AIR (HEIRARCHIAL CLUSTERING).ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "import seaborn as sns\n",
    "import matplotlib.pyplot as plt\n",
    "air= pd.read_csv(\"C:\\\\Users\\\\kumar\\\\Python Assign\\\\clustering\\\\Assignments\\\\EastWestAirlines.csv\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### WE ALWYAYS DO AGGLOMERATIVE HEIRARCHIAL CLUSTERING AS DIIVSIVE IS NOT SUITABLE FOR REAL WORLDPROBLLEMS"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "id                   0\n",
       "Balance              0\n",
       "Qual_miles           0\n",
       "cc1_miles            0\n",
       "cc2_miles            0\n",
       "cc3_miles            0\n",
       "Bonus_miles          0\n",
       "Bonus_trans          0\n",
       "Flight_miles_12mo    0\n",
       "Flight_trans_12      0\n",
       "Days_since_enroll    0\n",
       "award                0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "air.isnull().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>id</th>\n",
       "      <th>Balance</th>\n",
       "      <th>Qual_miles</th>\n",
       "      <th>cc1_miles</th>\n",
       "      <th>cc2_miles</th>\n",
       "      <th>cc3_miles</th>\n",
       "      <th>Bonus_miles</th>\n",
       "      <th>Bonus_trans</th>\n",
       "      <th>Flight_miles_12mo</th>\n",
       "      <th>Flight_trans_12</th>\n",
       "      <th>Days_since_enroll</th>\n",
       "      <th>award</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>3999.000000</td>\n",
       "      <td>3.999000e+03</td>\n",
       "      <td>3999.000000</td>\n",
       "      <td>3999.000000</td>\n",
       "      <td>3999.000000</td>\n",
       "      <td>3999.000000</td>\n",
       "      <td>3999.000000</td>\n",
       "      <td>3999.00000</td>\n",
       "      <td>3999.000000</td>\n",
       "      <td>3999.000000</td>\n",
       "      <td>3999.00000</td>\n",
       "      <td>3999.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>2014.819455</td>\n",
       "      <td>7.360133e+04</td>\n",
       "      <td>144.114529</td>\n",
       "      <td>2.059515</td>\n",
       "      <td>1.014504</td>\n",
       "      <td>1.012253</td>\n",
       "      <td>17144.846212</td>\n",
       "      <td>11.60190</td>\n",
       "      <td>460.055764</td>\n",
       "      <td>1.373593</td>\n",
       "      <td>4118.55939</td>\n",
       "      <td>0.370343</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>1160.764358</td>\n",
       "      <td>1.007757e+05</td>\n",
       "      <td>773.663804</td>\n",
       "      <td>1.376919</td>\n",
       "      <td>0.147650</td>\n",
       "      <td>0.195241</td>\n",
       "      <td>24150.967826</td>\n",
       "      <td>9.60381</td>\n",
       "      <td>1400.209171</td>\n",
       "      <td>3.793172</td>\n",
       "      <td>2065.13454</td>\n",
       "      <td>0.482957</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.00000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>2.00000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>1010.500000</td>\n",
       "      <td>1.852750e+04</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1250.000000</td>\n",
       "      <td>3.00000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>2330.00000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>2016.000000</td>\n",
       "      <td>4.309700e+04</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>7171.000000</td>\n",
       "      <td>12.00000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>4096.00000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>3020.500000</td>\n",
       "      <td>9.240400e+04</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>23800.500000</td>\n",
       "      <td>17.00000</td>\n",
       "      <td>311.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>5790.50000</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>4021.000000</td>\n",
       "      <td>1.704838e+06</td>\n",
       "      <td>11148.000000</td>\n",
