None)>"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 提取会员总累计积分\n",
    "ps = data['Points_Sum']\n",
    "# 绘制会员总累计积分箱线图\n",
    "fig = plt.figure(figsize = (5 ,8))\n",
    "plt.boxplot(ps, \n",
    "            patch_artist=True,\n",
    "            labels = ['总累计积分'],  # 设置x轴标题\n",
    "            boxprops = {'facecolor':'lightblue'})  # 设置填充颜色\n",
    "plt.title('客户总累计积分箱线图')\n",
    "# 显示y坐标轴的底线\n",
    "plt.grid(axis='y')\n",
    "plt.show()\n",
    "plt.close\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "相关性分析："
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Anaconda\\lib\\site-packages\\ipykernel_launcher.py:5: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  \"\"\"\n",
      "C:\\Anaconda\\lib\\site-packages\\ipykernel_launcher.py:6: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  \n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "相关性矩阵为：\n",
      "                 FFP_TIER  FLIGHT_COUNT  LAST_TO_END  SEG_KM_SUM  \\\n",
      "FFP_TIER        1.000000      0.582447    -0.206313    0.522350   \n",
      "FLIGHT_COUNT    0.582447      1.000000    -0.404999    0.850411   \n",
      "LAST_TO_END    -0.206313     -0.404999     1.000000   -0.369509   \n",
      "SEG_KM_SUM      0.522350      0.850411    -0.369509    1.000000   \n",
      "EXCHANGE_COUNT  0.342355      0.502501    -0.169717    0.507819   \n",
      "Points_Sum      0.559249      0.747092    -0.292027    0.853014   \n",
      "AGE             0.076245      0.075309    -0.027654    0.087285   \n",
      "ffp_year       -0.116510     -0.188181     0.117913   -0.171508   \n",
      "\n",
      "                EXCHANGE_COUNT  Points_Sum       AGE  ffp_year  \n",
      "FFP_TIER              0.342355    0.559249  0.076245 -0.116510  \n",
      "FLIGHT_COUNT          0.502501    0.747092  0.075309 -0.188181  \n",
      "LAST_TO_END          -0.169717   -0.292027 -0.027654  0.117913  \n",
      "SEG_KM_SUM            0.507819    0.853014  0.087285 -0.171508  \n",
      "EXCHANGE_COUNT        1.000000    0.578581  0.032760 -0.216610  \n",
      "Points_Sum            0.578581    1.000000  0.074887 -0.163431  \n",
      "AGE                   0.032760    0.074887  1.000000 -0.242579  \n",
      "ffp_year             -0.216610   -0.163431 -0.242579  1.000000  \n"
     ]
    },
    {
     "data": {
      "image/png": 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w4Bx7nRuEszIh2WvC6GsXOgcOICMtlbh1y6nXrDWhYZUK3aY4XOgcOG/SUlNYt3IJzVq2JaxSyZ5bfD0HrsIt0332ezZl5gC/jeH8sgPnZurAuVdxdOD8SVF24PxNUXfg/E1Rd+D8TVF04PxZcXTg/Ik6cCXDny9iKDDPVaNnSrbW+utl4CIiIiKF8qfqwHm+701ERET+pPz94gJf0UUMIiIiIi7zp0rgRERE5E9OARygBE5ERETEdZTAiYiIiGtoDpxDCZyIiIiIyyiBExEREddQAudQAiciIiLiMkrgRERExDWUwDmUwImIiIi4jBI4ERERcQ1/S+CMMe8BzYFvrbUvenn+PuBmz8MwYAnwALDNcwMYYq1dV5DXVQInIiIiUgjGmOuBQGttF6CBMabxuW2steOttdGeP/e5CJgEtAI+Pr28oJ03UAdORERE3MT48HZ+0cAsz/3ZQLdcyzamJlDVWrsc6AxcbYxZaox5zxhT4BFRdeBEREREvDDGDDbGLD/jNvicJsHAbs/9JKBqHpt7ABjvub8M6GOtvRgoDVxZ0No0B05ERERcw5dz4Ky1E4GJeTRJA4I890PIJRgzxgQAvYChnkVrrbUnPPeXAzmGXs9HCZyIiIhI4azgj2HT1sCOXNp1B5ZYa63n8QxjTGtjTCBwLbCmoC+sBE5ERERcw8+uQv0fsMgYUwO4ArjFGPOitfaZc9r1BRae8fh54COcmXZfWWvnFvSF1YETERERKQRrbYoxJhq4DHjVWrsPL2matfbpcx6vx7kStdDUgRMREREpJGvtEf64EtVn1IETERER1/CzIdQSow5cEWvyt+tKuoRis+WrL0q6hGJV69neJV1CsVq1aH1Jl1BsAqMvaCTC7yXE7y/pEopZVEkXUKzKlwks6RLkT0gdOBEREXEPBXCAvkZERERExHWUwImIiIhraA6cQwmciIiIiMsogRMRERHXUALnUAInIiIi4jJK4ERERMQ1lMA5lMCJiIiIuIwSOBEREXENJXAOJXAiIiIiLqMETkRERNxDARygBE5ERETEdZTAiYiIiGtoDpxDCZyIiIiIy6gDJyIiIuIyGkIVERER19AQqkMJnIiIiIjLKIETERER11AC51ACJyIiIuIySuBERETEPRTAAUrgRERERFxHCZyIiIi4hubAOZTAiYiIiLiMEjgRERFxDSVwDiVw4reqVApl7nsPl3QZIiIifqdIEjhjzAjgZmC/Z9FFwMPW2g+8tI2z1jby3DfAGKAdcAC4BZgITLbW/mKMeQZIBBoBfYAoYA3wlLU2xsu2A4HxQDNgG3A3UAWYAYQAi6y1/2eMWQDcYa1NNMZMAyYDHwCDrbWzPc+PAO4AOgBVgVjgbmvt9kL/RxXQiP7NaBAZzKIth5i0cEeO5wMDDN/+qyuJRzIAeOW7WHYcPsZL17egesVyHEo7weOz1vN7lvVVyUUmLDSISc/fSfmgsiVdSpEY/uzTbIuPp3uPngz+5/15tj186BD33TuIWZ//z0fVFdz4Ib1oViec75clMGrWihzPhwWXZepjfYisGMSq+IMMGfczgQGGTZPvYPu+FAAe/c8iNiQk+br0Anvi8kbUiyhPzLYjTF+yK8fzgQY+GdSBPcknAHjrp3i2HTrm6zIL5K1/XExUjQrMWbOHN77emOP5u3s14tpOdQCoUL40K+MP838zVrBi9NUkHEwH4MkPVrApMdmndefliwmvcjAxgSZtOxN9w535arN09pesi5kPwPH0NGo1asbVAx/mzSG3EV61OgBX3f0Q1eo08Nl+5Nfbo0awc8c2Onbpzs0D7sm13ZGkw7wy7HFGjZ0CQNyWTUyb8BYnT5ygS49Lue7mAb4qucgogXMUZQI30lobba2NBsbmc53eQD1rbTdgPfB3b42stc/gdO5WeF4jR+fN42agrLW2O7APuBZ4DvgC6Ap0M8Z0zWVdCzx0zusOAh4GvvW8rs86b72bRRJgYMDk5dQKD6JOpaAcbRpXDeGH9fsYNG0lg6atJO5AOpc0imDL/jTunLycbQePEd000lclF6nMLMudT04hNf14SZdywebOmU1WZhYzPvqExMRdJCTsyLP966+N4sQJ/93v/l0aEBhoiH78v9SvVoGG1SvmaHPbpU34ZMEWuj36GaFBpWnXKJKW9SOY9fNW+j79JX2f/tIVnbcejSIINIb7Pl5LjbBy1Aorl6NNw8hg5m4+xEOz1vHQrHV+33m7qn0tAgMMV7w4l7qRITSoGpKjzdT5cfR/5Sf6v/ITi7ccZMbP8bSoHcZ/F+/MXu5PnbcNSxZis7IY/OI4jhzYw+G9iflqc/Hl/Rk4/C0GDn+Luk1b0aH31exPiKflJZdmL/fHzttvC+eRmZXFa+Ons2/PbvYkJnhtl5aawlsvD+P48YzsZRPHjOJfTz7HqLFT+e3neezbu9tXZUsRK+kh1Ghggef+O8D8C9xeX+Bbz/1PgINAJ2ChtdYCvwEdc1k3Ewg1xjQq6IsaYwYbY5YbY5YfXvFNIcrOqUO9cGZvOABATHwSbeuE5WjTqlYFejSpzIf3dGBE/2YEBhiSj52iQWQwQWUCaRAZzM7D/v3LJDep6cdJSfPfTkxBLF+2lMv7XQFAl67dWLUyZ2J12pLFMQQFBVG5sv92vHu0rMHni+IAmLdqF12bV8/RJinlOM3rRlAxuAy1KoeQeCiNi6OqcsXF9Vj0+g2MH9KLwAD//xTdtnZFftpyCIBlO47QqmaFHG2aVw+la4Nw/nNba564vBGBfr5blzStwpdLdwKwYMM+OjXO/WetWlgQkRXKsXrHEdo3jODyNjWYPewy3vrHxX71/u3YuJqLukQD0LBVRxI2rytQm5Skg6QlJ1GzYRS7tm4kdmUME56+jy8mvEpmZqYvdqFA1q1aTvdelwHQtmNnNq5d7bVdQEAA/zf8FcoHB2cvS01JIbJKNYwxhFYIIyM9zSc1FyVjjM9u/qwoL2IYaowZBGzEGQ7Nj0hghzHmTuABnGFMgHeMMclAXZwELb+qAkkA1tqVAMaYUCDd8/wxIOcZ+A9jgCEFeD08rzURZ+iX1sPnFcl4ZVCZQA6kOEMyyRmnaFY9NEebDbtTGPz+Kg6lneTF65rTrXEEKxOOAnBbp1pknMrMHl4V33l+xDASdvwR1i5ftpTrrr8RgIoVK7Jpo/dPvKdOnmTihHd58+1xPPLQAz6ptTDKly3NnsPOIZWUdoK2DXP+bP62cS/9Otbl/mtasXnXEZJST7Bi60GueuYr9h05xuRHetOvQ12+XbrDx9UXTLnSARxMOwlAyvHfaeIlrdq8L41HPlvP4fRTDO3XhM4NKvFrvP+mi+XLlmKv57xwJO0kreqF59p2YO/GTPvJ6ayv3p7EDa/OZ3/yccbd04nLWlXnh9V7fFLz+Zw8cZzQ8MoAlA8JZc/2fQVqs+TH/3HxZf0BqNmwKXc/8zqh4RF8NvYltqxaTLMOl/hgL3I39rUX2b1rR/bj9atXcPlV1wIQUqEi8Vs2eV2vfHDOn9dmLVvzzX9nEhJakQP79lCvYZNiqVmKX1F24EaenvPmmROXH8lAqLV2sjHmIM58M4AhZ8yBK4gUnLluGGOu9dzPXgYEAztxhkvPdPrx/4DhwO8FfN0id+xkJmVLOwFp+TKBePsgsGV/GqcyndI37kmhbkR5mlYLZeovCWzdn0bvZpEM7F6Pd+bF+7L0v7xhI54/6/Gol1/MHhI9duwY1mZ5Xe+9yRO5+dbbqFAhr88YJS/9+CmCyjqnjpBypb1+Sn361o4MGfczqRmneKh/awb0acoH8zZz8ndn31fGHaBRjZxDr/4m41QWZUs5x2FQ6UCvQxbxh9Kzj8PN+1OpFZZzuoM/ST9+inJlAgEILleKgFxSBmOgW7MqjPx8LQAbdh3Nfv9Wb0+iQbWcHfeSUqZcEL+fdDraJ45neD3GcmuTlZXF9g2r6XPLIACq1W1AqdJlAKjZMIokPxhifPCxs38VTnz7VU6ccD7gH884hi3APOcH/v0M61Yt48Mp47nhtrv8PmXyyoUlF4eSHkL9FWfYE6B1EW3vMs/9y4CjwBIg2nPBxCXAUpyLLU5PbGiAM18O6xzR03AuqihRG/ekZA+bNqkWwp6jOYcTR17fgiZVQwgw0KtpJLH7UgkNKkXjKk5c3rp2RWyOvqr4WvPmF2UPm26J3UyNGjW9tluyOIZPPv6IgXfdSezmTYwYNtSXZebbyriD2cOmLetHsPNAao424SFluaheBAEBho5RVbHWMuXffWjpWXZNp/qs3X7Y16UXWOz+tOxh00ZVgtnrScXP9MwVTWgYGUyAge6NIog76N9DUmt2HKFzYyeJuqh2GLsOpXtt16VJJCu2/fEevTu4My1qhxFgDFe2q8X6nUd9Um9+1KjfhIRYZ0h0X0I8YZHV8t0mYfNaajVqlt2R+WzsS+zdEUdWViablv1KtboNfbQX+deoSTM2rnOGTbfHbaFKtRr5XjcwMJCadeoBEH3ZlcVRnvhIcXbgnj