Mini_Project_Clustering_CZ.ipynb

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   "source": [
    "# Customer Segmentation using Clustering\n",
    "***\n",
    "This mini-project is based on [this blog post](http://blog.yhat.com/posts/customer-segmentation-using-python.html) by yhat. Please feel free to refer to the post for additional information, and solutions."
   ]
  },
  {
   "cell_type": "code",
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   "source": [
    "%matplotlib inline\n",
    "import pandas as pd\n",
    "import sklearn\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "\n",
    "# Setup Seaborn\n",
    "sns.set_style(\"whitegrid\")\n",
    "sns.set_context(\"poster\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Data\n",
    "\n",
    "The dataset contains information on marketing newsletters/e-mail campaigns (e-mail offers sent to customers) and transaction level data from customers. The transactional data shows which offer customers responded to, and what the customer ended up buying. The data is presented as an Excel workbook containing two worksheets. Each worksheet contains a different dataset."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/colinechabloz/anaconda3/lib/python3.7/site-packages/pandas/io/excel.py:329: FutureWarning: The `sheetname` keyword is deprecated, use `sheet_name` instead\n",
      "  **kwds)\n"
     ]
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>offer_id</th>\n",
       "      <th>campaign</th>\n",
       "      <th>varietal</th>\n",
       "      <th>min_qty</th>\n",
       "      <th>discount</th>\n",
       "      <th>origin</th>\n",
       "      <th>past_peak</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>January</td>\n",
       "      <td>Malbec</td>\n",
       "      <td>72</td>\n",
       "      <td>56</td>\n",
       "      <td>France</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>January</td>\n",
       "      <td>Pinot Noir</td>\n",
       "      <td>72</td>\n",
       "      <td>17</td>\n",
       "      <td>France</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "      <td>February</td>\n",
       "      <td>Espumante</td>\n",
       "      <td>144</td>\n",
       "      <td>32</td>\n",
       "      <td>Oregon</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4</td>\n",
       "      <td>February</td>\n",
       "      <td>Champagne</td>\n",
       "      <td>72</td>\n",
       "      <td>48</td>\n",
       "      <td>France</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5</td>\n",
       "      <td>February</td>\n",
       "      <td>Cabernet Sauvignon</td>\n",
       "      <td>144</td>\n",
       "      <td>44</td>\n",
       "      <td>New Zealand</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
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      ],
      "text/plain": [
       "   offer_id  campaign            varietal  min_qty  discount       origin  \\\n",
       "0         1   January              Malbec       72        56       France   \n",
       "1         2   January          Pinot Noir       72        17       France   \n",
       "2         3  February           Espumante      144        32       Oregon   \n",
       "3         4  February           Champagne       72        48       France   \n",
       "4         5  February  Cabernet Sauvignon      144        44  New Zealand   \n",
       "\n",
       "   past_peak  \n",
       "0      False  \n",
       "1      False  \n",
       "2       True  \n",
       "3       True  \n",
       "4       True  "
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
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   ],
   "source": [
    "df_offers = pd.read_excel(\"./WineKMC.xlsx\", sheetname=0)\n",
    "df_offers.columns = [\"offer_id\", \"campaign\", \"varietal\", \"min_qty\", \"discount\", \"origin\", \"past_peak\"]\n",
    "df_offers.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(32, 7)"
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     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
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   ],
   "source": [
    "df_offers.shape"
   ]
  },
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   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We see that the first dataset contains information about each offer such as the month it is in effect and several attributes about the wine that the offer refers to: the variety, minimum quantity, discount, country of origin and whether or not it is past peak. The second dataset in the second worksheet contains transactional data -- which offer each customer responded to."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>customer_name</th>\n",
       "      <th>offer_id</th>\n",
       "      <th>n</th>\n",
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       "      <th>2</th>\n",
       "      <td>Johnson</td>\n",
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       "      <td>1</td>\n",
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       "      <th>3</th>\n",
       "      <td>Johnson</td>\n",
       "      <td>24</td>\n",
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       "      <th>4</th>\n",
       "      <td>Johnson</td>\n",
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       "  customer_name  offer_id  n\n",
       "0         Smith         2  1\n",
       "1         Smith        24  1\n",
       "2       Johnson        17  1\n",
       "3       Johnson        24  1\n",
       "4       Johnson        26  1"
      ]
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     "execution_count": 4,
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   ],
   "source": [
    "df_transactions = pd.read_excel(\"./WineKMC.xlsx\", sheetname=1)\n",
    "df_transactions.columns = [\"customer_name\", \"offer_id\"]\n",
    "df_transactions['n'] = 1\n",
    "df_transactions.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(324, 3)"
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     "execution_count": 5,
     "metadata": {},
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   ],
   "source": [
    "df_transactions.shape"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Data wrangling"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We're trying to learn more about how our customers behave, so we can use their behavior (whether or not they purchased something based on an offer) as a way to group similar minded customers together. We can then study those groups to look for patterns and trends which can help us formulate future offers.\n",
    "\n",
    "The first thing we need is a way to compare customers. To do this, we're going to create a matrix that contains each customer and a 0/1 indicator for whether or not they responded to a given offer. "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<div class=\"span5 alert alert-info\">\n",
    "<h3>Checkup Exercise Set I</h3>\n",
    "\n",
    "<p><b>Exercise:</b> Create a data frame where each row has the following columns (Use the pandas [`merge`](http://pandas.pydata.org/pandas-docs/stable/generated/pandas.DataFrame.merge.html) and [`pivot_table`](http://pandas.pydata.org/pandas-docs/stable/generated/pandas.pivot_table.html) functions for this purpose):\n",
    "<ul>\n",
    "<li> customer_name\n",
    "<li> One column for each offer, with a 1 if the customer responded to the offer\n",
    "</ul>\n",
    "<p>Make sure you also deal with any weird values such as `NaN`. Read the documentation to develop your solution.</p>\n",
    "</div>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
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       "      <th>3</th>\n",
       "      <td>1</td>\n",
       "      <td>January</td>\n",
       "      <td>Malbec</td>\n",
       "      <td>72</td>\n",
       "      <td>56</td>\n",
       "      <td>France</td>\n",
       "      <td>False</td>\n",
       "      <td>Peterson</td>\n",
       "      <td>1</td>\n",
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       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1</td>\n",
       "      <td>January</td>\n",
       "      <td>Malbec</td>\n",
       "      <td>72</td>\n",
       "      <td>56</td>\n",
       "      <td>France</td>\n",
       "      <td>False</td>\n",
       "      <td>Wood</td>\n",
       "      <td>1</td>\n",
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      ],
      "text/plain": [
       "   offer_id campaign varietal  min_qty  discount  origin  past_peak  \\\n",
       "0         1  January   Malbec       72        56  France      False   \n",
       "1         1  January   Malbec       72        56  France      False   \n",
       "2         1  January   Malbec       72        56  France      False   \n",
       "3         1  January   Malbec       72        56  France      False   \n",
       "4         1  January   Malbec       72        56  France      False   \n",
       "\n",
       "  customer_name  n  \n",
       "0        Thomas  1  \n",
       "1       Jackson  1  \n",
       "2      Mitchell  1  \n",
       "3      Peterson  1  \n",
       "4          Wood  1  "
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "full_data = df_offers.merge(df_transactions, how = 'inner', on = 'offer_id')\n",
    "full_data.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
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     "data": {
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       "(324, 9)"
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     "execution_count": 7,
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   "source": [
    "full_data.shape"
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   "cell_type": "code",
   "execution_count": 8,
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       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Barnes</th>\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>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>...</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>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Bell</th>\n",
       "      <td>0</td>\n",
       "      <td>1</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>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</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",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Bennett</th>\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>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</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>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Brooks</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</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>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Brown</th>\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>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</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>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Butler</th>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</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>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Campbell</th>\n",
       "      <td>0</td>\n",
       "      <td>1</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>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</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",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Carter</th>\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>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</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>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Clark</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</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>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Collins</th>\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>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</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>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Cook</th>\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>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</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",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Cooper</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</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>...</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>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Cox</th>\n",
       "      <td>0</td>\n",
       "      <td>1</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>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</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",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Cruz</th>\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>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</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>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Davis</th>\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>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</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>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Diaz</th>\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>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</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>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Edwards</th>\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>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Evans</th>\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>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Fisher</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Flores</th>\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>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>1</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>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Foster</th>\n",
       "      <td>1</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>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>1</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>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Garcia</th>\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>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</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>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Gomez</th>\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>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</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>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Gonzalez</th>\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>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>...</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>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Gray</th>\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>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</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",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Reed</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</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>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Reyes</th>\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>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>1</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>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Richardson</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</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>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Rivera</th>\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>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</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>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Roberts</th>\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>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</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>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Robinson</th>\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>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</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>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Rodriguez</th>\n",
       "      <td>0</td>\n",
       "      <td>1</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>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</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",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Rogers</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</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>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Ross</th>\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>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</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>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Russell</th>\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>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</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",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Sanchez</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</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",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Sanders</th>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Scott</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>1</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>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Smith</th>\n",
       "      <td>0</td>\n",