       "      <td>5.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>5.000000</td>\n",
       "      <td>263685.000000</td>\n",
       "      <td>86.00000</td>\n",
       "      <td>30817.000000</td>\n",
       "      <td>53.000000</td>\n",
       "      <td>8296.00000</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                id       Balance    Qual_miles    cc1_miles    cc2_miles  \\\n",
       "count  3999.000000  3.999000e+03   3999.000000  3999.000000  3999.000000   \n",
       "mean   2014.819455  7.360133e+04    144.114529     2.059515     1.014504   \n",
       "std    1160.764358  1.007757e+05    773.663804     1.376919     0.147650   \n",
       "min       1.000000  0.000000e+00      0.000000     1.000000     1.000000   \n",
       "25%    1010.500000  1.852750e+04      0.000000     1.000000     1.000000   \n",
       "50%    2016.000000  4.309700e+04      0.000000     1.000000     1.000000   \n",
       "75%    3020.500000  9.240400e+04      0.000000     3.000000     1.000000   \n",
       "max    4021.000000  1.704838e+06  11148.000000     5.000000     3.000000   \n",
       "\n",
       "         cc3_miles    Bonus_miles  Bonus_trans  Flight_miles_12mo  \\\n",
       "count  3999.000000    3999.000000   3999.00000        3999.000000   \n",
       "mean      1.012253   17144.846212     11.60190         460.055764   \n",
       "std       0.195241   24150.967826      9.60381        1400.209171   \n",
       "min       1.000000       0.000000      0.00000           0.000000   \n",
       "25%       1.000000    1250.000000      3.00000           0.000000   \n",
       "50%       1.000000    7171.000000     12.00000           0.000000   \n",
       "75%       1.000000   23800.500000     17.00000         311.000000   \n",
       "max       5.000000  263685.000000     86.00000       30817.000000   \n",
       "\n",
       "       Flight_trans_12  Days_since_enroll        award  \n",
       "count      3999.000000         3999.00000  3999.000000  \n",
       "mean          1.373593         4118.55939     0.370343  \n",
       "std           3.793172         2065.13454     0.482957  \n",
       "min           0.000000            2.00000     0.000000  \n",
       "25%           0.000000         2330.00000     0.000000  \n",
       "50%           0.000000         4096.00000     0.000000  \n",
       "75%           1.000000         5790.50000     1.000000  \n",
       "max          53.000000         8296.00000     1.000000  "
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "air.describe()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "NO NULL VALUES SO SCALING THE FEATURES"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\kumar\\Anaconda3\\lib\\site-packages\\sklearn\\preprocessing\\data.py:645: DataConversionWarning: Data with input dtype int64 were all converted to float64 by StandardScaler.\n",
      "  return self.partial_fit(X, y)\n",
      "C:\\Users\\kumar\\Anaconda3\\lib\\site-packages\\sklearn\\base.py:464: DataConversionWarning: Data with input dtype int64 were all converted to float64 by StandardScaler.\n",
      "  return self.fit(X, **fit_params).transform(X)\n",
      "C:\\Users\\kumar\\Anaconda3\\lib\\site-packages\\sklearn\\preprocessing\\data.py:645: DataConversionWarning: Data with input dtype int64 were all converted to float64 by StandardScaler.\n",
      "  return self.partial_fit(X, y)\n",
      "C:\\Users\\kumar\\Anaconda3\\lib\\site-packages\\sklearn\\base.py:464: DataConversionWarning: Data with input dtype int64 were all converted to float64 by StandardScaler.\n",
      "  return self.fit(X, **fit_params).transform(X)\n",
      "C:\\Users\\kumar\\Anaconda3\\lib\\site-packages\\sklearn\\preprocessing\\data.py:645: DataConversionWarning: Data with input dtype int64 were all converted to float64 by StandardScaler.\n",
      "  return self.partial_fit(X, y)\n",
      "C:\\Users\\kumar\\Anaconda3\\lib\\site-packages\\sklearn\\base.py:464: DataConversionWarning: Data with input dtype int64 were all converted to float64 by StandardScaler.\n",