89L8wY82Aubb4CthtjYoDLi+A1JwKVjDG/4AyVfodzNWl/IAbnKtQYYDTwuud1V1lrz4yo3gNy/kbysfmbD3J162o81rcxl7eoSvyBdB649OzJtBN/3s7I65sz675OrElMZsm2I8xcksjtnWuzeGg07eqGMWtpyX96vBB97xlT0iVcsF69+/DNV18yetTLzP7xe7r3jCY+Lo6xY948q93U6R/y3rQZvDdtBlFNmzHi+ZElVHHevl68jVt7NWHUwK7c0K0RGxOSGH7HxWe1Gf3pSsY+GM3+mYMIDynLrIVbeenj5bz3aB+WjLmJJbH7mb8m50Rzf7Mo7jB9m1XhwZ716dWkMtsPH2PQJXXOajMtZhfPXtGEKXe2ZcOeVFbs9J/J/d58tzKRv3etxwu3tKH/xbXZvDuZp65vmaNdr4uqExN7MPvxa19u4N3BnVnwfF+WxR9i4cb9OdYpKc06dmP1wtl8P30c62MWUKVWPebOfC/PNlHtOgMQt2YZdZu1ym7X64b/x+fjXmbc/91D7SbNadiqvU/3JT86d+/F/NnfMHnsa/wyfw4du3Rj5454Zkwel6/1P5g8jrvu/Zc70zfJZpy5/VJUimoOHEBouVJ0aViJFQlHOeyZh1OStnz1RUmXUKyOLMvvxdMFl5KcTEzMr7Rv35HKkSVzgUL4de8W2bbCgsvSu20tflm/h/1HS36eZYfoVudvVEghZQPpWDecNYnJJB07VWyvk5dN64q2s1uxfGmiW1QjZstBDiSX/MVC7z7c44K3kZGWSty65dRr1prQsEqFblMc2lTLeRHahUpLTWHVssVc1Lod4RGVi3z7BdGkWnmf9gQbPPqdzzou29640m97ua78SwzGmGrAzHMWx1pr7y2JeopL6vHfs69EFXerULEiffv9eYYrjqaf4PNf/hpzK9NOZDLfcyXqn0XysVN8uSznd9q5WVBIKC279LrgNm4RElqB7pcWxcCVuJUrO3DW2n04X0EiIiIifyEa+nWU9EUMIiIiIlJArkzgRERE5K9JAZxDCZyIiIiIyyiBExEREdfQHDiHEjgRERERl1ECJyIiIq6hAM6hBE5ERETEZZTAiYiIiGtoDpxDCZyIiIiIyyiBExEREddQAOdQAiciIiLiMkrgRERExDUCAhTBgRI4EREREddRB05ERETEZTSEKiIiIq6hixgcSuBEREREXEYJnIiIiLiGvsjXoQRORERExGWUwImIiIhrKIBzKIETERERcRklcEXs8we6lnQJxabWs71LuoRiFd7xwZIuoVgdWPx2SZdQbBKTMkq6hGIVHtyppEsoVlv2pZZ0CcXqt12HSrqEYtWkWh2fvp7mwDmUwImIiIi4jBI4ERERcQ0lcA4lcCIiIiIuowROREREXEMBnEMJnIiIiIjLKIETERER19AcOIcSOBERERGXUQInIiIirqEAzqEETkRERMRl1IETERERcRkNoYqIiIhr6CIGhxI4EREREZdRAiciIiKuoQDOoQRORERExGWUwImIiIhraA6cQwmciIiIiMsogRMRERHXUADnUAInIiIi4jJK4ERERMQ1NAfOoQRORERExGWUwImIiPy+M5AAACAASURBVIhrKIBzKIETERERcRklcCIiIuIamgPnUAInIsUuOfkoi2N+5eiRIyVdiojIn4ISOD/31isj2LVjGx27dOeW/3eP1zbpaamMGvEkWVmZlCsXxBPPvUrp0qU5knSYl599jFfHTfVx1YUz/Nmn2RYfT/cePRn8z/vzbHv40CHuu3cQsz7/n4+qK3pVKoXy0eiB9Bn4VkmXUmjPDx/K9vh4LunRk0GD7/PaJiUlmUcevI9LuvfkzdGjmDB5GvPm/MjsH78HIC01lRYtWzF02HO+LD1f3nn1ORJ3bKN95+7cNGBQru2OJh1m1PDHefmdKQB8PHU861evzH6uV7+rufH2f/ik5vwa9cKz7Ni+jS6X9GDAwHu9tklLS+W5px93zi1BQYx46XUOHdjPW6NHkp6eTrPmLXngkcd9XHnepr09kr07d9CyY1euvvnufLc7lp7GpFefJSsrizLlynHv/73IiRMZTH5tBKnJR6jbMIo7H3zSh3uSu28nvc6h3Qk0atOJS6693Wub9OQj/HfM89w57E0Akg8d4JsJozABAYRXrUG/fzzs2iTLpWUXuWJL4IwxI4wxd5yzrJIxJtUYU87zONgY84Ux5mdjzAzjeNAYs8AYk+H597pctp+jnTEmxLO9X4wx7xtjvHZQPbVt8qy3wBjTxvPvYM/z04wxd3mWLTbGLDPG/K2o/4/O59ef55GVmcnrE6azb08iu3cleG03f853XHfzHbz4xgTCK1VmxZJfSU1N4Y2Rz3I8I8PHVRfO3DmzycrMYsZHn5CYuIuEhB15tn/9tVGcOHHcN8UVg7DQICY9fyflg8qWdCmF9tNc5z2b+sFMdicmsjOX92zrllgeeewJBg7+J126dmPzpo3cePOtTJwynYlTptOmXXuuu+Hvvi0+H2IWziMrM4tR777P/r2J7Enc6bVdWmoKY14exonjf/w83nr3fYwcM4mRYyZRp35Del1+ta/KzpeFP80hKzOL8VM+ZM/uXSTu9H5umfP9t9x0+wBeHzuJShGVWfrbL0wY+yYDBv6TsZOmc/DAPlatWOrj6nO38rcFZGVm8dRrkzi0bzf79+zKd7slC37ksmtv5ZEXxlAxPIL1Kxez+Kcf6BTdl2fenMrxjGPs2LrJp/vjTeyyRdisTP7fiLc5emAvSfsSc7TJSE/l6wmvcuqMc+Tqn76h790PcdvTo0k5fJCDu7b7smwpBr4eQr0MKAf08Dy+E4ix1vYETgAdrLVjrbXRwG5rbbS19gtvG8ql3RBgq7W2G1AWuCmPWkZ61ou21q72LHvIS7sbPdt53xhTuUB7e4HWrVpO90svB6Btxy5sXLvKa7urr7uZth27AJB89Ahh4ZUIDAjgyedGUT442Gf1Xojly5Zyeb8rAOjStRurVq7Ite2SxTEEBQVRuXKkr8orcplZljufnEJquns7oSuWL6NP334AdO7SldWrVnpt177DxbRs3YaVy5exYf1aWrZuk/3cgf37STp8mOYtLvJJzQWxfvUKLul1GQBtOnRh0zrvx19AQACPDX+FoPI5j7WtmzcQEVmFiMgqxVprQa1auYxel/UFoGPnrqxd7f29u+7vt9CxU1cAko8cIaxSJRITdtCkaXMAwipFkJ6W5pui8yF23Uo6du8NQPO2nYjbuCbf7XpddQPN214MQGryUUIrhhNcoSJ7EuI5lpZK0qEDVKpc1Tc7koeETWtp2qknAPVbticxdn2ONgEmgGuHPEPZoPLZy3re9A8q16wLQEZaCkGhFXxTsBQbXw+h9gPGef6dDewG/p8x5gtrbe7jE/nXCZjsuf8L0BH4qADrHzTG9D53obV2uzFmKdAZ+Obc5z3J3WCAF0a/wy0DBha0bgDeGf0Cu3fuyH68bvUKLr/6WgBCK1QgbsuePNfftH4NaakpNG3RqlCv70vPjxhGwo4/PgEuX7aU666/EYCKFSuyaeNur+udOnmSiRPe5c23x/HIQw/4pNbi4MaO28jnh5/1nq1cvoz+190AQMWKYWzetDHXda21zPnxe0IrVKRUqT9OO5/O/Igbb7ql+IougHdff5HdZyRRG9asoM+VzvEXUqEC23I5/soHh+S6zW8++4hb7v5n0RZaCK+99By7Ev5471avXM5Vf7segAoVKrJlT97J0vq1q0lNSaFFy9b07H050ya9S/OLWrM05hcGP/BwsdaelxljX2Hf7j+S0S3rV9Ht8msACA6pwM74WK/rnTiRQVhEpNd28ZvXcSwthYZNLyKsUgTrlv3KvK9nUb12Pcr7Qafn1InjhIY7WUK5kFD2b9+fo01ZLx8mTtu4eAGVa9bN3oYbuXXot6j5ugPXBegGzAOw1n5tjAkC/muMmQ88Yq3NvIDthwLpnvvHgLyOtqHGmNOdxtOdtjdxUjhvM60PA2HeNmStnQhMBIg7kGELWHO2IY8/e9bj/4wZxckTJwDIyMjAZmXlum5qSjIT3hrF0BdfK+zL+9SwEc+f9XjUyy9mD4keO3YMa73v63uTJ3LzrbdRoULJn0j/as6do/baKy9lDxseO5ZOVh4/n8YYnhg6jPFjx7BwwU9c3u9KsrKyWL5sCfc/VHIdgDPd/+9nzno8+Z3RnPT8TB7POEZWLj+TuUlLTeXo0SNUr1m7yGosrMeeHn7W47dfe5kTp88tx/Let5TkZN5+7SWeH+XM1Rww8F7Wrl7JzBlT6XdVf8qXL5/rusXt3DlpMye+mX3OPHE8I9f9KleuvNd26anJfPyfN7jvqZcA+Prj97jjgScIKh/M7P99zG9zv6FHv2uLa3fypUzZcvx+yqn91PGMXM+V3hw5sJcl337KrU+9WlzliQ/5bAjVGNMKqAx8BtQzxtQ2xjQGfgDaAJHAHXlsIj9SgNMfh4M9j3Nz5hDq6U7jSpxOXwMv7SsBSRdYX4E0imrOBs+w6fa4WKpWr+G13alTp3j52ce5694hVKnmvY2/a978ouxh0y2xm6lRo6bXdksWx/DJxx8x8K47id28iRHDhvqyTDlD0+bNs4dNt8bG5vqeTZsyiW++ci42SU1NJdSTYqxauZyLWrby20/TDZs0Y9M6Z3bFjvgtBT62lv66gPadLimO0i5Yk2bNWecZNo3bGku16t7fu1OnTjH8yUe554GHqXbG+adRk6bs37eXm24f4JN686tOo6jsYdNd27dSuUr1fLf7/dQpJrwylOsH3EeEZ730tFR274gjKzOT7bEb/GL2fLX6TUiM3QDA/p3bqBhZLV/rZaSn8uW4l7hq8GOUyyOhcwNjfHfzZ76cA9cXeMkzb+1tz+NBwHWeDtR6nPlxF2IJEO253x0ozOzatz3rZjPG1AHaATEXUlxBdenei/k/fsukd15j0fw5dOzSnZ3b45k+aexZ7WZ/8wVxWzbxyfTJPDlkIAvn/ejLMotEr959+OarLxk96mVm//g93XtGEx8Xx9gxb57Vbur0D3lv2gzemzaDqKbNGPH8yBKqWKIv7cN333zFG6NfYc7sH+jWoyfb4uN4952zr6q9/sab+O6br7jnrjvIysykc1enU7P4119p275DSZSeL526RbNgzrdMGfc6v86fQ4fO3di1YxsfTh6Xr/VXLYuhRet2xVxl4XTv2ZvZ33/N2DdfZf7cH+nSrQc7tsUzefzbZ7X79svP2RK7iQ+mTORf997FT7OdK4dnzpjCTbcPoFy5oJIoP1dtO/dk8fwf+GTyGJb/Mo+WHS9hz87tfDHjP+dt98ucr9kZv4VvZ01j9FP3s2zRXK78+wCmjx3FQ7dcRnpaChf3uKyE9uwPTdp3Zf2vc5n7wQQ2L/mZyjXr8vOn5/+mgcVfzyTl0AHmvD+WD1/8Nzs3eZ8fKAVnjHnPGBNjjHkml+dLGWN2nnHhZEvP8uc8F0nm76Ry7natLfSIX94bNmYEMIA/Uqs2OBcprDbG9AIewBmu/BAwQDJwq7X2mGf9OGtto3y8TnY7Y0wIMAOoCmwFBlprf8+ltpuB05MHxgP34SSAezzrvgDchdOpzASGWWvnna+eCxlC9SY1NYXVy2Jo0bo9lSJKds5CrUrFe7JOSU4mJuZX2rfvSOVI31+gEN7xQZ+/pi8dWPz2+RsVUEpKMktifqNt+w4lelFJYlLxXG2dlprC6uWLadGqHeElePyFB5cp8m2mpiSzbEkMrdt2IKJyyZ5btuxLLbJtpaelsHHVUppc1JaK4REX3K4obEsu2gs9MtJT2bFuJbWbtiQkrFKRbrsw7upYx6dZVffXfymejosXi/7dLc99M8ZcD/zNWnuXMWYK8LK1dus5bdoBN1trnzhjWXvgVaAPMAz41Vo7tyC1FVsH7q+qqDtw/qS4O3AlTR049yquDpy/KI4OnD8pyg6cPyrqDpy/+Yt34N4GfrDWfmeMuQUIstZOPafN/TihVTqwDrgXJ8A6bq191xjTGbjCWjucAnDFF/kaYxacsyjZWtu/uNcVERER/+LLebNnfsuEx0TPhYunBeN8owY4I47e5k0sA/pYa/caY6YDV3rWiz9jvQJ/R40rOnCeeXM+X1dERET+us78lolcpAGnh6dC8H5twVpr7QnP/eVA43yulyf9LVQRERFxDT+7CnUFztejAbQGdnhpM8MY09oYEwhcC6zJ53p5ckUCJyIiIuKH/gcsMsbUAK4AbjHGvGitPfOK1Odx/qiAAb6y1s41xgQALxtjxuD8cYN+BX1hdeBERETENfzpuyOttSnGmGicPxX6qrV2H07Cdmab9UCrc5ZlGWP6AFcBY6y1Bf7jtOrAiYiIiBSStfYIMKsQ62Xg/HGDQlEHTkRERFzDjwK4EqWLGERERERcRgmciIiIuIY/zYErSUrgRERERFxGHTgRERERl9EQqoiIiLiGRlAdSuBEREREXEYJnIiIiLhGgCI4QAmciIiIiOsogRMRERHXUADnUAInIiIi4jJK4ERERMQ19EW+DiVwIiIiIi6jBE5ERERcI0ABHKAETkRERMR1lMCJiIiIa2gOnEMduCI2YNqyki6h2KxatL6kSyhWBxa/XdIlFKsqnR8q6RKKTfvbby7pEorVigVrSrqEYjXlhetKuoRi1alWREmXIH9C6sCJiIiIayiAc2gOnIiIiIjLKIETERER1zAoggMlcCIiIiKuow6ciIiIiMtoCFVERERcQ1/k61ACJyIiIuIySuBERETENfRFvg4lcCIiIiIuowROREREXEMBnEMJnIiIiIjLKIETERER1whQBAcogRMRERFxHSVwIiIi4hoK4BxK4ERERERcRgmciIiIuIa+B86hBE5ERETEZZTAiYiIiGsogHMogRMRERFxGSVwIiIi4