       "      <td>1</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>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>1</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>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Stewart</th>\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>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</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>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Sullivan</th>\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>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</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>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Taylor</th>\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>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</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>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Thomas</th>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</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",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Thompson</th>\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>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Torres</th>\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>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</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>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Turner</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Walker</th>\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>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</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>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Ward</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</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>...</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>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Watson</th>\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>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</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>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>White</th>\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>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Williams</th>\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>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</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>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Wilson</th>\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>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</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>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Wood</th>\n",
       "      <td>1</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>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>...</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>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Wright</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Young</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>...</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>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>100 rows × 32 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "offer_id       1   2   3   4   5   6   7   8   9   10 ...  23  24  25  26  27  \\\n",
       "customer_name                                         ...                       \n",
       "Adams           0   0   0   0   0   0   0   0   0   0 ...   0   0   0   0   0   \n",
       "Allen           0   0   0   0   0   0   0   0   1   0 ...   0   0   0   0   1   \n",
       "Anderson        0   0   0   0   0   0   0   0   0   0 ...   0   1   0   1   0   \n",
       "Bailey          0   0   0   0   0   0   1   0   0   0 ...   0   0   0   0   0   \n",
       "Baker           0   0   0   0   0   0   1   0   0   1 ...   0   0   0   0   0   \n",
       "Barnes          0   0   0   0   0   0   0   0   0   1 ...   0   0   0   0   0   \n",
       "Bell            0   1   0   0   0   0   0   0   0   0 ...   0   1   0   1   0   \n",
       "Bennett         0   0   0   0   0   0   0   1   0   0 ...   0   0   0   0   0   \n",
       "Brooks          0   0   1   0   0   0   0   1   0   0 ...   0   0   0   0   0   \n",
       "Brown           0   0   0   0   0   0   1   0   0   0 ...   0   0   0   0   0   \n",
       "Butler          1   0   0   1   0   0   0   0   0   0 ...   0   0   0   0   0   \n",
       "Campbell        0   1   0   0   0   0   0   0   0   0 ...   0   1   0   1   0   \n",
       "Carter          0   0   0   0   0   0   1   0   0   0 ...   0   0   0   0   0   \n",
       "Clark           0   0   0   1   0   0   0   0   0   0 ...   0   0   0   0   0   \n",
       "Collins         0   0   0   0   0   0   0   0   0   0 ...   0   0   0   0   0   \n",
       "Cook            0   0   0   0   0   0   0   0   0   0 ...   0   1   0   1   0   \n",
       "Cooper          0   0   0   1   0   0   0   0   0   0 ...   0   0   0   0   0   \n",
       "Cox             0   1   0   0   0   0   0   0   0   0 ...   0   1   0   1   0   \n",
       "Cruz            0   0   0   0   0   0   0   0   0   0 ...   0   0   0   0   0   \n",
       "Davis           0   0   0   0   0   0   0   0   0   0 ...   0   0   1   0   0   \n",
       "Diaz            0   0   0   0   0   0   1   1   0   0 ...   0   0   0   0   0   \n",
       "Edwards         0   0   0   0   0   0   0   1   0   0 ...   0   0   0   0   1   \n",
       "Evans           0   0   0   0   0   0   0   0   0   0 ...   0   0   0   0   1   \n",
       "Fisher          1   1   0   0   0   0   0   0   0   0 ...   0   0   0   0   0   \n",
       "Flores          0   0   0   0   0   0   0   0   0   0 ...   0   1   0   0   0   \n",
       "Foster          1   0   0   0   0   0   0   0   1   0 ...   1   0   0   0   0   \n",
       "Garcia          0   0   0   0   0   0   0   0   0   0 ...   0   0   0   0   0   \n",
       "Gomez           0   0   0   0   0   0   0   0   0   0 ...   0   0   1   0   0   \n",
       "Gonzalez        0   0   0   0   0   0   0   0   1   0 ...   0   0   0   0   0   \n",
       "Gray            0   0   0   0   0   0   0   0   0   0 ...   0   0   0   1   0   \n",
       "...            ..  ..  ..  ..  ..  ..  ..  ..  ..  .. ...  ..  ..  ..  ..  ..   \n",
       "Reed            0   0   0   0   1   0   0   0   0   0 ...   0   0   0   0   0   \n",
       "Reyes           0   0   0   0   0   0   0   0   1   0 ...   1   0   0   0   0   \n",
       "Richardson      0   0   1   0   0   1   0   0   0   0 ...   0   0   0   0   0   \n",
       "Rivera          0   0   0   0   0   0   1   0   0   0 ...   0   0   0   0   0   \n",
       "Roberts         0   0   0   0   0   0   0   0   0   0 ...   0   0   0   0   0   \n",
       "Robinson        0   0   0   0   0   0   1   0   0   0 ...   0   0   0   0   0   \n",
       "Rodriguez       0   1   0   0   0   0   0   0   0   0 ...   0   0   0   1   0   \n",
       "Rogers          0   0   1   0   0   0   1   1   0   0 ...   0   0   0   0   0   \n",
       "Ross            0   0   0   0   0   0   0   0   0   0 ...   0   0   0   0   0   \n",
       "Russell         0   0   0   0   0   0   0   0   0   0 ...   0   0   0   1   0   \n",
       "Sanchez         0   0   0   1   1   0   0   0   0   0 ...   0   0   0   1   0   \n",
       "Sanders         1   0   0   1   1   1   0   0   1   0 ...   0   0   1   1   0   \n",
       "Scott           0   0   0   0   0   1   0   0   0   0 ...   1   0   0   0   0   \n",
       "Smith           0   1   0   0   0   0   0   0   0   0 ...   0   1   0   0   0   \n",
       "Stewart         0   0   0   0   0   0   0   1   0   0 ...   0   0   0   0   0   \n",
       "Sullivan        0   0   0   0   0   0   0   1   0   0 ...   0   0   0   0   0   \n",
       "Taylor          0   0   0   0   0   0   1   0   0   0 ...   0   0   0   0   0   \n",
       "Thomas          1   0   0   1   0   0   0   0   1   0 ...   0   0   0   1   0   \n",
       "Thompson        0   0   0   0   0   0   0   0   1   0 ...   0   0   1   0   0   \n",
       "Torres          0   0   0   0   0   0   0   1   0   0 ...   0   0   0   0   0   \n",
       "Turner          0   0   0   1   0   1   0   0   0   0 ...   0   0   0   0   1   \n",
       "Walker          0   0   0   0   0   0   0   0   0   0 ...   0   0   0   0   0   \n",
       "Ward            0   0   0   1   0   0   0   0   0   0 ...   0   0   0   0   0   \n",
       "Watson          0   0   0   0   0   0   1   0   0   0 ...   0   0   0   0   0   \n",
       "White           0   0   0   0   0   0   0   0   0   0 ...   0   0   1   0   0   \n",
       "Williams        0   0   0   0   0   0   0   0   0   0 ...   0   0   0   0   0   \n",
       "Wilson          0   0   0   0   0   0   0   1   0   0 ...   0   0   0   0   0   \n",
       "Wood            1   0   0   0   0   0   0   0   0   1 ...   0   0   0   0   0   \n",
       "Wright          0   0   0   1   0   1   0   0   0   0 ...   0   0   0   0   1   \n",
       "Young           0   0   0   0   0   1   0   0   1   0 ...   0   0   0   0   0   \n",
       "\n",
       "offer_id       28  29  30  31  32  \n",
       "customer_name                      \n",
       "Adams           0   1   1   0   0  \n",
       "Allen           0   0   0   0   0  \n",
       "Anderson        0   0   0   0   0  \n",
       "Bailey          0   0   1   0   0  \n",
       "Baker           0   0   0   1   0  \n",
       "Barnes          0   0   0   1   0  \n",
       "Bell            0   0   0   0   0  \n",
       "Bennett         0   1   0   0   0  \n",
       "Brooks          0   0   0   0   0  \n",
       "Brown           0   1   1   0   0  \n",
       "Butler          1   0   1   0   0  \n",
       "Campbell        0   0   0   0   0  \n",
       "Carter          0   1   1   0   0  \n",
       "Clark           1   0   0   1   0  \n",
       "Collins         0   0   1   0   0  \n",
       "Cook            0   0   0   0   0  \n",
       "Cooper          0   0   0   0   1  \n",
       "Cox             0   0   0   0   0  \n",
       "Cruz            0   1   1   0   0  \n",
       "Davis           0   0   0   0   0  \n",
       "Diaz            0   1   1   0   0  \n",
       "Edwards         0   0   0   0   0  \n",
       "Evans           0   0   0   0   0  \n",
       "Fisher          1   0   1   1   0  \n",
       "Flores          0   0   0   0   0  \n",
       "Foster          0   0   0   0   0  \n",
       "Garcia          0   0   0   0   0  \n",
       "Gomez           0   0   0   0   1  \n",
       "Gonzalez        0   0   0   1   0  \n",
       "Gray            0   0   0   0   0  \n",
       "...            ..  ..  ..  ..  ..  \n",
       "Reed            0   0   0   0   0  \n",
       "Reyes           0   0   0   0   0  \n",
       "Richardson      0   0   0   0   0  \n",
       "Rivera          0   0   0   0   0  \n",
       "Roberts         0   0   0   1   0  \n",
       "Robinson        0   1   0   0   0  \n",
       "Rodriguez       0   0   0   0   0  \n",
       "Rogers          0   0   0   0   0  \n",
       "Ross            0   0   0   0   0  \n",
       "Russell         0   0   0   0   0  \n",
       "Sanchez         0   0   0   0   0  \n",
       "Sanders         0   0   0   0   0  \n",
       "Scott           0   0   0   0   0  \n",
       "Smith           0   0   0   0   0  \n",
       "Stewart         0   1   1   0   0  \n",
       "Sullivan        0   0   0   0   0  \n",
       "Taylor          0   1   1   0   0  \n",
       "Thomas          0   0   0   0   0  \n",
       "Thompson        0   0   1   0   0  \n",
       "Torres          0   0   0   0   0  \n",
       "Turner          0   0   0   1   0  \n",
       "Walker          0   1   0   0   0  \n",
       "Ward            0   0   0   0   0  \n",
       "Watson          0   1   0   0   0  \n",
       "White           0   0   1   0   0  \n",
       "Williams        0   0   0   1   0  \n",
       "Wilson          0   0   1   0   0  \n",
       "Wood            0   0   0   1   0  \n",
       "Wright          0   0   0   0   0  \n",
       "Young           0   0   0   1   1  \n",
       "\n",
       "[100 rows x 32 columns]"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "full_data_pivot = pd.pivot_table(full_data, values = 'n', index = 'customer_name', columns = 'offer_id', fill_value = 0)\n",
    "full_data_pivot"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## K-Means Clustering\n",
    "\n",
    "Recall that in K-Means Clustering we want to *maximize* the distance between centroids and *minimize* the distance between data points and the respective centroid for the cluster they are in. True evaluation for unsupervised learning would require labeled data; however, we can use a variety of intuitive metrics to try to pick the number of clusters K. We will introduce two methods: the Elbow method, the Silhouette method and the gap statistic."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Choosing K: The Elbow Sum-of-Squares Method\n",
    "\n",
    "The first method looks at the sum-of-squares error in each cluster against $K$. We compute the distance from each data point to the center of the cluster (centroid) to which the data point was assigned. \n",
    "\n",
    "$$SS = \\sum_k \\sum_{x_i \\in C_k} \\sum_{x_j \\in C_k} \\left( x_i - x_j \\right)^2 = \\sum_k \\sum_{x_i \\in C_k} \\left( x_i - \\mu_k \\right)^2$$\n",
    "\n",
    "where $x_i$ is a point, $C_k$ represents cluster $k$ and $\\mu_k$ is the centroid for cluster $k$. We can plot SS vs. $K$ and choose the *elbow point* in the plot as the best value for $K$. The elbow point is the point at which the plot starts descending much more slowly. "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<div class=\"span5 alert alert-info\">\n",
    "<h3>Checkup Exercise Set II</h3>\n",
    "\n",
    "<p><b>Exercise:</b></p> \n",
    "<ul>\n",
    "<li> What values of $SS$ do you believe represent better clusterings? Why?\n",
    "<li> Create a numpy matrix `x_cols` with only the columns representing the offers (i.e. the 0/1 colums) \n",
    "<li> Write code that applies the [`KMeans`](http://scikit-learn.org/stable/modules/generated/sklearn.cluster.KMeans.html) clustering method from scikit-learn to this matrix. \n",
    "<li> Construct a plot showing $SS$ for each $K$ and pick $K$ using this plot. For simplicity, test $2 \\le K \\le 10$.\n",
    "<li> Make a bar chart showing the number of points in each cluster for k-means under the best $K$.\n",
    "<li> What challenges did you experience using the Elbow method to pick $K$?\n",
    "</ul>\n",
    "</div>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<b> What values of 𝑆𝑆 do you believe represent better clusterings? Why? </b>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "SS means \"Sum of squares\" and we want this to be the smallest possible. "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<b> Create a numpy matrix `x_cols` with only the columns representing the offers (i.e. the 0/1 colums)</b>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "matrix([[0, 0, 0, ..., 1, 0, 0],\n",
       "        [0, 0, 0, ..., 0, 0, 0],\n",
       "        [0, 0, 0, ..., 0, 0, 0],\n",
       "        ...,\n",
       "        [1, 0, 0, ..., 0, 1, 0],\n",
       "        [0, 0, 0, ..., 0, 0, 0],\n",
       "        [0, 0, 0, ..., 0, 1, 1]])"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import numpy as np\n",
    "x_cols = np.matrix(full_data_pivot[:])\n",
    "x_cols"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<b> Write code that applies the `KMeans` clustering method from scikit-learn to this matrix. </b>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[251.91666666666666]\n",
      "[251.91666666666666, 228.08149075232927]\n",
      "[251.91666666666666, 228.08149075232927, 213.91964285714283]\n",
      "[251.91666666666666, 228.08149075232927, 213.91964285714283, 203.82698474126494]\n",
      "[251.91666666666666, 228.08149075232927, 213.91964285714283, 203.82698474126494, 193.8703174603175]\n",
      "[251.91666666666666, 228.08149075232927, 213.91964285714283, 203.82698474126494, 193.8703174603175, 188.99126984126985]\n",
      "[251.91666666666666, 228.08149075232927, 213.91964285714283, 203.82698474126494, 193.8703174603175, 188.99126984126985, 179.19632034632036]\n",
      "[251.91666666666666, 228.08149075232927, 213.91964285714283, 203.82698474126494, 193.8703174603175, 188.99126984126985, 179.19632034632036, 173.65079365079364]\n",
      "[251.91666666666666, 228.08149075232927, 213.91964285714283, 203.82698474126494, 193.8703174603175, 188.99126984126985, 179.19632034632036, 173.65079365079364, 167.54025974025976]\n"
     ]
    }
   ],
   "source": [
    "from sklearn.cluster import KMeans\n",
    "clusters = range(2,11)\n",
    "SS = []\n",
    "\n",
    "for k in clusters: \n",
    "    model = KMeans(n_clusters = k)\n",
    "    model.fit(x_cols)\n",
    "    SS.append(model.inertia_)\n",
    "    print(SS)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<b> Construct a plot showing 𝑆𝑆 for each  𝐾 and pick  𝐾 using this plot. For simplicity, test 2≤𝐾≤10. </b>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0, 0.5, 'inertia')"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.plot(clusters, SS, '-o')\n",
    "plt.xlabel('k number of clusters')\n",
    "plt.ylabel('inertia')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<b> Choosing k: </b> it is not straightforward to choose k here. The graph doesn't show a clear 'elbow', but I would choose 4. "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<b> Make a bar chart showing the number of points in each cluster for k-means under the best K.</b>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "KMeans(algorithm='auto', copy_x=True, init='k-means++', max_iter=300,\n",
       "    n_clusters=4, n_init=10, n_jobs=None, precompute_distances='auto',\n",
       "    random_state=None, tol=0.0001, verbose=0)"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "best_model = KMeans(n_clusters = 4)\n",
    "best_model.fit(x_cols)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[0 1 2 3] [32 34 15 19]\n"
     ]
    }
   ],
   "source": [
    "labels, counts = np.unique(best_model.labels_, return_counts = True)\n",
    "print(labels, counts)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x1a21d38a90>"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "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.barplot(labels, counts)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<b> What challenges did you experience using the Elbow method to pick K? </b>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The choice of K is arbitrary: at first, I had chosen k=3 according to the small 'elbow' on the graph. When counting the number of points in each cluster, I found the second cluster to be very small compared to the two others. I decided to do k=4 are got a better result I think. "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Choosing K: The Silhouette Method\n",
    "\n",