      "  return self.fit(X, **fit_params).transform(X)\n",
      "C:\\Users\\kumar\\Anaconda3\\lib\\site-packages\\sklearn\\preprocessing\\data.py:645: DataConversionWarning: Data with input dtype int64 were all converted to float64 by StandardScaler.\n",
      "  return self.partial_fit(X, y)\n",
      "C:\\Users\\kumar\\Anaconda3\\lib\\site-packages\\sklearn\\base.py:464: DataConversionWarning: Data with input dtype int64 were all converted to float64 by StandardScaler.\n",
      "  return self.fit(X, **fit_params).transform(X)\n",
      "C:\\Users\\kumar\\Anaconda3\\lib\\site-packages\\sklearn\\preprocessing\\data.py:645: DataConversionWarning: Data with input dtype int64 were all converted to float64 by StandardScaler.\n",
      "  return self.partial_fit(X, y)\n",
      "C:\\Users\\kumar\\Anaconda3\\lib\\site-packages\\sklearn\\base.py:464: DataConversionWarning: Data with input dtype int64 were all converted to float64 by StandardScaler.\n",
      "  return self.fit(X, **fit_params).transform(X)\n",
      "C:\\Users\\kumar\\Anaconda3\\lib\\site-packages\\sklearn\\preprocessing\\data.py:645: DataConversionWarning: Data with input dtype int64 were all converted to float64 by StandardScaler.\n",
      "  return self.partial_fit(X, y)\n",
      "C:\\Users\\kumar\\Anaconda3\\lib\\site-packages\\sklearn\\base.py:464: DataConversionWarning: Data with input dtype int64 were all converted to float64 by StandardScaler.\n",
      "  return self.fit(X, **fit_params).transform(X)\n",
      "C:\\Users\\kumar\\Anaconda3\\lib\\site-packages\\sklearn\\preprocessing\\data.py:645: DataConversionWarning: Data with input dtype int64 were all converted to float64 by StandardScaler.\n",
      "  return self.partial_fit(X, y)\n",
      "C:\\Users\\kumar\\Anaconda3\\lib\\site-packages\\sklearn\\base.py:464: DataConversionWarning: Data with input dtype int64 were all converted to float64 by StandardScaler.\n",
      "  return self.fit(X, **fit_params).transform(X)\n",
      "C:\\Users\\kumar\\Anaconda3\\lib\\site-packages\\sklearn\\preprocessing\\data.py:645: DataConversionWarning: Data with input dtype int64 were all converted to float64 by StandardScaler.\n",
      "  return self.partial_fit(X, y)\n",
      "C:\\Users\\kumar\\Anaconda3\\lib\\site-packages\\sklearn\\base.py:464: DataConversionWarning: Data with input dtype int64 were all converted to float64 by StandardScaler.\n",
      "  return self.fit(X, **fit_params).transform(X)\n",
      "C:\\Users\\kumar\\Anaconda3\\lib\\site-packages\\sklearn\\preprocessing\\data.py:645: DataConversionWarning: Data with input dtype int64 were all converted to float64 by StandardScaler.\n",
      "  return self.partial_fit(X, y)\n",
      "C:\\Users\\kumar\\Anaconda3\\lib\\site-packages\\sklearn\\base.py:464: DataConversionWarning: Data with input dtype int64 were all converted to float64 by StandardScaler.\n",
      "  return self.fit(X, **fit_params).transform(X)\n",
      "C:\\Users\\kumar\\Anaconda3\\lib\\site-packages\\sklearn\\preprocessing\\data.py:645: DataConversionWarning: Data with input dtype int64 were all converted to float64 by StandardScaler.\n",
      "  return self.partial_fit(X, y)\n",
      "C:\\Users\\kumar\\Anaconda3\\lib\\site-packages\\sklearn\\base.py:464: DataConversionWarning: Data with input dtype int64 were all converted to float64 by StandardScaler.\n",
      "  return self.fit(X, **fit_params).transform(X)\n",
      "C:\\Users\\kumar\\Anaconda3\\lib\\site-packages\\sklearn\\preprocessing\\data.py:645: DataConversionWarning: Data with input dtype int64 were all converted to float64 by StandardScaler.\n",
      "  return self.partial_fit(X, y)\n",
      "C:\\Users\\kumar\\Anaconda3\\lib\\site-packages\\sklearn\\base.py:464: DataConversionWarning: Data with input dtype int64 were all converted to float64 by StandardScaler.\n",
      "  return self.fit(X, **fit_params).transform(X)\n",
      "C:\\Users\\kumar\\Anaconda3\\lib\\site-packages\\sklearn\\preprocessing\\data.py:645: DataConversionWarning: Data with input dtype int64 were all converted to float64 by StandardScaler.\n",