hr6HjiHEjgRERERl1EHTkRERMRlNIQqIiIirqEBVIcSOBE/kJx8lMUxv3L0yJGSLkVERFxACZwfe6pfE+pHlOe3bUlMi9mZ4/lAA5/eezF7jh4H4I25cSQkZTD86igqlC1NYIDh6f9tJPXE774uPV/GD+lFszrhfL8sgVGzVuR4Piy4LFMf60NkxSBWxR9kyLifCQwwbJp8B9v3pQDw6H8WsSEhydel59vzw4eyPT6eS3r0ZNDg+7y2SUlJ5pEH7+OS7j15c/QoJkyexrw5PzL7x+8BSEtNpUXLVgwd9pwvS78gVSqF8tHogfQZ+FZJl1IoT/ZtTL2I8sRsS+L9xbtyPB9oYNY9HdmT7Bx7b86LZ9uhY5QtFcCE21pz9/RVvi65QMY/dCnN6lTi+2U7GPXJ8hzPhwWXZerjlxFZsTyr4g4wZNwCz7E3gO37PcfehIVsSDjs69Jz9eWE0RzcnUDjtp3oef2d+WqTkZbK52NHkp5ylBr1m3DNPY+ybPaXrI9ZAMDxY2nUatSMa+551Id7kj/vvPociTu20b5zd24aMCjXdkeTDjNq+OO8/M4UAD6eOp71q1dmP9er39XcePs/fFJzUdEX+Tp8msAZY4KNMV8YY342xswwxjxnjNlkjFngubUxxpQ1xnxkjFlkjJmcx7YWGGNqGWNqGGM2GGOqG2N2GGPu9zy/zxhzVy7rtjDGzDfGLDHGPOhZFudl29YYc6UxprwxJtMYE120/yO569k4ggADgz9cTY2K5agVXi5Hm4ZVQpi76SAPzlzLgzPXsu3QMbo0CGfJtiM8/Ok6lmxPol+LKr4quUD6d2lAYKAh+vH/Ur9aBRpWr5ijzW2XNuGTBVvo9uhnhAaVpl2jSFrWj2DWz1vp+/SX9H36S7/uvP00dzZZmVlM/WAmuxMT2Zmww2u7rVtieeSxJxg4+J906dqNzZs2cuPNtzJxynQmTplOm3btue6Gv/u2+AsQFhrEpOfvpHxQ2ZIupVB6NI4gwBj++dEa59gL83LsRQYzd/NBhnyyjiGfrGPboWMEGHj+mqaElPXvz8X9uzQgMMAQ/dhnzrFXw9uxF+Uce4/MIjSoDO0aVaFl/crMWriFvk99Qd+nvvCrztvGpQvJyspi0AtjOXJgL4f3JuarzZpFc2jVrQ/3vjSBExnH2B0fS8fL+3P38De5e/ib1G3aknaXXlUCe5S3mIXzyMrMYtS777N/byJ7EnN+wAdIS01hzMvDOHH8ePayW+++j5FjJjFyzCTq1G9Ir8uv9lXZUsR8PYR6JxBjre0JnAA6ACOttdGe22pPm0PW2u5AGWPMJXlsLwD4AHjYWrvXs6y1MaYKUDWP9Z4FngC6AgOMMYF5tG0NtMTH/1ft6oTxU+xBAJbuOELrmjlPshdVD6Vrw0pMvrMNT/VrQqCBX+KS+Hb9fgDCypfmyLFTviw733q0rMHni5w+87xVu+javHqONkkpx2leN4KKwWWoVTmExENpXBxVlSsursei129g/JBeBAb47yexFcuX0advPwA6d+nK6lUrvbZr3+FiWrZuw8rly9iwfi0tW7fJfu7A/v0kHT5M8xYX+aTmopCZZbnzySmkph8/f2M/1LZ2xTOOvaO0qpXz2GtRowJdG1Ri4u1teLJvYwI9P4avzo5jX4p/73ePljX5/Jczj70aOdokpZ5x7EWGkHgo1Tn2OtZj0Rt/Z/xDl/rVsbdj4xpadOkJQMNWHdgZuy5fbcqHVuDAru1kpKeRcvggFSMis9unJB0kLfkINRtG+WYnCmD96hVc0usyANp06MKmdd4T34CAAB4b/gpB5YNzPLd18wYiIqsQEemfH/LzEmB8d/Nnvu7A7QauM8Y0ttYOApZ5aRMNLPDcfxrYnMf2hgGrrLVzPI93AvWBNsCm89RxD1DBWnuxtTYzl3abcDpvrfPanjFmsDFmuTFm+f4lX+XxsvlXrnQgB1NPApBy/HcqBZfJWdy+VP71yToGzVhNqQBDl4aVsp+rUbEc7euGMX/LoSKpp6iVL1uaPYfTAUhKO0HV8KAcbX7buJc6VUK4/5pWbN51hKTUE6zYepCrnvmK7v/+nNKlAujXoa6vS8/VyOeHM/gfA7Jvn3z0AVWqOJ8jKlYMI+lw7omFtZY5P35PaIWKlCr1R4Lz6cyPuPGmW4q99qKUmn6clDT/7sTkJah0IIfSTh97p6hUvnSONpv2pfLwp+sY/KFz7HVuUIksC4fTT/q63AIrX640ew6nAU5HrWqYl2Nvw17qRIZy/zWtzzj2DnDVM1/S/dFP/e7YO3X8OBUqOZ2voOBQ0pJzziX11qZO1EUcPbifJT/8l8o16xAUUiG7/dIfv6TjZX/zzQ6cx7uvv8jQf92Tffv2vzOJqOx0vEIqVOBokveRiPLBIQSHhHp97pvPPuKq6911bpGz+TTrt9Z+bYwJAv5rjJkPJANDjTGnB/B7A5FAijHmceBm4EXgf7lssgew7YzHWUAm0BbIOanqD08CjwPLjTEvWmun5tLuABCB04HLdXvW2onARICury60ebxuvmWczKRsKad/HVQ6wOufDok7mM6pTOflNu9LpbanE1Q60PDMlVGM+nErmVlFUk6RSz9+iiDPUFNIudJe5zQ8fWtHhoz7mdSMUzzUvzUD+jTlg3mbOfl7FgAr4w7QyMvwT0k5d47aa6+8lD10cexYOllZWbmua4zhiaHDGD92DAsX/MTl/a4kKyuL5cuWcP9DDxdr3XK2s469MoFefzbjzzr20rKPPTdIP36KoDLnOfZu68iQcfOdY+/aNgzo04wP5m3649jbeoBGNcN8WndeypQL4tTJEwCcPJGB9XLe89ZmwefTuXrQI5QrH8xv337KqgU/0KHP1WRlZbFj42p63zLQp/uRm/v//cxZjye/M5qTJ5xzy/GMY2TZ3M8t3qSlpnL06BGq16xdZDX6kubAOXw9B64x8ANOQhYJ3MHZQ6iZOJ26UGvtaGAyEJLHJvsD5Y0x3c9YthW4HvA+XuVoZa19CegIPGaMaQCce8SffpwGNOPsjmKx27w/NXvoplGVEPYm50w0hl3VlEaRwQQY6NG4MlsPOInW0Cui+Hb9PjbvS/NlyQWyMu5g9rBpy/oR7DyQmqNNeEhZLqoXQUCAoWNUVay1TPl3H1p6ll3TqT5rt/vPPJxzNW3ePHvYdGtsLDVq1PTabtqUSXzzlfMZJTU1ldBQJwVYtXI5F7VspZOVj8XuT6NVTec9aBQZzD4vx96zV0ZlH3vdG0cQ5zn23GBl3IHsYdOW9Suf/9hrUhWLZcq/L6Nlfc+x17kBa7f5T7pfvUETdm52hk33J2wjLLJavtocT0/lwK7tZGVlsnvrpuwPyjs3r6Nmo6Z+e+w1bNKMTetWA7AjfgtVquUcBs/L0l8X0L5TXrOTxA18PYQ6CLjO01FbD+ScHQy/Av0891ufZ3upwDPAyDOWrQSqAHl9H8M4Y0xda20SznBqaeCwMaaOZz5cDeB0z2AdztfO+DTKWrj1MP1aVOGhXg3o3TSS7YeOMbhbvbPaTP0tgWFXRfH+Xe1ZvyeF5QlH6Vw/nJ6NI7iiRVXG3tKKm9oX7MD2la8Xb+PWXk0YNbArN3RrxMaEJIbfcfFZbUZ/upKxD0azf+YgwkPKMmvhVl76eDnvPdqHJWNuYknsfuavyTlZ2V9EX9qH7775ijdGv8Kc2T/QrUdPtsXH8e47Z1+Zef2NN/HdN19xz113kJWZSeeuzol18a+/0rZ9h5Io/S9tYdxh+javwoPR9bk0KpLth49xzyVnDxdOjdnJM1dGMXVAOzbsSWH5zqMlVG3BfR2zjVsvjWLUoG7c0L0RG3ceZvgdnc5qM/rTFYx9sBf7P7mH8NCyzPp5Ky/NXMZ7j17GkrdvYcnmfX517DXtcAlrF83hh+nvsiFmAVVq12PeJ+/l2aZJu050638bX096nZfvvoaM9FQuuqQ3AHFrllG3WauS2JV86dQtmgVzvmXKuNf5df4cOnTuxq4d2/hw8rh8rb9qWQwtWrcr5iqLjzG+u/kzY63v+iXGmBrAhzgdomQgFrgG2O9pMh74CpgOVMPpYI631n7gZVsLgDustYnGmNnAmzgXJgwBRgBfA1hrp3lZtycwGjgF/GytfdoY0w+nI5gFzLLWjva8xus48/JSgQXW2gV57WNRDaEChJYtRcd6YaxOTCYpveQvRli1aH2Rbi8suCy929bil/V72H80o0i3XRgHPvtnkW8zJSWZJTG/0bZ9BypXjjz/CsWoSueHSvT1i1P7228u0u1lH3u7kknygwuBVixYU6Tbc4692p5j71iRbrswprxw3QVvIyMtlfh1K6jbrBWhYZUK3aY4tKlW9MPNaakprF6+mBat2hEeUbnIt18QzaoH+7Src+eHa3zWcZlxe2u/7cb5tAP3V1CUHTh/U9QdOH9THB04f6IOnHsVdQfO3xRFB86fFUcHzp/4ugM34KO1Pvs9O/22Vn7bgfPvLywCjDHVgJnnLI611t6bj3XbAOd+k+jP1trhRVWfiIiIiK/5fQfOWrsPZwizMOuuLuy6IiIi4n/8/fvZfEV/C1VERETEZfw+gRMRERE5zV+/3sXXlMCJiIiIuIw6cCIiIiIuoyFUERERcQ0NoDqUwImIiIi4jBI4ERERcY0AXcQAKIETERERcR0lcCIiIuIaCuAcSuBEREREXEYJnIiIiLiGvsjXoQRORERExGWUwImIiIhrKIBzKIETERERcRklcCIiIuIa+h44hxI4EREREZdRAiciIiKuoQDOoQRORERExGWUwImIiIhr6HvgHErgRERERFxGCVwRi6oTXtIlFJvA6FYlXUKxSkzKKOkSilX7228u6RKKzYoPPynpEopVk79dV9IlFKvQ0n/uX0VlSikrKUr633To/0FERETEZdSBExEREXGZP3duLSIiIn8quojBoQRORERExGWUwImIiIhrBCiAA5TAiYiIiLiOEjgRERFxDSVwDiVwIiIiIi6jBE5ERERcQ1ehOpTAiYiIiLiMEjgRERFxDc2BcyiBExEREXEZJXAiIiLiGpoC51ACJyIiIuIySuBERETENQIUwQFK4EREREQKzRjznjEmxhjzTC7PVzTGfG+MmW2M+cIYU8YYU8oYs9MYs8Bza1nQ11UHTkRERFwjwIe38zHGXA8EWmu7AA2MMY29NLsdeMNaezmwD+gHtAI+ttZGe27rCvjfoA6ciIiIiDfGmMHGmOVn3Aaf0yQamOW5Pxvodu42rLXvWmvneB5GAgeAzsDVxpilngSvwFPaNAdORERExAtr7URgYh5NgoHdnvtJQLvcGhpjugDh1trFxphMoI+1dq8xZjpwJfBVQWpTB05ERERcw8+uYUgDgjz3Q8hlZNMYUwl4B7jBs2ittfaE5/5ywNvQa540hCoiIiJSOCv4Y9i0NbDj3AbGmDLAp8BT1toEz+IZxpjWxphA4FpgTUFfWAmciIiIuIaffY3I/4BFxpgawBXALcaYF621Z16ROhBnaHWoMWYoMB54HvgIMMBX1tq5BX1hdeBERP4/e/cdH1WZNXD8d0JNIBASQkc6Q5HeqyACrquy9o6ioutrb2tddy9l1wAAIABJREFUwQJiFwVFQMG2Cuqqa0e6KCC9M0CoIfSSDsTM8/5xb0Igk5DAZO7c4Xz95OPMnWdmzuVOOXOecpVS6jQYY1JEpA/QH3jZGLObk6ppxph3sZK2k7U+k+fWLlSXq1C2FC2qV6Ri2VJOh6LUWaNabDTT33/A6TBUAGSkpuBdsYi0lMOFblOhQyR4f0VhjDlkjJlqJ29BoxW4EDakc21qVSrPyqQUvlu7L9/tUWUieKB3PVYkpXJdu5q8PGszqUezAahUrjQP9anP8F82BTvsYntsQGPqx0Uxf/MhPlq4I9/tpQSm3N6RpGRrvOebMxPYvD8j2GGelrdffpbErZvp0LUXVw++vcB2hw8e4KVhj/Li2x8A8Nmkd1m9fGnubX0vvJgrb7g1KDEXxeMDm9jH7CAfLvB/zKYO7URS8hEA3phhHbNypSMYd30bhny0LNghB0xMdCQTnruJqMhyTody2oYPak7D+Ar8tmE/E+ZuzXd7qQjhh/u7k3goE4BRP3rZeiCDkZe3pGbl8uxPO8qjU1fzl88EOfKCTRk7ij2J22jeoSv9r7y5SG0y0lKZ+OJjtGjfjf9NHsNdw0cTUapUvm0VK8cEeW9O7c0Xh7N962Y6devFdbcM9dsmPS2VUcMex+fLpnz5SB5/7mWOHT2Sb1uZMmWCHL0KhFNW4ERkuIisy7Na8A8icrt92x8i0khESonIeBH5TUQ+FJEIu20du91Eu8SIiPxDRGblefytIjLAvjxbRPqISBX7eeaJyLN5bpufE0ch8VYXkV/s+z5gb+thr7WySESutrdtynOfTSJSX0TSRaSKvc2ISEX7+bba/wbfFvcf+HR1qFOJCBFGTE8gvmJZqlcsm69N3ZhIPlu2i+/X7mP17lTqVYnMve2adjUoWyqkxgn41btxHKVEuOuzldSKKU+dmPL52jSKr8D09fu5b+oq7pu6yjXJ2/y5M/Bl+3jpnQ/ZsyuRpMTtftulpaYw+sVnOHrkSO6264bcxYjRExgxegLnNGhE3wEXByvsU+rdJI4IEf75nxXUqlzYMdvHvVNWce8U65hFCDx3STMqlnP378Zsn+Gmxz8gNf3IqRuHoH7N44kQGDxxMXWqRHJObGS+Nk2qV+Tn1bu5ffJSbp+8lE170+nROI4Ne9K4aeJiNu/LoE+zeAei92/lgjn4fD7ue/FdDuzZxb6k/D8q/LXZtS2BQbfcwwVXDsbTtjOJmzf43RZqfp8zA58vm9ff+4jdSYns3LHNb7tZ037ksmtvZMQb46gSV5UlC3/3u81tIiR4f6GsqF2oI3JWCwYGA7eLSG9gozEmAbgGKGeM6YW1yvA/CnmsgUBnEaloXzfAfSe1eRiYYYzpCfTJSQSBq/LEUZDngc/t+14pItWAD4Bb7OceLSL5v3EsUUBumcQYk2Y/12T732BQIc8bUJ5qFVi0PRmA1bvTaBJfIV8b7750Nh/IpGl8FA1io9hkJzbNq1Xg6F8+ko/8FaxwT1u7upWZuWE/AIu2HqJ17Ur52rSoGU33hlV47/o2PDagMS7ISwFYvXwJPfr2B6Btx26sW+W/6hQREcEjw0YRGZX/GG9cv4a4+GrExVcr0ViLo13dysz0WhXhP7cepnWdyvnatKxVie4NYxl/Q1seH9gk95i9PG0Tu1PcmfjkSE0/Qkqae/ehY/0qTFuzF4D5CQdpd07+6lLrOpXo3bQqnw7tyPBBzSkVISRnZNEwvgKRZUvRML4C2w+Ezg+phDXLadu9LwCeNp3Ysj7/ovb+2jRq2ZZ6TVuSsGY5Ozauo76npd9toWbVssX0On8AAO07d2PNSv+fLRdffg3tO3UDIPnwIWJiYv1uU+5U7J/CxpgDdgXtA6yF58BKjH6wL0/BWtiuIL2AT4HzsRatywaiRaRxnjZ9OJ7U3QoUZyDCQCBn9scbQFkgzhizFkBEdgKeAu67GBgsIq8X4/mwV2a+A6Db7c/g6Xdlce7uV7nSERzKzAIg/Vg2Vavkr8Dl6HxODBnHssk2hlIRwiUtq/H2vG3c16veGcdR0sqXiWBf2jEAUo78RdPqFfO1Wb87jQe/XM2B9CyeurApXRvG8nvCwWCHekrvvPYCO7cf/yW8ZsUSLrjI+i1TsVIlNm9I8nu/qAr59znH91/+h2uH/DOwgZ6hyDKl2J97zLLwVM4f/7rdqTzwxX4OpGfx9N+OH7MD6ceCHa46SWTZUuxNsYYjJGdm0bxmdL42a3amcMeHy9ifdowXLmtBzyZxLN1mfQxf36UOmVnZud2roeDYkUwqx1kVwaiK0SRu3lXkNsYYlv8xk8iK0ZQqVbrAbU56++XnSdy+Nff6quVLGPB367MlulIl9nj9f7bkWLd6BWmpKTQ7t3Wh29wixGahOqaor8yncrpNgX7ADKyq20Z7W3WsFYgxxiwFEOsf+AsROYqVMH0iIk2BHViJ2984vurwaODePM8XD6SIyGv28+U8d87jrTLG5G2fV95YvhKRukB6ntszgPxlHksyMB+4tIDb/cq7UvOQz1cFZFDI0SwfZeyyRfnSEYUOpvxkSRKXtapOu1qVqFGpHDM3HSAzyxeIMEpcZpaPcqWtQnBkmVJ+S8IJ+9PJyrb+WdfvSaVOTP4un1Dwfw+feB7jiW+/wrGjVqXmSGYGPlO8Y5KWmsrhw4eoWbtuwGIMhMxj2cePWdlSOe/1EyTsy3PMdqdRt0poHrOzUcaxbMqVsY5fVNlSfj9bNuxJyz1+a5NSqBcXRbMa0Uyat42Ne9Lo1zye23rV5+0ZCcEMvUDlykeSdcxKSo8eycSY/B/DBbUREa4Y+hA/fTaR1Yvn0a5HP7/bnHTvv/59wvVxb77EsaPWvmRmZmIK+WxJTUnm3Tde4qkRrxa6TblPsbtQjTHZwENYi9ddZt+egrUCcc4Ytxvt7VfZXZA51bkLsVYbfhxrym2Ob7Cqbjk/BZOBaGPMw8DPQE6XZ04XakHJ28mxPA/E5ly3VbDb5H2H5738FnBPIY8fFFsPZdK0qlXIrBtT3m/l4qJmVele3+r+iCoTQUZWNi2qV6RfkzgeO78B58REMqRT7aDGXVzePWm53aaNq1VgV8rRfG2e/ltTGsVXIEKgV+M4Nu1LC3aYp6VR0+asW7UcgK0JG6hWo1ax7v/n77Pp0KVHSYR2Rk44ZvEV2J2cvzvx3xd5aJxzzJrEsWlver42yhlrk1Jyu02b1qhI0uH8x2/E5S1pWr0iEQJ9m8Xj3Z1KdGRpmlSzPpPa1K2MIXQmMNRp5GHzupUAJG1NILZajSK1mfn1pyye/TMAmelpREZF+90Wapp4WuR2m27Z5C3wsyUrK4uR/36UW/55L9XtNv62uU2ozUJ1SrGXERGRrkAqVhfnY2L9/P6d4wlZfwru8hwIXG2PT9uT021qrJ8Pkzl+DrHfsZI9sFY2Lo7fgf4iEoFV5UsE9olIKxGJA2oD6+19qWCPxcvOubMxZhPWqTEctTQxhW71Y7i2bU061a3MzuSjXN6q+gltZiccpHv9GB4/vyERIqzencaomZt5aeYWXpq5he2HM5m0aGcBzxAaftt0gIHNq3HPeQ3o27QqWw5kcHuPc05oM3n+Dv79t6Z8cFM71iSlssQeGxjquvTsw+xff+CDsa/x+6xf6di1Jzu2bubTiWOLdP9li+bTsk2Bp9VzzNxNBxjYohr39GnA+Z54thzIYGiPE7vrJ83fztMXeZg0uD1rklJYvD38lmMYOHS00yGcllnr93Fxmxo8MrAJA1pWJ2FvOnef3/CENuPnbGHE5S2YelcXViQms3DzIT5fmMgNXeuy4Kk+tK8Xw9Q/Q+ez5dzOvVgyZxrfThrDij9mUqNuA376z4RC2zRv342u/S9l8ZxpjH36HozPh6dtJ7/bQk233n2Z+csPjH/7VX6b+Sudu/di+5YEPhw/5oR2077/mgTvOqZ8OJHH7rmNOTN+8btNuZP4KzWf0EBkOFZ36R5703lAR2PMEhGZgFVd+wVrTFxdYAtwMzATuNEYkygiE7FOIzERqGeM8dmzS/cBDxhjGotIJaxk61JgNdYKxeWxJhY8ALwAlANySjR3GmO8fuJtCLyPdW6y/xhj3hKRHljj4SKwFtqbKiK3cLzb9nWsxG+iMeYCe8bsLGOM5Pk32GSM+eQU/54B60IFq6rWskY03n3ppITAhIRNO0smcapYrhSd6lVhRWIyBzOySuQ5imL89e0C/phpqSksX7yAlq3bUyWuasAfvziGfro0YI8VXa40nerHsHyHs8csx5JPpzgdQolqeullp25UDNHlS9OtUSxLth3mQJrz4xJHXHXuGT9GRloqG1YsomGLNlSqEnfabUpC8+oFjdo5fakpKSxbNJ9z23Yg1uHPlkbxkUGtVY2YsSlo5d+n+jUO2TrcKRM4VTyBTOBCTUklcKGiJBK4UBLIBC7UaALnboFI4EJZSSRwoUQTOGc4P73mNInIhVhj6fKaYp+yQimllFJhSAjZnCqoXJvAGWN+xprgoJRSSil1VtFzoSqllFJKuYxrK3BKKaWUOvuE+imugkUrcEoppZRSLqMVOKWUUkq5hlbgLFqBU0oppZRyGa3AKaWUUso1/J1/+WykFTillFJKKZfRCpxSSimlXEPHwFm0AqeUUkop5TJagVNKKaWUa+gQOItW4JRSSimlXEYrcEoppZRyjQgtwQFagVNKKaWUch2twCmllFLKNXQWqkUrcEoppZRSLqMVOKWUUkq5hg6Bs2gFTimllFLKZTSBU0oppZRyGe1CVUoppZRrRKB9qKAJXMCt3Ljf6RBKzLaEPU6HUKKqVOjidAglasnsFU6HUGKaXnqZ0yGUqA3/+9rpEEpUxmXNnQ6hRB3N8jkdggpDmsAppZRSyjV0EoNFx8AppZRSSrmMVuCUUkop5Rq6kK9FK3BKKaWUUi6jFTillFJKuYaezN6iFTillFJKKZfRCpxSSimlXEMLcBatwCmllFJKuYxW4JRSSinlGjoGzqIVOKWUUkopl9EKnFJKKaVcQwtwFq3AKaWUUkq5jFbglFJKKeUaWnmy6L+DUkoppZTLaAKnlFJKKeUy2oWqlFJKKdcQncUAaAVOKaWUUsp1tAKnlFJKKdfQ+ptFK3BKKaWUUi6jFTillFJKuYaeSsuiFTillFJKKZfRCpxSSimlXEPrbxatwCmllFJKuYxW4JRSSinlGjoEzqIVOKWUUieoFhvN9PcfcDoMpVQhtAIXwv59sYeG8RWYt/EA78/blu/2UiL8796uJB7KBOCVXzayaW86ADd3P4cjWdlMWbQzqDEXx5u3dsZTqxK/rkji9e/W5rt9SN/G/KPLOQBUiirD0oQD/OvjJSx55WK27bP28/FPlrAuMTmocRfHS8//m61bNtOtR28G33an3zZpaak8++Sj+HzZlI+MZPjI19i/dw9vvjKC9PR0mrdoxd0PPhrkyAv37n3n0/ycWH5atJWXpizOd3tMhXJMerQ/8ZWjWLZpL/eOnU2pCGHdxMFs2ZMCwEPj5rJm24Fgh14kwwc1p2F8BX7bsJ8Jc7fmu71UhPDD/d1z33ujfvSy9UAGIy9vSc3K5dmfdpRHp67mL58JcuRnLiY6kgnP3URUZDmnQymWr8e9zL7EbTRt15U+V9xUpDZ/TvuWVfNnAXAkPY06jZtz8W0P8Ma911Olek0A/j7kPmqc0zBo+1FUY15+lh3bNtOxay+uuun2AtsdPniAl4c/ysi3Pjhh+4gn7+f6W/+PBo09JR1qwOmZGCxBrcCJyHARWScic0VkhojUKqTtm0V4vLYi0vY04qggIl+LyBwR+VhC8NXQt1lVSkUIQyYtpXaVSOrGRuZr06R6BX5es4c7P17OnR8vz03e6lSJpHeTOL5YHLrJ29871KFUhPC3F6ZTL74iDatXzNdm0qxNDBo1k0GjZrJgwz4+npNAy7ox/HfB9tztoZy8zZ35K75sH+9+8ClJO3eQuD1/Eg7w608/cPUNg3ltzARi46ry5x/zGDfmDQbf9k/GTPiIfXt3s2zJn0GOvmCDujWkVITQ55EvaVCjEo1qVc7X5vrzPUyZvYGeD04lOrIs7RtXo1WDqkydu4GBT3zNwCe+DtnkrV/zeCIEBk9cTJ0qkZzj971XkZ9X7+b2yUu5ffJSNu1Np0fjODbsSeOmiYvZvC+DPs3iHYj+zGX7DDc9/gGp6UecDqXI1iyci/H5uOOFsRzam8SBXYlFatN5wCBuG/Ymtw17k3rNWtOx38Xs2ZZAqx7n524PxeRt/twZ+Hw+Xhr7IbuTEklK3O63XVpqCm+NeoajR048lnN+/ZEateq4MnlTxznRhTrCGNMbmATcW1AjY0xR6vdt7b/iugmYb4w5DzgKdDyNxyhRHetV4de1ewFYsPkgbevm/5JsVbsSvZpU5cNbO/Dviz2UsvPQp/7uYdvBTAaeW52IkEtNLT2aVePbP60PndlrdtOlScFfdjViIomvVJ7lWw/RoVEcA9rWYtoz/Xnz1s6UCtUdBJYtXUTf/gMB6NS1OyuXL/Xb7rKrrqVTl+4AJB86RExsLInbttK0WQsAYmLjSE9LC07QRdC7VW2+mrcJgBnLdtC9Rf7fYQdTj9CiXhyVK5SlTnxFEven0tlTnb91qs9vr1/Fu/edH7LHrmP9KkxbY7335iccpN05MfnatK5Tid5Nq/Lp0I4MH9ScUhFCckYWDeMrEFm2FA3jK7D9QEawQw+I1PQjpKS5J3kD2Lp2Oed26wNAo9ad2LZ+VbHapBzcR1ryQWo38rBj41q8S+cz7sm7+Hrcy2RnZwdjF4plzfIl9OjTH4C2nbqxbtUyv+0iIiJ4+JlRREZVyN2WmpLM5HffoGJ0JVYtWxSUeAMtIoh/oczJLtQqwBER+QyoBSQCQ4wxxwBEZLYxpo99eThQBugFVAIuBO4HLrNvv8kY009EIoEv7DYHgKuMMX/5ee6dwM0i8rUx5vY8zzHbGDNbRG6x290L7AWOATWAScaYcSc/mIjcAdwBcM6lD1G148Vn8M9iKV8mgr2pRwFIycyiWY3ofG3WJKUy65Pl7E87xrODmtOjSSyZx3yULxPBuNlb6NUkjvv7NeKN6QlnHE+gRZUrzS67++lQ2jFa169SYNvb+jVh8kwrYVi+5SBXvDyLPclHGDu0C/1b1+Tn5UlBiflUXh35LDu2bcm9vnzpYv5+6eUAVKpUmQ1J6wq9/+qVy0lNSaFlqzac128Akye8Q4tz2/Dn/HnccXfojEeKKl+GpANWQnkw9QjtGuVPvv9Ys4sLO9bn/y5pw/odhziYepQlG/fy96e/ZfehDCY+dAEXdqzHD39uDXL0pxZZthR7U6z3XnJmFs1r+nnv7Uzhjg+XsT/tGC9c1oKeTeJYuu0wANd3qUNmVnZu96oqeceOHiG6SlUAoipGk7Rld7HaLPzlGzr3HwRA7UbNGPL0a0RXiePLMSPZsGwBzTv2CMJeFOzd115g547jFfw1K5bQ76J/ABAdXYmE3f4/A6Mq5O/Z+O7LT+ne5wIGXHIFn0x4m8yMDDr3OK9kAlclyokE7ik74dkB/AmsNsZcZydQtwL5EiRbY2NMbxF5BjjfGPOEiHgBjDGT7TYtAJ/d7lKgInD45AcyxnxnJ3v/FZFZwIMFPGcUcBWwCrgAeNpffMaY8cB4gA7PzwrIoJfMY9mUK10KsL5Q/PXybtybRla29XTrklI4JzYKAb5fsZu9qUf5fuVuPrilfSDCCbj0I1mUL2vtX4XypQtcWVsEejavxoivVgKwZsdhjv3lA6xkrqGfxNYpjzw57ITrb736IkePWolAZkYGPuMr8L4pycm89epInnvJGjkw+LY7Wbl8KZ9/PIkL/z6IqKiokgu8mNKPZBFZ1vroqFi+jN/X5pPXd+LesbNIzczivn+0ZfAFzflkxrrcY7d0414a185f2QoFGceyKVfG+u0dVbaU3xlvG/Ycf++tTUqhXlwUzWpEM2neNjbuSaNf83hu61Wft2eE3o+ncFS2fCR/HTsGwNEjmRg/77WC2vh8PrasWc4F11rjyGrUa0jpMmUBqN3Iw8Fdzg9Fuevhp0+4PvHtVzh2zKqSZmZmYHwFf7acbPPG9dxy14NUia1K9z4DWLF4gesSuBAc9eQIx7pQjTE3AB5gob19AdC8kPt9ZP9/O1C2gDZLgdUiMg0YCPjtwxCRJsDPWN2v8cCNJzXJGfSyxxiTBmwDsgni+oHrdqfSzu42bVq9IruS8/+af/4fLWhSvQIRAn088WzYk8aOQ5nUrmKF37xmNLuSQ7MrZMXWQ3RtYv0aPrduDDv2p/tt161pPEs2Hx8r9c4dXWlZN4YIES5qX4fV2/Pl5yGjafMWrLK7TTdt9FKjZm2/7bKyshj2+EMMvfsBatQ83h3ZuGkz9uzexdU3DA5KvEW1dNPe3G7TVg2qsn1var42VSqW49z6cURECJ2aVsdg+ODh/rRqYG27pGtDVm7eH+zQi2RtUkput2nTGhVJOpz/PTTi8pY0rV6RCIG+zeLx7k4lOrI0TapZXVVt6lbG4L4JDG5Vq0FTtnmtLtHd2xKIia9R5Dbb1q+kTuPmuUnBl2NGsmvrJny+bNYt+p0a9RoFaS+KrlHT5qxbtRyArQkbqFajwOHk+dSsXZc9SVZSmuBdS7w9WUO5j9NdvGuArvblrvb1gvj7hs/EqpJhT0RoA/xujBmA1UXbq4DHuh24zBiTDawGymN1k+b0BV1YjH0oEbPX7+ei1jV4sH9j+reoRsK+dO7q0+CENhPmbuX5QS347I5OrNyZzJ9bDjF3w34qlS/NhMHtePTCJoybs6WAZ3DWj0sTuap7fZ6/ti2DOtdl/c5knri8Vb52fc+tyXzvvtzrr367hnfu6Mrs5wayKGE/c9fuCWbYxdLrvH5M++k7xrzxMrOm/0K3nr3ZujmBie++dUK7H779ig3edXzywXjuv/MWZk77CYDPP/6Aq28YTPny+QfRO+m7+Zu57nwPL93ekyt6NWbt9gMMu7HLCW1e+WIJY+7py54pQ6kSXY6pczYy8vNFvP9Qfxa+dS0L1+9m1or8A81Dwaz1+7i4TQ0eGdiEAS2rk7A3nbvPP3Eg+/g5WxhxeQum3tWFFYnJLNx8iM8XJnJD17oseKoP7evFMPVP5ys3Z2Lg0NFOh1BkzTv1ZPncafz00VhWz59NtTr1mf75+4W28bS3vno2rVhEveatc9v1veJmvhr7ImP/NZS6TVvQqHWHoO5LUXTp2Yc5037gg7Gv8fvsX+nQtSc7tm7m0/fHnvK+l117Cz9+M4Un7hnC2pVLueCiQUGIWJUEMSZ4vxLtbtJNxphP7OvlgMlAbawu1cLGwJ0wPs0YM1lEYoGpWBWzJ4CVwBQgGjiClaTlm6Zoz379FKuilgxcBzQE3gHWYlX45gK3GGP6iMhs4BZguDHmlsL2MVBdqADR5UvTtWEVlm5L5kD6sUA97GnblhDYZKlyVBn6tKzB/A372BsClcLVb10R8MdMTUlm0cL5tGnXkbiqVQP++MXR4Pr3AvZYMRXK0a9dXeatTmLPYecH6zftUFjxvviiy5emW6NYlmw7zIE05997G/73tdMhlKgPJz15xo+RmZbKplWLqd+8DdExsafdpiScWy3/JLQzlZaaworFC2jRpj1VYp39bGlRq0JQ+zS/WJ4UtMTlqra1Qra/NqgJ3NkgkAlcqAl0AhdqSiKBCyWBTOBCTaATuFCjCZy7lUQCF0o0gXNG2C/ka1fP8ko2xmjNWCmllHIhncRgCfsELqcbVimllFIqXIR9AqeUUkqp8OH07MtQof8OSimllFIuoxU4pZRSSrmGjoGzaAVOKaWUUspltAKnlFJKKdfQ+ptFK3BKKaWUUi6jFTillFJKuYYOgbNoBU4ppZRSymW0AqeUUkop14jQUXCAVuCUUkoppVxHK3BKKaWUcg0dA2fRCpxSSimllMtoAqeUUkop5TLahaqUUkop1xCdxABoBU4ppZRSynW0AqeUUkop19BJDBatwCmllFJKuYxW4JRSSinlGrqQr0UrcEoppZRSLiPGGKdjCCtTlyfpP6hL1Ygq73QIJWpneqbTIZSY6DLh3ZmQ8Ve20yGUqJuHjHQ6hBK1fvprTodQourFlQtqSeyXtfuC9j07sEV8yJb7tAKnlFJKKeUymsAppZRSyjVEgvdXtHjkfRGZLyJPF6dNUe5XGE3glFJKKaVOg4hcDpQyxnQDGopIk6K0Kcr9TkUTOKWUUkq5hgTzP5E7RGRxnr87TgqnDzDVvjwN6OknZH9tinK/QoX3yF+llFJKqdNkjBkPjC+kSQVgp335INC+iG2Kcr9CaQKnlFJKKdeICK15oWlApH25Iv57Nv21Kcr9CqVdqEoppZRSp2cJx7s/2wBbi9imKPcrlFbglFJKKeUaElpnYvgG+E1EagF/A64VkReMMU8X0qYrYPxsKxatwCmllFJKnQZjTArWhIQFQF9jzIqTkjd/bZL9bSvuc2sFTimllFLqNBljDnF8RmmR2xTlfoXRBE4ppZRSrlHUBXbDnXahKqWUUkq5jFbglFJKKeUaITaJwTFagVNKKaWUchmtwCmllFLKNUJsIV/HaAVOKaWUUspltAKnlFJKKdfQMXAWrcAppZRSSrmMVuCUUkop5Rq6DpxFK3BKKaWUUi6jFTillFJKuYYW4CxagVNKKaWUchmtwCmllFLKNSJ0EBygFTillFJKKdc5qxM4EYkVkVQRKS+Wt0Rknoj8V0TKishwEVknIrPtv7ZOx6yUCj0ZqSl4VywiLeVwodtUaKgWG8309x9wOgx1miSIf6HsbO9C7Q+UB3oDPqC+MaaniDwHXGW3GWGM+SRYAX097mX2JW6jabuu9LnipiK1+XPat6yaPwuAI+lp1GncnEF3PAzAdxPfoEm7LjTr0D1Yu1CoQO0CCkPSAAAgAElEQVTfxbc9wBv3Xk+V6jUB+PuQ+6hxTsOg7UdhJr81gl3bt9KqU3cuvmZIkdtlpKcx4eV/4/P5KFu+PHf+6wWOHs1k4qvDSU0+RL1GHm665/Eg7kl+3457hX07t9GkXRfOu9z/8Tu5TWZaKl+NGUF6ymFqNWjKJUMfYtG0b1k9fzYARzKsY3rJ0IeCuCf+TRk7ij2J22jeoSv9r7y5SG0y0lKZ+OJjtGjfjf9NHsNdw0cTUapUvm0VK8cEeW9OdDa894oiJjqSCc/dRFRkOadDOSOvjRzG9i0JdO7emxuG3OG3TXpaKiOe+Re+bB/lIyN56vlXKFOmDACHDh7gyQfv4t0PpwYzbBVAZ3UFDrgQGGv/vw8w297+NjAr2MGsWTgX4/NxxwtjObQ3iQO7EovUpvOAQdw27E1uG/Ym9Zq1pmO/iwHYum4lqYcPhUzyFsj927MtgVY9zs/dHipfIEv/mI0v28cTr05g/+6d7EnaUeR2C2f/Qv9/XMeDz4+mcpU4Vi9dwIKZP9Olz0CefmMSRzIz2LpxXVD3J6+1f87F5/Nx+/NjOLR3l9/j56/Nit9+pXXPC7hz5DiOZmawM8FLpwGDGDLsDYYMe4N6zVrR/vy/O7BHJ1q5YA4+n4/7XnyXA3t2sc/PsfPXZte2BAbdcg8XXDkYT9vOJG7e4Hebk86G915RZfsMNz3+AanpR5wO5bTNmz0dny+b0RM+YXdSIjt3bPPbbsYvP3DFtYMZNfo9YuPiWLxgXu5t499+jaNH3ftvoDSB6wa8APQD4oEUEbkJ+A643G7zVJ4u1FL+HkRE7hCRxSKyePpXp1+s27p2Oed26wNAo9ad2LZ+VbHapBzcR1ryQWo38pD91198O/5VqlSrzrpF8/I9jhMCuX87Nq7Fu3Q+4568i6/HvUx2dnYwduGUvKuW0qlXPwBatOvCprUrityu79+voEW7zgCkJh8munIVKlSqTNK2BDLSUjm4fy+xVasHZ0f82Lp2BS27nQdAo9Yd2e71d/zyt4mKrsTeHVvITE8j5cA+KsfF57a3jukhajfyBGcnCpGwZjltu/cFwNOmE1v8vD79tWnUsi31mrYkYc1ydmxcR31PS7/bnHQ2vPeKKjX9CClp7k5cVixdzHnnDwSgfedurF6xzG+7S6+4lg6duwFw+NAhYqrEArBs8ULKR0YSG1c1OAEHmvahAmdxF6qItAaqAl8C9YGlQLQxZqKI7AM62k1P2YVqjBkPjAeYujzJnG5Mx44eIbqK9YaKqhhN0pbdxWqz8Jdv6Nx/EADL5/5CfO369Lz0Ohb8/F+S9++l698uz/d4wRTI/avdqBlDnn6N6CpxfDlmJBuWLaB5xx5B2IsTfTxmFLt3bs+9vmH1MnoOuASAChUrsT3B6/d+R49mEmMnMie3S1i/ioy0FBo1O5eY2DhWLfqdGd9NpWbd+kRFVyrBvSlc1pEjVIq1Yo6sEM2uffmPn782rbqfz4alC1j483+pWvscIise34c/f/mWTv0vDc4OnMKxI5m5yWVUxWgSN+8qchtjDMv/mElkxWhKlSpd4DanhON772zy5kvPkbh9a+71lcsWc+EllwEQXakym7yFV+bXrlpBWmoKzc9tQ1ZWFv+ZPJ5hL77B8Md1HKCbnc0VuIHASGNMH+AtYLm9DaCNEwGVLR/JX8eOAXD0SCbG+IrcxufzsWXNchq0tOZZ7Nq6iU4XXEx0TCxtevZn85rlQdqLggVy/2rUa0h0lTgAajfycHDXzmDsQj433fM4j774Tu5fv0uu5tjRo7nx+/zsI0D58lF+26WnJvPZe69zy/1PAfDdZ+9z492Pccl1t1GjTj3+mP59EPbKv7LlI8k6ZsV87Ggmxpf/t4q/NrO/+oiLb3+QPlcMpmrtc1g2+2fAOqZb1y6nfovQmBtULk/s1msv//4V1EZEuGLoQ9Ss14jVi+cVuM0p4fjeO5s88NgzvDr2g9y/f1x1Pcfs7s8jmRl+j2eOlJRkxr7xIg8/+RwAUz5+n0suv4aKDv4YPFMSxP9C2dmewM20L8/EmsiwRUTmAwPytMvbhXpNSQZUq0FTttndUru3JRATX6PIbbatX0mdxs0Re32c2Oq1ObgnCYCkzV5i4p3ressRyP37csxIdm3dhM+XzbpFv1OjXqMg7UXhzmnsye023bFlI1Wr1Sxyu7+yshg36ikuH3wXcfb90tNS2bl1E77sbLZ41zh6EsCaDZuy3e5W27Nts9/j56/NkfRU9u7Ygs+Xzc6N63J3Yfv6VdRu3Cz3mDqtTiMPm9etBCBpawKx1fLvn782M7/+lMV2UpqZnkZkVLTfbU46G957Z5MmzVqweqXVbbp5k5fqNWv5bZeVlcULTz3Mrf+8P7fN0kUL+N9Xn/PI3beSsNHL6y8OC1rcKrDO2gTOGHOBMWa5fXmWMeYKY8xdxphuxph+xpgXjDHDjTHNjTF97L8pJRlT8049WT53Gj99NJbV82dTrU59pn/+fqFtPO27ArBpxSLqNW+d267D+RexZc1yJg67nz+nfUvPS64uydCLJJD71/eKm/lq7IuM/ddQ6jZtQaPWHYK6LwVp1/U8Fsz6mSkTR7N43gxadepB0vYtfP3xe6dsN+/X79iesIEfpk7mlSf+j0W/Teeiqwbz0ZiXuO/a/qSnpdC5d3+H9gyadezByt9+5eeP3mHN/NlUq1ufGVPeL7RN0/Zd6Dnoer6b8BovDrmEzPRUzu1hjf07+Zg67dzOvVgyZxrfThrDij9mUqNuA376z4RC2zRv342u/S9l8ZxpjH36HozPh6dtJ7/bnHQ2vPeKa+DQ0U6HcNq69z6fGT9/z7jRrzBnxjS6dO/Nti0JTHrv7RPa/fzdf9m0YR2ffTiBR+6+ldnTf+b1dyfnVvIaNfHw0BPPOrQXp08keH+hTPx1E6jTdyZj4AAy01LZtGox9Zu3ITom9rTbhKpQ3r8aUeUD8jjpaSmsXfYnTc9tR2W7q+lM2gXKzvTMM36MzLRUElYtoV7z1oUev1O1CbToMoEZY5aRlsqGFYto2KINlQo4JkVpE2gZf535RIFQfu/dPGRk0J7LCeunvxbwx0xNSWHpovm0atvB8ckI9eLKBTXV+XNzctASl84NK4dsGqcJXICdaQKnnBOoBC5UBSKBC1WBSuBCVSASuFCmCZy7BTuBWxTEBK5TCCdwZ20XqlJKKaWUW4X3z1allFJKhZeQrYkFl1bglFJKKaVcRitwSimllHKNUF+fLVi0AqeUUkop5TJagVNKKaWUa4T6+mzBohU4pZRSSimX0QqcUkoppVxDC3AWrcAppZRSSrmMJnBKKaWUUi6jXahKKaWUcg/tQwW0AqeUUkop5TpagVNKKaWUa+hCvhatwCmllFJKuYxW4JRSSinlGrqQr0UrcEoppZRSLqMVOKWUUkq5hhbgLFqBU0oppZRyGa3AKaWUUso9tAQHaAVOKaWUUsp1tAKnlFJKKdfQdeAsmsAFWNPYaKdDKDFRZUs5HUKJ+mPHfqdDKFFd6sQ5HUKJKVs6vDsTjmb5nA6hRK2f/prTIZSoZhc87HQIJSpz2RinQzgraQKnlFJKKdfQdeAs4f2zVSmllFIqDGkFTimllFKuoQU4i1bglFJKKaVcRitwSimllHIPLcEBWoFTSimllHIdTeCUUkoppVxGu1CVUkop5Rq6kK9FK3BKKaWUUi6jFTillFJKuYYu5GvRCpxSSimllMtoBU4ppZRSrqEFOItW4JRSSimlXEYrcEoppZRyDy3BAVqBU0oppZRyHa3AKaWUUso1dB04i1bglFJKKaVcRitwSimllHINXQfOohU4pZRSSimX0QqcUkoppVxDC3AWrcAppZRSSrmMVuCUUkop5R5aggO0AqeUUkop5TqawCmllFJKuYwmcCFu3GvP8fR9Q/jq04mFtjt86ADDHrw99/r+vbt59uE7eO7RfzL+jREYY0o61NPy1kvDeeSuwUz5aEKh7Q4dPMBj99yae33ThnU8/dCd/OvuW/h6ykclHeZp+WHCa3w4/D5+/+bTAtukJx/i4+cezL2evH8vn77wMP8Z+Sg/vf9GyB63HG+//CyP/d/NTP3oFK/Pgwd44t7jx++zSe/y1P1Deer+odx90+V8+ekHJR3qaXnzxeE8dOdgPptc8OszPS2Vfz98N089+E+ef+JBsrKy/G4LNWNefpbH7r6ZLz4+9bF78r5b820f8eT9bNnkLanwzthrI4dx/9Ab+XTS+ALbpKel8uRDd/H4/Xcy/PEHTjhOhw4e4K6brw5GqAFXLTaa6e8/4HQYJUaC+F8oC9kETkQqi8hMEZktIpedfN3p+IJh4W8z8WX7eOGtSezdtZNdidv9tktLTeGdl4dz5Ehm7rbp33/Fbfc9wTOvjOPAvj1s37IpWGEX2R9zZ5Dt8/Hqux+xO2knSYnb/LZLS03hzRefOWH/xo9+ifsff5aXxkzijzkz2L1rZ7DCLhLvot8wvmxuHv4Wh/fu4uDuxHxtMtNT+W7cy2QdPZK7bfnM7xk45D6uf/IVUg7sY9+OLcEMu1jmz52BL9vHS+98yJ5diSQV8voc/eIzHD1yfD+vG3IXI0ZPYMToCZzToBF9B1wcrLCL7Pc5M/D5snn9vY/YnZTIzh3+X5+zpv3IZdfeyIg3xlElripLFv7ud1somT93Bj6fj5fGfsjupMKP3VujTjx2AHN+/ZEaterQoLEnGOEW27zZ0/H5shk94ZNCj92MX37gimsHM2r0e8TGxbF4wbzc28a//RpHjx7xe79QFhMdyYTnbiIqspzToagSFrIJHNAG+MMY08cY87Wf62Fv7coldDvvAgBad+jK+tXL/baLiIjggadfJCqqQu62a2+9mzr1GgCQmnKY6EoxJR9wMa1atpheffsD0K5TV9auLHj//jVsFFEVju9fakoK8dVqICJEV4ohMz0tKDEX1bZ1K2nW5TwAGrTqQKJ3db42ERLBP+59mnKRUbnbzrv6VqrWrgdAZloKkdGVghPwaVi9fAk97OPXtmM31q1a5rddREQEjwwbRWSe12eOjevXEBdfjbj4aiUa6+lYtWwxvc4fAED7zt1Ys9L//l18+TW079QNgOTDh4iJifW7LZSsWb6EHn3sY9ep8GP38DMnHrvUlGQmv/sGFaMrsWrZoqDEW1wrli7mvPMHAtaxW73C//5desW1dOhsHafDhw4RU8U6TssWL6R8ZCSxcVWDE3AAZfsMNz3+Aanp7ks+i0okeH+hTEKxi0ZE7geGADHAVmAWcFme61cBdwNdgChgH3CtMeYvP481BKhujBklIrcANYCXgPFAU/u+19jNPwbqAfuBK40xWSIyG/gWGGKMaV1AvHcAd9hXxxtjCq7ZF8Lj8bwH5P1Jex7Q1uv1rvB4PAOA9l6vd9TJz53zfB6PZ7bX6+1z0mNeA1zo9XqHnE5MgVTc/cu7b/b9c/fP4/FMBJYDB4EngNZerzdkXswej+d94K2iHLtQP245Ann88mz7BBjm9XoTSjb6UwvA/nUDXvB6vf0K2+aEQH62eDye57E+i0cALwJfe73e/wVhNwoUyGPn8XjKAr9gfed8c/Jr1g3s/bse6ON0LCVh097MoH3WN64WGbJpXEguI2KMGS0iK4A+xpjhACIy56TrAL8ZY0aKyFhgEPCVn4f7EvgVGAVcCfzTblvGGHOeiLwO/B1YAPwATAEmAe2BhUBNKyT/yZsd73ishPCMeL3eO/Ne93g8o4FI+2pF/FdM7yjouT0eT0PgEeCCM40tEE5j/wrcN+BOoC/wHPBSKCVvtjRO89iF2nHLEeDjh8fjiQGqhULyBme2fx6PJxZ4G7giz/3zbXNKgD9b2gGPeL3e3R6PZyrQH3A0gQvwsXsceMfr9R72eEKzi7gI7sD6DApLIZtRBVkod6EWxRL7/yuB+v4aGGNSgQQROQ+IMMYkYv1S62ZX13oD1YEs4GLgC6Ahx9/8ycBbJRT/qSwBetqX22BVH4vE4/FUAT4DbvV6vcmBDy0gTnv/vF5vNpAzgrrgWQLOOa19c8lxy3Hax882CPgxkAEFWJH2z67YfAE84fV6txW0LcScybHbhPUZCdARcO3+FXCcLgDu9ng8s4G2drVfqZATkhW4YuiMVepuB/xUSLuPsKpqw+zrXuBzY8wwEekJGOByYDUwEqsKlyPDGOMLdOBF9A3wm8fjqQX8Dejq8XhaANd7vd6nT3Hfx4FzgLftX5HDvF7vnBKNtvjOZP8AXgAeC8HqG+Tft2s9Hs8LYXLccpzp8RsIvFqSAZ6hou7fbVgV+6c8Hs9TwLtA7MnbvF7vFELHmRy7l4GJ9n5lYH12hprTPnZer7d3zo121/HtqNCiJTggRMfAAYhIH07sMj35+nCsN14lYDdwgzEmu4DHKoX1C6yZMSZdRCKA97DGwBngRqwy+1QgFcgEJhljPhWR2caYPiWxj0VhV2T6A3O9Xu/uk28/eSyH2xS2f+G8b+D+/YPwPn4Q3vsX7q/PcD52pxLu+5ewL3hj4BrFh+4YuJBN4E7FTuBmG2Nmn6JdLPA9MC0n+VNKKaWUO23edyRoiUvD+PKawAWDiHyONcs0r78ZYzL9tVdKKaWUu2gCZ3H7GLgTGGOudToGpZRSSpWcUF+fLVjcPgtVKaWUUuqsE1YVOKWUc0Skd0G3GWPmBjMWpVT40gKcRRM4FxIRD3DMGLMlz7aOxpjFDoZ1xkSknjEmFNeUChh7Uk1XrJXsDwLzjTGhvt5bUc3E+mxdh7UO12asWd4Ark/gRORrY0zYn4fZPrNM15yrWAuZ5z+bvYuISGtjzEr7shh78LeIXGWM+cLZ6JQ6PZrAuYyIjAZaAeVE5GtgI/AQVjLg9i+XScD5TgdRUkTkEuBDrDN8HMRaK2yyiFx7qtnULhGN9dpsY//1xkoAlhR2JxfxiMj1/m4wxvwn2MGUoCHA9YCP4wm4273J8c+WGXku34W1kG9YEJHlxpi2TsdR4rQEB2gC50bdjDGdRaQ0kAj8F7jdGLPR4bgCoZmI+F27yBhzh7/tLvM00N4YszVng4icg3UmiV5OBRVA52Elbq2AOKwkdRXhk8BFAo3J//URLklOjj3AdKwzLAjW/oXTD6tw/vqfLCL3GWOcOnuQCiJN4NznLxGpifUhtBnrbASISC1jTJKjkZ25PYTmabECxQccFZFaebb9BZRyKJ5Ayzkt1jpgL1Ae63y1fbDOM+x2W40xzzkdRBCUAVoZYzKcDiSAatjVUwGq573sbFgBNwioae9fJlb3dzgl3yqPsFoH7mwgIrOwfhHn/IrMuez6N6qITDDGDHU6jpKS59idsJkwOHYAIlI7z1Vj/x0wxhxzKKSAEpGoMEtq/BKRxVjVxj0529z++hSRYSdtyvu5eTYk5WFl24GjQUtc6sWVK1bFVkTeB1oAPxhjXiigTWXgc6wf7+nANVg/8DfbfwD3GmNWFfZcWoFznweMMSsARCQi5zytInK1s2EFxMcFzWQMk1mMk/1sC6dfUJ9w4v4IVjVgvDHmdYdiCqQ0Ecn94s97gzEmXKqoAG87HUAJGAlcCczGGitcxd4eTu+/nNNGtsdKwAFqG2M+czCks4qIXA6UMsZ0E5EPRKRJAcObbgBeN8b8KiLvAhdiDYn6zBjzWFGfTxM493mD4+NRpue5/E+sc7m62bN5LhusQfEdgBSsWZtu1yfPZQM0xBronwB85ERAgWSM6XvyNvsL5Q/A9QmcMSYCrB9OwKXAvVjnTn7DybhKUCTWF8t+rMk3bvYZsBr4FbgJGI41XrM/VnIXLr7Eek02AJKwEtWwS+BCeCHfPhz/Hp4G9MSaaHgCY8w7ea7GYw056QpcLCJ9scYO32mM+auwJ9MEzn2kgMuul5MAiEgjrC/HPlgzbN93MKyAMcYMARCR84H7sErmlwPfOhlXCatJmIzxs7s9bgcGA7OAocaYzYXfy32MMXmTtXEi8k6Bjd2jujHmSgARGWuM+QX4RUT+4XBcgVYVq9I41RhzjYj85nRAbmcvq5N3Et14Y8x4+7b3AE+e287j+PfVQaxqaGGP3Q2oYoxZICLZwAXGmF0i8hFwEfC/wu6vCZz7nDAAN5wG44rIBViJTTQw2hjzgMMhBZSI3I61f/uwqjZL7ZtqYv1adjUR2cjxLqmcbsb9wFOOBRVYO4EoYDHQEiu5yRlHNcDRyALopGEM1bDG87jdUhH5BqsLNUVE7gcGAsscjSrwtgNXY02WegKo5HA8JSKYlQs7WStodYQ78163l/nK6b6uSCFnu7LXBH0buMLetNIYc9S+vBhocqrYdBKDy/gZjJvLGPNsQbe5gYhswfrSz7Y35R1o3NSxwALEnsSQV9hMQMlx0rjMBkDpMFniBhGpZ188eZwf4bQA9UmfMUeB74wxa5yKJ1BEpB9WVb8qUBZYAfQyxlzlZFyBZHfvxwFZWNX9340xXmejCrwdB4M3iaFubNEnMYjIYKCaMeZVEXkW8PpbI1JEygI/AaOMMb/a26YCIzje1T/SGDO9sOfTCpzLuD1JO4UbT7pugN1h1E0VzhNQEJH/w+pe7CoiNwOPAokistgY87Sz0QXEdE5M3nJ+bDyHtWaa64lIB2PMs3YicCNQDmuMpqvZX5iCVUHtBtTDGjQ+1sm4SoABumD1yKzFGg8XdkJ4DNw3wG/2UlF/w/osbAFcf9Jn4G1Y3atPichTwLtYnyP/wXqd/u9UyRtoBU6FEBGZRJ6qlP3/BsBSY8xDTsYWCCIyM6fSVtBlNxOR+UB3oDKwCOhkjDksIr8ZY8JhoeJ8RKQiMMsY08npWM6UPe7miDHmDhF5E2tw9Rqs4+jqs7yISApWMjoWq8rxRTi8505mV3F2YC0M/iDwfDjuZ+Kh4FXg6lQp9jIiVbAmx8w1xuwumagsWoFzmZPGGeVuJgy6GXMG+Z9MRH4PdiwlJGwnoNgysWYNDwU+sJO3uljdVeEqm/A5lnWNMX1FpDHW7PY2xhgjIjOdDiwA6gEDsMa9zQOqisgDwMycc6SGiXhjzNX2j8Lf7UpqGArdt5wx5hBBWhFCEziXMcaccmCjW510hoIcDbFWhg8HYTsBxXYP8G+srqmXRaQ9VsXjEUejCpACfjwZrIpOODgkIo9grZP2PFDBXtfK9ewv1Sn2HyLSCiuZexUrsQsXG0XkA6z1F4cBG5wOSJUc7UJVIcPPmQoM1mrwrxpjlvq/l3uE8wSUohCRYW7fTxEph7VsQCUgGVhvjMlyNqrAEJEorDXSdhpjvrcrcbdjzQjf5Wx0qqhEZBDWa9SLNZYq7L7kdx4+FrR9qh1TNmTLfZrAuYyIDDLGhO26Yfa6OJ2wviBTgIXGmIXORhUcIvK128caFcbtY/1E5ErgLawKYwbWMgG1sE5585WTsQVDuL8+w4GI/Av4JAzOi10oTeAs2oXqPvcTpgu/isgorJWr52N9QZ4DXCcic4wxjzsaXHCEw9kmChOyH4RF9CjQ0u6OA0BE4rCWAwj7BI7wf32Ggz3Ae/b6hP8BvjbGZDocU8C5/YMkUDSBc5+uInLyuIawmMSAtSZTj7wb7A+icJnEcCrhXg4Ph/2rBhzKc70G4bFfRXG27Kdr2WfR+NBejHkS8KKITDTGPO9waKoEaALnPgv9nXMyTKSLyD+BuUA6VhdVH/uycj+3/3B+CPjWXkE9HeuMIXuxxokp5TgReQwYhLV2313ADKzP07BK4EJ4Hbig0gTOfb50OoASdBPWCe3vxPpyTAYWkn+B33Dl6o8lexVyv4wxHwGuPjWavSxDL6xlUQSIBXbk7VINc65+fZ4l9gADjDFpORtE5GIRKZfnNE0qTOgkhjDj9oHGInIZsM8YM09EbgAOGWN+dDquQLDXZLqaEydp/Im1qKjPydgCQUR2cXwF+CVY53o9BLkJnKvZ58+8AnjDGPO1iDwHXAh8aIwJmxX97ddpRaxxqL2AxcaYsFzR/2zh9glEJ9uVHLxJDDUrh+4kBk3gwoyIzHJrF6t9JobdwBRjzHJ7Onx/INoYc7Oz0Z05e//KYy0kmgFUwPqCzChoEWO3EZF4oC3QDutcjNWAJeFwvkkRmWuM6X3StghgnjGmu0NhBZyIfIU1fmogVpWxujHmAmejUmfCzd8L/uxOzgpa4lKjcpmQTeC0CzX8uDkjb5Q3kbGXS/lWROY6GFMgNTo5AQDGiMg8R6IJMHv8Yhus059lAwuwqnBLnIwrgDLsbuIfgYNYVar+wBFHowq8OHsduHuMMReG0ZlQzmZu/l5QBdAEToWSdXaV6gdO/IJMdDSqwNlvL5Vy8iSNvU4GFUCP2f/fDviwkrk2wC1Yp2Zyu8FYg8Gfw6ospmDNkL7NyaBKQKqIfAMsEZGLCNMToisXC9maWHBpF2qYcXOp3O6Ougar66Y6x78gxxtjXF/lEJEKwL1AF6xJGilYkzTGGGNcP9NWRGr4O3mziFxojPnZiZiCSUTuNMa853QcZ0pEygMtjDFLRaQNsNUYk+x0XOr0ichnxpjrnI4jUHanBLELtVLodqFqAucyIlIDuA9rDNXos2lwsYi8a4y5y+k4TtdZcBaN1cB9xpiZ9vUewEgg0xhzoaPBBUG4DRTPISI9jTFh0c0f7uwfwYOA+sAm4PtwPJXWniAmcNVDOIGLcDoAVWwfA2uAw8A7DscSbB6nAzhD9zsdQAkbADwlIq+IyI9YJ3l/+mxI3mwh+0FfHCLy60mbXnQkEHU6Pgf6YQ3RuAj41NlwVEnSMXDuU9YY8ynknptRuUfOWTSE44OKw+UsGjluAZ4BGmJVAlJFpFa4n5vR5upKh4i0xpo9XDvPmn4VCL9JGuGsmjHm6pwrIjLLyWBKii7ka9EEzn3iReR6rC/+avZlAIwx/3EuLFUE4XwWDbB+7Rus1+Yu4F2OJ6th17Xoh9u/VsTP/zYIDWEAAAkQSURBVA9grV2o3CFDRB7HmvndGUgWkd7GmHCZya/y0ATOfaYATfxcdvWv/yJy+xdkOJ9Fg1Mlp24fw1gQEWlijNkIjHI6ljNhjFkBrBARTzgsvHyWWgiUA3LWJVyGNdM9rBI4cf1XQWDoJAaXEZH/M8aE5dg3EelijFlYyO3VjTF7ghlTMLn9LBqn4vZB/nnjF5HRxpj7T94eDkSkGlbFtGzONk3oQlvOBCkRiTPGHHA6npK2L/WvoCUu8dGlQzZb1EkM7pM77k1ExjgZSAkodLB0OCdvthinA1CFyvtB3qqA7eHgZ6Ax1n7l/KnQljNB6gtHowgWCeJfCNMuVPfJ+5Jq4VgUJaOtiEw7aVvOIP8BTgQUZFoOD21lRaQm1g/fcnkuly38bq6Taox5wekgVLEY+9y8DUTkmRNuMOY5h2JSJUwTOPepnmcSQ40wm8TgBYY6HYQqMSH+e/aUjmFN1JCTLh91MqgS8JuIfAZ8hLUcBToIPuRdhnXWk0uA2bj/vVaosN65YtAEzn3iOd69EW6TGKYbY7Y5HYSDXP+5JCLdgPVAL6xThQG5Py6udSquQDDG9BWR2lhrMDYDyufc5FxUJSIL6xh2tq8bwmwQfBjqa4+B+1qT7bOHTmJwmbynyhKRMcaYe5yOKVBE5GYK+DIMh0HUZ8GZGCYAZYCngG+AiVhVgVbGmF5OxhYIIjIMa0bfrcBXWGPF2gJVjTGdC7mrUiUqZyJNuE2oKciB9OBNYoirELqTGLQC5z55X7jhNgauTp7LtwIfOBVICbkfCNsEDmhqjDkPQESm5JwXVETCpSLQzxjTG3K/MJ+0L89xNiylcsfA1dcxcGcPTeDcp0a4joEzxozIuSwiF+S9HiZyzsSQVzidiSFRRN7EGoOzVEQGARcC+U5w71IpIvIA1v69Y5/o/ULC5EwFIvK6MeYhe/X+k88UEvZVHZc7q8bAKYt2obqM3Y3jj3H7Ly0R6Z7n6tvAvTlXjDF/BD+iwMrb/R2ORKQscBNWN2M8kAz8Dow3xrg+yRGRGOBhrP2ryvH9G3k2rL2lQp+I3GeMecvpOErawfTsoCUusRVKhWwyrAmcChkiMonjp2KCPN3FxphbHQkqgETkbmPMWKfjKCki0toYs9K+HGGM8dmXrzLGuH59KhHpnXMxz2YDOktTqWDSBM6iCZwKGSJSBrgKmAVczvGFbY0xZqRjgQWQiHQwxiwREQFuxJrJ+HGYVKjynqnA72U3O+nE4AaIBtpjrZsWNoswi0hprDGozYE1wGRjzF/ORqXU/7d3L6FWVXEcx78/o2s+igzqaqESZoFgNrGMHuCj7AFlIYZo9EASIiqoSUMJCWyQVgY9yMpEKgepRSVoUQn2phQaOGgQSWJoaKkZ9W+w9vXu6l452bl77bPP7zM6e49+F/He/1lr/f+r34HD1RVwY0bWt4DzTQxWJ+uBC0ljDBYDnxWfG7HtKOllYGnx+ARwPWmrcX22UEOntr/0TlZEzCy2wJcA35A6bh8CxmcN1n5rgHGkLtvzimczqxk3MVid9EbEfABJqyNiC7BF0rzMudplQjFL7ALSXZPTIiIkbcsdrE3KDTa9khb2fc4bqz0kzQHuJ628rYqIBzNHGirjI+L24vN7kj7IGcbMBuYCzurkS0lvkrqoDkp6AJgLfJU1VfsckPQwqWPsUWCUpFszZ2qn14GFwOfABmA5MBxYmzNUGz1P2jr9A3hc0gqa1UXcZ4+kR4BPgBnAnsx5zGwAPgNntSJpNv/oYoyITVlDtYmkkaQuzR8i4q1iJW4JsDIiOn7UhqRXgKMRcY+kVaR/w13A9Ii4JW+6oSFpEnBdE5pTJI0ldX4fJf3fu4h0Bu6FiDiWM5tZ2c9HqjsDd+aI+p6B8wqc1UpEbAW25s4xFCLiMPBs6dURYDfwJLAgS6j2Gl/aIp5J87aI+4rwWaT5b3OAvUBTfr61wEvAGOCy0jaqmdWQCzizihRz0q6m/4//RFLx9kzOXG3U6C3iohCdTrrEfjNwcd/NEw3RExHrACTNzx3GbDBqXo/USXEBZ7UhaTf/vgu1SWeMfiKdCVsNzAY2RMRgg5k70WLSFvFjpS3iKcCivLHaZhWp+L4SmEBq2pgWEV/njdU2Z5eaUM5p0i0vZk3kM3BmFZE0BriW1JhxOWma/3JgW98AXOsMkiaTirm5wNSImJg50v/W5FterFkOHv2zssLljNOG1Xa5zwWcWSaSppKKgOUR0ZM7j51Y6SaGv70Ghhcjb8ysAi7gEm+hWu1JujcimnJO7LiI2AnslNSEBoZuUD7UfwPwNsUWP+ACzqwita2oKuYVOKs9SZ9GxKW5cwyVpv98TSTp/eJWBjOr2KEKV+BO9wqcmZUPhZdfA2dVncX+O0nnlh57JI2jWAyICA+7NatKbUuqarmAs9roggJn8iDvm3JTQdOto79L+ljx3LeFOitXKDPrTt5CtdqQtA94iv4/ise/Z0XEsly5zAAkCZgH7IuIjyUtAg4A74R/kZpV5tBvFW6hDvcWqlkrdvWNK5D0dETclzuQWcmLwI/Aa8XzL6RmhgXAnZkymXUdD/JNXMBZnZS/VU3JlsJsYJMi4q6+h4jYCGyU9FHGTGbWpVzAWZ2MLU2CH+tJ8FYz30paQxofsh8YDVwDfJ81lVmXkRfgAJ+BsxrxJHirM0nDgNtIty/0AgeB7cBzEXE0ZzazbvLrseoKl1E99S0XXcCZmbVA0vkR8d0A72+KiE05Mpl1o8MVFnAja1zADcsdwMysQ7wqaY2kSQCS5knaAdyYOZeZdSGfgTMza0FEXCHpKuDdYqTIF8DNEbE3czSz7lLbNbFqeQXOzKwFku4GVgBvAEtJTQwrJV2SNZiZdSWfgTMza4GkzcAdEbFfkiIiJM0AtkfEKbnzmXWLI79TWeEy4tT6rvd5Bc7MrDWjI2J/8XkrQETsAD7MF8nMupXPwJmZtab8rV+DvDezIVbfvtBquYAzM2tNedB0b/lz3lhm1o18Bs7MrAUnGDRNRCyrMouZmQs4MzMzsw7jJgYzMzOzDuMCzszMzKzDuIAzMzMz6zAu4MzMzMw6jAs4MzMzsw7zF0YZFVpYH0JfAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 720x720 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "<function matplotlib.pyplot.close(fig=None)>"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 提取属性并合并为新数据集\n",
    "data_corr = data[['FFP_TIER','FLIGHT_COUNT','LAST_TO_END',\n",
    "                  'SEG_KM_SUM','EXCHANGE_COUNT','Points_Sum']]\n",
    "age1 = data['AGE'].fillna(0)\n",
    "data_corr['AGE'] = age1.astype('int64')\n",
    "data_corr['ffp_year'] = ffp_year\n",
    "\n",
    "# 计算相关性矩阵\n",
    "dt_corr = data_corr.corr(method = 'pearson')\n",
    "print('相关性矩阵为：\\n',dt_corr)\n",
    "\n",
    "# 绘制热力图\n",
    "import seaborn as sns\n",
    "plt.subplots(figsize=(10, 10)) # 设置画面大小 \n",
    "sns.heatmap(dt_corr, annot=True, vmax=1, square=True, cmap='Blues') \n",
    "plt.show()\n",
    "plt.close\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 数据预处理"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "1.数据清洗：\n",
    "主要包含：\n",
    "* 丢弃票价为空的记录（因为数量较少）\n",
    "* 保留票价不为0的，或者平均折扣不为0且总飞行公里数大于0的记录\n",
    "* 丢弃年龄大于100的记录"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "原始数据的形状为： (62988, 44)\n",
      "删除缺失记录后数据的形状为： (62299, 44)\n",
      "数据清洗后数据的形状为： (62043, 44)\n"
     ]
    }
   ],
   "source": [
    "# 处理缺失值与异常值\n",
    "\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "\n",
    "datafile = 'air_data.csv'  # 航空原始数据路径\n",
    "cleanedfile = 'data_cleaned.csv'  # 数据清洗后保存的文件路径\n",
    "\n",
    "# 读取数据\n",
    "airline_data = pd.read_csv(datafile,encoding = 'utf-8')\n",
    "print('原始数据的形状为：',airline_data.shape)\n",
    "\n",
    "# 去除票价为空的记录\n",
    "airline_notnull = airline_data.loc[airline_data['SUM_YR_1'].notnull() & \n",
    "                                   airline_data['SUM_YR_2'].notnull(),:]\n",
    "print('删除缺失记录后数据的形状为：',airline_notnull.shape)\n",
    "\n",
    "# 只保留票价非零的，或者平均折扣率不为0且总飞行公里数大于0的记录。\n",
    "index1 = airline_notnull['SUM_YR_1'] != 0\n",
    "index2 = airline_notnull['SUM_YR_2'] != 0\n",
    "index3 = (airline_notnull['SEG_KM_SUM']> 0) & (airline_notnull['avg_discount'] != 0)\n",
    "index4 = airline_notnull['AGE'] > 100  # 去除年龄大于100的记录\n",
    "airline = airline_notnull[(index1 | index2) & index3 & ~index4]\n",
    "print('数据清洗后数据的形状为：',airline.shape)\n",
    "\n",
    "airline.to_csv(cleanedfile)  # 保存清洗后的数据\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "2.属性规约：\n",
    "* R(recency)指最近一次消费时间与截止时间，代表客户是否可能对该产品感兴趣。\n",
    "* F（frequency）指客户在某段时间内消费的次数，代表客户满意度扥。\n",
    "* M（monetary）指客户在某段时间内的消费金额，代表客户的消费能力。"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "航空公司价值分析的LRFMC模型：\n",
    "由于票价收到运输距离和舱位等级影响，同样消费额的不同客户对航空公司的价值不同，例如一位购买长航线，低舱位与一位购买短航线，高级舱位的客户价值不同。因此，\n",
    "* 本案选择一定时间内客户积累的里程M和客户在一定时间内乘坐舱位所对应的折扣系数的平均值C两个特征来代替消费金额。\n",
    "* 另外，入会时间长短L也影响着客户的价值。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "筛选的属性前5行为：\n",
      "     FFP_DATE  LOAD_TIME  LAST_TO_END  FLIGHT_COUNT  SEG_KM_SUM  avg_discount\n",
      "0  2006/11/2  2014/3/31            1           210      580717      0.961639\n",
      "1  2007/2/19  2014/3/31            7           140      293678      1.252314\n",
      "2   2007/2/1  2014/3/31           11           135      283712      1.254676\n",
      "3  2008/8/22  2014/3/31           97            23      281336      1.090870\n",
      "4  2009/4/10  2014/3/31            5           152      309928      0.970658\n",
      "构建的LRFMC属性前5行为：\n",
      "            L   R    F       M         C\n",
      "0  90.200000   1  210  580717  0.961639\n",
      "1  86.566667   7  140  293678  1.252314\n",
      "2  87.166667  11  135  283712  1.254676\n",
      "3  68.233333  97   23  281336  1.090870\n",
      "4  60.533333   5  152  309928  0.970658\n"
     ]
    }
   ],
   "source": [
    "# 属性选择、构造\n",
    "\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "\n",
    "# 读取数据清洗后的数据\n",
    "cleanedfile = 'data_cleaned.csv'  # 数据清洗后保存的文件路径\n",
    "airline = pd.read_csv(cleanedfile, encoding = 'utf-8')\n",
    "# 选取需求属性\n",
    "airline_selection = airline[['FFP_DATE','LOAD_TIME','LAST_TO_END',\n",