    "There exists another method that measures how well each datapoint $x_i$ \"fits\" its assigned cluster *and also* how poorly it fits into other clusters. This is a different way of looking at the same objective. Denote $a_{x_i}$ as the *average* distance from $x_i$ to all other points within its own cluster $k$. The lower the value, the better. On the other hand $b_{x_i}$ is the minimum average distance from $x_i$ to points in a different cluster, minimized over clusters. That is, compute separately for each cluster the average distance from $x_i$ to the points within that cluster, and then take the minimum. The silhouette $s(x_i)$ is defined as\n",
    "\n",
    "$$s(x_i) = \\frac{b_{x_i} - a_{x_i}}{\\max{\\left( a_{x_i}, b_{x_i}\\right)}}$$\n",
    "\n",
    "The silhouette score is computed on *every datapoint in every cluster*. The silhouette score ranges from -1 (a poor clustering) to +1 (a very dense clustering) with 0 denoting the situation where clusters overlap. Some criteria for the silhouette coefficient is provided in the table below."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<pre>\n",
    "\n",
    "| Range       | Interpretation                                |\n",
    "|-------------|-----------------------------------------------|\n",
    "| 0.71 - 1.0  | A strong structure has been found.            |\n",
    "| 0.51 - 0.7  | A reasonable structure has been found.        |\n",
    "| 0.26 - 0.5  | The structure is weak and could be artificial.|\n",
    "| < 0.25      | No substantial structure has been found.      |\n",
    "\n",
    "</pre>\n",
    "Source: http://www.stat.berkeley.edu/~spector/s133/Clus.html"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Fortunately, scikit-learn provides a function to compute this for us (phew!) called [`sklearn.metrics.silhouette_score`](http://scikit-learn.org/stable/modules/generated/sklearn.metrics.silhouette_score.html). Take a look at [this article](http://scikit-learn.org/stable/auto_examples/cluster/plot_kmeans_silhouette_analysis.html) on picking $K$ in scikit-learn, as it will help you in the next exercise set."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<div class=\"span5 alert alert-info\">\n",
    "<h3>Checkup Exercise Set III</h3>\n",
    "\n",
    "<p><b>Exercise:</b> Using the documentation for the `silhouette_score` function above, construct a series of silhouette plots like the ones in the article linked above.</p>\n",
    "\n",
    "<p><b>Exercise:</b> Compute the average silhouette score for each $K$ and plot it. What $K$ does the plot suggest we should choose? Does it differ from what we found using the Elbow method?</p>\n",
    "</div>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<b> Using the documentation for the `silhouette_score` function above, construct a series of silhouette plots like the ones in the article linked above.</b>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "For n_clusters = 2 The average silhouette_score is : 0.09365573283492713\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "For n_clusters = 3 The average silhouette_score is : 0.11889942863638556\n"
     ]
    },
    {
     "data": {
      "image/png": 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B93JLVfvvRViPfk+ohb2C7oQ0k9Tve+6X/t1juE3boYd+UnOB0+huNv4h3SePPb2NsEwy/W2RnJlZlZlNc/cWd19HaJaB7uapjHqrgVhLqJY5x8wmpn0+LXpdssUv+iY14z4EoTDA+6PPUllYaiWbEL3u1WMcT0avt0bV7R+P4vt9tJBTG1p6mXt+lspmVwHHReN5iHDWkqnt/iRCVe6V7n4F2ROVbM9M341QDfiYu3+GcFDM1vEl5ZdR3OcROrr8JnVGa2aXEjqAXhtVcb47+s1hPUdiZtsT2uEOcfcz3X0uoTZkON1NS735N2GD/GA0zka6q4LTs+eel2BmsxDA3Z8mdJgDuDJLP5hUk8UI4EHfsiNRan14NlqO7yKc4d1HyNpPooBl14f5l4rnkLT+LXea2etmlulqic3WdzMbRtg55TP9nut3b9tISi7LK9s6DSFpg6gZIlov3rmV4VPb/2FRPw3M7DBCE82zPWpotjbdTSx0sv4xocPYR919BmEbnkyYjyeR2/Lvi+OiGCYTznhbCc0tPaWWx3XR8jif0Lcj20Gh53zdi+79YWq64wn9hy5ny+WYaR94FmGenuShk2C2ZsOUVD+IDxL6Wj1EqBU9gbAvyNT/ISVJ8W5emNN6kEFqnh8OYGbNwGtm9kSWfmc9jzt7kpasWLig4MdAl7ufQFi/lgG7m9loMs/zVAyXRcv924Sm31t6THvT8jOzDxIShufc/d2EY3AnGfbtAO4+zd0TWf5ezPCTe4EXLFzMAJDqA7M4w7Cb9NaJ8nFClcZo4Ekzu8PMFhF2PKvozlIK9Q3CgjrfzB4gzOC3EFbMVFvSo9HrL83sZ4TOP+muJfTT+JqZ/T0a/hN015KkdkLzzewfUdaZ+uxMM7vBw00z7iRk0v81s4cJWfnphN61PaV2Cl+OqkFTvcFzvZRwCSGDnmdm/yCcRY/f2jjcfQlhYW8bfZS+s2km7KgfM7M/0n0QvjfDqJYReoufa2YPm9lfCQnHBkIHrFz8H2ED+UE0z58itGX+wd2znUXl6qIolu0J9yHp6f4s71PuiOJ5p5k9Rmjy+DxwfJRhF7rs8p1/dxEOlHsBj5vZI4Tk8WW6dyjpHotejzSz3xCWYXrHslymn1q/bzOzE+h9GymWHxLOSj9pZvcType1Vivaof2McBXAoqgstxMOEuf1MYZlhKTxG2Z2r5ndR5j3KwjrRaHLf2suNLN7CcuhFvi+h8uNe7qKUMafm9ndhLPWTxGaojK5Inr9uZn9jbBOtaR9nyrTWWZ2J1E1Pd1lWkU46Mw0swUWOu29RDggPmxmjxLWqfTf9PRANJ5KQl+ZLsK6mTr4bu2GfCsJV1zcb+GmWnG4mlB78RkzW0DYJocBd/uW95qB7uPOBWb2U0ItQ3ot1BuExO36aD36J2Hf/GTUpJRpnqcupf9btNz/QGgq2FqzzQbCyfytFq7G+idhnmfat/fFNdHrX6OYvkLYV2z15le5ZIMfJlw6tIzQaW1bwsZ9oGe4/Kwvog418wk7/R0JbWvXA4d596U15xI2sEmEg8qJPcbxPKHK7p+EM7AKQmJyXvT9k8ANhIU5hbDS3Eyo6htN90b7AcLOZAzh7OGOKI70araUq+juqzGHsIG/AOwYnTH2Vu5lhGaLVwh9H54i6iUO7LuVn6amuZjQfpnyGcKC7yBc5rie0NHxWxmmvZbQZvkHQk/0PQjz7ih3z3S2lCn+Owmdah8gZOY1hGy64DtUuvtSuje0C3rUgKUOOqmOeVskENGO7e2EqsHphDOD3xIu/4LCl11e88/dk4Tqw1sI6990ws7o6Oi7nsM/RVierxOW5V+I+prkMf3LCdutAQ29bSPFEtUgvY/QBr8roZ04tYPKdCCF0F/nS4R23f0JnemO8+gyzj7E8BKhGvfvhBOCuYQD6hFRX52Cln8vTibsT5oISfwXs8R4H2E+PU1IJtcB53qWy5zd/VeEZbWOsJ/8Mpv3N7gZ+C5hu9+FcHJxPzDazGZF68xXCVXj2xMOWCcTkutUzeel0WvG/U90kE1dWZCqpk/1bftPL8eEiwjr8xxCf5SSi07A3k6oVd6LsM+6gtChNZOfEI5F1YRldC7dffhw94cIndofJTQrTCFs40dH328xz939F4TOnK9F41xKqAHKWnvj7r8l1Jq9QDi5bib0gcqnH2JW7n4Dof/ZCsK+4WFC7Wamk5tNEslkX2uCJC5mdgXhTOWb7r7VTi4ipWZmhxASyifc/fbos18QkvNDo457g47plt8yxAyZuzAOBhau138fIYPuJFQVi5SbakLtR1dU1d5IODtbxeY1ZiJFY2ZvJVw5kM1fPdyNV4pECcTAUkuotn4ZuCiq5hYpK+7+Zwt3tTuNcJltO6Gq/bNR84FIf5hC96XtmawuVSBDhZowREREJG/FuqRGREREhhAlECIiIpI3JRAiIiKSNyUQIiIikjclECIiIpI3JRAiIiKSNyUQIiIikjclECIiIpI3JRAiIiKSNyUQIiIikjclECIiIpI3JRAiIiKSNyUQIiIikjc9zrsMLVy48N/AdsA64NmYwxERGShmAk3AC/Pmzdst7mAGOyUQ5Wk7YET0NzHmWEREBprt4g5gKFACUZ7WASMqKipoaGgo7YTXrQOgqamppNPN6u9/735/4IH9MomyK3MJDLUyD7XywtAuM2EfKv1MCUR5ehaY2NDQgJmVdMILFy4EKPl0s5o9u/t9Mtkvkyi7MpfAUCvzUCsvDO0yo6bfklAnShEREcmbEggRERHJmxIIERERyZsSCBEREcmbEggRERHJmxIIERERyZsSCBEREcmb7gMh5W333eOOQEREMlACIeWt+8YwIiJSRtSEISIiInlTAiEiIiJ5UwIhIiIieVMfCClv117b/f7UU+OLQ0RENqMEQjZXtRyaHmJl57/jjgSAMaedtun9yo/1U4XZyJfC+MukzCVRcJmrGF5xGDWJicWLSUQGFCUQsrnmO2D4XSzvLI/WrTFp75d3frd/JjJ6QzT++v4ZfzkqsMwViQY6eZ1xlecUMyoRGUCUQMjmEu1QuY4kDVTQEHc0m0lQ0z8jTrb37/jLUQFl7mQ1XcmNdLGx2FGJyACiBEIyqqSZqsSouMPYTHVifP+MuOPNaPwj+mf85aiPZW5PvkYXG6hNbE99Yqf+iExEBgglECKSk2Syk3aWUJeYy5iKUxiReGfcIYlIjJRAiEhWyWQnXaynixa6WAckqE6MY1Tl8XGHJiIxUwIxSLW3t3P++eezZMkS2traOOOMMzjkkEPiDkvKXFeyjU7eSEsaNlIR9YepTIygmgnUMiPuMEWkDCiBGKRuu+02mpub+frXv86qVat417vepQRCerWRZ6mgjsrEMKoZTwVN1CamUZuY2f3H9LjDFJEyoARikDriiCM4/PDDN/1fWVkZYzQycHRQnZjAmIpTaKjYnRqmUpEYQleniEjOlEAMUo2NjQCsW7eOs88+m09/+tMxRyQDSVPFAbpJlIhslRKIQWzZsmWceeaZnHDCCRx11FFxh9MnLUeOjTuEIaMz+SZdtAMJEnpMjoj0QgnEILVy5Uo++tGPcuGFF7LvvvvGHU6fLbtlr7hDGNCSySTQSZJOoIMknSTpgMo1kOikLbmWJB0kaaeLddQlZtFUcQBV9NM9N0Rk0FACMUj94Ac/YM2aNVx99dVcffXVAFx33XXU1dXFHJn0VVdyI0naIUoCeiYFWyQJUeIQahOqSFBJgiqgEirbIVkJ1FORqCfBMCrZjtEVH2FkxfEkEokYSyoiA4ESiEHqggsu4IILLog7DCmS9uQy2nmVBDUkomRgU1KQqCJB7ZafR8lCZaKBCpqooIlKmqhINLH0jTegq57xU3ekMtFEBY3UJLajNjE15pKKyEChBEJkAOhiAzWJydQndqSK8dFBP5UQNHa/j/7f9J4GEoktr8BZ+vpCAEZNn1fqoojIIKEEQsraqEt80/s3LrQYIykPoypOYFjFwXGHISKiBELK26jLntn0frAlEKGDY8emTozhL9v7NioZHnfIIiKbKIEQ6UfJZJIOVpCktUdCEBKE0GehmgqqSFANVJOgmopEI4nofSL6rorR1Ca2j7tIIiKAEgiRftXJ63SwnKrEaCpoICQL1VEHx2oqE01U0hz+EiOo2vS+mUpGUJkYSSXh8wT1ujpCRMqGEgiRPkgmu6JLJbM1O6ReW6lJzGBUxfHUJXbokSiMIJGojrsoIiJ9ogRCBIAkyWRbdODP1C+hI7oHQ+r/zs2aFza9JqqooA7SPqthEmMqTqUiURt3IUVEikYJhAw6oXNi12a1AWxWM5CeEHRA/XrChZL10UE/NC+kEoCKRENaopD6vIbKxIjQzBDVKoT3I6ja1PzQHDU/jCGR0KYmIoOL9moyoCWTSZJsoIM36GQNSdqihIEMNQRV0U2X6rr/p5q21hZIVjGsdgqVDKeSEVQkhkfvm7sTgk2JQjMVNKk/gogMaUogZMBJJjvoYj2drKaDVQBUMYqaxORNd2SsoC462A+PagPC+wqGU5nY/LPHFz8PXfVsN2bPmEsmIjJwKIGQshRqFto3+6w1+RRdbAC6SFBPJSOoS8yiim1pqtiXxsQ+1CQmRzUIefQ36HqtuMGLiAwBSiAko3aW0pFcGcu0Q/+EDdDjkdLViQlUUEcFw6hJTKIuMZemxH7UJeaQSOjx0yIipaQEQjbXORw6xlCdiO+pnQkqqaCeykQz6z725KaHPU2qvJyaxGSq2Eb9D0REYqYEQja36mhYP5fJzfHd8bCCeqoTk8J9En7YnSjoIkgRkfKhBEI2l6yG1tk0VugpjSIikp0SiByZ2fIijCbp7uOKMB4REZFYKYHI3ZgijCNZhHGIiIjETglE7g6OO4Ah6dRTu99fe218cYiIyGaUQOTI3f8edwxD0nXXdb9XAiEiUjZ08XwRmVmFmU02sx3TPtP1hiIiMugogSgCM9vdzH4LrAZeBB6NPp8KPGdmn4wxPBERkaJTAlEgM/so8ADwLqAJSER/ANOivyvN7MdxxCciItIflEAUwMx2B66J/v0asBvwYNogC4GLgA7gI2b2wZIGKCIi0k+UQBTm84R5eIa7f8HdHwM6U1+6+zp3vwT4GKFW4qPxhCkiIlJcSiAKcyCw3N232jzh7j8DXiXUUIiIiAx4SiAKMxJ4JcdhXwbq+zEWERGRklECUZiVwIzeBjKzCmA6sKLfIxIRESkBJRCF+TswwsxO72W4M4HRwL39H5KIiEj/050oC/NV4L2EyzTHAr8EKlNfmtkE4HTgC0AX8O04gszH0rZOFrS0s+CpxXGHAsA+Z3560/sH+ymml99oBSiLMrd3dfHUmvUsb23jzO0nccSEYjyCRUSk+JRAFMDd/2NmpwLXAl+K/gAwszcJ94VI+Yy7P1LiEPN219p27lrbBq1L4g4FgOuOOL77n2f7J6bWDRsBqNsYf5kTQGNVJcOrq/jRs0s5fNvRJBK6mamIlB8lEAVy9+vN7ElC8nAIUBN9NYxwSed9wKXu/teYQsxLJ0nWdyVpIhzIhoKKinCAjqO8L7RsoL6ykuHVIWkYXl1FZSJBY1Ull+w8XcmDiJQtJRBF4O4PA+8ws1pgFjAcaAGec/d1sQbXR8OrqxhVWx13GCVR07oBgBF1Nb0MWVyvb2ynsbKSvUYP54CxI9llZBM7Nw9jRI02SxEpf9pTFZG7bwQWxR2HDAyvtbYxsaGWk2ZMUF8HERlwlEAUgZkNAz4MHAEY0EB4sNYTwB+AX7p7e3wRDlz/e/6nNr2/9MtXxhhJcXQlk6zr6GRVWzvrOzpprqnmwLEj4w5LRCRvSiAKZGaHAD8DxtL9EC2ACcAOwHHAF8zsPe7+VAwhDmh7PTA4rnxd19HJ8tY2Vre1U1tRwciaanZsbuSAbZqpHyJ9TURkcFECUQAzmw3cDtQBTxESiSeAdYROlLsCHwHmAHeZ2e7uvjymcCUm7V1dPL1mPRPra5k0opbpw+rZb0wz+4wZwcxhujmpiAxMSiAK80VC8vBT4GPu3tnj+1vM7CuE+0McDfwvcFZpQ5S4vb6xnebqKg4aN5KzZ09mUkNd3CGJiBRMd6IszKGEvg6nZ0gwcsklAAAgAElEQVQeAHD3VkL/iBbgmBLGJmUgmUyyvLWdbeqqOXbyNkoeRGTQUAJRmBHAM1GSkJW7rwH+C2xTkqikbLza2kZlAqY11rP36BFxhyMiUjRKIArzFDAruv9DVmZWCUwFnitJVBKrZDLJ2vYOXmrZwPLWNmYNa+CjMyZQWaGbQonI4KEEojDfApqB7/Qy3IWE2oer+j0iiU0qaXhs1TpebGmlKpFgzvBGzrLJvGWcLtUUkcFFnShzZGZHZvj4DcKtqk8xs50Iz8R4lHAVRiOwPaH/w5HA74A7ShOtlNry1jZe3dDGmNpqZo9oYFJDHftv08xbxjaz/fDGuMMTESk6JRC5+wOQzPJdAtg7+sv2/buAY9E8H3RaO7tYtmEjM4c18N4p4zh0/ChmDqvXcyxEZFDTwSx3i8meQMgQk0wmaeno5NXWNta0dzC+rhYb3sDpsyYqcRCRIUEJRI7cfVrcMQxFV33mgrhD2KS9q4s17Z2sae9gTXsHAOPrapk1rIFDx4/i/dPGK3kQkSFDCYSUtb8c9Z5Yp9+ZTLJsw0ZWt3WwsbOLYdVVjKiuYnxdDWPrajh8wmiOmrjNkHlyqYhIihKIEjKz+e6+IO44JDfJZJIX1oVHfU9rrKe5poodm5vYZWQTu44cxvSmeipU4yAiQ5QSiAKZ2SzgU4QHZzWw5aWxVYTbXY8HhqN5PmC82tpGa2cXu48axnlzt2Pn5iZqKnXls4gI6GBWEDObATxMSAxSp6JJNn8qZ7qlpYhLCrd0w0ZWtrYze3gDn7Ip7DF6eNwhiYiUFZ1OFebzhNtZLyE8KOuzhOThT8BpwKXAs9Gwd7v7pFIH+Nhjj/GhD32o1JMtmm+fesKmv1Jp7+ri1Q0b2WFEI5/dYapuAiUikoFqIApzCNAFvMPdHwcws88Ao9z9uuj/ywn3kHirmR3j7reWKrjrrruO2267jfr6gfvI6JnP/Lfk0+xMJqlKVDC5sY7Dth1d8umLiAwEqoEozLbAS6nkIfJvYDczq4ZNT+M8LfruNEpoypQpfPe73y3lJAeFjZ1JqisSNFVVxh2KiEjZUgJRuJU9/n8GqAZmpT5w9+cITRm7lTAuDj/8cKqqVMmUj65kkmWtGxlRXcWOzboFtYhINkogCvMaMLHHZ89Hrzv2+HwtMKrfI5KCvLBuA1WJBLOHN/LOiXr6uohINkogCnM/sK2ZHZ/22ROEjpRvT31gZsMID9ZaUdrwJB8bOjtZ297JDiMauXSX6Yytq4k7JBGRsqX67cJ8HzgB+JmZHQ2cTHg65xLgw2b2HOHpnGcRns55X1yBSu9WtXUwsraK+WNHMn1YQ9zhiIiUNdVAFMDd/wFcSJiP73b3dndvJ1y+mQAuBm4FDiPcH+LSUsc4adIkfv3rX5d6sgNOMplkQ0cnNRUVTGqojTscEZGypwSiQO5+GaFz5AVpn11LuOLiOaCD0KzxXnd/IJYgJau2ri6Wd3TxdFsn7V1JhldX6eoLEZEcqAmjCKLLOB/v8dl1wHXxRCS5aOvqYtHqddQnk0ytqWTPbUfzzoljeJvu/SAi0islEDIkJZNJ1rR1UFdZwezqKj40qo7j9zQ9jltEJEdKIHJkZp8oxnjc/epijGeouOT/rijauDq6krzZ3sGbbR2sbu+gpiLBmNpqxifamVlXqeRBRCQPSiBydxWhI2ShlEDk4Z/7HViU8axobWPx+laGVVXRXFPFpIZaJjfWsdfo4Ux9Y1lRpiEiMpQogcjdvRQngZASe2NjO6+s38jcEY3sMWoEe44ezp6jhzOpoZZEIsHCha/FHaKIyICjBCJH7n5Q3DFI/la3tfNSSys2vIFTZkzkfdPGxx2SiMigoARCBo3OZJINnV1s6OgMr52dtHR0sf3wBk7cbjzHTx0Xd4giIoOGEggpa9cfd9im9yfdfOem98lkkjXtnazt6NiUNLR1JamrrKC+spKGqgqGV9fQWFXJMZPGcvL0CeokKSJSREogpKyNfr3nw06Dlo5OXmjZwJjaakbXVFNfX0tDVSWTGuqY1ljH1MY6pjXVM62xjnH1urOkiEixKYGQAak9maShspK9R4/g/dPGMa2xnokNtVRX6OaqIiKloARCBrQJDbUcNE5PSRcRKTUlEDJgdHR18WZ7J6vb2nmzvZORNVV6mIuISEyUQBTAzPYFHnf3dXHHMhQ8umodw6vDjaAmN9QxpbGOIyaMiTssEZEhSQlEYW4AxprZdHd/I+5gBovUFRYrN7Zt9vmeo4ezU3MTe0Q3gppYX6srK0REYqIEojCTAVfyUBydySSvb2zn1Q1tVCZgm7qazb6/6YCdaNSjtkVEyoISiMIsIdRAVLp7Z9zBDFSdySSvbmjjtdY2hlVVsl1THdOb6jm8x2O1lTyIiJQPJRCF+RzwK+DnZnaBuz8bd0ADSVcyyYrWdpZu2MiI6ip2GNHATs3DePfksew7ZgSVFWqeEBEpV0ogCnMw8CjwXuC9ZvYGsAxozTJ80t33LlVw5c7XrKciATa8gXmjhvOh7bZlx+ZG9WsQERkAlEAU5pM9/h8d/WWjp3mmWdfRyR6jhnH+jtux75gRmROHRx4pfWAiItIrJRCFOTnuAAaqZDIJJEmQYJ9syQPAvHkljUtERHKjBKIA7n5D3DEMRBs6O1nZ2k51ooKRtVVUqMlCRGTA0Y38+oGZNcUdQ7npSiZ5rbWNJ99cx1NvricJbD+8gZ2aNatERAYi1UAUgZlNAv4HeDswC0gAVWY2AbgJ+Ia73x5jiLFbsbGd11vbmNhQx9i6Gg4Y28zB40YqgRARGaCUQBTIzA4HfgkMJyQO0N1ZcjtgPnCAmV3s7pfEEGJZaO/qormmmuOmjOW0WZOorcyx8iu9eSOpPqgiIuVCTRgFMLMZwG+BEcBvgHcB/04b5BngekJi8SUze0epYywHyWSS1W0dNFRVsFNzU+7Jg4iIlC3tyQtzHtAAXODu73f3W4H1qS/dfbm7fxQ4l5BEfCKeMOOzvqOTp9eG+z1s11jP/ts0xx2SiIgUgZowCvM2YBXw1V6G+xbweWCvfo+oSJZu2MjKje0FjaMrmWRDZxcTG2qZ1ljHmTaZGtU+iIgMCkogCjMW+E9vz8Fw904zewHYpTRh9d3wigrGVFVQ11BblPE111Rz9KRteP/UcQyr1uomIjJYaI9emNXA1ByHnQiU/VM7j26uYW59JdvP3r4o45vaWM/YHk/VFBGRgU8JRGEeAI42s+Pc/eZsA5nZ+4BtgVtLFlkfVScSzK6rYt7oEXGHIiIiZUwJRGG+DRwDXGdmDcCv0780syrgw8CVhEs7v1fyCEVERPqBerQVwN3vBS4kXMb5E2AtUUdJM3uM0MHyOqAR+La73xVTqCIiIkWlBKJA7n4ZcBzwFFAJVBMu2dyJkDi8BHzM3T8bW5AiIiJFpiaMInD33wG/M7PpwA6Eu1K2AM+4+5OxBiciItIPlEAUwMzeArzp7o8BuPvzwPNZhj0UmOXu3y9hiAPfkiVxRyAiIhkogSjMPcAC4MAchv0y4UFbSiDyMWFC3BGIiEgGSiByZGYjCPdy6KnRzHbYyk8ThHtFzOmXwERERGKgBCJ3tcD9wLC0z5LAbsDjOY7jb8UOSkREJA66CiNH7r4cuIhQo5D6o8f/mf4A1hGSj9NLF/EgsXRp95+IiJQN1UDkwd2vAK5I/W9mXcB97v6W+KIa5CamtRolk/HFISIim1ECUZiLgcVxByEiIlJqSiAK4O4Xb+17M5tDuLnUk+7eVZqoRERE+p8SiAJFV2d8Eljp7tdEn00AbiN0sAR40cxOjm59LSIiMuApgSiAmY0EHgJmEBKGa6KvrgV2j963AdsBfzKznaKbTZWtpUuXsmDBAhYsWFCyab7wwgvsvPPOHH/88QwbNqz3H4iISOyUQBTmf4CZwLPADQBmNhU4EugCDgXuAy4HzgE+B5wRS6Q5uuuuu7jrrtI+86u6upqnn36auXPnss8++5R02iIi0jdKIApzFNAOHObuL0WfHRO9/sPd7wEws/OBjwGHlTzCPHV2drJ+/XqamppobGws6riTySQtLS2sXbuW1tZWAEaOHMnkyZM54IAD2GuvvYo6PRER6T9KIAozHfC05AHgCMINpv6U+sDd283secKDtgaE4cOHM2rUqILH09bWxvLly1m7di0tLS3U19fT3NzMsGHDaGpqYubMmZx55pnMmaMbdYqIDCRKIAqTINRAAGBmNUDqnhB39xh2GKFZY0jo6Ohg2bJlrFixgtGjRzNhwgSampqYPn06c+fOZe7cueywww6MGTMm7lBFRKQPlEAU5jlghpnVu/sG4BCgAVjh7v9MDWRmswi1FU/FE2Zptba28t///peRI0ey4447ctBBB3HIIYcwZ84cdZIUERkklEAU5m/ALsCPzewm4KuE5ovfpgYws72AHxJuG/7HOIIspWQyyWuvvcaYMWM45JBDOP3005k1a1bcYYmISJEpgSjM5cB7gPcBxxOaNFYClwGY2SHAHdHnzwBfiyfM/pVMJlm7di2rVq1i1apVVFVVMX36dE488cTCkwfdvlpEpCwpgSiAu68wsz2AzwM7Epo0vunuqSc/OeFBWr8BPufuq+KJtP+sW7eOZ555hurqakaNGsXs2bOZMmUKBx10ELvuumvc4YmISD9RAlEgd19BuL9Dpu9eMbNt3L2txGGVzBtvvMG4cePYeeedOfDAA9l///2ZOXMmiUSi9x+LiMiApQSinw3m5CF1X4exY8dywgkncNhhZX+bCxERKRIlEAUwsw/n+xt3/2l/xBKHV199la6uLqZMmcKee+7ZPxNZuLD7/bx5/TMNERHJmxKIwlxPuOoiF4lo2AGfQHR1dbF48WLWrl3L9ttvz5lnnklzc3P/TGyPPbrfq0OliEjZUAJRmH+RPYFoAMYDI6NhfgGsKFFc/WLjxo2sWLGCFStWMGzYMHbZZRfOPvts5s+fH3doIiJSYkogCuDue/Q2jJnNJ9RU7AH0Uz1//2pra+PFF19k3bp1jBkzhjlz5jB79mzOPvts3eNBRGSIUgLRz9x9gZm9F3gEuJAsV2yUs5UrV1JZWckee+zB/PnzOfLII5kzZ46utBARGcKUQJSAu//LzJ4i3HRqwCUQ69evp7m5mbPOOotDDz007nBERKQMVMQdwBDSCWwbdxB9sWHDBhoaGpg2bVrcoYiISJlQAlECZrYf4VHer8YdS76SySStra3U1dUxefLkuMMREZEyoSaMApjZJ7bydQKoBQw4Ifrs1n4PqsjefPNNEokEtbW11NbWxh2OiIiUCSUQhbmK3O4DkQCeBy7t33CKp6Ojg2effZaWlhZmzpzJ3Llz4w5JRETKiBKIwtzL1hOIDmAVcD/wY3dfU5KoiuDFF19k7Nix7LHHHpx44okcc8wxcYckIiJlRAlEAdz9oLhj6C8NDQ3MnTuXK6+8knHjxsUXyLYDst+piMigpwRCMtp222058cQT400eAJYu7X0YEREpOSUQOTKzhmKMx93XF2M8/aWzsxOAESNGsP/++8ccjYiIlCslELlbW4RxJCnzeb506VJqamqoqqpi1KhRcYcjIiJlqqwPZmWmGPdtLut7P7e2trJq1SpGjhxJY2Nj3OGIiEgZUwKRu+3iDqC//eIXv2DVqlVUVlZy0kknxR1OcPvt3e+POiq+OEREZDNKIHLk7i/FHUM+urq6uOiii3B3ampquOyyy5g6dWrW4ZPJJI899hirVq1izJgxDB8+vITRbsXRR3e/T+Zyyw0RESkF3cq6j8ysvpfvDzOz2DoR3HXXXbS1tfGrX/2Kc845h6985StZh00mk9xwww0sWrSI1tbWEkYpIiIDlRKIPJnZMDP7FvCqmU3KMkwV8FvgFTO7wsxK3qFg4cKFzJ8/H4Bdd92VRYsWZR32nnvu4cYbb8TdSeosX0REcqAEIg9mNhF4GPgUMAw4MMugswnPwagDzgL+aWbjSxJkZN26dTQ1NW36v7Kyko6OjozDLlq0iOXLlzNu3LjyaboQEZGypgQiR2ZWAdxCeDjW68BngT9lGtbdFwGjgLMJt7KeDfzOzEp2FUZTUxMtLS2b/u/q6qKqKnOXl+XLl7Nx40YaGopyqwsRERkClEDk7kRgHvAMsIu7f8vd38g2sLu3uPtVwF7AMmBv4PiSRArsvvvu3HvvvQA8+uijbL/99lmHXb16Ne3t7VRXV5cqPBERGeCUQOTuBMKNoD7l7sty/ZG7P09oxkgAH+yn2LZw2GGHUVNTw/vf/34uv/xyzjvvvIzDdXR08Morr7Bx40Y9rltERHKmyzhztxuwyt3/3Iff/p7QlDGvuCFlV1FRwSWXXNLrcHfccQerVq3adPdJERGRXKgGInejgBf78kN3TwLPAaOLGVChXn75Za655hpeeOEFJkyYEHc4IiIygCiByN16YEwBv28GNhYploIlk0m++93v8vzzz9PU1MTo0WWV24iISJlTApG7p4CJfbk5lJmNBqYBLxc7qL7o7Ozkyiuv5KGHHmL16tVMmTIl7pCy23337j8RESkbavTO3V+APYFzgC/m+duzCfP6vmIH1RdXXnklt9xyC88//zwzZswo774PCxfGHYGIiGSgGojcXUNogjjHzN6V64/M7J3A+YQrOH7UT7HlrK2tjbvvvptnnnmGWbNm6cZRIiLSJ0ogcuTuSwk1CTXAr8zsGjOzbMOb2Vwz+xHhCoxK4Efu/nBpos1u6dKltLe3U1VVtdmdKkVERPJRxnXX5cfdf2hmswh3oTwFOMXMlgGLCHenrCJ0tNwBGBv9LAH8HPhE6SPe0k033cTy5ctpbm6OOxQRERnAlEDkyd3PNbO7gauB6cCE6C9JSBbSPQlc4u6/Lm2Uma1evZr77ruP5cuXs9NOO8UdTm6uvbb7/amnxheHiIhsRglEH7j7HVFNxIHAWwnPumgmXOr5KvA08P/c3eOLcnNdXV38/Oc/Z/Xq1TQ1NVFTUxN3SLk57bTu90ogRETKhhKIPopuDnVP9FfW1q5dy7e//W3uvvtuXnrpJaZPnx53SCIiMsApgRjkHn30Ua644gqeeOIJli1bxowZM3TlhYiIFEwJxCD2wAMPcPHFF/P888/T2dnJnDlzqKurizssEREZBJRADGK///3veeGFF6ivr2fy5MkkEj37eIqIiPSN7gMxCCWTSW644Qb+9a9/sXbtWiZOnKjkQUREiko1EIPQT37yE2688UaeeeYZpk2bRmVlZdwhiYjIIKMEYpB56aWXuPnmm3n66aeZPn26bhglIiL9Qk0Yg8ytt97K0qVLGT16tJIHERHpN0ogBpklS5awbt06Ro4cGXcoIiIyiKkJYxBZu3Yt7s6GDRsGz+Wa73xn3BGIiEgGSiAGkV/96le89tprNDY2DpxbVffm9tvjjkBERDJQE8Yg8eCDD/LrX/+al19+mYkTJ8YdjoiIDHJKIAaJe+65h1deeYVx48bR1NQUdzgiIjLIKYEYBDZu3MiSJUvo7Oykvr4+7nBERGQIUB+IAa6jo4PLLruMhQsX0tLSMvietHnRRZnfi4hIrJRADHALFixgwYIFLF68mNmzZ1NVNcgW6cUXd79XAiEiUjbUhDGArVmzht/97ne89tprTJw4Uc0XIiJSMkogBqhXX32Vz372s9x///20tLQwatSouEMSEZEhZJDVdw8NyWSSb33rWzz88MO8+eabzJkzZ/A1XYiISFnTUWeAee6557jlllt45JFHWLlyJTvttJOSBxERKTkdeQaAZDLJfffdx2233cZjjz3G8uXLWblyJVOmTFHyICIisdDRp8y1tLTwne98h7vuuoslS5awdu1axowZw5w5cwbP8y5ERGTAUQJRxlauXMlXv/pVFi1axNKlS5kwYQLTp0+nsrIy7tBERGSIUwJRxhYvXsy9995LV1eXahxERKSsKIEoYx0dHYwfP57m5mYSiUTc4YiIiGyiBKLMLV++nOXLl5dsei0tLYwYMaJk0+vVxz8edwQiIpKBEogyVlNTw7bbblvSaTY1NVFXV8fo0aNLOt2srr027ghERCQDJRBlbObMmVx55ZUlneaTTz7JsGHDMLOSTldERAYWJRBlrKmpqeQH8s7OzpJOT0REBiY9C0NERETyphoIKW+nntr9Xv0hRETKhhIIKW/XXdf9XgmEiEjZUBOGiIiI5E0JhIiIiORNCYSIiIjkTQmEiIiI5E0JhIiIiORNCYSIiIjkTQmEiIiI5E0JhIiIiORNN5IqTzMB1q9fj7vHEkBc093CNdd0v+/nmMqmzCU01Mo81MoLQ7PMRPtQ6V+JZDIZdwzSw8KFC1cDI+KOQ0RkgHpz3rx5zXEHMdipBqI8vQBsB6wDno05FhGRgWIm0ETYh0o/Uw2EiIiI5E2dKEVERCRvSiBEREQkb0ogREREJG9KIERERCRvSiBEREQkb0ogREREJG9KIERERCRvSiBEREQkb0ogREREJG9KIERERCRvSiBEREQkb0ogREREJG9KIERERCRvepx3zMzseOB/gB2ATuB+4BJ3f7iAcR4M3A182N1/Fn12KHA+sDNQAywEvuLuf8ljvNsDFwMHAKMJjxq/Frja3bv6Gm9/KFJ53w58GtiT8IjgZcCfgMvc/ZWiB12gYpQ5wzj/BBwBHOzu9xQjzmIq0nJuBD4PHA9sB6wH7iNsh48UPegCFanM+wAXAPsR1u2XgdsIZV5V9KCLxMxOAn4CzHf3+/L43QTgS8BhwLbAYuBnwNfcfWM/hDokqAYiRmZ2EfArYC7wN+Bx4EjgH9HBqy/jnAX8HEikfXYScCdhZ/Ew8ACwP/BnMzs1x/HuAvwTeD/wEvBnYDLwXeCnfYm1vxSpvF8A/ggcCnj0HuA04F9mNrvIYRekGGXOMM4zCMlDWSrSch5FSNovBIYRlvPLwFHAfWa2Z/Ej77silfkYYAHwDuAZQpnrCMnyw2a2TfEjL5yZ7UvY3+T7u0nAQ8CpwGrg/wHDgUsI8626mHEOJUogYmJm8wgZ8UvAHHc/1t0PJOy4AH5iZg15jvMtwL2EDDv12bbAD4A3gT3c/Uh3P5yw01kDXGlmE3sZb4KQJAwHPuTuB7j7u4Htgf8AJ5rZe/KJtb8Uqbw7AJcB64AD3H1fdz8WmAlcDWxDOAsqC8Uoc4ZxzgC+XvRgi6SIZf424Uz+l8B0d3+3u+8CfA6oBX7YLwXogyKt21XANYR9/3vcfe9o3Z4B3E5Yxy/sx2L0iZm9G/gLobYkX1cDk4D/dffd3f04QjnvAg4Czi5WnEONEoj4nBO9fsndl6Q+dPf/B1wPjAPel8uIzGyUmV1BaLbYhnAGlXIWYUf4bXdflDadfwJfI5x59HbmchhhJ3tPqkkkGscK4BPRv+WyERajvB8CKoFvufsDaeNoJ5ylrQD2MbOpRY69r4pR5k3MrIKQMLYBTxQ31KIpuMxmNoWwrJ8HTnL3trTxfIPQNNBYRmfkxVjOOxP2LY+5++/SxtEKXBr9+5ZiBl0IM5tkZj8FfkvYJl/L8/cGvBN4Dvhy6nN3bwE+Rmg2PqtoAQ8xSiDicwSQJLQ79vT76DXXZoyzgU8BLwIHA3/tMR2AWwqYTtZxuPs/gOXAAWY2LMd4+1MxyttGqFm5t+cXURLxQvTvhL4E2A+KUeZ05xKqyD8JvFpAXP2pGGV+N6Gp73uZ2sHdfQ93nxklyuWgGGVO9VUaG9VGpBsTvb7Rh9j6y2WEJO8RYB/gqTx/fzhhGd/es5+Wuy8G/gVMjWodJU9KIGIQVUWOBJZk6bCU2kh2ynGUrxASiLnuvqDHdzsQdhr/zfC7p6Pv5kbNFNnMjV4XZfneCetSrBthVIaCy+vuX3L3Xdz97gzTaKS7nLF3pCxWmdPGtzNwEfBbd/9FEUMtmiKWeffo9WEzazKzU8zs+2b2XTM7Npf5VSpFLPMThBrKicCNZjbDzBrM7BBCVX8X8K2iBl+Yp4CPAHu7++N9+H1v+65897WSRglEPFJ9FJZl+T71+bhcRubuP3T376RXwUYaCVWer2f4DnfvAFYCDYQOZCWJtx+NpDjl3ZpzCe2w/3T3l3sbuASKVmYzqwFuJHQ0O6P4oRZNsco8M3odQzjAXAecTqh5+T1wZ5nUqkGRyhzVoB0HLCF0iH4WaCH0B6gB3u7utxc9+j5y96+4+08LuMproOy7BiRdxlkkZvZzYF4Og/6e7h7967MM0xq99qXDULraXqYDsCFtWmuyDNPYy3jSxxGn3uKE3MqbkZkdSbh8rotw2V85KGaZLyW0kb+rjKrtMylWmUdErz8htJF/gJBI7Ew4Gz+E0OHwhEKCLZJiLudnCVdqnUPo57GcsO+aAHzOzB5x93JqxijEQNl3DUhKIIpnKmA5DLct3e2QyV6GLbQKNdnjdWvT2Nq0eos3l3GUQi7ztU+xmtk7gJsJHbm+UEb3RChKmc1sf+CzwM/cPVMbezkp1nKui17bgEPdfXX0/z/M7HBCs8AHzOwid3+6z9EWR7GW82jCPS4mEsp8T/R5LXAVcAqhj0XZdKQs0EDZdw1ISiCKxN0PyHXY6J4KAPVZBknt2FoKCqq7JiPbdHKd1rpexlOseAvVW5zQh1jN7KOEM9Eqwo12vtq38PpFwWWO+nXcQKjOHQg90ou1nFPf/SIteQDA3V81s9uAE4EDCclEnIpV5s8Bs4HPpyfB7r7RzD4BzAfmm9n8DP2pBqKBsu8akNQHIh6pyzbHZ/m+t3a7XG0gbEBjMvS4Tl0TPgZo7bkD7WFp9Nrf8RZqDcUpb/pvLgV+RKh5+B93/1IR4y2GYpT5DMJ9AF4HrjKzn6X+6O6E9sXos/nFL0LeirWcU800L2b5/qXodUyW70upWGU+KHq9s+cXUf+Iu6J/dyso2vIxUPZdA5ISiBi4+0pCu+OkLJ205kSvfel13EE48PYAABVQSURBVNOThIPf9hm+M8I60Nt0Uj2Yt7jKIurxPZtwPfWTfQ+zcO6epDjlxcwSZvZDwu1+NwLvd/crihhuURSpzKn2350JZ9zpf6kd76HR/zMKj7owRVzOqe+zXY6bKnvs/UGKWObm6LUjy/epz2vyjbFMZd13RYq5rx1ylEDE58+EncFRGb47Nnr9Y4bv+jKd9HH2ZTpbG8d+hJtX3efua/MPr+iKUV6AbxJuNLMGONzdf12E2PpLQWV294vcPZHpj3BzMgjPwki4+/XFC7sgxVjOf4pe39XzrD66IuXg6N9yqcovRplTly0e2fMLM6sE3hr9+1je0ZWn1Dw7OrpB2ibRjcR2A15y91hPfgYqJRDx+T6hY89XzWy71IdRZ72TCFVqN6X/wMxmR3/53OL6J4S+EOdGt89OjWsPwpUEGwg9zlOfz4imMSJtHH8nXD9+mJl9PG3YbdJ++808YupPBZfXzI4gPOCsA3iHu/+9VMH3UTGW8UBTjDLfRThQzgKuiA6gqTtxfoPwYK073d37uzA5KkaZr41evxh1nE0NW0W4dflOhG09/WZ0A4KZTYnKu6nJyd1fICQRRnj2RWrYRsJtyispn33XgKNOlDFx9wfN7OuEDX+Rmd1NuH77QKAdODHD3fFSN5A5GLgnx+m8aGbnAN8DHoimkyCcaVQRnti5PO0ndxOuKDmZcEtt3L0r6kh4N3CtmX2M0LZ4EOH69OvK5drxYpSXcCMlCLfNPd3MTs8yuf9z90w39SmpIpV5QCnSet1pZh8gHCzPBN5pZv8mHERnEG641KcHkfWHIpX5j2b2FeALwAIze5DQnLobMIWwzh/v7p2lKVVR/ZSw/7yY7m0YwrL9ByFpOoZw47v9CP0f/kQ4mZM+UA1EjNz9XEJtw38Jbcw7EJ4Ut6+7/62I07ma0FTyIKGX9Z6ES7kOS3+2RS/jeBjYm3BP+lnA2widzE6nzG46VEh5o9qd1BMYJ7Jln4D0v7K5+UwxlvFAU6T1+r/ArnQ/5fFIoJpwkN7L3V8sctgFKVKZz4vGcRehD8CRhMsdrwJ2G2zV+e7+PLAXIYnahvAU0lXAecC7o5twSR8kksnebkUgIiIisrn/3965x21VVXn8i2N44eJlVLyhKMKKTAU0k7yh+fGSKVgkKF5AnSZzNC2sqWkcPmrYeIksx8pGhzQFHSV1yFt4rdRSAYUYfiihYKbmJUNNHcX5Y+3DczjveR6e877vI2+wvp/P+znvc559zr48+5y99tprrR0aiCAIgiAIKhMCRBAEQRAElQkBIgiCIAiCyoQAEQRBEARBZUKACIIgCIKgMiFABEEQBEFQmRAggiAIgiCoTESi7EKY2USgPbs97pCi1N2HR2I7XdJlnVm2VmFm4/AQvY9J2iOd6wcsTkl6SXo9nZ8CnAhcImnCB17YFmJmfYC3y3ZSNLNBXSHi5eoihZj+Vzzo2lZ4EKCvSrrazP4emAwcCvTGoyoejIcv3h44QtKMduY7EX8eb5I0qoPVaDdm1h9YknbL7BKY2dN0sH2Dv31CgOhaLMFDrhbZA1gPeBJ/QRZ5q5WFClqLmZ2Jh9/dG/hz7vyWwKXAjtSiY66NfIOaYD0f3/l1Sfp8Ax7K+R18D4f1qW3D/TeNmX0IOAc4G9+qu8sIEEEAIUB0KSRdBVxVPJ+T9id1od0QO4uf4WF5/7q6C7IamVzn/KHA0cBjH2BZuiJHp+O3JH0zO5m0D9nukYdLmpn77pN4SOqlHcj3MmAaviPr6mAbfDv5IOiShAARrFYkvQa8trrLEXRpNk/H4rbam+f+X+k7SYs6mqmkl4CXOnqfIFhTCSPKIAi6OtlEp7g77YoJUMnOtUEQtJjQQKyhmNm++G5zw3BVroAfAz+S1GYHNTPbAd/i92Bga2AZvrQwWdLdFfPeMZf3DvjarYAbgf+Q9GYu7TgKRpRN5rELvj48HOiJG11eA1xcZmxmZgOACbn6vYEvDVwh6b8LaftRYsSZ+/6jwFwASd1K8toPOAvfMnhj3G7lLuACSU/l0k1kZaPZuWYGvl37FHzZCmB3M3sfeEZSv9z16+E7oY4FPoxPCARcB1wmqZJtjJn1Ar4IjAZ2Av4Otzm4Cu83ywvpNwbOBD6T0i9P+U9L+ZcuS6Utlb+I2/b0AP4AzAC+LemPuXT34UbBGfem9vkJbkybv2fWp8dLmtLIyM/M9gFOx/tnH1zLcDe+RKJcuonUMaJMbfVlYBS+9fe7wDy8L19V3A47V5eP4f316+n/9YEFwH8CP8zaOGcwnLEs1X2HejuEmtmBqR6vAFvWeQ7G47/nbyV9PHd+19Qm++FLJ+vi/faXuNHyrLI8C/eeSAOjUzO7GPgK8BNJ4wrfVe7LZjYc+BKwK7AtrsmcBfyXpOtXVd6g44QGYs3kWOA+/IW1GB8sh+L73l9aTGxmh+AD4ueBLXBjtL/i297ONLOmPUPMbBDwKHAKPlAvAJ7HX5YXAncn47COMBx4BDgSH3xewrclnoRvN14s00jgcbx+mwFP4OvaBwE3mNl1ydK/w5jZN4H7gZH48zUXHzBOAh43s8NyyYtGs7PT59dS/Z5M519P5x/J5bMp8ABuPzEUb+On8JfpRcCvko1As+XeHvgN8O10j6dx+4E9gMvxgTGffgDepv+Gb0O/EO9rQ/Df+eFkBJq/ppuZXQHcjAtyb+ODbh/gDOAJM8sLkXNTvbPtluelzwvTcXYu7a/T3wurqOc/4+12NG6YPBcXYo4HHk0DaUOSgDkLmIgPdovxfjgMuAKYkQbEMk4A7sG34l6C/9ZD8O3D87YwC/HnKOOhVL9GQuG9+G+2Kd6+ZRyTjtfk6nNiqs8p6doF6T5b4++Sh5JQ3BLa05fN7FhcWBoJdMef6beBQ4BpSVgJWkwIEGsmw4CpwDaShuKub9kD9U9mtlWWML0Mb8BfoucBm0gaKqkvMAIfaCemQbgZzgU2wQWVPpJ2l/RhYHd8oN8LGNPB+u2Oz4z6SRqcynpG+u4IM8vPrAbibbEB8P1Upo+lmfxhuNfDMfhg0CHM7DN4G/4FGCNp86RV2QJ3Q9wQf7ltB240K2mf3C1OkLSPpNmSPocLRCmp9knnMqYAewIPAgMlDZC0G67x+WVqoysrFH8KLoQ9DPSXtGv63YbjAswJZjY21fNDwP8A2+HCUvY77IIPqE/gL/8bCnmcBfwD8BxwkKStU/v0wYWUzYDpaXaPpNNT+2Q2MqendpiUzp+Q3Tid30fS7fUqaGYHABfgXhynAlul/LfBtSY9cS1ZXZKgOR3XuNwKbCtpZ0mDgJ2B/8WNX/+9zi1Ox4WxLSUNxmfO2bN5WiZ0SZoE5H/vg1P9nq9XtqRZvDZ9bPOMJVfhA3GB7PrcuctxbdOEVK49JA0ABuJCYndcO9kqplChL5vZOsB38PFrtKS+6Znui2tt3gfOSu+2oIWEALFmshAYl8UUSGrRf8EHy274IJ5xNu4/f7WkcyS9k30h6VZqL45mtRC7pOOUvApV0ux0j5vouNvpq8CovLpb0vdxdTv40kHG13A18R2Szsgvn0i6AxifPn6lyoy9Duel45l5Faqk/5N0Pj6g9sYH0naTZulHAC8DI/MGg5KW4mr1ZcAIM9utift9gpqgMELSCjdISffjLqbgs3Twwcnw2f4ISc/m0i/ENVdvAPtmGhczWx93xwQ4Lr8sJukNSafhGpC+uLamFXw9Hb8jacVygaQ38H7wMjDAzPaqdwN8uWYI/oyNlrRC4yFpPq7ZWA6camZblFw/HzhF0l/SNe/hz+ab+CC+ZwfqBzXNwojU5nmOTnncIelP6dzwdHxY0iX5pRdJv6cm3Hykg+UqpZ19eQtc6HwVWGn5UdLVuBZoKrBRK8oc1AgBYs1khqR38yeSYJA9nPmB8oh0nFrnXtNwiX5wXnPRgCyPH5jZ/maWN3S7XNKoos1BO7hX7r1RJAu2lK/fp9Lx8rIbSboZVyVvQM0lsDIp2M9H8MGj3vpr1saH1fm+WUak48zcQLACSS/iavJm8zo8HWeka4v8CJ9dH1lIf23Z75AEiunp46fTcW/8d3lB0r11yjGtQpkrYWY9qA2WbVyl0xr73vgM/OEGt8ra4Gdl6/KS5uHLIt0p7093qGCDlJ7Np9PH3g3yXiVJiJkF9KL2O2W0Wb6QdL2kHrjdTRmZwL1hR8rVgPb05ZdwrdQmwJVmtnPhmi9IOk7S4y0qc5AII8o1k+fqnM+MAdeHFYZgfdO5SWn9voz38L4yEPhjnTQZ5+Ivzr1wO4zXzOwe4Hbg1vyMrQM0W7/eQLYOP7v0itp32+H1ay/ZDG05cFcyeCuSvYQHmFm34kDSjrz2NbNf1UmzQzqWFqRA/3ScV/alpGXUtDtQa6dGbToL11hkabMy92xQ5k3TsZkyV6Uvbkz8Dq49aINUM6BsQFaPzyVjzDK2TceyevyhzjWZwWln2OJcjdsSjCHZBCV1/jB8ee3WkmveMbO9cQ1if3yJJltGgNZNNiv3ZUnvmtk5+DLpeGC8mS3FDZVvw4W0N0vvFHQqIUCsmTTr0paf7QxpIv0qVYKSfmNmQ3G17JHpmqPS3w/MbBpwWh0NQrM0W79euf+XNUiXCR69GqRZFVlbrovPZBuxTsqrvQGKsry2Tn+NaEaNm2lsXm+YqkbWTlXaNCtzD1bdPq1QPWd1fLMDghvU6rFj+mtEWT3eKTmXp41XTzuYii89HG5mPeVeRJn24cai5iTZ7lzAygL0clygnI4v27SKdvVlSd8zsydxT5jhuIB4cvpbZmYXpmXDoIWEALF280bu/80kvdwZN5Xv23CcmXXHZz2fxNWpQ3E3rR64QNFq8gNib+oHrMpeTGUDaNkLvUydm7XlvGRM2EqyvCZIuqQT7pfN1no0mT5rp0bq9mKbZmWeIemIkvStZoUqvoPan6weoyS18fjpCkh60czuwpfvjsRdITMB4qf5tGZ2MG442g03jL0JN4KVpDfT91UFiHpCUKPnpnJfTgazt5vZRvgSzEH4kux2wHlmtkxSG6+zoPMIAWItRtKfzexPeES/QUAbFWKyOj8AX6NdrIJ/eyHtOkA/oK+k+9Pa7v3p7xwzOwm3ph6Zmxm1DEmvmdnz+DLGEErCGptZN2ralyxGQ95+ZD3azrTLbEEyl8sdzax73hg1l1cfYADejvVU2c2Q5TWoXgIzG4LbrixKSxCNyFT6pYZyyRhwBm7fcjzunz8Yb7dryq7BLeeh1qbNlLkf/lv9vo4tRkdYhM+qu+Mq+qeKCVIcg2F4bJB6wsGTuGq/UT2G4X1mkerEwvgAuAYXII4ys9n40sRSfFkxzwR8wJ8iaTxt2bbkXD2y56aeC2uj56bpvpxcZAcA60qak7SZNwM3m9kZuI3LicBxlLitB51HGFEGt6XjF+p8Pxb4BTAHd3NrxJb4i/keMytTR87M/d8pcReaIKvfqXW+H4m78b2L+9GDW3dnlK1jH1lybj4uZG1Izr2wwAW4W9q0wvlsNlycuS2vc/7n6fhZM9usmEmakd2N2ygcXfy+hDvT8dPJJ7/IUXgcj0HJODfL/9iUVzH/bakZx92Vjg/g2oj+ZnZQnXJcicc76Aytykokr4eH0sc2v08alMbh8RPqCsnU6n5iiZdDFkTtAdyQ8hPF7yuSD9xVdWnjFnyJ7BBq2odrSzQv/dKxjT1LEq7HpY/NTDaz56bNM5PskfYvnqd9ffkovH2vS2VcQfKsyYwuP6h3zFpLCBDBhbhb5Vgz+1b+pZjUl9m24D9eld2CpOfwGc46wLV5IcLMelKLa/BgB20gqnARbqB2qJldamYr1Khmdig1//LJmYFncut7Ip2flA2SZrZumuGMK2aSXsyZG+d3zWyFH3667mxqLqPFATLTxGxf5/xWaTkoy+s+fJDaGPi5me2Uy2trfDa2CW7wel2xrCVln4m7UG4E3JgPAGVm++PBpcB978G9TIS70t2cBIYs/UB8UNgQH7BvSXksoxYo6dq8EGFmG5jZZNz49j3gu6sqczvJ+t/XzOy4XP498Sit2+PamEbbU0/FZ807ATcV2moAXt91gTmqGMG1hLyGrtg3GpI0HzfiNiiZ2/BPS5Jm2qdTkoYMgORxdR0e8AqSYfIqeDAddzLfYTa7Vx/chXmTknLeR/W+PAPX8AwCJhee6e1wrQq44XbQQkKAWMtJbl8n4IaJ3wBeNLNHzEMB34m/gGbi8RSa4WTcp3s48LSZzTezOfgLYCweZvfznViFhkhagKsy38KDTb1gZr9N9bsdfzndgBt95jkHnwHuByw1s0dx749Lge9RYk8h3011Mm5LMNXMnjOzR/DIehemZOcm19E8mbAyLbV9FkVwHq6d2Ap4smClfgweMXRPYKGZzTOzx/GoiMPx2eenKqjQR+Nq/gOAJWY2y8wW4QLhxsCVycc+czscibu/DgcWm9kcM5uLRzHcNdVpdGHJ61zcb38L4Bdm9nRq1+fxkNgA/yipJbuPSroND+jVHbjGzJ41s8fwvnk83jdHF12gC/d4G6/7s/gSwdJU9/m4G/FH03cj6t2jQnlfSfcCeCD1jZ0bXVMgW17qCcyW9LuSNOfjhp274M/rHDP7Hf7bjsGXH98D1qujncqXdzY1993JZvZMWj5ZggsiF9W5tFJfTkufx+PPxpeA581sdvoNFqW6PEZN8A1aRAgQASkuw2B8Nv4KPgBshodOPhN/eFdlPZ7dazEe/viHwDO4pfpA3H1tMrBznRdZy5A0nZXrtxspuBTwWUmjVdg3QNIt+Iz4DlyQGIS/0I6XVDcQlKQv42rjW3EV6mBc/XwnHnSpLCDXSfjySTe8rQakey3EwwsvwpeH+mezxKTt2RP4Kv6y3D6V8Tk8bsNukuZUaKNncLuFc/EZ9iBcw/BrYKykUwrpF6S6nY9rIwbilvBZn/l4CgSUv+ZdXFAZgy+L9cJ/i7fwbd33k1QlemZlkmX+gbjB4Hp4X3+VCm2WhO7d8LovwOue2VVcDAyVtKSTijwKb9MNUh79GydfifvxwRvKtQ+kmBdD8YH/RTzex5a4NuFkvK2yEOrNGL+OwYPTZSHKt8G1CEOpaSiKZajcl9PzuX8q9zJccNsGdx+eAOzdhO1P0EG6vf9+RzyagiAIgiBYGwkNRBAEQRAElQkBIgiCIAiCyoQAEQRBEARBZUKACIIgCIKgMiFABEEQBEFQmRAggiAIgiCoTAgQQRAEQRBUJgSIIAiCIAgqEwJEEARBEASVCQEiCIIgCILKhAARBEEQBEFl/h8sbLOzzydeQQAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "For n_clusters = 4 The average silhouette_score is : 0.12347053919571699\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "For n_clusters = 5 The average silhouette_score is : 0.14092516241984757\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "For n_clusters = 6 The average silhouette_score is : 0.1371798939109807\n"
     ]
    },
    {
     "data": {
      "image/png": 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MEdrzPvc9Nwwktx4G3AYG0N9+mCpguGjYb4Te9YaZbcQnwGfhqyh3xbcxur+fceTkhu97XHqsT389+6H5dlJ7ABfQW238HXovHvt6I36d9PfqLznLtcN6IpzeUnziPei5YFslEBvxxTKfcM5Nyes+M3x/dashtk9uwZ0FEBYBvzPslsvCchvZTuH7gX3G8c/w/Y6wuP39YXz/L1zJuR0tf577dstls2uB08LxPIK/aumv7v4cfFHu183sawycqAz0zPR98cWAT5jZRfiT4kANX3JuDeO+FN8Y9We5K1rn3OfxDUBvDIs4TwmHObbvSJxzc/H1cEeb2YfMbA98aUgzvVVL2/IYfod8dzjORnqLgvOz575/wRzIYgAz+xe+wRzA1wdoB5OrshgN/MW2bkiU2x7+Ha7Ht+Gv8B7AZ+3nUMK6247ll4vn6Lx6xT8451Y75/r7t8QW27tzbhT+4FTM9Ptu39vaR3IKWV8DbdPgkzYIqyHC7eItg/Sf2/+PDdtp4Jw7Fl9F8+8+JTSDTbeH842sv4dvMHaemc3G78PT8MvxHApb/9vjtDCGafgr3g58dUtfufVxU7g+LsO37RjopNB3uR5I7/EwN91J+PZDV7P1euzvGPgR/DI9x3wjwYGqDXNy7SDejW9r9Qi+VPRM/LGgv/YPOQHlu3lhQdtBP3LL/DgA59wYYLlz7ukB6vv7nncOIC9Zcf4PBd8DsmZ2Jn77eg3Yzzk3jv6XeS6GL4Tr/av4qt9f9Jl2z/pzzr0bnzA8Z2an4M/BGfo5tgOY2UwziwzwWtrPILmE5+hwehOBcfTWFvRrW40on8QXnYwD/umc+71z7in8gWctvvFIOVyLX1GXOecexi/gw/EbZq4u6fHw/Vbn3A/xdTX5bsS30/iSc+7esP8P0ltKkjsILXDOPRhmnbluH3LOfd/MnsFnd3sDzzjnHsVn5R/At67tK3dQuCosBs21Bi/0r4Sv4jPo+c65B/FX0ZMGG4eZvYpvHzI57JR/sBmDP1A/4Zz7Nb0n4fv6GdVr+NbilzjnHnXO/RmfcLTjG2AV4r/wO8i3w2W+BF+X+SszG+gqqlCLwljm4u9D0tdDA3zO+X0Yz1ucc0/gqzw+BZxuZm2Uvu6KXX5/xJ8oDwSedM79DZ88vkzvASXfE+H78c65n+HXYX7DskKmn9u+73TOncm295Fy+Q7+qvTDzrmH8PM34JVMeED7If5fAE+F8/JL/Eni0u2M4TV80nitc+4+59wD+GW/Er9dlLr+B3OFc+4+/HqoA643/3fjvr6Jn8cfOef+hD+IfwxfFdWfr4XvP3LO3Y3fpjbl/Z6bp4845/5AWExP7zytxZ90dnHO3e98o70X8SfER51zj+O3qfxh+no4HE8M31Ymi982cyffwW7Itwr/j4uHnL+pVi1chy+9uMg5dz9+nxwF/Mm2vtcM9J53LnfO/QBfypBfCrUGn7jdHG5Hf8Ufm/8ZVin1t8xzf6W/O1zvv8K3mxis2qYdfzF/h/P/xvorfpn3d2zfHrfhqwTf45z7C37bjdNbItWvQrLBs/F/HXoN32htMn7nXmj9/P1se4QNahbgD/rz8HVrNwPHWu9fay7B72BT8SeVd/UZx/P4Iru/4q/AovjE5NLw93/i/8+awf8NbBR+oT2IT5ByO+0Z+INJK/7q4fdhHPnFbDnfpLetxm74HfwFYF54xbit+X4NX23xCr7twxLCVuLAIYMMmpvmS/j6y5yL8K2Ju/F/y9mMb+j4lX6mvRGfbf4K3xJ9f/yyO9HM+rta6i/+P+Ab1T6Mz8yT+Gy65DtUmtkyene0y/uUgOVOOrmGeVslEOGB7c34osGd8VcGt+P//gWlr7uilp+ZBfjiw1/gt7+d8Qejt4a/9e1/CX59rsavy98RtjUpYvpX4/dbBzRsax8pl7AE6R34Ovh98PXEN4Q/93ciBd9e50p8ve5h+MZ0p1n4N87tiOFFfDHuvfgLgj3wJ9Q3hW11Slr/23Au/njShE/iPz1AjA/gl9O/8MlkG3CJDfA3ZzP7CX5dteGPk1exZXuD24D/we/3e+MvLh4Cxjnn5oTbzDX4ovG5+BPWufjkOlfy+fnwvd/jT3iSzf2zIHfVmmvb9o9tnBMW4bfn3fDtUaouvAB7M75U+UD8Metr+Aat/flf/LkogV9Hl5B3VW5mj+AbtT+Or1aYjt/H3xr+vtUyN7P/wzfmXB6Ocxm+BGjA0hszux1favYC/uJ6DL4NVDHtEAdkZh34/eW3+H8CduGrqb492HCRINjekiCpFefc1/BXKl82s4trHY9IPufc0fiE8mkz+2XY7f/wyfkxYcO9Ycfplt8ywoyYuzAOB87/X/8d+Aw6gy8qFhlqEvjSj2xY1N6Ivzpby5YlZiJl45w7ChiowTjAn83fjVfKRAnEjqUOX2z9MrAoLOYWGVLM7LfOuU/hW4wvxLeHuAd/h8FNgw0rUoLp9P61vT/rqhXISKEqDBERESlauf5SIyIiIiOIEggREREpmhIIERERKZoSCBERESmaEggREREpmhIIERERKZoSCBERESmaEggREREpmhIIERERKZoSCBERESmaEggREREpmhIIERERKZoSCBERESmaHuc9BC1evPgxYBbQBvy7xuGIiOwodgGagBfmz5+/b62DGe6UQAxNs4DR4WtKjWMREdnRzKp1ACOBEoihqQ0YHY1GaWhoqO6E29oAaGpqqup0i3bvvb2fFy4saVQ7zDyXkeZ5+Btp8wu984w/hkqFKYEYmv4NTGloaMA5V9UJL168GKDq0y3arrv2fg6Ckka1w8xzGWmeh7+RNr/QO8+o6rcq1IhSREREiqYEQkRERIqmBEJERESKpgRCREREiqYEQkRERIqmBEJERESKpgRCREREiqb7QMiOab/9ah2BiMiIpgRCdky9N4wREZEaUBWGiIiIFE0lELKF+PrVjH7iAXhh5Fzht774ov9QlXmOwP4LYebcKkxLRKRylEDIFsb8/W7GPXjXiCqbGt/e7j+kUpWfWCIJLz0LF32p8tMSEakgJRDD2Mknn8yoUaMAmDp1KldfffU2h4l0dxNr3wRkYez4CkdYgn+v6P28y4SSRpVNZ/yHZH1J49mm9k2weQ2kuyo7HRGRKlACMUx1dnYCcMstt2zfCMZNHNoJxK1/7f180J4ljao7vt5/aG6GIAvZvFeQ2fJ7Ntunn8yW34O87n37zXRDqrGkWEVEhgolEMPUkiVLaG9v57zzzqO7u5uLLrqIffbZp9ZhVU8246/0+3v1Obk3dHb6z7E4RKO9r0je52isz/fw91gMogn/e99hItHe7vnj2WP/Wi8dEZGSKYEYpurr63nve9/L29/+dpYuXcr73/9+fvvb3xKPD8NVvnLZ1klCkIV40rc5SIbvqUYYPRai8S1O8u0bN0I0SnJsCyTrel+JvM/J+kF+K6DfurBbIgmRSK2XmIhIyYbh2UQAZs2axYwZM4hEIsyaNYsxY8awcuVKJk+eXOvQChcE0LEJOjuhqxO6OsJX55b9ZTJQl4Km0b0JQzIFY1vD13j/GhN+TzVucaJ//ul/EiSS7HfgQTq5i4gUSAnEMHXbbbfxr3/9i0WLFrF8+XLa2toYP34It2nIl83CupWw6nVfSlCX8if7xlE+Eairhz+92tv/GR+Glgl5yUIrjBpTcDKQzbVLUPIgIlIwJRDD1Gmnncall17KGWecQSQS4aqrrtoxqi+6OuH5Z6ChEabOhmk7w7RdYPxOMH4ytE727zfkJUOnvq928YqIjFA7wBlFtkcymeTLX/5yrcMoThDAqtdgzDjY51A44V2w9yG+FEJERIYUJRBSe0EAa1bA6td9w8edZsDJ58K8A2odmYiIDEAJhNTe2lWwbpWvspgxB449TcmDiMgQpwRCaiebhY3rYM1yGDUajjgR3v1xVVmIiOwAlEBI5QVZ6OryDSTTHb1/y9y8EeobfKPIcRNh/yMKTx7e8paKhiwiIoNTAiGlCQJ/i+buNKTT/r07vJlT7t4N6S5/f4b8Gyo1joKdpsOsXeHQ4/wTKhuaCp/uL39ZuXkSEZFtUgIh/QuC8K6OnXmJQZ9XOg2ZtL89cyLhG0DGE/5Vl4Lmsb03bBrb6v+C2Tqp9++Yk6bB5Om6/4KIyA5ICcRIFASwuQ3WroQ1K/37ulWwZiXNTz/i+1n2IiSW+ZKDXFKQux10frd4Apqa/Y2bRrf4pKF5rL/rYy5RGDfBDyMiIsOGEoiR5pnH4FtX+GqHLZ4f0QnpLurbNpIeMx5m7+YTgrGteYlBXoKQe40a40sfRERkRFECMdJsbgMCIOLbJCTqtvi5c8N6gkgELv4yzHI1CbEgixb1/1lERKpCCcRIM3+Bf5x0Ntvvz88//hjZWIL5Qzl5APjsZ3s/K4EQEak6JRAjUX1qwJ+ydQP/JiIikqM79oiIiEjRlECIiIhI0ZRAiIiISNGUQIiIiEjRlECIiIhI0ZRAiIiISNGUQIiIiEjRlECIiIhI0XQjKdkxvf/9tY5ARGREUwIhO6Ybb6x1BCIiI5oSCNlS/RJo+TlLu8ds9yiytJMNNgIwNvp2xsXOLld0IiIyRCiBKJBzbkUZRhOY2cQyjKdyGh+HhqfYkG0vYSQR4owhHmmlI7CyhSYiIkOHEojCtZZhHEEZxlF5kW7ijCPO+CIGCujkWaKMIhmZQiIyhebIsYyJnlyxMEVEpHaUQBTuyFoHUE0R6ohG6gruvyt4FYiSiuzO1Pi1JCPTKhccwPnn935WewgRkapTAlEgM7u31jEMVengNbpZTSqyOxNjF1U+eQC46abez0ogRESqTveBKCPnXNQ5N805Ny+vW6SWMVVaOlhGmpWkIrsyMXYRjdGDah2SiIhUgRKIMnDO7eecux1YBywFHg+7zwCec859uIbhVUw2aCfN62HJwyU0R4+tdUgiIlIlSiBK5Jw7D3gYeBvQBETCF8DM8PV159z3ahFfpXQHa+hgCcnINEZHj6c5elStQxIRkSpSAlEC59x+wA3h1y8B+wJ/yetlMbAI6Abe45x7d1UDrIBMsJGOYAldvEJdZC7N0TczLnpercMSEZEqUyPK0nwKn4RdYGbfA3DOZXI/mlkb8Dnn3PPAD4DzgB/WItBSBUFAF0vJsIFkZCeSzGBc7GyaI8cTiQzrZh4iItIPlUCUZiGwIpc8DMTMfgi8ji+hqKrVq1ezcOFCnnvuue0eRxAEdLOcLG00ROYzPvoRZsS/x+joCUoeRERGKJVAlGYs8GSB/b4M7F3BWLaSTqe54oorqK+v367hgyAgw2rSLAMS1EVmMz52AaOjbypvoCIissNRCURpVgGzt9WTcy4K7AysrHhEea655hre+c53MmHChKKHzbKJDp4hzQqSkZmMihzJpNinaY4cV/5ARURkh6MEojT3AqOdcx/YRn8fAsYB91U+JO/nP/85LS0tLFiwYLuGz7CGRGQ8TZE3MDn2GWbEv01z9ChVWYiICKAqjFJdA7wd/zfNCcCtQCz3o3NuJ+ADwH8CWeCr1Qrs9ttvJxKJ8PDDD/PMM89wySWXcP311zN+/Laeb+Ef15GITGVM9DQmRD9ENJKqfMDFuvLKWkcgIjKiKYEogZn9wzl3PnAjcGX4AsA5tx5/X4ici8zsb9WK7Uc/+lHP57POOotFixYVkDwATY9Ctp444xgXPXtoJg8AixbVOgIRkRFNVRglMrObgTcAvwXS9N5IahS+1OE+4Fgz+3qtYixUe/ZpiKQhSNAYPZhEpPi2EyIiMjKoBKIMzOxR4ATnXB0wB2gGNgHPhfeCqKlbbrmloP5WZ78PieWQbaQpckiFoxIRkR2ZEogyMrNO4Klax7E9OgJjc/AYxNZDtrHW4YiIyBCnBKIMnHOjgLOBNwEOaMA/WOtp4FfArWaWrl2Eg+sKXmV99i4yrK91KIU78cTez7/8Ze3iEBEZoZRAlMg5dzT+9tQT6H2IFsBOwO7AacB/OudONbMlNQixX53Bi2zI/o7Nwd/oCl6km3V0B1W9TUVpfvWrWkcgIjKiKYEogXNuV+CXQD2wBJ9IPA204RtR7gO8B9gN+KNzbj8zW1GjcLfweua/2JRdTIY1BHQRYzQJJtOtm+TOAAAgAElEQVSVUbtaERHZNiUQpfk0Pnn4AfBeM8v0+f0Xzrkv4u8P8VbgM8BHqhvi1tqyD9EVvEqaZdTjiNKUd4Oo5TWNTUREdgy63CzNMfi2Dh/oJ3kAwMw68O0jNgEnVTG2fq3O/IhlmUV0BM8Qp5VYZJTuLikiIkVTAlGa0cCzYZIwIDPbADwDFHAnp8roDlaxLnsHa7I/oCP4J3FaSTKjVuGIiMgOTlUYpVkCzHHO1YV/4eyXcy4GzAC2/5na26E9+zSbgofZHPydzuAFMqwnHbxOnIkkIhOrGYqIiAwzSiBK8xV8+4dvABcM0t8V+NKHqj3AYWP2fl7LfJ7uYCUZ1pOlgxjNJJhAjNZqhSEiIsOUEogCOeeO76fzGuAB4H3OuT3xz8R4HP8vjEZgLr79w/HAz4HfVyPWbNDO6uz36AyeJ0o9SaaFDSVVYyUiIuWhBKJwvyL3qMqtRYCDwtdAv78NOJkKLvMgCOjkOdZmb6UjWAJkSTJTjSRFRKTslEAU7iUGTiCqKggCsmwkwzq6g7VkWEs6eJ224D46gmfpDlbTzQrqmavkQUREKkIJRIHMbGa1p9nNalZnfkA3a8mwjkyw1n8O1hPQSUB6i1d3sJaADuK0UM+uQ/dR3OVwww21jkBEZERTAjGEdQUvsjzz1a0ShYA0EWJEiBMh0fNKMIkYo0dGW4fzz691BCIiI5oSiCpyzi0ws/sL7T8gC0SI0rhFohAhMTKSBBERGbKUQJTIOTcH+Bj+wVkNbH1zrjj+dteTgGaKWuZBWFXRzy0mKtUaI7YOsg0VGrmIiAwXSiBK4JybDTyKTwxyrRUDtnwqZ75lxYw/QoxYZNT2B7g90lHINBCjubrTFRGRHYoSiNJ8Cn8761eAG4B24FrgN8AvgKnAGcAuwJ/M7NhiRl4XmcVOsc+XNeBteXb5vyDTREPLAVWdbtHmz+/9vHhx7eIQERmhlECU5mggC5xgZk8COOcuAlrM7Kbw+9X4e0gc5Zw7yczuKHTkMcbSHD24AmEPoq0FYOi3sfj732sdgYjIiDbEzxJD3mTgxVzyEHoM2Nc5l4Cep3HmbnM92O2uRUREdhhKIEq3qs/3Z4EEMCfXwcyeA/4N7FvFuERERCpGCURplgNT+nR7Pnyf16f7RqCl4hGJiIhUgRKI0jwETHbOnZ7X7Wn8vzDenOvgnBuFf7DWyuqGJyIiUhlqRFma64EzgR86594KnIt/OuerwNnOuefwT+f8CP7pnA/UKlAREZFyUglECczsQeAK/HI8xczSZpYGPo8vhfgscAdwLP7+ENX9T6aIiEiFKIEokZl9Ad848vK8bjfi/3HxHNCNr9Z4u5k9XJMgRUREykxVGGUQ/o3zyT7dbgJuqk1EIiIilaUSCBERESmaSiAK5Jz7YDnGY2bXlWM8I96dd9Y6AhGREU0JROG+SXmegTmkE4i12bU81P0QTyx7omYxZIMss1OzObLlyIF7OvHE6gUkIiJbUQJRuPuo3EO0h4w/p//MXV13EXl5oAeKVl6UKNPqp/GGMW8gEU3ULA4RERmYEogCmdkRtY6hGJlMhssvv5wXXniBWCzG1VdfzfTp07c5XJo0bUEbme4MrYnWisWXDtK0Z9rZnN1MOpvu6V4XrWNK3RSObjlayYOIyBCmBGKYuvvuuwG49dZbeeSRR7j66qu5/vrrCx5+UnIS45Pjyx5XEAQs2byE9kw7DbEGWuItNMQaSEVTNMQamJScxJ5Ne/K+nd5X9mmLiEj5KIEYpo455hiOOOIIAJYtW0Zra+VKEwqVCTKsSa+hI9vBAc0HsGvjrsyon8HM+pnMTM1kRv0MGmINhY1sp516Py9bVpmARURkQEoghrF4PM4ll1zCH/7wB77xjW/UJIYgCFjbvZbV6dVs6N7AqNgoZtTPYO9Re/PZnT+7/SN+7bXyBSkiIkVTAjHMXXPNNVx88cWcfvrp3HXXXTQ0FHiFXwbrutfxSscrRIgwMTmRWfWz2LNpTw4bcxhHjT2qanGIiEj5KYEYpn7xi1+wfPlyLrjgAlKpFJFIhFgsVrXpr+paxbLOZUyrn8ac1BxOnnAybxjzBsYlxlUtBhERqRwlEMPUG9/4Ri699FLe9a530d3dzWWXXUZdXV1Vpt2V7WJVehUTkhM4bcJpfHDqB0lGk1WZtoiIVIcSiBI45w4BnjSztlrH0ldDQwNf//rXqzrNIAh4pfMVVnatpDXRSmuylWNajlHyICIyDOlZGKX5PvCKc66l1oEMBeu617G+ez17Nu3JOye9k2+5bzGvaV6twxIRkQpQCURppgFmZmtqHchQsLZ7LeOT4zlvp/M4feLptQ5HREQqSCUQpXkVmOCcq17rxCEoE2RY3rmcdd3raIo1sWfTnrUOSUREKkwJRGk+CbQCP3LO7VLrYKopCALauttY2r6UJzY+wcbMRuak5jAnNYfZqdm1Dk9ERCpMVRilORJ4HHg78Hbn3BrgNaBjgP4DMzuoWsGVWxAEbM5uZl16HavTqyECrYlW5jXNY79R+3Fi64kcNPogYpERXSAjIjIiKIEozYf7fB8Xvgaywz3NMwgC1nWv62kgGSXKmPgYZjfMZlrdNBaOXchRY49iZmpmdQP729+qOz0REdmCEojSnFvrACrthY4X6Mh00JJoYXLDZHaq24n9m/dnwZgF7NW0F9FIjWrB5s+vzXRFRARQAlESM/t+rWOolBfaX+DlzpchgAOaD+CMSWdwQPMBzKqfRSQSqXV4IiJSY0ogKsA51zQUby5ViOnR6UyOTmbUqFEARCIR3jr+rbxj4jtqHJmIiAwlSiDKwDk3FfgP4M3AHCACxJ1zOwE/Bq41s1/WMMSCHZo4lH3j+7LXnnsBECVKU7ypxlGJiMhQo79xlsg5dxzwJPBxYFcghk8gAGYBC4BfOOeuqE2ExUtFUjTHm2mONw/d5CES6X2JiEjVKYEogXNuNnA7MBr4GfA24LG8Xp4FbsYnFFc6506odowiIiKVoASiNJcCDcDlZvZOM7sD2Jz70cxWmNl5wCX4JOKDtQlTRESkvJRAlOaNwFrgmm309xVgNXBgxSMSERGpAiUQpZkAPGdmmcF6Cn9/AWiuSlQiIiIVpgSiNOuAGQX2OwXQUztFRGRYUAJRmoeBVufcaYP15Jx7BzAZ+EtVohIREakw3QeiNF8FTgJucs41AD/N/9E5FwfOBr6Ofw7Gt6oeoYiISAWoBKIEZnYfcAX+b5z/C2wkbCjpnHsC38DyJqAR+KqZ/bFGoYqIiJSVEogSmdkXgNOAJfibSCXwf9ncE584vAi818wurlmQIiIiZaYqjDIws58DP3fO7Qzsjv+3xSbgWTP7Z02DExERqQAlECVwzh0OrDezJwDM7Hng+QH6PQaYY2bXVzHEollHN79Y18XYR56pdSiDGv3Le3o+ry8x1pWrNgEwfojPczlVep5TsSgf33U6M5tSFRm/iNSeEojS3APcDywsoN+r8A/aGtIJxGObu3miPU3HstW1DmUbYr0fS4y1qysNQLJrqM9z+VR6nielkhy6eoMSCJFhTAlEgZxzo/H3cuir0Tm3+yCDRvD3ititIoFVQHcAY5NxWusTtQ6lKjau7wZgVPPIOdlVap7XdHazrL2TZDRCcyK27QFEZIelBKJwdcBDwKi8bgGwL/5pnIW4u9xBDSSdTnPZZZfx6quv0tXVxYUXXsjRRx9d8PD1sSip2Mg4AXRF/RM9R8r8QmXmuTOTZUVHF3uMbuSNk8dx1MSWso1bRIYeJRAFMrMVzrlF+Oda5AT0Prp7IAHQBvwD+EBlotvanXfeyZgxY/jv//5v1q5dy9ve9raiEoihrmXVip7Pa1on1DCSkSsIAjoyWTZ1Z9iUybCiI01TPMaeY5r41O4ziOhR6yLDmhKIIpjZ14Cv5b4757LAA2Z2eO2i6t+b3vQmjjvuuJ7vsWF2df39t/fO24l3PzZIn1Ju7ZkML7Z1sKk7QyIapTEepTEeY9fmBhriMQ5qHa3kQWQEUAJRms8CL9U6iP40NjYC0NbWxkc/+lE+/vGP1zgiGS7WdHZTF4uyy6gGdmqoY5emFHOaG5gzqoHZTSmaEjqsiIwE2tNLYGafHex359xu+L8L/NPMstWJqtdrr73Ghz70Ic4880xOPPHEak9ehqlsEJCMRnnr1PFcOHdqrcMRkRpRAlGi8N8ZHwZWmdkNYbedgDvxDSwBljrnzg1vfV0Vq1at4rzzzuOKK67gkEMOqdZkZZjJBAGbuzNs7s6wKZNlc3eG9kyG6Q0pxiZ1+BAZyXQEKIFzbizwCDAbnzDcEP50I7Bf+LkLmAX8xjm3Z3izqYr79re/zYYNG7juuuu47rrrALjpppuor6+vxuRlCAmCgEzgk4HuICATBGzIZMkA7e2dYTf/eybb2093ENCdDUjFfBuHpniMifVJGmIxZjXVc1Dr6FrPmojUkBKI0vwHsAvwb+D7AM65GcDxQBY4BngAuBr4BPBJ4MJqBHb55Zdz+eWXV2NSMgS0d2d4vaOLdNaf/DN5SUA2CIhFIj2veCRCOhMQi0A063+rj0aIRdmin1gkQkM8xozGemY3pZg9qoGdm1LMaqqnfpg1yhWR4imBKM2JQBo41sxeDLudFL4/aGb3ADjnLgPeCxxb9QhlRHi9o4sIML4+0XPyj0UixKMRYkRoTMRojIevWIx1K16nIRph11nTaIzFaIxHaUrEe35vjEdpiMcYV5cgEdUz90Rka0ogSrMzYHnJA8Cb8Pd++E2ug5mlnXPP4x+0JVJ2mSBgTDLOCTu1cuLU8b3JQjxGKhYl2udvlYsXrwdg/i793VxVRGTblECUJoIvgQDAOZcEcveE+FOffkfhqzVEgN62Cdkgv9qBns/ZPt8H+z0gYGwywcGto5k3pqnWsyYiI4ASiNI8B8x2zqXMrB04GmgAVprZX3M9Oefm4EsrltQmTKm1dV1plrV30Z0NehKGLAFRIsSiYZUD9HyORiLEIvRUQ9RFokQjbNGWIZbfTyTChPoku45urPWsisgIoQSiNHcDewPfc879GLgGX31xe64H59yBwHeAKPDrWgQp1RUEAekgIJ0NSGezdGUDlm3uZHpjPQ3xaG+CQIRU2NYgFYvSEIv1fo7HaAifR5KK+996u/d2a4j7fpLRiO7+KCJVpQSiNFcDpwLvAE7HV2msAr4A4Jw7Gvh92P1Z4Eu1CXP4qdbtq4MgIICwyoCeqoN0ENAVJgg+UQjoCj93BwHxSIRENEIyGiERjTI5Vcf8cc18crcZNIQn//pYVCd9EdlhKYEogZmtdM7tD3wKmIev0viymS3L9YJ/kNbPgE+a2draRDoyZMOTfDYIyLDlCT+/rUE2gCy97Qc2d2XIAon1m7bsNxxHBHqqD3JVC/FItCc5aIxHw2TBvyciEcYkE4xNxhlXl2BsMkFLMsHRk1rYqaGu1otJRKQslECUyMxW4u/v0N9vrzjnxptZV5XDGpayQUBnJktHNktHJus/h9+7s74hYe4kH8Wf6KORXJsCtnzHlxBEIxCP+ffRDXU9CULPcERIxiLUR6Ok4jHqo1HqY1FGJ+O09JMkjE3GGZOME9dfH0VkmFMCUWE7avLwwqZ21qe7ax0G4EsJOjK+eiAZnsDrw/YALckEdTF/5R+LREiFVQOpsJ9ULEZdbOvu9WGbgvpYlBf//SzJSIT95u26VT910YiSARGRfiiBKIFz7uxihzGzH1QilnJpikVoiUeZ1NBQ61B6xCIR6sIT+6RUHTulkuz1nNFal2R8IsGYQw9mXDJBYjsbEi5ethRAf38UESmCEojS3Iz/10UhImG/QzqBOK45ycxkjNlz59Q6lB71sSiTU0km1id7SwP23qW3h6DQVSAiIuWiBKI0f2fgBKIBmASMDfv5P2BlleLabolIhHmpOPMnjKl1KCIiMoQpgSiBme2/rX6ccwvwJRX7AwdUOiYREZFqUOuwCjOz+4G3A3OBK2ocjoiISFkogagCM/s7/jbWp9Y6FhERkXJQAlE9GWByrYMQEREpByUQVeCcOxT/KO/Xax2LiIhIOagRZQmccx8c5OcIUAc44Myw2x0VD0pERKQKlECU5psUdh+ICPA88PnKhiMiIlIdSiBKcx+DJxDdwFrgIeB7ZrahKlHleeKJJ7j22mu55ZZbqj1pEREZxpRAlMDMjqh1DIO56aabuPPOO0mlUrUOpfwmqz2qiEgtqRHlMDZ9+nT+53/+p9ZhVMayZb0vERGpOpVAFMg5V5anS5nZ5nKMpxDHHXccr7zySlHDrFqV5b77uvjLX16qUFRDz0svdQDUfJ4zmYCOjizt7Vk2b87Q3p6lvb33vasru0X/n/nMbA48cHSNohWRkU4JROE2lmEcAUN8md9zTxd33dVJELxc61Cqpr3dJxCpVK3nOUIsBrFYpOcVjZL3ecvfb775VSUQIlIzQ/pkNsQU/5zoyoyjorq7AzZtyhIEGcaPT9Y6nKrIPcyzoSFW9LDZbEBbW4aNGzOk01kikQipVJT6+lhRyUDu98FEIhEaGmKkUlGam+NcfPHM4mdWRKRMlEAUblatA6imCROSQzqBOGD5fT2f/zrx8JLGtX59O0EQMGpUgu7uYIBXtt/u2Sw0NsZobU0walScUaNizJyZYsaMFKlUNDzh+5P+1t973xsaotTVRYlEhnyOKSICKIEomJm9WOsYtsfUqVP56U9/Wuswyu6Kv/5Hz+fj3vhXslnfhiCbDchkfMlA7nPf7v67757JBGze3E0mA4nERuLxSN4rSjweIZGIhCUK/nN+P8lklBkzUsyb18QeezSxxx6NjB6dqOGSERGpDiUQ28k5lzKz9kF+PxZYbGZrqhjWDisIAjZuzNDWluk5sWezvSf8vif/fE8/vWmLKoJc9YB/7+0ej/ftHiEeh82bM8RiMHHiGEaNitPc7EsStnz33XPd/PcYDQ0xlRqIyIikBKJIzrlRwGeB9zrn9jCzrf7m4JyLA7cDcefcjcCnzWxTlUMtyaZNGRoaMhWfTnd3wNq1adau7SaZjNDcHO+5su/bdiAa7T3582rvOI44oiVsd+CrA/y7b4fQ+3nL3+rq/PemphjPP/80jY0RDjlkn4rPr4jIcKEEogjOuSnAH4G5+AaRC4Ef9dPrrvjnYCSAjwBvdM4dZWY7xMO0EokImzZl2LRpwAKWsolGI4weHWe33RqZNSvFgQeOZsyYOPX1sZ4Tf/7nXBJAS+84fvjDPUuKYe1a3Q5FRKRYSiAK5JyLAr/APxxrFfBF4Df99WtmTznnWoBzgUX4hOLnzrnDzKyQZ2fUzAEHJFi+PEtLy4SqTC8ej7Dnnk0sWDCWWbNSqg4QEdlBKIEo3LuA+cCzwBFm9tpgPYdVFt90zv0auB84CDgd+EmlAy3F7NlxPvaxOPPn71rrUEREZAhT2W3hzsTfCOpj20oe8pnZ8/hqjAjw7grFJiIiUlVKIAq3L7DWzH67HcP+P/xTOeeXNyQREZHaUAJRuBZg6fYMGLZ7eA4YV86AREREakUJROE2A60lDD8G6CxTLCIiIjWlRpSFWwLMd861FHtzKOfcOGAmvgGmlMN++9U6AhGREU0JROF+BxwAfAL4dJHDfhS/rB8od1Aj1uLFtY5ARGREUxVG4W7AV0F8wjn3tkIHcs69BbgM/w+O71YoNhERkapSAlEgM1uGL0lIAj9xzt3gnHMD9e+c28M59138PzBiwHfN7NHqRCsiIlJZqsIogpl9xzk3B7gYeB/wPufca8BTwGr88mwFdgdyt3KM4G93/cHqRywiIlIZSiCKZGaXOOf+BFwH7AzsFL4CfLKQ75/A58xs+D1Pu9ZuvLH38/nn1y4OEZERSgnEdjCz34clEQuBo/DPuhiD/6vn68C/gLvMzGoX5fZbv349y5cvr3UYg5p4wQU9n5efdFJJ41q9erUfzxCf5/7U19czevToWochIiOQEojtFN4c6p7wNWzce++93HbbbTQ1NdU6lEH9Pu/zWWedVdK4NmzYAEBzc3NJ46mFeDzOlVdeyUEHHVTrUERkhFECIVt47bXXWL58Oc899xx1dXW1DqcgS5YsKWn4rq4uAJLJZDnCKYtsNks6nd5mf3vssQeJRKIKEYmIbEkJhPRr1qxZjB8/vtZhDOzll3s+7r333iWNav369QBVqwrIZrN0dHT0vDo7O+nu7iadTpNOp+nu7iabzZJMJkkkEiQSCeLx+Bafk8kk8XicefPmse+++1YlbhGRfEogRCpo06ZNtLW1bZEwpNNpkskkqVSK+vp6Ghsbt0oU6urqGDNmDGPGjGH06NEDfp46dSqRSN+2uyIilacEQqRCOjo6MDNaWlp6GjvW19eTSqWYPHkyU6ZMYcqUKUyePLknKcglBg0NDUoMRGRIUwIhUgHZbJZVq1aRSqWYOXMm5557LlOmTGHq1KlMnDiReFy7nojs2HQUEymzDRs2sHTpUurr65k5cyannXYap5xySq3DEhEpKyUQw1Q2m2XRokWYGclkki984QvMmDGj1mENW9lsls2bN7NmzRpWr17NzJkzmTdvHu9973vZf//9ax2eiEjZKYEYpv74xz/S1dXFT37yEx5//HG++MUvcv3119c6rGGjvb2dtra2LRpJplIpmpqa2HPPPTnzzDM544wzVFUhIsOWjm7D1OLFi1mwYAEA++yzD0899VSNIyqvRydM2HZPFdDV1cVLL71EW1sbzc3NNDY20traSmNjI7NmzWLOnDkcd9xxDPKcNRGRYUEJxDDV1ta2xd0kY7EY3d3dw+aK+PMHHliR8WYyGbq6ukin03R1dW31ubOzkwkTJrDbbrtx0EEHMXfuXObOncvs2bOpr6+vSEwiIkPR8DibyFaamprYtGlTz/dsNjtskodyCIKAjRs3smbNGjZs2EA6nSYSiWxxA6dkMkkymaS+vp7m5uae+zMcdthhXHDBBUyoUSmIiMhQoDPKMLXffvtx9913c/zxx/P4448zd+7cWoc0JHR1dbFixQpWr15NLBZj3LhxtLS0kEgkGDduHPX19YwbN47W1lbGjRvX88p9Hz9+POPGjav1bIiI1JwSiGHq2GOP5cEHH+Sd73wnQRBw1VVX1TqkmgqCgBUrVvDqq6/S2trKnDlzmDFjBkceeSRBEDB69GiOOOIImpubdQMnEZECKIEYpqLRKJ/73OdqHUbFnJH3pPQfF9Bg8aWXXmLTpk3svvvuLFy4kJNOOol58+YRiURYvHgxUL1nYYiIDAdKIGSHdOazz/Z83lYCkclk2LBhAzNnzuSjH/0oJ554YqXDExEZ9pRAyLAVBAFr167l5ZdfprGxkYaGBvbbb79ahyUiMiwogZBhacOGDbz88ssEQcCsWbPYe++9+cAHPsCUKVNqHZqIyLCgBEKGnba2Np5//nmmTZvG7NmzOeOMMzjuuOOIxWK1Dk1EZNhQAiE7lEwmQzqd3qLbK6+8QjqdJp1O093dTUdHB5MnT+aUU07hwx/+sG7wJCJSAUogZEjq6upi7dq1bNy4sedukLnEIZFIbNFvJBKhsbGRRCLR82pubuaMM85Q8iAiUiFKIGTI6OzsZPXq1axdu5bOzk7GjBnD2LFje+4MmUgkSKVSjB07Fr773Z7hLr74YsaOHdvTf+6VTCZrODciIsObEgipqSAIWL9+PStWrKCtrY2WlhamTZtGa2sr+++/PwceeCCTJk3qSRBSqZS/0VNeAnHmmWfWcA5EREYmJRBSMxs3buTFF18kEokwYcIEnHMsWLCAww8/nH322Ye6urpahygiIgNQAiFVEwQB7e3tbNiwgfXr19Pe3s706dOZO3cuJ5xwAscee6zuBikisoNQAiEVt3HjRlasWMGGDRuIRqOMHj2a8ePHM378eE4//XROPfXU4ksb3v/+ygQrIiIFUQIhFZHNZmlvb+f1119n48aNTJ48malTpzJ58mT22Wcf9tlnH/bbb7/tL3G48cbyBiwiIkVRAiElyWazdHR00N7evsWrq6uLuro6WlpamDt3LqeddhoLFy5k6tSpetqliMgwoARC+vXCCy+wdOnSgvqtq6ujoaGBVCrFuHHjSKVSpFIppkyZwq677sqZZ57JhAkTKhuwiIhUlRII2cIuu+zC9OnTaWpqKqj/SCTCpEmTmD59es9rxowZTJkyRfdhEBEZxpRAyBb2339/5s+fz/z58wsepiZVEuef3/tZ7SFERKpOCYRsJRKJDP12Cjfd1PtZCYSISNVFax2AiIiI7HiUQIiIiEjRlECIiIhI0ZRAiIiISNGUQIiIiEjRlECIiIhI0ZRAiIiISNGUQIiIiEjRdCOpoWkXgM2bN2NmNQmgVtMt2A039H4uU6xDfp4rQPM8/I20+Q3tUusARoJIEAS1jkH6WLx48TpgO59zLSIy4q2fP3/+mFoHMdypBGJoegGYBbQB/65xLCIiO4pdgCb8MVQqTCUQIiIiUjQ1ohQREZGiKYEQERGRoimBEBERkaIpgRAREZGiKYEQERGRoimBEBERkaIpgRAREZGiKYEQERGRoimBEBERkaIpgRAREZGiKYEQERGRoimBEBERkaIpgRAREZGi6XHeNeacOx34D2B3IAM8BHzOzB4tYZxHAn8CzjazH4bdjgEuA/YCksBi4Itm9rsixjsX+CzwBmAc/lHjNwLXmVl2e+OthDLN75uBjwMH4B8R/BrwG+ALZvZK2YMuUTnmuZ9x/gZ4E3Ckmd1TjjjLqUzruRH4FHA6MAvYDDyA3w//VvagS1SmeT4YuBw4FL9tvwzciZ/ntWUPukycc+cA/wssMLMHihhuJ+BK4FhgMvAS8EPgS2bWWYFQRwSVQNSQc24R8BNgD+Bu4EngeODB8OS1PeOcA/wIiOR1Owf4A/5g8SjwMHAY8Fvn3PkFjndv4K/AO4EXgd8C04D/AX6wPbFWStq1IUAAABsxSURBVJnm9z+BXwPHABZ+BrgA+Ltzbtcyh12ScsxzP+O8EJ88DEllWs8t+KT9CmAUfj2/DJwIPOCcO6D8kW+/Ms3zScD9wAnAs/h5rscny48658aXP/LSOecOwR9vih1uKvAIcD6wDrgLaAY+h19uiXLGOZIogagR59x8fEb8IrCbmZ1sZgvxBy6A/3XONRQ5zsOB+/AZdq7bZODbwHpgfzM73syOwx90NgBfd85N2cZ4I/gkoRk4y8zeYGanAHOBfwDvcs6dWkyslVKm+d0d+ALQBrzBzA4xs5OBXYDrgPH4q6AhoRzz3M84ZwP/XfZgy6SM8/xV/JX8rcDOZnaKme0NfBKoA75TkRnYDmXatuPADfhj/6lmdlC4bc8Gfonfxq+o4GxsF+fcKcDv8KUlxboOmAp8xsz2M7PT8PP5R+AI4KPlinOkUQJRO58I3680s1dzHc3sLuBmYCLwjkJG5Jxrcc59DV9tMR5/BZXzEfyB8Ktm9lTedP4KfAl/5bGtK5dj8QfZe3JVIuE4VgIfDL8OlZ2wHPN7FhADvmJmD+eNI42/SlsJHOycm1Hm2LdXOea5h3Muik8Yu4Cnyxtq2ZQ8z8656fh1/Txwjpl15Y3nWnzVQOMQuiIvx3re6/+3d+bxWlVVH/9ihAPgUCgKzghLJAfQVBxxyFlBMycccKhXM01TM6vXeNXUHELLtDSNHMnXKSM1BcdMc8KBkB9KIKgpOaSIAy/C+8fah+dw7nme+wzn4d4L+/v53M+5zzn7nL3XGfZee+2118brlhck3ZG6xqfAueHnDkUWuhHMbE0zux64Hf8m367xfAP2AaYC5yf7Jc0BjsWHjU8qrMBLGVGBaDv2ABbg445Z7gzbaocxTga+C0wHdgIezOQDcFcD+ZS9hqTHgVnAdmbWvcryNpMi5J2LW1YezR4ISsS08LNXPQVsAkXInOZM3ET+HeCtBsrVTIqQ+QB8qO9XeePgkraQtEFQlNsDRcic+CqtFqwRaXqE7Xt1lK1ZnIcrec8AWwOTazx/d/wZ/ynrpyVpBvAcsE6wOkZqJCoQbUAwRa4CvFHGYSn5SDau8pKv4wrEAEmPZY5thFcaL+ecNyUcGxCGKcoxIGwnljku/F1q048wyNCwvJJ+ImlTSeNz8uhKSc42d6QsSubU9TYBRgK3S7q5wKIWRoEyDwrbp8ysm5kdZ2ZXmdkvzWxYNfdrcVGgzP/ALZS9gRvMrI+ZrWBmu+Cm/vnAzwstfGNMBo4CtpL0Uh3nt1Z31VrXRlJEBaJtSHwU/lXmeLK/ZzUXk/RbSb9Im2ADXXGT57s5x5A0D3gHWAF3IFss5W0iq1CMvJU4Ex+HfVrSzNYSLwYKk9nMugA34I5mJxRf1MIoSuYNwrYH3sBcAxyPW17uBB5oJ1Y1KEjmYEE7EHgDd4h+FZiD+wN0AfaU9KfCS18nki6UdH0Ds7w6St3VIYnTOAvCzG4CNq8i6Z2UPPo/LpPm07Ctx2EozbKt5APwSSqvD8uk6drKddLXaEtaKydUJ28uZrYXPn1uPj7trz1QpMzn4mPk+7cjs30eRcm8Utj+Dh8jPxRXJDbBe+O74A6HhzVS2IIo8jm/is/UOg3385iF1129gDPM7BlJ7WkYoxE6St3VIYkKRHGsA1gV6dagNA65oJW0jZpQF2S2lfKolFdr5a3mGouDau5rXWU1s72B23BHrh+0o5gIhchsZtsCpwM3SsobY29PFPWclwvbucCukv4Tfj9uZrvjwwKHmtlISVPqLm0xFPWcv4zHuOiNy/xw2L8scAVwHO5j0W4cKRuko9RdHZKoQBSEpO2qTRtiKgAsXyZJUrHNaahQJUtGuXyqzeujVq5TVHkbpbVyQh1lNbNj8J5oZzzQzs/qK15TaFjm4Nfxe9yc2xE80ot6zsmxm1PKAwCS3jKzu4HhwI64MtGWFCXzGcCGwPfTSrCkz8zs28D2wPZmtn2OP1VHpKPUXR2S6APRNiTTNlcvc7y1cbtq+QT/gHrkeFwnc8J7AJ9mK9AMb4Zts8vbKB9SjLzpc84FrsUtD6dK+kmB5S2CImQ+AY8D8C5whZndmPxRckL7Udi3ffEi1ExRzzkZpple5vhrYdujzPHFSVEyDwnbB7IHgn/EuPBzYEOlbT90lLqrQxIViDZA0jv4uOOaZZy0+odtPV7HWSbhjV+/nGOGvwOt5ZN4MLeYZRE8vjfE51NPqr+YjSNpAcXIi5l1MrPf4uF+PwMOkXRZgcUthIJkTsZ/N8F73Om/pOLdNfzu03ipG6PA55wcLzcdN5G9zf1BCpR55bCdV+Z4sr9LrWVsp5StuwJF1rVLHVGBaDvuwyuDfXOODQvbe3KO1ZNP+pr15FPpGtvgwav+Kml27cUrnCLkBbgUDzTzIbC7pFsLKFuzaEhmSSMldcr7w4OTga+F0UnS6OKK3RBFPOd7w3b/bK8+zEjZKfxsL6b8ImROpi3ulT1gZl8Adg4/X6i5dO2T5J7tFwKkLSQEEhsIvCapTTs/HZWoQLQdV+GOPT8zs/WSncFZbwRuUrslfYKZbRj+aglx/TvcF+LMED47udYW+EyCT3CP82R/n5DHSqlrPILPH/+amX0zlXbV1LmX1lCmZtKwvGa2B77A2Txgb0mPLK7C10kRz7ijUYTM4/CGsi9wWWhAk0icl+ALaz0gSc0WpkqKkPnqsP1RcJxN0nbGQ5dvjH/r6WB0HQIzWzvIu3DISdI0XIkwfO2LJG1XPEz5F2g/dVeHIzpRthGSnjSzi/EPf6KZjcfnb+8I/B8wPCc6XhJAZifg4SrzmW5mpwG/Ap4I+XTCexqd8RU7Z6VOGY/PKDkaD6mNpPnBkXA8cLWZHYuPLQ7B56df017mjhchLx5ICTxs7vFmdnyZ7H4qKS+oz2KlIJk7FAW915+b2aF4Y3kisI+ZTcAb0T54wKW6FiJrBgXJfI+ZXQj8AHjMzJ7Eh1MHAmvj7/xBkj5fPFIVyvV4/fk/lL5h8Gf7OK40DcUD322D+z/ci3fmInUQLRBtiKQzcWvDy/gY80b4SnGDJT1UYD5X4kMlT+Je1l/Fp3J9Lb22RSvXeArYCo9J3xfYDXcyO552FnSoEXmDdSdZgbE3LX0C0n/tJvhMEc+4o1HQe/0ysBmlVR73Ar6IN9JbSppecLEboiCZzwrXGIf7AOyFT3e8Ahi4pJnzJf0T2BJXolbFVyF9HzgLOCAE4YrUQacFC1oLRRCJRCKRSCSyKNECEYlEIpFIpGaiAhGJRCKRSKRmogIRiUQikUikZqICEYlEIpFIpGaiAhGJRCKRSKRmogIRiUQikUikZqICEYlEIpFIpGZiJMp2hJmNBOpZ7XG9EKXuYTwS20mSriiybM3CzEbgIXqflbRF2LcuMC0k6S7po7B/NHAUcKmk0xd7YZuImfUEPstbSdHM+reHiJdtRQgx/d940LU18CBA35d0vZl9GRgF7AGsiEdV3A0PX7wOsK+ksXXmOxL/Hm+XdGCDYtSNmfUBZoTVMtsFZjadBu9vpOMTFYj2xQw85GqWLYBlgVfwCjLLp80sVKS5mNkpePjdbYH/pPavDlwOrE8pOubSyA8pKdaT8JVfZ4Tft+KhnOfiazgsR2kZ7g6NmX0ROBs4A1+qu90oEJEIRAWiXSHpOuC67P6Utn9+O1oNsSjuxMPyftLWBWlDRpXZvwdwEPDsYixLe+SgsP2ppB8nO4P1IVk9cm9J41LHdsFDUs9sIN8rgDH4iqxtQW98OflIpF0SFYhImyLpA+CDti5HpF2zathml9VeNfX/IsckTW00U0nvAO80ep1IZEklOlFGIpH2TtLRya5Ou7ADlLNybSQSaTLRArGEYmbb46vNDcZNuQKuAX4jqcUKama2Hr7E725AL2A2PrQwStL4GvNeP5X3evjYrYDbgF9J+jiVdgQZJ8oq89gYHx8eAnTDnS5vAC7JczYzs77A6Sn55uBDA1dL+t9M2nXJceJMHf8K8BKApE45ee0AnIovGbwy7rdyP3CBpFdT6UayqNPsS2YGvlz7aHzYCmBzM1sAvCZp3dT5y+IroQ4HNsQ7BAJuBq6QVJNvjJl1B74NHAxsAHwB9zm4Dn9v5mfSrwycAhwQ0s8P+Y8J+ecOS4Ullb+N+/Z0Bd4AxgIXSvpXKt3DuFNwwkPh/vwed6ZNXzN5p4+WNLqSk5+ZbQechL+fPXErw3h8iESpdCMp40QZ7tX3gAPxpb/nARPxd/m67HLYKVm+ir+vZ4X/lwMmA78Ffp3c45TDcMLsIPt65VYINbOdgxzvAauX+Q6Oxp/nU5K2Su3fJNyTHfChk874e/sY7rT8XF6emWuPpILTqZldApwG/F7SiMyxmt9lMxsCfBfYBFgTt2Q+B/xO0h9aK2+kcaIFYsnkMOBhvMKahjeWg/B17y/PJjaz3fEG8VvAargz2if4srfjzKzqmSFm1h94BjgOb6gnA2/hleVFwPjgHNYIQ4Cngf3wxucdfFni8/HlxrNlGga8gMvXA3gRH9feFbjVzG4Onv4NY2Y/Bh4BhuHf10t4g3EM8IKZ7ZlKnnWanRB+fxDkeyXs/yjsfzqVz5eAR3H/iUH4PX4Vr0wvBv4afASqLfc6wN+BC8M1puP+A1sAV+INYzp9X/ye/gRfhn4K/q4NxJ/zk8EJNH1OJzO7GrgLV+Q+wxvdnsDJwItmllYiXwpyJ8stTwy/p4TthFTax8Pf263I+QP8vh2EOya/hCsxRwDPhIa0IkHBfA4YiTd20/D3cDBwNTA2NIh5HAk8iC/FPQN/1gPx5cPTvjBT8O8o4YkgXyWl8CH8mX0Jv795HBq2N6TkOSrIc1w4d3K4Ti+8LnkiKMVNoZ532cwOw5WlYUAX/Jv+DNgdGBOUlUiTiQrEkslg4Bagt6RB+NS35IP6jpmtkSQMleGteCV6LrCKpEGS1gKG4g3tyNAIV8M5wCq4otJT0uaSNgQ2xxv6rYFDGpRvc7xntK6kzUJZTw7H9jWzdM+qH34vlgd+Gcr01dCT3xOf9XAo3hg0hJkdgN/DD4FDJK0arCqr4dMQV8Art7XBnWYlbZe6xJGStpM0QdI3cIUoJNV2YV/CaGBL4G9AP0l9JW2KW3weC/fo2hqKPxpXwp4E+kjaJDy3IbgCc6SZDQ9yfhH4E7A2riwlz2FjvEF9Ea/8b83kcSrwTeBNYFdJvcL96YkrKT2AO0LvHkknhfuT+MicFO7D+WH/kcmFw/7tJN1bTkAz2wm4AJ/FcQKwRsi/N2416YZbycoSFM07cIvL3cCakgZI6g8MAF7GnV9/VuYSJ+HK2OqSNsN7zsm3eWKidEk6H0g/792CfG+VK1uwLN4Ufrb4xsJU4Z1xhewPqX1X4tam00O5tpDUF+iHK4ldcOtksxhNDe+ymS0D/Bxvvw6WtFb4ptfCrTYLgFND3RZpIlGBWDKZAoxIYgoEs+iP8MayE96IJ5yBz5+/XtLZkuYmByTdTaniqNYKsXHYjk6bUCVNCNe4ncannb4PHJg2d0v6JW5uBx86SDgTNxPfJ+nk9PCJpPuAo8PP02rpsZfh3LA9JW1ClfR/ks7DG9QV8Ya0bkIvfV/gXWBY2mFQ0kzcrD4bGGpmm1ZxvW0oKQpDJS2cBinpEXyKKXgvHbxxMry3P1TS66n0U3DL1Rxg+8TiYmbL4dMxAQ5PD4tJmiPpRNwCshZurWkGZ4XtzyUtHC6QNAd/D94F+prZ1uUugA/XDMS/sYMlLbR4SJqEWzbmAyeY2Wo5508CjpP0YTjnc/zb/BhvxLdsQD4oWRaGhnue5qCQx32S/h32DQnbJyVdmh56kfRPSsrNRg2WK5c63+XVcKXzfWCR4UdJ1+NWoFuAlZpR5kiJqEAsmYyVNC+9IygGyceZbij3DdtbylxrDK7Rb5a2XFQgyeMqM9vRzNKObldKOjDrc1AHD8lnb2RJgi2l5dsrbK/Mu5Cku3BT8vKUpgTWTAj2sxHeeJQbf03u8Z5ljlfL0LAdl2oIFiJpFm4mrzavvcN2bDg3y2/w3vV+mfQ35T2HoFDcEX7uE7bb4s/lbUkPlSnHmBrKXBNm1pVSY9liqnQYY98W74E/WeFSyT24M29cXtJEfFikC/nv033K+CCFb3N6+LlihbxbJSgxzwHdKT2nhBbDF5L+IKkr7neTR6Jwr9BIuSpQz7v8Dm6VWgW41swGZM45XtLhkl5oUpkjgehEuWTyZpn9iTPgcrDQEWytsO/8MH6fx+f4u9IP+FeZNAnn4BXn1rgfxgdm9iBwL3B3usfWANXKtyKQjMNPyD2jdGxtXL56SXpo84H7g8NblqQS7mtmnbINSR15bW9mfy2TZr2wzS1Ihj5hOzHvoKTZlKw7ULpPle7pc7jFIkmblLlbhTJ/KWyrKXOtrIU7E8/FrQctkEoOlBVI5PhGcMbMY82wzZPjjTLnJA6nRfjiXI/7EhxC8AkK5vzB+PDa3TnnzDWzbXELYh98iCYZRoDmdTZrfpclzTOzs/Fh0qOBo81sJu6ofA+upH2ce6VIoUQFYsmk2ilt6d7OwCrSt2oSlPR3MxuEm2X3C+fsH/6uMrMxwIllLAjVUq183VP/z66QLlE8uldI0xrJveyM92QrsUzIq94ARUlevcJfJaox4yYWm48qpiqR3Kda7mlS5q60fn+aYXpOZPy4AcUNSnKsH/4qkSfH3Jx9aVrM6qmDW/Chh73NrJt8FlFifbgtazkJvjsXsKgCPR9XKO/Ah22aRV3vsqRfmNkr+EyYIbiCeGz4m21mF4Vhw0gTiQrE0s2c1P89JL1bxEXl6zYcbmZd8F7PLrg5dRA+TasrrlA0m3SDuCLlA1YlFVNeA5pXoeeZc5N7OTE4EzaTJK/TJV1awPWS3lrXKtMn96mSuT17T5Myj5W0b076ZrPQFN+g9SeR40BJLWb8tAckzTKz+/Hhu/3wqZCJAnFjOq2Z7YY7jnbCHWNvx51gJenjcLxWBaKcElTpu6n5XQ4Os/ea2Ur4EMyu+JDs2sC5ZjZbUotZZ5HiiArEUoyk/5jZv/GIfv2BFibE4HW+Ez5GO02Z+e2ZtMsA6wJrSXokjO0+Ev7ONrNjcG/qYameUdOQ9IGZvYUPYwwkJ6yxmXWiZH1JYjSk/UeWpWVPO88XJJlyub6ZdUk7o6by6gn0xe9jOVN2NSR59S+XwMwG4r4rU8MQRCUSk36uo1xwBhyL+7ccgc/P3wy/bzfknYN7zkPpnlZT5nXxZ/XPMr4YjTAV71V3wU30r2YThDgGg/HYIOWUg1dw034lOQbj78xUlYmFsRi4AVcg9jezCfjQxEx8WDHN6XiDP1rS0bRkzZx95Ui+m3JTWCt9N1W/y2GKbF+gs6TngzXzLuAuMzsZ93E5CjicnGnrkeKITpSRe8L2+DLHhwMPAM/j09wqsTpeMT9oZnnmyHGp/wuJu1AFiXwnlDk+DJ/GNw+fRw/u3Z2QN469X86+SbiStQKp6YUZLsCnpY3J7E96w9me2/wy+/8ctl83sx7ZTEKPbDzuo3BQ9ngOfwnbfcKc/Cz743E8+gfn3CT/w0Je2fzXpOQcd3/YPopbI/qY2a5lynEtHu+gCKvKIoRZD0+Eny2eT2iURuDxE8oqyZRkPypnlkMSRO1R3JFym+zxGkkH7qp1aOOP+BDZ7pSsDzflWF7WDdsW/ixBuR4RflbT2Uy+mxbfTPBH2jG7n/re5f3x+3tzKONCwsyaxOlycdUxSy1RgYhchE+rHG5mP01XisF8mSwLfk1rfguS3sR7OMsAN6WVCDPrRimuwd8a9IGohYtxB7U9zOxyM1toRjWzPSjNLx+VOHiGaX0vhv3nJ42kmXUOPZwR2UxCxZxM47zMzBbOww/nnUFpymi2gUwsMeuU2b9GGA5K8noYb6RWBv5sZhuk8uqF98ZWwR1eb86WNafs4/AplCsBt6UDQJnZjnhwKfC59+CzTIRPpbsrKAxJ+n54o7AC3mD/MeQxm1KgpJvSSoSZLW9mo3Dn28+By1orc50k79+ZZnZ4Kv9ueJTWdXBrTKXlqW/Be80bALdn7lVfXN7OwPOqMYJrDmkLXfbdqEiwfNyG+6Ak04ZvzEmaWJ+OCxYyAMKMq5vxgFcQHJNb4W9hu4H5CrPJtXriU5hXySnnw9T+Lo/FLTz9gVGZb3pt3KoC7rgdaSJRgVjKCdO+jsQdE38IzDKzp81DAf8Fr4DG4fEUquFYfE73EGC6mU0ys+fxCmA4Hmb3WwWKUBFJk3FT5qd4sKm3zeypIN+9eOV0K+70meZsvAe4AzDTzJ7BZ39cDvyCHH8K+Wqqo3BfglvM7E0zexqPrHdRSHZOmDqaJlFWxoR7n0QRnIhbJ9YAXsl4qR+KRwzdEphiZhPN7AU8KuIQvPe5Vw0m9INxM/9OwAwze87MpuIK4crAtWGOfTLtcBg+/XUIMM3Mnjezl/AohpsEmQ7ODHmdg8/bXw14wMymh/v6Fh4SG+C/JDVl9VFJ9+ABvboAN5jZ62b2LP5uHoG/mwdnp0BnrvEZLvvr+BDBzCD7JHwa8VfCsaHlrlFDed8L1wJ4NLwbAyqdkyEZXuoGTJD0j5w05+GOnRvj3+vzZvYP/Nkegg8/fg4sW8Y6lS7vBErTd0eZ2Wth+GQGrohcXObUmt7lMPR5BP5tfBd4y8wmhGcwNcjyLCXFN9IkogIRIcRl2Azvjb+HNwA98NDJp+Afb2ve48m1puHhj38NvIZ7qvfDp6+NAgaUqciahqQ7WFS+TQnBpYCvSzpYmXUDJP0R7xHfhysS/fEK7QhJZQNBSfoebja+Gzehboabn/+CB13KC8h1DD580gm/V33Dtabg4YWn4sNDfZJeYrD2bAl8H68s1wllfBOP27CppOdruEev4X4L5+A97P64heFxYLik4zLpJwfZzsOtEf1wT/jkndkqBAJKnzMPV1QOwYfFuuPP4lN8WfcdJNUSPbNmgmf+zrjD4LL4u/4+NdyzoHRviss+GZc98au4BBgkaUZBRT4Qv6fLhzz6VE6+CI/gjTfkWx8IMS8G4Q3/LDzex+q4NeFY/F4lIdSrcX49BA9Ol4Qo741bEQZRslBky1Dzuxy+zx1DuWfjiltvfPrw6cC2Vfj+RBqk04IFjcxoikQikUgksjQSLRCRSCQSiURqJioQkUgkEolEaiYqEJFIJBKJRGomKhCRSCQSiURqJioQkUgkEolEaiYqEJFIJBKJRGomKhCRSCQSiURqJioQkUgkEolEaiYqEJFIJBKJRGomKhCRSCQSiURqJioQkUgkEolEaub/AWXoYsKkKAiUAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "For n_clusters = 7 The average silhouette_score is : 0.1161092456616906\n"
     ]
    },
    {
     "data": {