      "  return self.partial_fit(X, y)\n",
      "C:\\Users\\kumar\\Anaconda3\\lib\\site-packages\\sklearn\\base.py:464: DataConversionWarning: Data with input dtype int64 were all converted to float64 by StandardScaler.\n",
      "  return self.fit(X, **fit_params).transform(X)\n",
      "C:\\Users\\kumar\\Anaconda3\\lib\\site-packages\\ipykernel_launcher.py:14: UserWarning: Pandas doesn't allow columns to be created via a new attribute name - see https://pandas.pydata.org/pandas-docs/stable/indexing.html#attribute-access\n",
      "  \n"
     ]
    }
   ],
   "source": [
    "from sklearn.preprocessing import StandardScaler\n",
    "scaler= StandardScaler()\n",
    "air.id=scaler.fit_transform(air[['id']])\n",
    "air.Balance=scaler.fit_transform(air[['Balance']])\n",
    "air.Qual_miles=scaler.fit_transform(air[['Qual_miles']])\n",
    "air.cc1_miles=scaler.fit_transform(air[['cc1_miles']])\n",
    "air.cc2_miles=scaler.fit_transform(air[['cc2_miles']])\n",
    "air.cc3_miles=scaler.fit_transform(air[['cc3_miles']])\n",
    "air.Bonus_miles=scaler.fit_transform(air[['Bonus_miles']])\n",
    "air.Bonus_trans=scaler.fit_transform(air[['Bonus_trans']])\n",
    "air.Flight_miles_12mo=scaler.fit_transform(air[['Flight_miles_12mo']])\n",
    "air.Flight_trans_12=scaler.fit_transform(air[['Flight_trans_12']])\n",
    "air.Days_since_enroll=scaler.fit_transform(air[['Days_since_enroll']])\n",
    "air.Award=scaler.fit_transform(air[['award']])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
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       "      <th>id</th>\n",
       "      <th>Balance</th>\n",
       "      <th>Qual_miles</th>\n",
       "      <th>cc1_miles</th>\n",
       "      <th>cc2_miles</th>\n",
       "      <th>cc3_miles</th>\n",
       "      <th>Bonus_miles</th>\n",
       "      <th>Bonus_trans</th>\n",
       "      <th>Flight_miles_12mo</th>\n",
       "      <th>Flight_trans_12</th>\n",
       "      <th>Days_since_enroll</th>\n",
       "      <th>award</th>\n",
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       "      <th>0</th>\n",
       "      <td>-1.735125</td>\n",
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       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>-1.734263</td>\n",
       "      <td>-0.539457</td>\n",
       "      <td>-0.186299</td>\n",
       "      <td>-0.769578</td>\n",
       "      <td>-0.098242</td>\n",
       "      <td>-0.062767</td>\n",
       "      <td>-0.701088</td>\n",
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       "      <td>1.379957</td>\n",
       "      <td>0</td>\n",
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       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>-1.733402</td>\n",
       "      <td>-0.320031</td>\n",
       "      <td>-0.186299</td>\n",
       "      <td>-0.769578</td>\n",
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       "      <td>-0.539253</td>\n",
       "      <td>-0.791649</td>\n",
       "      <td>-0.328603</td>\n",
       "      <td>-0.362168</td>\n",
       "      <td>1.411920</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>-1.732540</td>\n",
       "      <td>-0.583799</td>\n",
       "      <td>-0.186299</td>\n",
       "      <td>-0.769578</td>\n",
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       "      <td>-1.104065</td>\n",
       "      <td>-0.328603</td>\n",
       "      <td>-0.362168</td>\n",
       "      <td>1.372208</td>\n",
       "      <td>0</td>\n",
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       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>-1.731679</td>\n",
       "      <td>0.239678</td>\n",
       "      <td>-0.186299</td>\n",
       "      <td>1.409471</td>\n",
       "      <td>-0.098242</td>\n",
       "      <td>-0.062767</td>\n",
       "      <td>1.083121</td>\n",
       "      <td>1.499394</td>\n",
       "      <td>1.154932</td>\n",
       "      <td>0.692490</td>\n",