    "                                     'FLIGHT_COUNT','SEG_KM_SUM','avg_discount']]\n",
    "print('筛选的属性前5行为：\\n',airline_selection.head())\n",
    "\n",
    "\n",
    "\n",
    "# 代码7-8\n",
    "\n",
    "# 构造属性L\n",
    "L = pd.to_datetime(airline_selection['LOAD_TIME']) - \\\n",
    "pd.to_datetime(airline_selection['FFP_DATE'])\n",
    "L = L.astype('str').str.split().str[0]\n",
    "L = L.astype('int')/30\n",
    "\n",
    "# 合并属性\n",
    "airline_features = pd.concat([L,airline_selection.iloc[:,2:]],axis = 1)\n",
    "airline_features.columns = ['L','R','F','M','C']\n",
    "print('构建的LRFMC属性前5行为：\\n',airline_features.head())"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "3.数据变换\n",
    "对数据实施标准化。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "标准化后LRFMC五个属性为：\n",
      " [[ 1.43579256 -0.94493902 14.03402401 26.76115699  1.29554188]\n",
      " [ 1.30723219 -0.91188564  9.07321595 13.12686436  2.86817777]\n",
      " [ 1.32846234 -0.88985006  8.71887252 12.65348144  2.88095186]\n",
      " [ 0.65853304 -0.41608504  0.78157962 12.54062193  1.99471546]\n",
      " [ 0.3860794  -0.92290343  9.92364019 13.89873597  1.34433641]]\n"
     ]
    }
   ],
   "source": [
    "# 数据标准化\n",
    "from sklearn.preprocessing import StandardScaler\n",
    "data = StandardScaler().fit_transform(airline_features)\n",
    "#保存为二进制文件\n",
    "np.savez('airline_scale.npz',data)\n",
    "print('标准化后LRFMC五个属性为：\\n',data[:5,:])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 模型构建"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "1.使用K-MEANS聚类算法对客户进行分群，结合业务，对客户分成5类（稍后会解释）："
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "各类聚类中心为：\n",
      " [[ 1.16088402e+00 -3.77441153e-01 -8.66305084e-02 -9.45410571e-02\n",
      "  -1.56699730e-01]\n",
      " [ 4.64671934e-02 -1.83347106e-03 -2.30222312e-01 -2.34604695e-01\n",
      "   2.17826398e+00]\n",
      " [-7.00400970e-01 -4.15181398e-01 -1.60852710e-01 -1.60602909e-01\n",
      "  -2.56563446e-01]\n",
      " [ 4.83708578e-01 -7.99390422e-01  2.48317171e+00  2.42445742e+00\n",
      "   3.09239622e-01]\n",
      " [-3.13194484e-01  1.68639463e+00 -5.73898809e-01 -5.36735791e-01\n",
      "  -1.75093685e-01]]\n",
      "各样本的类别标签为：\n",
      " [3 3 3 ... 2 4 4]\n",
      "最终每个类别的数目为：\n",
      " 2    24636\n",
      "0    15734\n",
      "4    12120\n",
      "3     5337\n",
      "1     4216\n",
      "dtype: int64\n",
      "         ZL        ZR        ZF        ZM        ZC\n",
      "0                                                  \n",
      "3  1.160884 -0.377441 -0.086631 -0.094541 -0.156700\n",
      "1  0.046467 -0.001833 -0.230222 -0.234605  2.178264\n",
      "0 -0.700401 -0.415181 -0.160853 -0.160603 -0.256563\n",
      "2  0.483709 -0.799390  2.483172  2.424457  0.309240\n",
      "4 -0.313194  1.686395 -0.573899 -0.536736 -0.175094\n"
     ]
    }
   ],
   "source": [
    "\n",
    "from sklearn.cluster import KMeans  # 导入kmeans算法\n",
    "\n",
    "# 读取标准化后的数据\n",
    "airline_scale = np.load('airline_scale.npz')['arr_0']\n",
    "k = 5  # 确定聚类中心数\n",
    "\n",
    "# 构建模型，随机种子设为123\n",
    "kmeans_model = KMeans(n_clusters = k,n_jobs=4,random_state=123)\n",
    "# n_jobs计算用的进程数目\n",
    "fit_kmeans = kmeans_model.fit(airline_scale)  # 模型训练\n",
    "\n",
    "# 查看聚类结果\n",
    "kmeans_cc = kmeans_model.cluster_centers_  # 聚类中心\n",
    "print('各类聚类中心为：\\n',kmeans_cc)\n",
    "kmeans_labels = kmeans_model.labels_  # 样本的类别标签\n",
    "print('各样本的类别标签为：\\n',kmeans_labels)\n",
    "r1 = pd.Series(kmeans_model.labels_).value_counts()  # 统计不同类别样本的数目\n",
    "print('最终每个类别的数目为：\\n',r1)\n",
    "# 输出聚类分群的结果\n",
    "cluster_center = pd.DataFrame(kmeans_model.cluster_centers_,\\\n",
    "             columns = ['ZL','ZR','ZF','ZM','ZC'])   # 将聚类中心放在数据框中\n",
    "cluster_center.index = pd.DataFrame(kmeans_model.labels_ ).\\\n",
    "                  drop_duplicates().iloc[:,0]  # 将样本类别作为数据框索引\n",
    "print(cluster_center)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "2.客户价值分析\n",
    "构建雷达图如下："
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 576x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "<function matplotlib.pyplot.close(fig=None)>"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "%matplotlib inline\n",
    "import matplotlib.pyplot as plt \n",
    "# 客户分群雷达图\n",
    "labels = ['ZL','ZR','ZF','ZM','ZC']\n",
    "legen = ['客户群' + str(i + 1) for i in cluster_center.index]  # 客户群命名，作为雷达图的图例\n",
    "lstype = ['-','--',(0, (3, 5, 1, 5, 1, 5)),':','-.']\n",
    "kinds = list(cluster_center.iloc[:, 0])\n",
    "# 由于雷达图要保证数据闭合，因此再添加L列，并转换为 np.ndarray\n",
    "cluster_center = pd.concat([cluster_center, cluster_center[['ZL']]], axis=1)\n",
    "centers = np.array(cluster_center.iloc[:, 0:])\n",
    "\n",
    "# 分割圆周长，并让其闭合\n",
    "n = len(labels)\n",
    "angle = np.linspace(0, 2 * np.pi, n, endpoint=False)\n",
    "angle = np.concatenate((angle, [angle[0]]))\n",
    "\n",
    "# 绘图\n",
    "fig = plt.figure(figsize = (8,6))\n",
    "ax = fig.add_subplot(111, polar=True)  # 以极坐标的形式绘制图形\n",
    "plt.rcParams['font.sans-serif'] = ['SimHei']  # 用来正常显示中文标签\n",
    "plt.rcParams['axes.unicode_minus'] = False  # 用来正常显示负号 \n",
    "# 画线\n",
    "for i in range(len(kinds)):\n",
    "    ax.plot(angle, centers[i], linestyle=lstype[i], linewidth=2, label=kinds[i])\n",
    "# 添加属性标签\n",
    "ax.set_thetagrids(angle * 180 / np.pi, labels)\n",
    "plt.title('客户特征分析雷达图')\n",
    "plt.legend(legen)\n",
    "plt.show()\n",
    "plt.close"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "由上图可知：\n",
    "* 客户群1在所有特征上的值都很小，且在L处的值最小，说明客户群1是新入会员较多。\n",
    "* 客户群2在C处的值最大，在F,M处的特征值较小小，说明客户群2是偏好乘坐高级舱位的客户群。\n",
    "* 客户群3在F,M上的值最大，且在特征R上的值最小，说明客户群3频繁乘机且近期都有乘机记录。\n",
    "* 客户群4在L处特征最大，在R处特征较小，其他特征适中，说明客户群4的入会时间较长，飞行频率也较高，是较有高价值的客户群。\n",
    "* 客户群5在R处的值最大，且在L,M,F,C处的值都相对较小，说明客户群3已经很久没乘机。\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 结论："
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "本例定义4个等级的客户群体，依照重要性排列如下：\n",
    "1. 重要保持客户：\n",
    "   * 如上图中的客户群3。\n",
    "   * 这列客户平均折扣数（C）较高（一般所乘航班的舱位等级较高），最近乘机距今的时间长度（R）较低，飞行次数（F）或飞行里程（M）较高。\n",
    "   * 他们是航空公司的高价值客户，是最为理想的客户，航空公司应优先将资源投放在他们身上，对他们进行差异化管理和一对一营销，提高这类客户的忠诚度和满意度。\n",
    "2. 重要发展客户：\n",
    "   * 如客户群2。\n",
    "   * 这类客户的平均折扣系数（C）较高，最近乘机距今的时间长度（R）较低，且飞行次数（F）或飞行里程（M）较低。这类客户入会时间（L）较短。\n",
    "   * 他们是航空公司的潜在价值客户。航空公司要努力促使这类客户增加在本公司的乘机消费，也就是增加他们的钱包份额。通过客户价值的提升，加强这列客户的满意度，提高他们转向竞争对手的转移成本，使得他们逐渐成为公司的忠诚客户。\n",
    "3. 重要挽留客户：\n",
    "   * 如客户群4。\n",
    "   * 这类客户的平均折扣系数（C），飞行次数（F）或飞行里程（M）较高，但是最近乘机距今的时间长度（R）较高或者说乘坐频率变小。\n",
    "   * 客户价值变化的不确定性很高。由于这些客户价值衰退的原因各不相同，所以掌握客户的最新信息，维护与客户的互动就显得尤为重要。航空公司应根据这些客户最近消费时间和消费次数的变幻推测客户消费的异动情况，并列出客户名单，对其重点联系，采用一定的营销手段，延长客户的生命周期。\n",
    "4. 一般客户和低价值客户：\n",
    "   * 如客户群5（一般客户）和客户群1（低价值客户）\n",
    "   * 这类客户的平均折扣系数（C）很低，飞行次数（F）或飞行里程（M）较底，入会时间（L）较短，是公司的一般客户。"
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