      "image/png": 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M69G73bDWvotLgE/AVVFuhWtj9FCGaaSkxu9/Xnqq33A9x6F17aS2AT5Db7XxL+j98djf+3DbJNNnoORsP1xi8vAAw/QYrATiXVyxzJeNMVPS+s8MvpdtMsbmSa24EwCCIuCPB/1SWVhqJ5scfO/abxovBN+3BMXtnwri+3OwkVMHWvoy9++XymbXAUcH03kc96slU939Sbii3J9Ya39M9kQl2zvTd8QVAz5jrT0bd1HM1vAl5cYg7vNwjVH/mPpFa4z5Dq4B6NVBEedRwTgH95+IMWY+rh7uQGvtmdbabXClIaPorVoazFO4A/ITwTTr6C0KTs+e+9+Cmc1iAGvtS7gGcwA/ydIOJlVl0Qj8027akCi1P7wSbMcP437hPYzL2k+igG23GesvFc+Bae1b7jbGvGOMyXS3RJ/93RjTgDs55TP//vv3YMdISi7bK9s+DS5pg6AaItgvPjDA8Knj/+CgnQbGmINxVTSv9CuhGWi+PYxrZH0trsHYKdbaObhjeBpuPZ5Ebtt/cxwdxDAN94u3A1fd0l9qe1wTbI/zcW07sl0U+q/XXek9H6bmOxHXfugSNt2Omc6Bn8et05OsaySYrdowJdUO4hO4tlaP40pFj8OdCzK1f0jxKd7DC3PaDzJIrfNDAIwxTcBKY8zzWdqd9b/u7EJasmLcDQXXAklr7XG4/WsFsJMxZiyZ13kqhouC7f4jXNXvX/rNu2f7GWM+gUsYXrXWHoW7BifIcG4HsNbOtNZ6WT5LM40TVBlPBf6Tayn0YI0on8UVZYwFXjDG3GWMeQ534lmHazxSDN/HbajzjTGP4Vbwe3E7Zqou6eng+0ZjzG9wDT3SXY1rp3GZMeaBYPjP0ltKkjoJ7WOMeSTIOlP9zjTG/J+19r+47G574L/GmCdwWfnpuEYl/aVOChcHxaCp1uC53kq4DJdBLzTGPIL7FT1xoGlYa5fh2odMCnqln2yacCfqZ4wxf6P3IvxghkmtwLUWP9cY84Qx5l5cwtGOa4CVi//BHSA/D9b5i7i6zNuttdl+ReVqURDLfNxzSPp7NEt3yl1BPB8wxjyDq/I4B/iotbaFwrddvuvvH7gL5a7As8aYf+GSxzfpPaGkeyb4PtwY80fcNkxvWJbL/FP7963GmOMY/Bgpll/gfpV+zhjzKG75spZqBSe03+DuAnguWJbbcBeJ8zYzhhW4pPH7xpgHjTEP49b9atx+Uej2H8iFxpgHcduhCrjSutuN+7sct4y/Ncbcg/vVehauKiqTHwffvzXG3Ifbp9LrqVPL9HljzN0ExfT0LtM63EVnrjHmoaDR3uu4C+ITxpincftU+jj9PRZMJ4xrK5PE7Zupi+9AD+Rbg7vj4lHjHqpVCVfgSi/ONsY8hDsmG4B77KbPmoHe684FxpjrcaUM6aVQa3GJ23XBfvQk7tz8QlCllGmdp26lvy/Y7rfj2k0MVDLQjvsxf4txd2M9iVvnmc7tm2t68P3fAYdKk0s2eCLu1qEVuEZrk3AH9742w+1nmyNoULMP7qS/AFe3dh1wsO29teZc3AE2FXdROb7fNF7DFdk9ifsFFsIlJucF/38B+D/cxpyO22luwhX1jaX3oD0WdzIZh/v1cFcQR3oxW8rl9LbV2Bp3gC8BFgS/GAdb7hW4aou3cG0fXiRoJQ7sMcCoqXm+gau/TDkb15o4jrvNsQ3X0PGHGeb9Lq7O8nZcS/SdcevuCGttpl9LmeK/G9fo5jFcZh7DZdMFP6HSWruc3gPtgn4lYKmLTqph3iYJRHBiOwxXNDgb98vgZtztX1D4tstr/VlrfVzx4V9w+99s3Mnog8H/+g//Im57voPblncStDXJY/6X4I5bA9QOdowUS1CC9DFcHfwOuHriq4J/Z7qQgmuv801c8eleuMZ0R9vgNs7NiOF1XDHuA7gfBNvgLqiHBm11Ctr+gzgZdz6pxyXxX88S48O49fQSLplsAc61WW5zttb+HretWnDnyYvp297gJuB/ccf99rgfF48CY40x84J95lJc0fh83AXrZFxynSr5/E7wnfH8E1xkU3cWpIrpU23b/jPINWERbn/eGtcepeyCH2CH4UqVd8Wds36Ma9Caya9w16IobhudS28bPqy1j+MatT+Nq1aYjjvGPxj8f5N1bq39Ha4x58pgmstxJUBZS2+stTfjSs2W4H5cN+HaQOXTDnEwqbYRubbJwvP9zS0JkkoxxvwY90vlB9bar1Q6HpF0xpgDcQnl89ba24J+v8Ml5wcFDfdGHKNHfssWZot5CuNIYNz9+h/DZdAJXFGxyFATxZV+JIOi9jrcr7N19C0xEykaY8wBQLYG4wD3Wvc0XikSJRDDSxWu2PpNYFFQzC0ypFhr7zDGnINrMb4vrj3E/bgnDA56b7nIZppO763tmawvVyBbClVhiIiISN6KdUuNiIiIbEGUQIiIiEjelECIiIhI3pRAiIiISN6UQIiIiEjelECIiIhI3pRAiIiISN6UQIiIiEjelECIiIhI3pRAiIiISN6UQIiIiEjelECIiIhI3pRAiIiISN70Ou8haPHixU8Bs4AW4JUKhyMiMlzMBeqBJQsXLtyx0sGMdEoghqZZQGPwmVLhWEREhptZlQ5gS6AEYmhqARpDoRC1tbXlnXFLCwD19fVlnW9GDzzQ273vviWbzZBa5jLRMo98W9ryQu8y486hUmJKIIamV4AptbW1GGPKOuPFixcDlH2+GW21VW+375dsNkNqmctEyzzybWnLC73LjKp+y0KNKEVERCRvSiBEREQkb0ogREREJG9KIERERCRvSiBEREQkb0ogREREJG9KIERERCRveg6EDF077VTpCEREJAslECPYVVddxb333kt3dzfHHnssxxxzTKVDyk/vQ2FERGSIUQIxQj3++OM89dRT3HDDDbS3t3PttdfmNJ7X2UnNiy+SbNlyngRbZy1AbsscieAtXIhXXV3iqEREhjYlECPUww8/zPz58znzzDNpaWnhnHPOyWm8xrvvZvRtt5GIxUoc4dAxIUgcErm8M6CqitDxxxM+7bQSRyUiMrQpgRih1q1bx/Lly/n5z3/OW2+9xRlnnMEdd9yB53kDjhduaSGybh1+SwuMHVumaCsr3NEBgB+PZx/I92HtWhg3DjZuLFNkIiJDlxKIEaqpqYnZs2cTi8WYPXs2VVVVrF27lrG5JgWzZuE1N5c2yEF4r7/e0+3PmFGy+XStX4/X3U11TQ10d2f+dHVBfT3e9Ol4e+9dslhERIYLJRAj1MKFC7n++us5+eSTWbVqFe3t7TQ1NVU6rLxEnn22p7u7iAmE39YGb77pkoLubmo6OvAjEairg2jUfWIxvNra3r+jURgzhvBXvkJo552LFouIyHClBGKE2n///XnyySc5+uij8X2fCy+8kHA4XOmwysr3fUgkXAlCPN77vXo1NDXhjR4N0Sjt7e0kRo2ieu5c16+pCcaMcd2jR/d8M3YsXjRa6cUSERkSlECMYLk2nBxOfN93SUBXV08JQp/koP93OAyRiCtBSH03NuLNmEHksstg/Hhef/ll8DwmLFxY6cUTERk2lEDIkOL7PnR2uuQgvb+1vf1DIYjF3CeoXvCqqqC+vjdRCJIFr64OGhtd0hB809SEt+OOePPmuYkP0rBUREQ2pQRChgy/tRWWLnWlB/2es+BNnOgShqoqvIYGaG52jTybmlxy0NQEo0a55CCVKIwapSoHEZESUQIhFeMnk9DaCu++6z5tbXjTprk7QGbMgO9+t2fY8I9+5BKGceNc40YREakoJRBSdv6GDbB8uUseamqgoQFv/HhobCR01FGEjj/ePekxLYEIqX2CiMiQogRCMlu+HL+7u/jTjcfhnXfwZs50VQxz5uBtsw3eggXuM8xuNRUR2VIpgZA+krEYiYYG17AwmSz+DEIhvK23JvSJTxA66ijXnkFERIYdJRDSx8YDDiBZV8eoadNKNg9v6631MCYRkWFOCYT0kWhqYsMhhxAeCm0OPvCBSkcgIiJZKIGQoeu22yodgYiIZBGqdAAiIiIy/CiBEBERkbwpgRAREZG8qQ2EDF2LFmXuFhGRilMCIUPXt77V260EQkRkSFEVhoiIiORNCYSIiIjkTQmEiIiI5E1tIKQfn0hoJcn4a5UOpE92W8p4ouG3gnmMLtk8NuFVEQpPKd/8RESKTAnECHbkkUfSELysaurUqVxyySWDjlNffQ9NtX+mY31dqcMbVG1ad8f6T5VsPhMaNwbzGFWyefTneVGidV8mUn1w2eYpIlJMSiBGqM7OTgB+/etf5zVeJLSGSGg1yfgSPK928BHKxE+UsgSiK5jHmpLNI53vtxEKTcJPLi/L/ERESkEJxAj14osv0t7ezimnnEI8Hufss89mhx12yHn8UHgWXqi5hBHmYmlPVyiyoGRz6WrdAEBNXWPWYXzfB5J9P35i034kwXfffoZ+7tNWsmURESkXJRAjVHV1NaeeeirHHHMMS5cu5VOf+hR33HEHkciWucl9Pwl0gd8JdOL7nUAC/CTRcBuQJBmP0ZsY9CYMPolgKiE8QpD6eGndaX+nhvG8MO4Q6zus500EqvBCE8q2/CIixbZlXk22ALNmzWLGjBl4nsesWbNoampi9erVTJo0qdKhlYQrIejumyAE3fid+HTjEQOvCrwqPK8KqAIvRNKP4BPCC48iPSFwCUB6ghCMF4zbtzu2aX+vCo/07vT5N+CFZ1VkXYmIFIMSiBHqpptu4qWXXmLRokWsXLmSlpYWmpsrXSWRn/gJ0/v87ftJ8NtwJQkd+KkSBb8Tn048In0v3KEG8MYBVYS8KrxQM154Il5oAl54Ap7XCF4VS1cswSfK2Ck7pCUW6UlGDIjgeV75V4KIyBClBGKEOvrooznvvPM49thj8TyPiy++eNhVX3T/aLs+f/uJl8HvBq8az6vC82rAa3K3RHpVeKHRLjlIJQihCb0JQ6gZz8u8/O3diwEIR7fL+H8REdnU8LqiSM5isRg/+MEPKh1G0fh+N/gthKK7EI7t2jdJ6ClRqK50mCIiWwwlEDkyxqwqwmR8a61azmXh+wkg7koZiOPaNHTjE4fkBvCqCUVmEmv4eoUjFRERJRC5G1eEafhFmMaw5Bo5doLfiu+39UsS4vh0A+ARBS8CwbdHFM+LQniSa5dQ8VtLRUQElEDkY/9KBzBcuJKEoHGj3wqpDyHw6vFCteBVQ1qyEPKiuFsbG/FCjeA1ETnrTnd3AzGSV56L5zUSim5byUUTEZGAEogcWWsfqHQMQ4nvJ8Bvx90y2dHvlsl4z+2LnlcHoQl4oTq8UDOhyDy88Fy80DiXLHiNEGrECzUB1X3vdLjuWz2doWvvLvciiojIAJRAFJExJgRMARqttc8F/Txr7YipuvD9JCTfJpl8O7jN0d0R4W6ZbA4Sh1q88Hi80CRCkVmEIvMIhedDaKxuhRQRGSGUQBSBMWYn4OvAwUAdrq1DxBgzA7jPGPNDa+3l5Yil94FKXfQ+UCm9O1VK0JWxu67qcQCSiSV4yXVs+ijmOHj1hCLbEI4avNBkvPAkvNDE4HuSK13w9KZ4EZGRTAlEgYwxpwBXAtEM/54ZfH5ijNnJWntKIfPy/U66Np5LMv7qIEMmST2K2e//vgY/w7sb0vpHw610JyZTE06S6XHNHiG8yAyidZ8lHM393RoiIjKyKIEoQFDycBXuKnwZcAMumdgtGGQxsAhXOvFJY8y91trfbO78/MSKIHnovfD7/RMCP5U8DMQDwsEnrReQ8D08L06k5mOEY3ulPZEx1vs4ZlVFiIhs8ZRAFOYc3E/zz1hrrwUwxqTevIS1tgX4tjHmNeB64BRgsxMILzyDWMMikt1PBo9X7vs+BrxYcNdCeJApZbdkxYsk/TrGz/yIkgQREclKCURh9gVWpZKHbKy1vzHGXAbsWMjMPM8jHFtIOLawkMkMqKM70jMvERGRbNTSrTCjgbdyHPZNoKaEsYiIiJSNEojCrAHmDDZQcHvnbGB1ySMSEREpAyUQhXkAaDTGnD7IcGcCY4EHSx+SiIhI6akNRGEuBY7B3aY5HriRtBaMxpjJwOnA13C3SfyoEkEOW9/8ZqUjEBGRLJRAFMBa+x9jzKeBq4Fuz+DwAAAgAElEQVRvBh8AjDEbgPq0wc+21v6rzCEOb4sWVToCERHJQlUYBbLWXgfsDdwBdOOeqOABDbhShweBg621P6lUjCIiIsWmEogisNY+AbzfGFMFzANGAa3Aq8GzIEREREYUJRBFZK3tBJ6rdBwiIiKlpgSiCIwxDcCJwKGAAWqB9cDzwO3Ajdba7spFOEwdcURv9223VS4OERHZhBKIAhljDsQ9nno8PW+UAGAy8B7gaOBrxpiPWGtfrECIeWn32vlP1X9Y1bWqoOn4+CxPLgfgI7GPMDo0Ov+J3H57QTGIiEjpKIEogDFmK+A2oBp4EZdIPA+04BpR7gB8Etga+EfwRs7CrswldlfdXfy17q9E2grfNWJejAavgVa/lbNqzipCdCIiMlQogSjM13HJw/XAqdbaRL///8UY813c8yE+CHwD+Hx5Q8xPu9fO+vB6WpOtNHvNOY3j47PaX03Mi9HkNVHv1dPgNVBFFQBHxI4YZAoiIjLcKIEozEG4tg6nZ0geALDWdhhjTgSWAR9iiCcQKTNDM2kODZ5AJP0kLydfpslrYn54PgdED2BBeAHvCb+HyaHJeimXiMgIpQSiMI3As9bajoEGstZuNMb8F9i2PGGVXsJPsN5fzxp/DR4eO0d25hu132Cr8FaVDk1ERMpACURhXgTmGWOqgls4MzLGhIEZwKtli6wEEn6Ctf5a1vprafFbaPAaGOONYUpoCotqFzEvPK/SIYqISJkogSjMD3HtH34KfGaA4S4Emkl71PVw0uq38nbybdb76xnljaI51Mw8bx7bhrdl9+ju7B3Zm3GhcZUOU0REykgJRI6MMYdn6L0WeBg4zRizLe6dGE/j7sKoA+bjng9xOPAn4K7yRFs83X43LyVeYnJoMtPD09kxvCP7R/dnt8huNIYaKx2eiIhUiBKI3N0O+Fn+5wG7BZ9s//8wcCTDZJ23+C20J9tZm1zL6NBoDogewJdqvsSE0IRKhyYiIkPAsLiYDRFvkD2BGLLeeecdjjrqKK699lrmzJkz6PBdXhcALbQwhjGYsGFGeAanV5+u5EFERHoogciRtXZmpWPIV3d3NxdeeCHV1dUDDhf34yxLLmNpcilPVT9FtV/N7PBsjowdyV7RvTAhU5nbMa+6qvzzFBGRnCiBGMEuvfRSPv7xj3P11Vdv8r/F8cU80P0AS5NLeSPxBq1+K220sSa8htFJ99jpU6tPLXfIfX3605Wdv4iIZBWqdABbEmPMPuWa15/+9CfGjBnDPvtsOssNyQ1c1HYR13Zcy+1dt/N4/HFeSrzEO8l3WBNZQ6vXyqerdfEWEZHsVAJRIGPMPOAs3Iuzatk0KYvgHnc9ERhFmdb5zTffjOd5PPbYY/z3v//l3HPP5corr6S5uZl7u+9lRXIFnXQyIzSDGmoIe2EAujvcS0OrGbjaQ0REtmxKIApgjJkDPIFLDFKNBHz6vpUz3fJyxAXw29/+tqf7hBNOYNGiRYwdN5YHux/k9u7b2ehvZKw3lnqvvlwhiYjICKIEojDn4B5n/RZwFdAOfB/4O/AXYCpwLDAXuMdae3Algkz6SR7pfoTvtX4Pm7AsSy5zpQ/ejEqEk7uFC3u7Fy+uXBwiIrIJJRCFORBIAu+31j4LYIw5Gxhjrb0m+PsS3DMkDjDGfMhae0u5gkv6Sf7W/TfCV4b5RfIXrOheQaffyeTQZMZ6Ywl5Q7wJzL//XekIREQkiyF+BRnyJgGvp5KHwFPAjsaYKLi3cdL7mOuBHnddVMsSy/hK21f4fvv3eSj+EG8m36TZa2bb8LY0h5qHfvIgIiJDmq4ihVvT7++XgSjQ82Ypa+2rwCvAjuUK6kcdP+Ke7nt4NfEqk73JbBPahrGhYVDqICIiw4KuJoVZCUzp1++14HtBv/7vAmNKHhHwdPxp/pP4D2uSa1gQXsCY0JjKPAhKRERGLCUQhXkUmGSM+Whav+dxd2EcluphjGnAvVhrdakDejv5Nj/u+DFLE0uZGJpIxFMzFxERKT5dXQpzJXAc8BtjzAeBk3Fv51wGnGiMeRX3ds7P497O+XApg9mQ3MB5reexOLGYBAkmehNLOTsREdmCqQSiANbaR4ALcevxKGttt7W2G/gOrhTiW8AtwMG450N8p5Tx3Nh1I88nnqfNb8OEjNo7iIhIyegKUyBr7UW4xpEXpPW7GnfHxatAHFetcYy19rFSxPCu/y7PxZ/j3u57WZ5czszQzJ4nS4qIiJSCqjCKILiN89l+/a4Brin2vJYmlvJy4mVeT77uPonXWeOvod1vp8VvwcOj1qst9mxFRET6UAIxTCxPLue6jut4IP4AG/2NtPvttPvttNFG3I9T49VQQw2zw7MrHaqIiGwBlEDkyBjz2WJMx1p7Ra7DLksu47rW61juL2d1cjXLk+670Wukxquh2Wum1qslRmxk3qZ5662VjkBERLJQApG7y3ENIQuVcwKxIrmCm7puooMO4n6csd5YFoQXEPNiRQhjGDjiiEpHICIiWSiByN2DFCeByFmIEJNCk6imeuSWMoiIyLCkBCJH1tr9yj3PmBej0Wss92xFREQGpQRiCEv4CVr8lrLOs91rpxbdxSEiIgNTAjGEddDB68nXyzpPD48wYTyGQHXJ5Mm93cuXVy4OERHZhBKIIWycN44jY0eWdZ4r162kzq9jp8hOZZ1vRitWVDoCERHJQgnEEDYnPIfD6w4v6zwXr1sMwJhQWV4cKiIiw5QeZS0iIiJ5UwIhIiIieVMCUQBjzB7GmPpKxyEiIlJuSiAK83/AW8YYNRgQEZEtihKIwkwDllpr11Y6EBERkXJSAlGYZcB4Y0y40oGIiIiUkxKIwnwVGAf81hgzt9LBiIiIlIueA1GY/YGngWOAY4wxa4EVQEeW4X1r7W7lCk5ERKRUlEAU5nP9/h4bfLIp69s8h71//avSEYiISBZKIApzcqUDGNEWLqx0BCIikoUSiAJYa/+v0jEU23Ntr/PHtQ8zqvWvJZ1PR7KT7mQCgLkNU/ifHT9FbaS6pPMUEZHiUQJRAsaYemtted/D3U8ikeCCCy5gyZIlhMNhLrnkEqZPnz7oeM+1v47tWEbbyqUljc/zoDFaz7iqRl7e+BadiW4lECIiw4gSiCIwxkwFvgQcBswDPCBijJkM3AB831p7Wzljuu+++wC48cYbefzxx7nkkku48sorcxo3ic/EmjE0VzflPV/f91nVsY7WeAcdyS46E91EvBDV4RjV4SqqwzGqwlFGReuYWT+R/SbsyPsm78Loqoa85yUiIpWjBKJAxphDgBuBUbjEAXobS84C9gH2NsZ8y1r77XLFddBBB7HffvsBsHz5csaNG5fX+NFQhGgov90jnkzwSssyIl6IsVWjgqQhRkO0lim1zUyuGceU2nFMrh3HVo3TmVgzUHtTXDFFiq/2pyIiQ4kSiAIYY+YANwO1wB9wpQ0XAjsEg7wMXAecBHzTGLPYWlvaxgVpIpEI5557LnfffTc//elPSz6/ZW2rqQ7F2Hms4bjZBzGltpkpteNojNbjpScDIiIy7OlBUoU5D5c8XGCt/bi19hagLfVPa+0qa+0pwLm40onPljvASy+9lDvvvJNvfOMbtLW1DT5CAd6NtzO+uomvbPNx3jd5V7ZpmkVTrEHJg4jICKQEojDvA9YBlw4y3A+Bd4BdSx5R4C9/+QtXXXUVADU1NXieRzhcmiduJ/0kazo20BHvpCZSxaz6SSWZj4iIDB1KIAozHnjVWpsYaKDg/0tw7STK4n3vex8vvPACxx9/PKeeeirnn38+VVVVRZ/P2+1reXrtK7zTuYE5DVOYN2oqddGaos9HRESGFrWBKMx6YEaOw04ByvbWztraWn7yk5+UdB6+7/Nm6yq2HT2bbZpm8v4pe/DeCduXdJ4iIjI0qASiMI8B44wxRw80kDHmY8Ak4J9liaoMEn6SJS0rqI1UMaV2HD/a+fMcPHkXqsKxSocmIiJloBKIwvwI+BBwjTEmdSdGD2NMBDgR+Anu1s6flT3CEmjpbue1luXUR2rYYfRcTp//ITWUFBHZwiiBKIC19kFjzIXAt4FfAb8EkgDGmGeA2bi7NDzgh9baf1Qq1s2V8JO0xTtojXfQGm+nNd5BPJlgRv0Edhg9j68tOJ7p9RMqHaaIiJSZEogCWWsvMsa8AHwH2BpI3eqwbfC9FPiOtfZXFQhvs3Ul4zy/fgnt8U5qI1XURmoYFa1jUs1YGqK1fGDqnpw4+xBi4WilQxURkQpQAlEE1to/AX8yxswG3oO726IVeNla+0JFg8uR7/ts6G5hSedKwD0UanSsgfeMncGchinMbZjKvFFTmdswhRn1E/N+SqWIiIwsugoUwBjzXmCDtfYZAGvta8BrWYY9CJhnrc3thRRF1JXoZmN3K+92t7Gxuy34buXt9rW83bGWFW3v8HbHWlq621jduZbGcC2jqhpojNVx9Iz9OWnuYeUO2Vm2rDLzFRGRQSmBKMz9wEPAvjkMezHuRVtFSSBWtq/lvref6kkGUslBqrst0dkzbNJPEk8miPvBJ+k+ncluOhNddCS66Ex2k/CTxLu7mRwby/ZNczh57uHsPX7bAaIoscmTKzdvEREZkBKIHBljGnHPcuivzhjzngFG9XDPiti6WLG0xzs57bHLSCSTQUIQ700Mgu/utP5J36f3/V6bCnkeNcHtl52JEGFCnDTnMA6ctLBYIYuIyAijBCJ3VcCjQPp7p31gR+DZHKdxXzECqQ7H2H3ce/jnmhcIE6IqQ0NGDxgVrWNUtI7qcIxc77JcuXIVdaEqdho7vxihiojICKUEIkfW2lXGmEW491qk+PS+wjsbH2gB/gOcXoxYPM/j69udWIxJbWLx4sUAjKkq21O3s1u+vLdb1RkiIkOKEog8WGt/DPw49bcxJgk8bK19b+WiGsGmpNUY+dmrYEREpPyUQBTmW8AblQ5CRESk3JRAFMBa+62B/m+MST1Y6gVrbbI8UYmIiJSeEogCBXdnfA5YY629Kug3GbgV18ASYKkx5mRr7YMVClNERKSo9DbOAhhjRgNP4t6FcWjav64GdsI1sOwGZgF/D55UKSIiMuwpgSjMl4C5wKvA/wEYY2YAh+NeqnUAUA/8AKgBvlqZMEVERIpLCURhjsCVMBxsrf1L0O9Dwfcj1tr7rbVx4HxgPXBwBWIUEREpOiUQhZkNWGvt62n9DsU9++HvqR7W2m7cOzL0MAMRERkRlEAUJtXGAQBjTAxIPRPinn7DNuCqNURERIY9JRCFeRWYY4ypCf4+EKjF3ZHxZGogY8w8XGnFkvKHKCIiUny6jbMw9wHbA9caY24ALsVVX9ycGsAYsyvwC1yy9rdKBJmPZzeG+eOKKkatWlnpULghrfvYv5cunrVr6wAYMwSWuVw2d5k94Mg5dXx0fn0JohKR4UQJRGEuAT4CfAz4KO78uga4CMAYcyBwV9D/ZeCyyoSZu+dbIrzUGqatraPSoTDlmqW9fywrXTxdXe4wiLVXfpnLZXOXuSbiMbYmrARCRJRAFMJau9oYszNwDrAAV6XxA2tt6i1QFvcirT8CX7XWritXbN3d3Zx//vksW7aMrq4uzjjjDA488MCcxk3iMakuTHNNuMRRDg0bNrQD0NhYV+FIymdzlnlDZ5JV7Qmqwzm+2lVERjQlEAWy1q4my/MdrLVvGWOarbVdZQ6LW2+9laamJr73ve+xbt06PvzhD+ecQABEQh6R0JZxoYgEi7mlLC/kt8y+7xP3YV1nkvqox/zRm74+XkS2PEogSqwSyQPAoYceyiGHHNLzdzi8ZZQmSP5836cj4dOVgK6kT2fCpyv1SUJXwifkQSzs0VQVpi6ittciogSiIMaYE/Mdx1p7fSli6a+uzhVNt7S08IUvfIEvfvGL5ZhtUc155Zme7lfnbl/BSEY2u66brqRPLOQRC3tUhT0aoiFi1R5VYZc4NMRCNNeEmVwXZv9p1ZUOWUSGACUQhbkOd9dFLrxg2LIkEAArVqzgzDPP5LjjjuOII44o12yL5sdf7n29yBG3rKhgJCNbW9xn+3Ex5o2OMrMhQnOta/8yrjrM+Now42pC1KjUQUT6UQJRmH+TPYGoBSYCo4NhfgesLlNcrFmzhlNOOYULL7yQPfbYo1yzlWEk7sO6jgTxpNuFL95zDHVRJQoikhslEAWw1u482DDGmH1wJRU7A7uUOqaUn//852zcuJErrriCK664AoBrrrmG6moVP49Uvu+TBJJJSPiQ8H2SvutO+r7rl3TtGta0helKeowmwbSGCDURj9gW1IhURAqnBKLErLUPGWOOAf4FXEiZ3sh5wQUXcMEFF5RjVlJEXQmftniSRLLfhT+t2yUIQbfft9vDJ+x5hDwIhzzCHoQ9CHle8A3RsMekqiT1EZ89Zo9hu3Ex9ppUTVS3Z4pIHpRAlIG19t/GmBdxD53SK70lo6Tv89w7XdRHQz0X+3AIwp5HNARhL9TbD49wKBgmLTmoCntUR0LURLzeT9ijJhKiOuJRG/EYFQvByjXMrEmw+y5jKr3YIjJMKYEonwQwqdJByNCV8F1L2/mjo+wzubrngl8T8agOkoA+iUGkb7+qcO7P7ljckijtwojIiKcEogyMMXsC7wHeqHQsUhq+7+NDb7VCMkP7g/RqiAxVEp0J15ixMRbinJ2bKrtAIiKDUAJRAGPMZwf4twdUAQY4Luh3S8mDkpJ4pz3Bus5kv4aJfRMCD7+nrUFvO4S+7Q9S7RFiIQh5QVVFUCURCrn/T2vQYSkiQ5/OVIW5nNyeA+EBrwHfKW04Ugg/raQgnux718KyljgzGiI9bRJCGRonptofuOoGj+q0qoXeagiP6nCo5/+Zhp1ar8NSRIY+nakK8yADJxBxYB3wKHCttXZjWaLagqXe2xBP+sSTEPf94I4G93f/5CDuQ3tHmIQP4c4uQp57T0TqDoZI8D1zVIQjZtVx4LTqngaJqaQglQiEdRukiGxBlEAUwFq7X6VjGMneGT0BcBnau11JlxT40J1KDtK/UwlC0icccnctuIu/u1shlRTEQlATDhEJShLCIWgLtRP2fEY3xqiLhqiLetRFQ9QH3fXREKOrQhw9r04PWhIRCSiBkIpLBg0I2+O9n46EzzaXPh6UAkC0Jd5zO2MkSBBqwiEiYYh6HpFQ7xtEG6LuVsVRVb1JQF0kRH0s+A4ShFRy8Mp/36Ym5LPXLkalCCIiOVICkSNjTG0xpmOtbSvGdEptyYZuVraV/la/pO8enhQL425JDHuMruptI9AYC9EQC7mEoM+nN0lI718X9Qh5+SUB62OuFkrJg4hI7pRA5O7dIkzDZ4iv83GxJM2xJDPrY2WZn+dBdTjE5Pow0+ojTG+IMK0hwrT6CNMawlTrJU4iIkPSkL6YDTHF+Hk65H/iHji2mwUNcbbeZnZZ5hf2YFJdhJgeoywiMqwogcjdrEoHUA6eBxOrfGY3RisdCtx2W2/3MHwduYjISKYEIkfW2tcrHcMW54Mf7O32c3nchoiIlIsqmDeTMaZmkP8fbIzRm4pERGREUgKRJ2NMgzHmh8DbxpipWYaJADcDbxljfmyMqStrkCIiIiWmBCIPxpgpwBPAWUADsG+WQbfCvQejGvg88KQxZmJZghQRESkDJRA5MsaEgL/gXo71DvAV4O+ZhrXWPgeMAb6Ae5T1VsCfjDG61UBEREYENaLM3fHAQuBlYD9r7YqBBrbWtgKXG2P+BjwE7AZ8FPh9qQMVEREpNZVA5O443IOgzhoseUhnrX0NV43hAZ8oUWxZPfPMM5xwwgnlnq2IiIxwKoHI3Y7AOmvtHZsx7p9xVRkLixvSwK655hpuvfVWamoGvGFEREQkbyqByN0YYOnmjGit9YFXgbHFDGgw06dP53//93/LOUsREdlCqAQid23AuALGbwI6ixRLTg455BDeeuutvMbZuD7BU4+38doLS0oUVe6OSev+469LF8/SJRsByrrMfhK6upJ0dyXp7EzQ1ZmkqzNBZ2eyp7urq293Iu7z4eNmcOTHp+Pl+cIwEZFiUwKRuxeBhcaYMdbatfmMaIwZC8zENcAc0h69t4V7/rqRkNdR6VDYvmZuT/dVP7Qlm097m3tBak1tWfM7QiGPUCj17T5en7+D7nDwPw/+9LvX2fuACTRPqC5rrCIi/SmByN2dwC7Al4Gv5znuF3Dr+uFiB1VsXV0+re8miXd3Mba5qqKxfG7ry3u6S1nX5gWv8Q4N9DpvH1Ysa6O+IUosFsILQ8jz0i7wBAlA36TA65cgpA+XbxmC58ExJ85i3PjKbhcREVACkY+rgK8CXzbG/Mta++dcRjLGfAA4H3cHxy9LGF9RTZxczdjmkf8r1/d91q7tJB73qakK093t093tqha6u90n3pWku9unqjrMVts2stvezURjIWJVIaqqwsSqQsSqwlRVhfr0H3CYWEjVECIyrCmByJG1drkx5gvA1cDvjTG/An5orc1Ytm6M2QY4GzgRCAO/sNY+UbaAA1OnTuUPf/hDuWdbNL7vk0z6JBOQTOv2k0F3kuDbdWfun/1/8W6fRKKbSNSjrr6DaNRd4KtrwjQ0Rt3fUY9INMSoxihHfHQ6hx2Z8QnmIiJbFCUQebDW/sIYMw/3FMrTgNOMMSuA53BPp4zgGlq+BxgfjOYBvwU+W/6Ih6Z4PMm7G7t5d0Oc7q5Enwu63z8hwN+0TcBA7QWCKoVIGEKhkKte8HrbEvQfJxIJ0dX9Lg2NYeZvNYWmMVWMHhOjKe0zZmwVo5qixGLhSq86EZEhQwlEnqy15xpj7gGuAGYDk4OPD5tUa78AfNtaO+yKAN5e3kF3d3FfoZ1M+rRs7Ka9LUH9qAgNjTEaR0ezNho88K3bwHMX+ce3/jBVQTVA9iqC3u5orG//WCzzMA2jojz33NMALFy4XVGXV0RkJFMCsRmstXcFJRH7Agfg3nXRhLvV823gJeCv2ao3hrJYzKOuIUTIi5FMFjeB8DyPSVNrGdUYZf42jWy302imzaxLSwr6Jgi19fv1jHvi4suKGouIiBRGCcRmCh4OdX/wGTH2PKCemroQ06bNKPq0QyGPWXMb2Hq7RmpqtOuJiAxnOotLH6Oawux7SAMLF86qdCgiIjKE6VHWIiIikjclECIiIpI3JRAiIiKSNyUQIiIikjclECIiIpI3JRAiIiKSNyUQIiIikjc9B0KGrg98oNIRiIhIFkogZOi67bZKRyAiIlkogZA+fN9n5cqVvPbaa5UOpWzeeustAEaPHp3T8J7nMX36dMJhvZ1TRLZcSiCkj3vuuYc///nP1NXVVTqUstm4cSMAo0aNymn4cDjMoYceytlnn13KsEREhjQlECNUMplk0aJFWGuJxWJcdNFFzJgx+Auy1qxZw+rVq1myZAm1tbVliLTyurq6ALfs2SQSCTo7O6mqqsIYw1ZbbVWu8EREhiQlECPUP/7xD7q6uvj973/P008/zXe/+12uvPLKnMefNWsWzc3NJYxwcMemvQ39BmNKNp8NGzYA0NjY2NMvHo/T1tZGW1sb7e3trF+/nvHjxzN9+nQ+97nPcdhhh5UsHhGR4UAJxAi1ePFi9tlnHwB22GEHnnvuuQpHlL/jXn65p7tUCUQqUejs7GTjxo20t7fT1tZGIpGgtraWmpoa6urqmDBhAjvttBNnnnkmM2fOLEksIiLDiRKIEaqlpYX6+vqev8PhMPF4nEhky9zkvu/T2tpKS0sLHR0ddHR00N7eTiKRIBQKUVVVxejRo2lubqa2tpaGhgZmzpzJrFmzmDlzJnPmzGGrrbbC87xKL4qIyJCwZV5NtgD19fW0trb2/J1MJre45MH3fTZs2MD69etZv3494XCYhoYGampqGDNmDNXV1dTX1xONRpkwYQK7774706ZNY9asWUyaNIlQSM9ZExHJZsu6omxBdtppJ+677z4OP/xwnn76aebPn1/pkMpu6dKltLW1MWbMGLbaaiumTp3KTjvtxPTp05k6dSrTpk2jubmZf//73wAsXLiwwhGLiAwfSiBGqIMPPphHHnmEj3/84/i+z8UXX1zpkMquvb2d6dOns/fee3PKKacwa9YsVUGIiBSJEogRKhQK8e1vf7vSYZRdMpmktbWVd999l46ODmpraznrrLMqfkeJiMhIowRChr1kMsmqVatYv349LS0t1NTU0NDQwLx58zjkkEOUPIiIlIASCBnW2tvbee2114hGo0ycOJGGhgZmzZrFggUL2Hbbbdljjz0qHaKIyIikBEKGpba2NlasWMGGDRuYOnUqCxYs4JOf/CTbb799nwdCiYhIaSiBkGFn48aNvPrqq0ycOJG5c+dy6KGHctJJJ20xj94WERkKlEDIkHXH9Omb9Gtvb+eNN95g2rRpHHnkkZxyyilq4yAiUgFKIGTI+tl22/V0x+Nxli9fzpo1a5g8eTKzZ8/ms5/9LA0NDRWMUERky6UEQoaMZDJJIpEgHo/3+XR1dbFq1SqamprYbrvteP/7388nPvEJJQ8iIhWkBEIy2rBhA9FotGjTy5QY9P8kk0nC4TCRSIRoNNqne/78+ey8886cfvrpzJ49u2hxiYjI5lECIX2kXiwVj8dZuXJl0aabSgYikQhVVVXU1dX1/J36RKNR6uvrGTVqFA0NDTQ0NPR0G2PYa6+99CRJEZEhQgmE9LHnnnuyceNGxo8fX9Tp1tbWZkwM0rvr6ur6Jgif/nRv96mnFjUeEREpjBII6WPq1KmcdtppQ+PFUtdc09t99dWVi0NERDah9xWLiIhI3pRAiIiISN6UQIiIiEjelECIiIhI3pRAiIiISN6UQIiIiEjelECIiIhI3pRAiIiISN70IKmhaS5AW1sb1tqKBFCp+fZx1VW93WWIZ0gsc5lpmUe+LW15A3MrHcCWwPN9v9IxSD+LFy9eDzRWOg4RkWFqw8KFC5sqHcRIpxbPpYoAABydSURBVBKIoWkJMAtoAV6pcCwiIsPFXKAedw6VElMJhIiIiORNjShFREQkb0ogREREJG9KIERERCRvSiBEREQkb0ogREREJG9KIERERCRvSiBEREQkb0ogREREJG9KIERERCRvSiBEREQkb0ogREREJG9KIERERCRvSiBEREQkb3qdd4UZYz4KfAl4D5AAHgW+ba19ooBp7g/cA5xorf1N0O8g4HxgOyAGLAa+a629M4/pzge+BewNjMW9avxq4AprbXJz4y2FIi3vYcAXgV1wrwheAfwduMha+1bRgy5QMZY5wzT/DhwK7G+tvb8YcRZTkbZzHXAO8FFgFtAGPIw7Dv9V9KALVKRl3h24ANgTt2+/CdyKW+Z1RQ+6SIwxJwG/Avax1j6cx3iTgW8CBwOTgDeA3wCXWWs7SxDqFkElEBVkjFkE/B7YBrgPeBY4HHgkuHhtzjTnAb8FvLR+JwF3404WTwCPAXsBdxhjPp3jdLcHngQ+DrwO3AFMA/4XuH5zYi2VIi3v14C/AQcBNugG+Azwb2PMVkUOuyDFWOYM0zwDlzwMSUXazmNwSfuFQANuO78JHAE8bIzZpfiRb74iLfOHgIeA9wMv45a5GpcsP2GMaS5+5IUzxuyBO9/kO95U4HHg08B64K/AKODbuPUWLWacWxIlEBVijFmIy4j/v70zj9druvr4N6QJEmNDkEgQyRKpkFCEIIaaSZAKYoihfQ2lFFUdyIuiptAqrTE1pl5TSY0JQZWaYojIL0QihpqViCGN5P1j7ZPn5LnnufcZzpN7b7K/n8/9nPucs8/Ze51h77XXXnvtN4HekoZI2havuACuM7PlKrzmNsBjuIad7FsD+BPwGbCppN0k7YxXOp8Dl5pZlyau2wZXElYADpY0UNI+QC/gJWC4me1bSVnrRU7ybgCcDXwBDJQ0QNIQYD3gcmBVvBfUIshD5oxr9gAuyL2wOZGjzKPwnvwYYF1J+0jaCDgFaA9cXRcBqiCnd7st8Ge87t9X0ubh3e4B3IO/46fXUYyqMLN9gAdwa0mlXA50BX4jqb+kobic44BBwPF5lXNJIyoQzcdJYXuGpHeSnZL+DowGOgPDyrmQma1iZpfgwxar4j2ohOPwinCUpEmpfJ4Bzsd7Hk31XH6AV7ITkiGRcI0PgWPCz5byEeYh78HA0sDFkp5MXeO/eC/tQ2ALM+uec9mrJQ+ZF2BmS+EK4xzglXyLmhs1y2xm3fBn/QYwQtKc1HUuxIcGOrSgHnkez7kvXre8KOmO1DW+Bs4KP7fJs9C1YGZdzex64Hb8m3y/wvMN2AOYBpyT7Jc0GzgCHzY+LrcCL2FEBaL52AWYj487FnNn2JY7jHE88FNgBrAd8HBRPgB31ZBPyWtIegL4ABhoZsuXWd56koe8c3DLymPFB4ISMT38XLOaAtaBPGROcypuIv8J8F4N5aoneci8Dz7U98escXBJm0paLyjKLYE8ZE58lVYL1og0ncL2kyrKVi/OxpW8Z4EtgCkVnr8z/ozvKfbTkjQTeB7oHqyOkQqJCkQzEEyRKwPvlHBYSj6SDcu85Nu4AtFH0uNFxzbAK41XM86bGo71CcMUpegTtpNKHBf+LjXrRxhkqFleSWdI2kjS+Iw8OlCQs9kdKfOSOXW9vsBI4HZJN+dY1NzIUeb+Yfu0mXU0syPN7Aoz+4OZDSnnfi0qcpT5FdxC2QW4wcx6mNlyZrYDbuqfB1yca+FrYwpwKLC5pJerOL+puqvSujaSIioQzUPio/DvEseT/Z3LuZikqyX9Pm2CDXTATZ4fZxxD0lzgI2A53IFskZS3jqxMPvI2xqn4OOwzkt5qKvEiIDeZzawdcAPuaHZ0/kXNjbxkXi9sO+ENzFXAUbjl5U7goRZiVYOcZA4WtKHAO7hD9OvAbNwfoB2wq6R7ci99lUg6T9L1Nczyai11V6skTuPMCTO7CdikjKR3UvDo/7JEmq/DthqHoTTtm8gH4KtUXp+XSNOhieukr9GcNFVOKE/eTMxsN3z63Dx82l9LIE+Zz8LHyPduQWb7LPKSecWwvQ4fIz8AVyT64r3xHXCHwwNrKWxO5PmcX8dnap2E+3l8gNddawKnmNmzklrSMEYttJa6q1USFYj86A5YGenWoDAOOb+JtLWaUOcXbRvLo7G8mipvOddYFJRzX6sqq5ntDtyGO3L9ogXFRMhFZjPbCjgZuFFS1hh7SyKv57xM2M4BdpT0n/D7CTPbGR8WOMDMRkqaWnVp8yGv5/xdPMZFF1zmCWF/e+Ay4Ejcx6LFOFLWSGupu1olUYHICUkDy00bYioALFsiSVKxza6pUAVLRql8ys3riyauk1d5a6WpckIVZTWzw/GeaFs80M7vqiteXahZ5uDX8RfcnNsaPNLzes7JsZtTygMAkt4zs7uB4cC2uDLRnOQl8ynA+sDP00qwpG/M7Bhga2BrM9s6w5+qNdJa6q5WSfSBaB6SaZurlzje1LhduXyFf0CdMjyukznhnYCviyvQIt4N23qXt1Y+Jx950+ecBVyDWx5OlHRGjuXNgzxkPhqPA/AxcJmZ3Zj8UXBC+1XYt3X+IlRMXs85GaaZUeL4m2HbqcTxRUleMg8K24eKDwT/iHHhZ7+aSttyaC11V6skKhDNgKSP8HHHriWctHqHbTVex8VMxhu/XhnHDH8Hmson8WBuMMsieHyvj8+nnlx9MWtH0nzykRcza2NmV+Phfr8B9pd0SY7FzYWcZE7Gf/viPe70X1Lx7hh+96i91LWR43NOjpeajpvI3uz+IDnKvFLYzi1xPNnfrtIytlBK1l2BPOvaJY6oQDQf9+OVwZ4Zx4aE7b0Zx6rJJ33NavJp7Bpb4sGr/iFpVuXFy5085AW4CA808zmws6RbcyhbvahJZkkjJbXJ+sODk4GvhdFG0uj8il0TeTzn+8J27+JefZiRsl342VJM+XnInExb3K34gJktDWwffr5YcelaJsk92ysESFtACCTWD3hTUrN2florUYFoPq7AHXt+Z2brJDuDs94I3KR2S/oEM1s//FUS4vo63Bfi1BA+O7nWpvhMgq9wj/Nkf4+Qx4qpazyKzx//gZn9KJV21dS5F1VQpnpSs7xmtgu+wNlcYHdJjy6qwldJHs+4tZGHzOPwhrIncEloQJNInBfiC2s9JEn1FqZM8pD5yrD9VXCcTdK2xUOXb4h/6+lgdK0CM+sW5F0w5CRpOq5EGL72RZK2Ax6mfGlaTt3V6ohOlM2EpKfM7AL8w59kZuPx+dvbAv8FhmdEx0sCyGwHTCgznxlmdhLwR+DJkE8bvKfRFl+x84PUKePxGSWH4SG1kTQvOBKOB640syPwscVB+Pz0q1rK3PE85MUDKYGHzT3KzI4qkd1vJWUF9Vmk5CRzqyKn9/pbMzsAbyyPBfYws4l4I9oDD7hU1UJk9SAnme81s/OAXwCPm9lT+HBqP6Ab/s7vJ+nbRSNVrlyP15//S+EbBn+2T+BK02A88N2WuP/DfXhnLlIF0QLRjEg6Fbc2vIqPMW+ArxQ3QNIjOeZzOT5U8hTuZf19fCrXD9JrWzRxjaeBzfGY9D2BnXAns6NoYUGHapE3WHeSFRi70NAnIP3XYoLP5PGMWxs5vdevAhtTWOVxN+A7eCO9maQZORe7JnKS+bRwjXG4D8Bu+HTHy4B+i5s5X9IbwGa4ErUqvgrpp8BpwD4hCFekCtrMn99UKIJIJBKJRCKRhYkWiEgkEolEIhUTFYhIJBKJRCIVExWISCQSiUQiFRMViEgkEolEIhUTFYhIJBKJRCIVExWISCQSiUQiFRMViEgkEolEIhUTI1G2IMxsJFDNao/rhCh1E/BIbMdJuizPstULMxuBh+h9TtKmYd/awPSQZHlJX4T9o4FDgYsknbzIC1tHzKwz8E3WSopm1rslRLxsLkKI6d/gQdfWwIMA/VzS9Wb2XWAUsAuwAh5VcSc8fHF3YE9JY6vMdyT+Pd4uaWiNYlSNmfUAZobVMlsEZjaDGu9vpPUTFYiWxUw85GoxmwLtgdfwCrKYr+tZqEh9MbMT8PC7WwH/Se1fHbgUWJdCdMwlkV9SUKwn4yu/zgy/b8VDOc/B13BYhsIy3K0aM/sOcDpwCr5Ud4tRICIRiApEi0LStcC1xftT2v45LWg1xLy4Ew/L+1VzF6QZGVVi/y7AfsBzi7AsLZH9wva3kn6d7AzWh2T1yN0ljUsd2wEPSf1WDfleBozBV2RtDrrgy8lHIi2SqEBEmhVJnwGfNXc5Ii2aVcO2eFntVVP/L3RM0rRaM5X0EfBRrdeJRBZXohNlJBJp6SQdneLVaRd0gDJWro1EInUmWiAWU8xsa3y1uQG4KVfAVcCfJTVYQc3M1sGX+N0JWBOYhQ8tjJI0vsK8103lvQ4+divgNuCPkr5MpR1BkRNlmXlsiI8PDwI64k6XNwAXZjmbmVlP4OSUfLPxoYErJf1fUdq1yXDiTB3/HvAygKQ2GXltA5yILxm8Eu638iBwrqTXU+lGsrDT7MtmBr5c+2h82ApgEzObD7wpae3U+e3xlVCHA+vjHQIBNwOXSarIN8bMlgeOAYYB6wFL4z4H1+Lvzbyi9CsBJwD7hPTzQv5jQv6Zw1JhSeVjcN+eDsA7wFjgPEn/TqWbgDsFJzwS7s9fcGfa9DWTd/owSaMbc/Izs4HAcfj72Rm3MozHh0iUSjeSEk6U4V79DBiKL/09F5iEv8vXFi+HnZLl+/j7elr4fxlgCnA18KfkHqcchhNmBdnXKbVCqJltH+T4BFi9xHdwGP48n5a0eWp/33BPtsGHTtri7+3juNPy81l5Fl17JI04nZrZhcBJwF8kjSg6VvG7bGaDgJ8CfYGuuCXzeeA6SX9tqryR2okWiMWTA4EJeIU1HW8s++Pr3l9anNjMdsYbxB8Dq+HOaF/hy96OM7OyZ4aYWW/gWeBIvKGeAryHV5bnA+ODc1gtDAKeAfbCG5+P8GWJz8GXGy8u0xDgRVy+TsBL+Lj2jsCtZnZz8PSvGTP7NfAoMAT/vl7GG4zDgRfNbNdU8mKn2Ynh92dBvtfC/i/C/mdS+awCPIb7T/TH7/HreGV6AfCP4CNQbrm7A/8CzgvXmIH7D2wKXI43jOn0PfF7ega+DP1U/F3rhz/np4ITaPqcNmZ2JXAXrsh9gze6nYHjgZfMLK1EvhzkTpZbnhR+Tw3biam0T4S/95uQ8xf4fdsPd0x+GVdiDgaeDQ1powQF83lgJN7YTcffwwHAlcDY0CBmcQjwML4U90z8WffDlw9P+8JMxb+jhCeDfI0phY/gz2wV/P5mcUDY3pCS59Agz5Hh3CnhOmvidcmTQSmuC9W8y2Z2IK4sDQHa4d/0N8DOwJigrETqTFQgFk8GALcAXST1x6e+JR/UT8xsjSRhqAxvxSvRs4CVJfWXtBYwGG9oR4ZGuBzOBFbGFZXOkjaRtD6wCd7QbwHsX6N8m+A9o7UlbRzKenw4tqeZpXtWvfB7sSzwh1Cm74ee/K74rIcD8MagJsxsH/wefg7sL2nVYFVZDZ+GuBxeuXUDd5qVNDB1iUMkDZQ0UdIPcYUoJNXAsC9hNLAZ8E+gl6SekjbCLT6Ph3t0TQXFH40rYU8BPST1Dc9tEK7AHGJmw4Oc3wHuAbrhylLyHDbEG9SX8Mr/1qI8TgR+BLwL7ChpzXB/OuNKSifgjtC7R9Jx4f4kPjLHhftwTth/SHLhsH+gpPtKCWhm2wHn4rM4jgbWCPl3wa0mHXErWUmConkHbnG5G+gqqY+k3kAf4FXc+fV3JS5xHK6MrS5pY7znnHybxyZKl6RzgPTz3inI916psgXL4k3hZ4NvLEwV3h5XyP6a2nc5bm06OZRrU0k9gV64ktgOt07Wi9FU8C6b2VLAxXj7NUzSWuGbXgu32swHTgx1W6SORAVi8WQqMCKJKRDMor/CG8s2eCOecAo+f/56SadLmpMckHQ3hYqjXCvEhmE7Om1ClTQxXON2ap92+ikwNG3ulvQH3NwOPnSQcCpuJr5f0vHp4RNJ9wOHhZ8nVdJjL8FZYXtC2oQq6b+SzsYb1BXwhrRqQi99T+BjYEjaYVDSW7hZfRYw2Mw2KuN6W1JQFAZLWjANUtKj+BRT8F46eONkeG9/sKS3U+mn4par2cDWicXFzJbBp2MCHJQeFpM0W9KxuAVkLdxaUw9OC9uLJS0YLpA0G38PPgZ6mtkWpS6AD9f0w7+xYZIWWDwkTcYtG/OAo81stYzzJwNHSvo8nPMt/m1+iTfim9UgHxQsC4PDPU+zX8jjfkkfhn2DwvYpSRelh14kvUFBudmgxnJlUuW7vBqudH4KLDT8KOl63Ap0C7BiPcocKRAViMWTsZLmpncExSD5ONMN5Z5he0uJa43BNfqN05aLRkjyuMLMtjWztKPb5ZKGFvscVMEj8tkbxSTBltLy7Ra2l2ddSNJduCl5WQpTAismBPvZAG88So2/Jvd41xLHy2Vw2I5LNQQLkPQBbiYvN6/dw3ZsOLeYP+O9672K0t+U9RyCQnFH+LlH2G6FP5f3JT1SohxjKihzRZhZBwqNZYOp0mGMfSu8B/5UI5dK7sGdWePykibhwyLtyH6f7leRD1L4NmeEnys0kneTBCXmeWB5Cs8pocHwhaS/SuqA+91kkSjcy9VSrkao5l3+CLdKrQxcY2Z9is45StJBkl6sU5kjgehEuXjybon9iTPgMrDAEWytsO+cMH6fxbf4u9IL+HeJNAln4hXnFrgfxmdm9jBwH3B3usdWA+XKtwKQjMNPzDyjcKwbLl+1JD20ecCDweGtmKQS7mlmbYobkiry2trM/lEizTphm1mQInqE7aSsg5JmUbDuQOE+NXZPn8ctFknapMwdGynzKmFbTpkrZS3cmXgObj1ogFRwoGyERI4fBmfMLLqGbZYc75Q4J3E4zcMX53rcl2B/gk9QMOcPwIfX7s44Z46ZbYVbEHvgQzTJMALUr7NZ8bssaa6ZnY4Pkx4GHGZmb+GOyvfiStqXmVeK5EpUIBZPyp3Slu7t9CsjfZMmQUn/MrP+uFl2r3DO3uHvCjMbAxxbwoJQLuXKt3zq/1mNpEsUj+UbSdMUyb1si/dkG2OpkFe1AYqSvNYMf41Rjhk3sdh80WiqAsl9quSeJmXuQNP3px6m50TGL2tQ3KAgx7rhrzGy5JiTsS9Ng1k9VXALPvSwu5l1lM8iSqwPtxVbToLvzrksrEDPwxXKO/Bhm3pR1bss6fdm9ho+E2YQriAeEf5mmdn5YdgwUkeiArFkMzv1fydJH+dxUfm6DQeZWTu817MDbk7tj0/T6oArFPUm3SCuQOmAVUnFlNWAZlXoWebc5F5OCs6E9STJ62RJF+VwvaS31qHM9Ml9aszcXnxPkzKPlbRnRvp6s8AUX6P1J5FjqKQGM35aApI+MLMH8eG7vfCpkIkCcWM6rZnthDuOtsEdY2/HnWAl6ctwvFIFopQS1Nh3U/G7HBxm7zOzFfEhmB3xIdluwFlmNktSg1lnkfyICsQSjKT/mNmHeES/3kADE2LwOt8OH6OdrqL57UVplwLWBtaS9GgY2300/J1uZofj3tRDUj2juiHpMzN7Dx/G6EdGWGMza0PB+pLEaEj7j7SnYU87yxckmXK5rpm1SzujpvLqDPTE72MpU3Y5JHn1LpXAzPrhvivTwhBEYyQm/UxHueAMOBb3bzkYn5+/MX7fbsg6B/ech8I9LafMa+PP6o0Svhi1MA3vVbfDTfSvFycIcQwG4LFBSikHr+Gm/cbkGIC/M9NUIhbGIuAGXIHY28wm4kMTb+HDimlOxhv80ZIOoyFdM/aVIvluSk1hbey7KftdDlNkewJtJb0QrJl3AXeZ2fG4j8uhwEFkTFuP5Ed0oozcG7ZHlTg+HHgIeAGf5tYYq+MV88NmlmWOHJf6P5e4C2WQyHd0ieND8Gl8c/F59ODe3QlZ49h7ZeybjCtZy5GaXljEufi0tDFF+5PecHHPbV6J/X8P233NrFNxJqFHNh73Udiv+HgGD4TtHmFOfjF743E8egfn3CT/A0Nexfl3peAc92DYPoZbI3qY2Y4lynENHu8gD6vKQoRZD0+Gnw2eT2iURuDxE0oqyRRkPzRjlkMSRO0x3JFyy+LjFZIO3FXp0Mbf8CGynSlYH27KsLysHbYN/FmCcj0i/Cyns5l8Nw2+meCPtG3xfqp7l/fG7+/NoYwLCDNrEqfLRVXHLLFEBSJyPj6tcriZ/TZdKQbzZbIs+FVN+S1Iehfv4SwF3JRWIsysI4W4Bv+s0QeiEi7AHdR2MbNLzWyBGdXMdqEwv3xU4uAZpvW9FPafkzSSZtY29HBGFGcSKuZkGuclZrZgHn447xQKU0aLG8jEEtO9xP41wnBQktcEvJFaCfi7ma2XymtNvDe2Mu7wenNxWTPKPg6fQrkicFs6AJSZbYsHlwKfew8+y0T4VLq7gsKQpO+FNwrL4Q3230IesygESroprUSY2bJmNgp3vv0WuKSpMldJ8v6damYHpfLviEdp7Y5bYxpbnvoWvNe8HnB70b3qicvbFnhBFUZwzSBtoSt+NxolWD5uw31QkmnDN2YkTaxPRwYLGQBhxtXNeMArCI7JTfDPsF3PfIXZ5Fqd8SnMK2eUcwKVv8tjcQtPb2BU0TfdDbeqgDtuR+pIVCCWcMK0r0Nwx8RfAh+Y2TPmoYAfwCugcXg8hXI4Ap/TPQiYYWaTzewFvAIYjofZ/XGOIjSKpCm4KfNrPNjU+2b2dJDvPrxyuhV3+kxzOt4D3AZ4y8yexWd/XAr8ngx/CvlqqqNwX4JbzOxdM3sGj6x3fkh2Zpg6miZRVsaEe59EEZyEWyfWAF4r8lI/AI8Yuhkw1cwmmdmLeFTEQXjvc7cKTOjDcDP/dsBMM3vezKbhCuFKwDVhjn0y7XAIPv11EDDdzF4ws5fxKIZ9g0zDioa8zsTn7a8GPGRmM8J9fQ8PiQ3wP5LqsvqopHvxgF7tgBvM7G0zew5/Nw/G381hxVOgi67xDS772/gQwVtB9sn4NOLvhWODS12jgvJ+Eq4F8Fh4N/o0dk4RyfBSR2CipFcy0pyNO3ZuiH+vL5jZK/iz3R8ffvwWaF/COpUu70QK03dHmdmbYfhkJq6IXFDi1Ire5TD0eTD+bfwUeM/MJoZnMC3I8hwFxTdSJ6ICESHEZdgY741/gjcAnfDQySfgH29T3uPJtabj4Y//BLyJe6r3wqevjQL6lKjI6oakO1hYvo0IwaWAfSUNU9G6AZL+hveI78cVid54hXawpJKBoCT9DDcb342bUDfGzc8P4EGXsgJyHY4Pn7TB71XPcK2peHjhafjwUI+klxisPZsBP8cry+6hjO/icRs2kvRCBffoTdxv4Uy8h90btzA8AQyXdGRR+ilBtrNxa0Qv3BM+eWc2D4GA0ufMxRWV/fFhseXxZ/E1vqz7NpIqiZ5ZMcEzf3vcYbA9/q5/SgX3LCjdG+GyT8FlT/wqLgT6S5qZU5GH4vd02ZBHj8aTL8SjeOMN2dYHQsyL/njD/wEe72N13JpwBH6vkhDq5Ti/7o8Hp0tClHfBrQj9KVgoistQ8bscvs9tQ7ln4YpbF3z68MnAVmX4/kRqpM38+bXMaIpEIpFIJLIkEi0QkUgkEolEKiYqEJFIJBKJRComKhCRSCQSiUQqJioQkUgkEolEKiYqEJFIJBKJRComKhCRSCQSiUQqJioQkUgkEolEKiYqEJFIJBKJRComKhCRSCQSiUQqJioQkUgkEolEKiYqEJFIJBKJRCrm/wHbqjuK6ridhAAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "For n_clusters = 8 The average silhouette_score is : 0.11339573832632867\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "For n_clusters = 9 The average silhouette_score is : 0.12505960527779877\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "For n_clusters = 10 The average silhouette_score is : 0.11928332134753233\n"
     ]
    },
    {
     "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": [
    "from sklearn.cluster import KMeans\n",
    "from sklearn.metrics import silhouette_samples, silhouette_score\n",
    "\n",
    "import matplotlib.pyplot as plt\n",
    "import matplotlib.cm as cm\n",
    "import numpy as np\n",
    "\n",
    "Ks = range(2,11)\n",
    "\n",
    "for n_clusters in Ks: \n",
    "    \n",
    "    #create subplots\n",
    "    f, (ax1) = plt.subplots(1,1)\n",
    "    #f.set_size_inches(18, 7)\n",
    "    \n",
    "     # The 1st subplot is the silhouette plot\n",
    "    # The silhouette coefficient can range from -1, 1 but in this example all\n",
    "    # lie within [-0.1, 1]\n",
    "    ax1.set_xlim([-0.1, 1])\n",
    "    # The (n_clusters+1)*10 is for inserting blank space between silhouette\n",
    "    # plots of individual clusters, to demarcate them clearly.\n",
    "    ax1.set_ylim([0, len(x_cols) + (n_clusters + 1) * 10])\n",
    "    \n",
    "    #create silhouette plot\n",
    "    #initialize the clusterer with n_clusters value see 10 for reproducibility \n",
    "    clusterer = KMeans(n_clusters=n_clusters, random_state=10)\n",
    "    cluster_labels = clusterer.fit_predict(x_cols)\n",
    "\n",
    "    #the silhouette_score gives the average value for all the samples\n",
    "    silhouette_avg = silhouette_score(x_cols, cluster_labels)\n",
    "    print(\"For n_clusters =\", n_clusters,\n",
    "          \"The average silhouette_score is :\", silhouette_avg)\n",
    "    \n",
    "    #compute the silhouette scores for each sample\n",
    "    sample_silhouette_values = silhouette_samples(x_cols, cluster_labels)\n",
    "    \n",
    "    y_lower = 10\n",
    "    for i in range(n_clusters):\n",
    "        # Aggregate the silhouette scores for samples belonging to\n",
    "        # cluster i, and sort them\n",
    "        ith_cluster_silhouette_values = \\\n",
    "            sample_silhouette_values[cluster_labels == i]\n",
    "\n",
    "        ith_cluster_silhouette_values.sort()\n",
    "\n",
    "        size_cluster_i = ith_cluster_silhouette_values.shape[0]\n",
    "        y_upper = y_lower + size_cluster_i\n",
    "\n",
    "        color = cm.nipy_spectral(float(i) / n_clusters)\n",
    "        ax1.fill_betweenx(np.arange(y_lower, y_upper),\n",
    "                          0, ith_cluster_silhouette_values,\n",
    "                          facecolor=color, edgecolor=color, alpha=0.7)\n",
    "        \n",
    "        #label the silhouette plots with their cluster numbers at the middle \n",
    "        ax1.text(-0.05, y_lower + 0.5 * size_cluster_i, str(i))\n",
    "        \n",
    "        y_lower = y_upper + 10  # 10 for the 0 samples\n",
    "\n",
    "    ax1.set_xlabel(\"The silhouette coefficient values\")\n",
    "    ax1.set_ylabel(\"Cluster label\")\n",
    "    \n",
    "    #the vertical line for average silhouette score of all the values\n",
    "    ax1.axvline(x=silhouette_avg, color=\"red\", linestyle=\"--\")\n",
    "    \n",
    "    ax1.set_yticks([])  # Clear the yaxis labels / ticks\n",
    "    ax1.set_xticks([-0.1, 0, 0.2, 0.4, 0.6, 0.8, 1])\n",
    "    \n",
    "    plt.suptitle((\"Silhouette analysis for KMeans clustering on sample data \"\n",
    "                  \"with n_clusters = %d\" % n_clusters),\n",
    "                 fontsize=14, fontweight='bold')\n",
    "        \n",
    "\n",
    "    plt.show()\n",
    "    \n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<b> Compute the average silhouette score for each K and plot it. What K does the plot suggest we should choose? Does it differ from what we found using the Elbow method?</b>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[0.09365573283492713]\n",
      "[0.09365573283492713, 0.11889942863638556]\n",
      "[0.09365573283492713, 0.11889942863638556, 0.12347053919571699]\n",
      "[0.09365573283492713, 0.11889942863638556, 0.12347053919571699, 0.14092516241984757]\n",
      "[0.09365573283492713, 0.11889942863638556, 0.12347053919571699, 0.14092516241984757, 0.1371798939109807]\n",
      "[0.09365573283492713, 0.11889942863638556, 0.12347053919571699, 0.14092516241984757, 0.1371798939109807, 0.1161092456616906]\n",
      "[0.09365573283492713, 0.11889942863638556, 0.12347053919571699, 0.14092516241984757, 0.1371798939109807, 0.1161092456616906, 0.11339573832632867]\n",
      "[0.09365573283492713, 0.11889942863638556, 0.12347053919571699, 0.14092516241984757, 0.1371798939109807, 0.1161092456616906, 0.11339573832632867, 0.12505960527779877]\n",
      "[0.09365573283492713, 0.11889942863638556, 0.12347053919571699, 0.14092516241984757, 0.1371798939109807, 0.1161092456616906, 0.11339573832632867, 0.12505960527779877, 0.11928332134753233]\n"
     ]
    }
   ],
   "source": [
    "from sklearn.cluster import KMeans\n",
    "from sklearn.metrics import silhouette_score\n",
    "\n",
    "Ks = range(2,11)\n",
    "silhouette = []\n",
    "\n",
    "for k in Ks: \n",
    " \n",
    "    model = KMeans(n_clusters=k, random_state=10)\n",
    "    model.fit(x_cols)\n",
    "    pred = model.predict(x_cols)\n",
    "    silhouette.append(silhouette_score(x_cols, pred))\n",
    "    print(silhouette)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0, 0.5, 'silhouette score')"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.plot(Ks, silhouette, '-o')\n",
    "plt.xlabel('k number of clusters')\n",
    "plt.ylabel('silhouette score')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<b> Interpretation: </b> the best silhouette score is for k=5, which is different than the result with KMeans (k=4). "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Choosing $K$: The Gap Statistic\n",
    "\n",
    "There is one last method worth covering for picking $K$, the so-called Gap statistic. The computation for the gap statistic builds on the sum-of-squares established in the Elbow method discussion, and compares it to the sum-of-squares of a \"null distribution,\" that is, a random set of points with no clustering. The estimate for the optimal number of clusters $K$ is the value for which $\\log{SS}$ falls the farthest below that of the reference distribution:\n",
    "\n",
    "$$G_k = E_n^*\\{\\log SS_k\\} - \\log SS_k$$\n",
    "\n",
    "In other words a good clustering yields a much larger difference between the reference distribution and the clustered data. The reference distribution is a Monte Carlo (randomization) procedure that constructs $B$ random distributions of points within the bounding box (limits) of the original data and then applies K-means to this synthetic distribution of data points.. $E_n^*\\{\\log SS_k\\}$ is just the average $SS_k$ over all $B$ replicates. We then compute the standard deviation $\\sigma_{SS}$ of the values of $SS_k$ computed from the $B$ replicates of the reference distribution and compute\n",
    "\n",
    "$$s_k = \\sqrt{1+1/B}\\sigma_{SS}$$\n",
    "\n",
    "Finally, we choose $K=k$ such that $G_k \\geq G_{k+1} - s_{k+1}$."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Aside: Choosing $K$ when we Have Labels\n",
    "\n",
    "Unsupervised learning expects that we do not have the labels. In some situations, we may wish to cluster data that is labeled. Computing the optimal number of clusters is much easier if we have access to labels. There are several methods available. We will not go into the math or details since it is rare to have access to the labels, but we provide the names and references of these measures.\n",
    "\n",
    "* Adjusted Rand Index\n",
    "* Mutual Information\n",
    "* V-Measure\n",
    "* Fowlkes–Mallows index\n",
    "\n",
    "See [this article](http://scikit-learn.org/stable/modules/clustering.html) for more information about these metrics."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Visualizing Clusters using PCA\n",
    "\n",
    "How do we visualize clusters? If we only had two features, we could likely plot the data as is. But we have 100 data points each containing 32 features (dimensions). Principal Component Analysis (PCA) will help us reduce the dimensionality of our data from 32 to something lower. For a visualization on the coordinate plane, we will use 2 dimensions. In this exercise, we're going to use it to transform our multi-dimensional dataset into a 2 dimensional dataset.\n",
    "\n",
    "This is only one use of PCA for dimension reduction. We can also use PCA when we want to perform regression but we have a set of highly correlated variables. PCA untangles these correlations into a smaller number of features/predictors all of which are orthogonal (not correlated). PCA is also used to reduce a large set of variables into a much smaller one."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<div class=\"span5 alert alert-info\">\n",
    "<h3>Checkup Exercise Set IV</h3>\n",
    "\n",
    "<p><b>Exercise:</b> Use PCA to plot your clusters:</p>\n",
    "\n",
    "<ul>\n",
    "<li> Use scikit-learn's [`PCA`](http://scikit-learn.org/stable/modules/generated/sklearn.decomposition.PCA.html) function to reduce the dimensionality of your clustering data to 2 components\n",
    "<li> Create a data frame with the following fields:\n",
    "  <ul>\n",
    "  <li> customer name\n",
    "  <li> cluster id the customer belongs to\n",
    "  <li> the two PCA components (label them `x` and `y`)\n",
    "  </ul>\n",
    "<li> Plot a scatterplot of the `x` vs `y` columns\n",
    "<li> Color-code points differently based on cluster ID\n",
    "<li> How do the clusters look? \n",
    "<li> Based on what you see, what seems to be the best value for $K$? Moreover, which method of choosing $K$ seems to have produced the optimal result visually?\n",
    "</ul>\n",
    "\n",
    "<p><b>Exercise:</b> Now look at both the original raw data about the offers and transactions and look at the fitted clusters. Tell a story about the clusters in context of the original data. For example, do the clusters correspond to wine variants or something else interesting?</p>\n",
    "</div>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<b> Use scikit-learn's PCA function to reduce the dimensionality of your clustering data to 2 components. </b>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "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": [
    "from sklearn.decomposition import PCA\n",
    "model = PCA(n_components = 2)\n",
    "pca_features = model.fit_transform(x_cols)\n",
    "xs = pca_features[:,0]\n",
    "ys = pca_features[:,1]\n",
    "plt.scatter(xs, ys)\n",
    "plt.axis('equal')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [],
   "source": [
    "clusterer = KMeans(n_clusters = 4, random_state = 10)\n",
    "cluster_labels = clusterer.fit_predict(x_cols)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<b> Create a data frame with the following fields: customer name, cluster id the customer belongs to, and the two PCA components (label them `x` and `y`)</b>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_pca = pd.DataFrame({'customer_name': full_data_pivot.index, 'cluster_id': cluster_labels, 'x':pca_features[:,0], 'y':pca_features[:,1]})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "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>customer_name</th>\n",
       "      <th>cluster_id</th>\n",
       "      <th>x</th>\n",