       "      <td>1.363975</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>-1.730817</td>\n",
       "      <td>-0.567483</td>\n",
       "      <td>-0.186299</td>\n",
       "      <td>-0.769578</td>\n",
       "      <td>-0.098242</td>\n",
       "      <td>-0.062767</td>\n",
       "      <td>-0.709992</td>\n",
       "      <td>-1.208203</td>\n",
       "      <td>-0.328603</td>\n",
       "      <td>-0.362168</td>\n",
       "      <td>1.367365</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>-1.729955</td>\n",
       "      <td>0.112270</td>\n",
       "      <td>-0.186299</td>\n",
       "      <td>0.683121</td>\n",
       "      <td>-0.098242</td>\n",
       "      <td>-0.062767</td>\n",
       "      <td>0.428076</td>\n",
       "      <td>1.395256</td>\n",
       "      <td>-0.328603</td>\n",
       "      <td>-0.362168</td>\n",
       "      <td>1.392549</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>-1.729094</td>\n",
       "      <td>-0.523459</td>\n",
       "      <td>-0.186299</td>\n",
       "      <td>-0.769578</td>\n",
       "      <td>-0.098242</td>\n",
       "      <td>-0.062767</td>\n",
       "      <td>-0.492582</td>\n",
       "      <td>-0.791649</td>\n",
       "      <td>-0.150036</td>\n",
       "      <td>-0.098503</td>\n",
       "      <td>1.365428</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>-1.728232</td>\n",
       "      <td>3.666042</td>\n",
       "      <td>-0.186299</td>\n",
       "      <td>0.683121</td>\n",
       "      <td>6.675367</td>\n",
       "      <td>-0.062767</td>\n",
       "      <td>-0.637398</td>\n",
       "      <td>3.269747</td>\n",
       "      <td>2.421330</td>\n",
       "      <td>2.801807</td>\n",
       "      <td>1.370271</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>-1.727371</td>\n",
       "      <td>0.310220</td>\n",
       "      <td>-0.186299</td>\n",
       "      <td>0.683121</td>\n",
       "      <td>-0.098242</td>\n",
       "      <td>-0.062767</td>\n",
       "      <td>0.467168</td>\n",
       "      <td>1.707671</td>\n",
       "      <td>0.492805</td>\n",
       "      <td>0.428826</td>\n",
       "      <td>1.362038</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>-1.726509</td>\n",
       "      <td>-0.332566</td>\n",
       "      <td>-0.186299</td>\n",
       "      <td>-0.043229</td>\n",
       "      <td>-0.098242</td>\n",
       "      <td>-0.062767</td>\n",
       "      <td>-0.408600</td>\n",
       "      <td>-0.166819</td>\n",
       "      <td>-0.328603</td>\n",
       "      <td>-0.362168</td>\n",
       "      <td>1.375598</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>-1.725647</td>\n",
       "      <td>0.227471</td>\n",
       "      <td>-0.186299</td>\n",
       "      <td>2.135821</td>\n",
       "      <td>-0.098242</td>\n",
       "      <td>-0.062767</td>\n",
       "      <td>1.820451</td>\n",
       "      <td>0.770426</td>\n",
       "      <td>-0.328603</td>\n",
       "      <td>-0.362168</td>\n",
       "      <td>1.358648</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>-1.724786</td>\n",
       "      <td>-0.299905</td>\n",
       "      <td>-0.186299</td>\n",
       "      <td>-0.043229</td>\n",
       "      <td>-0.098242</td>\n",
       "      <td>-0.062767</td>\n",
       "      <td>-0.248255</td>\n",
       "      <td>0.874564</td>\n",
       "      <td>-0.328603</td>\n",
       "      <td>-0.362168</td>\n",
       "      <td>1.358648</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>-1.723924</td>\n",
       "      <td>-0.302733</td>\n",
       "      <td>-0.186299</td>\n",
       "      <td>-0.769578</td>\n",
       "      <td>-0.098242</td>\n",
       "      <td>-0.062767</td>\n",
       "      <td>-0.575074</td>\n",
       "      <td>-0.583373</td>\n",
       "      <td>-0.328603</td>\n",
       "      <td>-0.362168</td>\n",
       "      <td>1.355743</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>-1.723063</td>\n",
       "      <td>-0.555296</td>\n",