       "      <th>y</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Adams</td>\n",
       "      <td>2</td>\n",
       "      <td>1.007580</td>\n",
       "      <td>0.108215</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Allen</td>\n",
       "      <td>1</td>\n",
       "      <td>-0.287539</td>\n",
       "      <td>0.044715</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Anderson</td>\n",
       "      <td>3</td>\n",
       "      <td>-0.392032</td>\n",
       "      <td>1.038391</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Bailey</td>\n",
       "      <td>2</td>\n",
       "      <td>0.699477</td>\n",
       "      <td>-0.022542</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Baker</td>\n",
       "      <td>1</td>\n",
       "      <td>0.088183</td>\n",
       "      <td>-0.471695</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  customer_name  cluster_id         x         y\n",
       "0         Adams           2  1.007580  0.108215\n",
       "1         Allen           1 -0.287539  0.044715\n",
       "2      Anderson           3 -0.392032  1.038391\n",
       "3        Bailey           2  0.699477 -0.022542\n",
       "4         Baker           1  0.088183 -0.471695"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_pca.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(100, 4)"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_pca.shape"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<b> Plot a scatterplot of the `x` vs `y` columns </b>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 464.975x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.lmplot(x = 'x', y = 'y', data = df_pca, hue = 'cluster_id', fit_reg = False)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<b> How do the clusters look? Based on what you see, what seems to be the best value for K? Moreover, which method of choosing K seems to have produced the optimal result visually?</b>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "I played aroung with the values of k, and visually it seems like k = 4 is the best, without too much overlapping. KMeans gave the same result, but not as easy to visualize as PCA. Personally, I think Silhouette gave us the wrongest result, even though it is visually nice. The best to visualize is for me PCA. "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<b> Now look at both the original raw data about the offers and transactions and look at the fitted clusters. Tell a story about the clusters in context of the original data. For example, do the clusters correspond to wine variants or something else interesting? </b>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {
    "scrolled": true
   },
   "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>offer_id</th>\n",
       "      <th>campaign</th>\n",
       "      <th>varietal</th>\n",
       "      <th>min_qty</th>\n",
       "      <th>discount</th>\n",
       "      <th>origin</th>\n",
       "      <th>past_peak</th>\n",
       "      <th>customer_name</th>\n",
       "      <th>n</th>\n",
       "      <th>cluster_id</th>\n",
       "      <th>x</th>\n",
       "      <th>y</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>January</td>\n",
       "      <td>Malbec</td>\n",
       "      <td>72</td>\n",
       "      <td>56</td>\n",
       "      <td>France</td>\n",
       "      <td>False</td>\n",
       "      <td>Thomas</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>-1.041879</td>\n",
       "      <td>0.122314</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>January</td>\n",
       "      <td>Malbec</td>\n",
       "      <td>72</td>\n",
       "      <td>56</td>\n",
       "      <td>France</td>\n",
       "      <td>False</td>\n",
       "      <td>Jackson</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>-0.839578</td>\n",
       "      <td>-0.328823</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1</td>\n",
       "      <td>January</td>\n",
       "      <td>Malbec</td>\n",
       "      <td>72</td>\n",
       "      <td>56</td>\n",
       "      <td>France</td>\n",
       "      <td>False</td>\n",
       "      <td>Mitchell</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>-0.342938</td>\n",
       "      <td>0.302186</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1</td>\n",
       "      <td>January</td>\n",
       "      <td>Malbec</td>\n",
       "      <td>72</td>\n",
       "      <td>56</td>\n",
       "      <td>France</td>\n",
       "      <td>False</td>\n",
       "      <td>Peterson</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>-0.797379</td>\n",
       "      <td>0.475995</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1</td>\n",
       "      <td>January</td>\n",
       "      <td>Malbec</td>\n",
       "      <td>72</td>\n",
       "      <td>56</td>\n",
       "      <td>France</td>\n",
       "      <td>False</td>\n",
       "      <td>Wood</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>-0.551215</td>\n",
       "      <td>-0.395670</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   offer_id campaign varietal  min_qty  discount  origin  past_peak  \\\n",
       "0         1  January   Malbec       72        56  France      False   \n",
       "1         1  January   Malbec       72        56  France      False   \n",
       "2         1  January   Malbec       72        56  France      False   \n",
       "3         1  January   Malbec       72        56  France      False   \n",
       "4         1  January   Malbec       72        56  France      False   \n",
       "\n",
       "  customer_name  n  cluster_id         x         y  \n",
       "0        Thomas  1           0 -1.041879  0.122314  \n",
       "1       Jackson  1           0 -0.839578 -0.328823  \n",
       "2      Mitchell  1           3 -0.342938  0.302186  \n",
       "3      Peterson  1           3 -0.797379  0.475995  \n",
       "4          Wood  1           1 -0.551215 -0.395670  "
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "final = full_data.merge(df_pca, how = 'left', on = 'customer_name')\n",
    "final.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "cluster0  origin      \n",
       "False     France          85\n",
       "          Australia       36\n",
       "          Chile           27\n",
       "          South Africa    25\n",
       "          Oregon          23\n",
       "          Italy           14\n",
       "          California      12\n",
       "          Germany         12\n",
       "          New Zealand     10\n",
       "True      France          43\n",
       "          Chile           10\n",
       "          Italy           10\n",
       "          California       4\n",
       "          Germany          4\n",
       "          Australia        3\n",
       "          New Zealand      3\n",
       "          Oregon           3\n",
       "Name: origin, dtype: int64"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "final['cluster0'] = final.cluster_id == 0\n",
    "final.groupby('cluster0')['origin'].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "cluster1  origin      \n",
       "False     France          85\n",
       "          Australia       28\n",
       "          Italy           22\n",
       "          Chile           16\n",
       "          Oregon          14\n",
       "          South Africa    12\n",
       "          Germany         11\n",
       "          California       6\n",
       "          New Zealand      4\n",
       "True      France          43\n",
       "          Chile           21\n",
       "          South Africa    13\n",
       "          Oregon          12\n",
       "          Australia       11\n",
       "          California      10\n",
       "          New Zealand      9\n",
       "          Germany          5\n",
       "          Italy            2\n",
       "Name: origin, dtype: int64"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "final['cluster1'] = final.cluster_id == 1\n",
    "final.groupby('cluster1')['origin'].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "cluster2  origin      \n",
       "False     France          95\n",
       "          Chile           31\n",
       "          Australia       27\n",
       "          Italy           24\n",
       "          California      16\n",
       "          Germany         16\n",
       "          Oregon          15\n",
       "          South Africa    14\n",
       "          New Zealand     13\n",
       "True      France          33\n",
       "          Australia       12\n",
       "          Oregon          11\n",
       "          South Africa    11\n",
       "          Chile            6\n",
       "Name: origin, dtype: int64"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "final['cluster2'] = final.cluster_id == 2\n",
    "final.groupby('cluster2')['origin'].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "cluster3  origin      \n",
       "False     France          119\n",
       "          Chile            37\n",
       "          Australia        26\n",
       "          Oregon           26\n",
       "          South Africa     24\n",
       "          California       14\n",
       "          Italy            12\n",
       "          New Zealand      12\n",
       "          Germany           9\n",
       "True      Australia        13\n",
       "          Italy            12\n",
       "          France            9\n",
       "          Germany           7\n",
       "          California        2\n",
       "          New Zealand       1\n",
       "          South Africa      1\n",
       "Name: origin, dtype: int64"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "final['cluster3'] = final.cluster_id == 3\n",
    "final.groupby('cluster3')['origin'].value_counts()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "What we've done is we've taken those columns of 0/1 indicator variables, and we've transformed them into a 2-D dataset. We took one column and arbitrarily called it `x` and then called the other `y`. Now we can throw each point into a scatterplot. We color coded each point based on it's cluster so it's easier to see them."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<div class=\"span5 alert alert-info\">\n",
    "<h3>Exercise Set V</h3>\n",
    "\n",
    "<p>As we saw earlier, PCA has a lot of other uses. Since we wanted to visualize our data in 2 dimensions, restricted the number of dimensions to 2 in PCA. But what is the true optimal number of dimensions?</p>\n",
    "\n",
    "<p><b>Exercise:</b> Using a new PCA object shown in the next cell, plot the `explained_variance_` field and look for the elbow point, the point where the curve's rate of descent seems to slow sharply. This value is one possible value for the optimal number of dimensions. What is it?</p>\n",
    "</div>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<b> Using a new PCA object shown in the next cell, plot the `explained_variance_` field and look for the elbow point, the point where the curve's rate of descent seems to slow sharply. This value is one possible value for the optimal number of dimensions. What is it? </b>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(32,)"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import sklearn.decomposition\n",
    "pca = sklearn.decomposition.PCA()\n",
    "pca.fit(x_cols)\n",
    "var_pca_df = pca.explained_variance_\n",
    "var_pca_df.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0, 0.5, 'explained variance')"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
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    }
   ],
   "source": [
    "plt.plot(range(1,33), var_pca_df, '-o')\n",
    "plt.xlabel('number of variables')\n",
    "plt.ylabel('explained variance')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<b> Answer: </b> the number of variables seems to be equal to 4 or 5"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Other Clustering Algorithms\n",
    "\n",
    "k-means is only one of a ton of clustering algorithms. Below is a brief description of several clustering algorithms, and the table provides references to the other clustering algorithms in scikit-learn. \n",
    "\n",
    "* **Affinity Propagation** does not require the number of clusters $K$ to be known in advance! AP uses a \"message passing\" paradigm to cluster points based on their similarity. \n",
    "\n",
    "* **Spectral Clustering** uses the eigenvalues of a similarity matrix to reduce the dimensionality of the data before clustering in a lower dimensional space. This is tangentially similar to what we did to visualize k-means clusters using PCA. The number of clusters must be known a priori.\n",
    "\n",
    "* **Ward's Method** applies to hierarchical clustering. Hierarchical clustering algorithms take a set of data and successively divide the observations into more and more clusters at each layer of the hierarchy. Ward's method is used to determine when two clusters in the hierarchy should be combined into one. It is basically an extension of hierarchical clustering. Hierarchical clustering is *divisive*, that is, all observations are part of the same cluster at first, and at each successive iteration, the clusters are made smaller and smaller. With hierarchical clustering, a hierarchy is constructed, and there is not really the concept of \"number of clusters.\" The number of clusters simply determines how low or how high in the hierarchy we reference and can be determined empirically or by looking at the [dendogram](https://docs.scipy.org/doc/scipy-0.18.1/reference/generated/scipy.cluster.hierarchy.dendrogram.html).\n",
    "\n",
    "* **Agglomerative Clustering** is similar to hierarchical clustering but but is not divisive, it is *agglomerative*. That is, every observation is placed into its own cluster and at each iteration or level or the hierarchy, observations are merged into fewer and fewer clusters until convergence. Similar to hierarchical clustering, the constructed hierarchy contains all possible numbers of clusters and it is up to the analyst to pick the number by reviewing statistics or the dendogram.\n",
    "\n",
    "* **DBSCAN** is based on point density rather than distance. It groups together points with many nearby neighbors. DBSCAN is one of the most cited algorithms in the literature. It does not require knowing the number of clusters a priori, but does require specifying the neighborhood size."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Clustering Algorithms in Scikit-learn\n",
    "<table border=\"1\">\n",
    "<colgroup>\n",
    "<col width=\"15%\" />\n",
    "<col width=\"16%\" />\n",
    "<col width=\"20%\" />\n",
    "<col width=\"27%\" />\n",
    "<col width=\"22%\" />\n",
    "</colgroup>\n",
    "<thead valign=\"bottom\">\n",
    "<tr><th>Method name</th>\n",
    "<th>Parameters</th>\n",
    "<th>Scalability</th>\n",
    "<th>Use Case</th>\n",
    "<th>Geometry (metric used)</th>\n",
    "</tr>\n",
    "</thead>\n",
    "<tbody valign=\"top\">\n",
    "<tr><td>K-Means</span></a></td>\n",
    "<td>number of clusters</td>\n",
    "<td>Very large<span class=\"pre\">n_samples</span>, medium <span class=\"pre\">n_clusters</span> with\n",
    "MiniBatch code</td>\n",
    "<td>General-purpose, even cluster size, flat geometry, not too many clusters</td>\n",
    "<td>Distances between points</td>\n",
    "</tr>\n",
    "<tr><td>Affinity propagation</td>\n",
    "<td>damping, sample preference</td>\n",
    "<td>Not scalable with n_samples</td>\n",
    "<td>Many clusters, uneven cluster size, non-flat geometry</td>\n",
    "<td>Graph distance (e.g. nearest-neighbor graph)</td>\n",
    "</tr>\n",
    "<tr><td>Mean-shift</td>\n",
    "<td>bandwidth</td>\n",
    "<td>Not scalable with <span class=\"pre\">n_samples</span></td>\n",
    "<td>Many clusters, uneven cluster size, non-flat geometry</td>\n",
    "<td>Distances between points</td>\n",
    "</tr>\n",
    "<tr><td>Spectral clustering</td>\n",
    "<td>number of clusters</td>\n",
    "<td>Medium <span class=\"pre\">n_samples</span>, small <span class=\"pre\">n_clusters</span></td>\n",
    "<td>Few clusters, even cluster size, non-flat geometry</td>\n",
    "<td>Graph distance (e.g. nearest-neighbor graph)</td>\n",
    "</tr>\n",
    "<tr><td>Ward hierarchical clustering</td>\n",
    "<td>number of clusters</td>\n",
    "<td>Large <span class=\"pre\">n_samples</span> and <span class=\"pre\">n_clusters</span></td>\n",
    "<td>Many clusters, possibly connectivity constraints</td>\n",
    "<td>Distances between points</td>\n",
    "</tr>\n",
    "<tr><td>Agglomerative clustering</td>\n",
    "<td>number of clusters, linkage type, distance</td>\n",
    "<td>Large <span class=\"pre\">n_samples</span> and <span class=\"pre\">n_clusters</span></td>\n",
    "<td>Many clusters, possibly connectivity constraints, non Euclidean\n",
    "distances</td>\n",
    "<td>Any pairwise distance</td>\n",
    "</tr>\n",
    "<tr><td>DBSCAN</td>\n",
    "<td>neighborhood size</td>\n",
    "<td>Very large <span class=\"pre\">n_samples</span>, medium <span class=\"pre\">n_clusters</span></td>\n",
    "<td>Non-flat geometry, uneven cluster sizes</td>\n",
    "<td>Distances between nearest points</td>\n",
    "</tr>\n",
    "<tr><td>Gaussian mixtures</td>\n",
    "<td>many</td>\n",
    "<td>Not scalable</td>\n",
    "<td>Flat geometry, good for density estimation</td>\n",
    "<td>Mahalanobis distances to  centers</td>\n",
    "</tr>\n",
    "<tr><td>Birch</td>\n",
    "<td>branching factor, threshold, optional global clusterer.</td>\n",
    "<td>Large <span class=\"pre\">n_clusters</span> and <span class=\"pre\">n_samples</span></td>\n",
    "<td>Large dataset, outlier removal, data reduction.</td>\n",
    "<td>Euclidean distance between points</td>\n",
    "</tr>\n",
    "</tbody>\n",
    "</table>\n",
    "Source: http://scikit-learn.org/stable/modules/clustering.html"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<div class=\"span5 alert alert-info\">\n",
    "<h3>Exercise Set VI</h3>\n",
    "\n",
    "<p><b>Exercise:</b> Try clustering using the following algorithms. </p>\n",
    "<ol>\n",
    "<li>Affinity propagation\n",
    "<li>Spectral clustering\n",
    "<li>Agglomerative clustering\n",
    "<li>DBSCAN\n",
    "</ol>\n",
    "<p>How do their results compare? Which performs the best? Tell a story why you think it performs the best.</p>\n",
    "</div>\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Your turn"
   ]
  }
 ],
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