       "      <td>-0.186299</td>\n",
       "      <td>-0.769578</td>\n",
       "      <td>-0.098242</td>\n",
       "      <td>-0.062767</td>\n",
       "      <td>-0.709992</td>\n",
       "      <td>-1.208203</td>\n",
       "      <td>-0.328603</td>\n",
       "      <td>-0.362168</td>\n",
       "      <td>1.352837</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>-1.722201</td>\n",
       "      <td>-0.447647</td>\n",
       "      <td>-0.186299</td>\n",
       "      <td>1.409471</td>\n",
       "      <td>-0.098242</td>\n",
       "      <td>-0.062767</td>\n",
       "      <td>1.337470</td>\n",
       "      <td>0.353873</td>\n",
       "      <td>-0.328603</td>\n",
       "      <td>-0.362168</td>\n",
       "      <td>1.352837</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>-1.721339</td>\n",
       "      <td>-0.215469</td>\n",
       "      <td>-0.186299</td>\n",
       "      <td>1.409471</td>\n",
       "      <td>-0.098242</td>\n",
       "      <td>-0.062767</td>\n",
       "      <td>1.317634</td>\n",
       "      <td>0.458011</td>\n",
       "      <td>-0.328603</td>\n",
       "      <td>-0.362168</td>\n",
       "      <td>1.351868</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>-1.720478</td>\n",
       "      <td>-0.591917</td>\n",
       "      <td>-0.186299</td>\n",
       "      <td>-0.769578</td>\n",
       "      <td>-0.098242</td>\n",
       "      <td>-0.062767</td>\n",
       "      <td>-0.532296</td>\n",
       "      <td>-0.687511</td>\n",
       "      <td>-0.328603</td>\n",
       "      <td>-0.362168</td>\n",
       "      <td>1.349447</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>-1.719616</td>\n",
       "      <td>0.177363</td>\n",
       "      <td>-0.186299</td>\n",
       "      <td>0.683121</td>\n",
       "      <td>-0.098242</td>\n",
       "      <td>-0.062767</td>\n",
       "      <td>0.425011</td>\n",
       "      <td>0.562149</td>\n",
       "      <td>-0.328603</td>\n",
       "      <td>-0.362168</td>\n",
       "      <td>1.348478</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>-1.718754</td>\n",
       "      <td>-0.498668</td>\n",
       "      <td>-0.186299</td>\n",
       "      <td>0.683121</td>\n",
       "      <td>-0.098242</td>\n",
       "      <td>-0.062767</td>\n",
       "      <td>-0.277367</td>\n",
       "      <td>-0.687511</td>\n",
       "      <td>-0.328603</td>\n",
       "      <td>-0.362168</td>\n",
       "      <td>1.345088</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>-1.717893</td>\n",
       "      <td>0.466189</td>\n",
       "      <td>-0.186299</td>\n",
       "      <td>2.135821</td>\n",
       "      <td>-0.098242</td>\n",
       "      <td>-0.062767</td>\n",
       "      <td>1.726281</td>\n",
       "      <td>1.186979</td>\n",
       "      <td>-0.150036</td>\n",
       "      <td>0.165161</td>\n",
       "      <td>1.345088</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>-1.717031</td>\n",
       "      <td>1.112309</td>\n",
       "      <td>2.430152</td>\n",
       "      <td>-0.769578</td>\n",
       "      <td>-0.098242</td>\n",
       "      <td>-0.062767</td>\n",
       "      <td>-0.159220</td>\n",
       "      <td>0.458011</td>\n",
       "      <td>0.957080</td>\n",
       "      <td>2.010813</td>\n",
       "      <td>1.345088</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>-1.716170</td>\n",
       "      <td>-0.526158</td>\n",
       "      <td>-0.186299</td>\n",
       "      <td>-0.769578</td>\n",
       "      <td>-0.098242</td>\n",
       "      <td>-0.062767</td>\n",
       "      <td>-0.567123</td>\n",
       "      <td>-0.062681</td>\n",
       "      <td>2.135622</td>\n",
       "      <td>2.538142</td>\n",
       "      <td>1.339277</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>-1.715308</td>\n",
       "      <td>-0.072708</td>\n",
       "      <td>-0.186299</td>\n",
       "      <td>-0.769578</td>\n",
       "      <td>-0.098242</td>\n",
       "      <td>-0.062767</td>\n",
       "      <td>-0.605097</td>\n",
       "      <td>-0.062681</td>\n",
       "      <td>-0.221463</td>\n",
       "      <td>-0.098503</td>\n",
       "      <td>1.339277</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>-1.714446</td>\n",
       "      <td>1.310497</td>\n",
       "      <td>0.460058</td>\n",
       "      <td>-0.769578</td>\n",
       "      <td>-0.098242</td>\n",
       "      <td>-0.062767</td>\n",
       "      <td>-0.543311</td>\n",
       "      <td>0.978703</td>\n",
       "      <td>0.171385</td>\n",
       "      <td>0.692490</td>\n",
       "      <td>1.846813</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>-1.713585</td>\n",
       "      <td>-0.524749</td>\n",
       "      <td>-0.186299</td>\n",
       "      <td>-0.769578</td>\n",
       "      <td>-0.098242</td>\n",
       "      <td>-0.062767</td>\n",
       "      <td>-0.653051</td>\n",
       "      <td>-0.791649</td>\n",
       "      <td>-0.328603</td>\n",
       "      <td>-0.362168</td>\n",
       "      <td>1.842939</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26</th>\n",
       "      <td>-1.712723</td>\n",
       "      <td>-0.546632</td>\n",
       "      <td>-0.186299</td>\n",
       "      <td>-0.769578</td>\n",
       "      <td>-0.098242</td>\n",
       "      <td>-0.062767</td>\n",
       "      <td>-0.659180</td>\n",
       "      <td>-0.999926</td>\n",
       "      <td>0.547804</td>\n",
       "      <td>0.165161</td>\n",
       "      <td>1.839549</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27</th>\n",
       "      <td>-1.711862</td>\n",
       "      <td>-0.642828</td>\n",
       "      <td>-0.186299</td>\n",
       "      <td>-0.769578</td>\n",
       "      <td>-0.098242</td>\n",
       "      <td>-0.062767</td>\n",
       "      <td>-0.709992</td>\n",
       "      <td>-1.208203</td>\n",
       "      <td>-0.328603</td>\n",
       "      <td>-0.362168</td>\n",
       "      <td>1.838096</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28</th>\n",
       "      <td>-1.711000</td>\n",
       "      <td>-0.137335</td>\n",
       "      <td>-0.186299</td>\n",
       "      <td>0.683121</td>\n",
       "      <td>-0.098242</td>\n",
       "      <td>-0.062767</td>\n",
       "      <td>0.688553</td>\n",
       "      <td>0.874564</td>\n",
       "      <td>-0.257176</td>\n",
       "      <td>-0.098503</td>\n",
       "      <td>1.834706</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29</th>\n",
       "      <td>-1.710138</td>\n",
       "      <td>-0.539685</td>\n",
       "      <td>-0.186299</td>\n",
       "      <td>-0.769578</td>\n",
       "      <td>-0.098242</td>\n",
       "      <td>-0.062767</td>\n",
       "      <td>-0.517222</td>\n",
       "      <td>-0.375096</td>\n",
       "      <td>0.028531</td>\n",
       "      <td>-0.098503</td>\n",
       "      <td>1.829379</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3969</th>\n",
       "      <td>1.703557</td>\n",
       "      <td>-0.606069</td>\n",
       "      <td>-0.186299</td>\n",
       "      <td>-0.769578</td>\n",
       "      <td>-0.098242</td>\n",
       "      <td>-0.062767</td>\n",
       "      <td>-0.624312</td>\n",
       "      <td>-0.270958</td>\n",
       "      <td>-0.328603</td>\n",
       "      <td>-0.362168</td>\n",
       "      <td>-1.294779</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3970</th>\n",
       "      <td>1.704418</td>\n",
       "      <td>-0.208582</td>\n",
       "      <td>-0.186299</td>\n",
       "      <td>-0.769578</td>\n",
       "      <td>-0.098242</td>\n",
       "      <td>-0.062767</td>\n",
       "      <td>-0.606463</td>\n",
       "      <td>-1.104065</td>\n",
       "      <td>-0.328603</td>\n",
       "      <td>-0.362168</td>\n",
       "      <td>-1.297685</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3971</th>\n",
       "      <td>1.705280</td>\n",
       "      <td>-0.522486</td>\n",
       "      <td>-0.186299</td>\n",
       "      <td>-0.769578</td>\n",
       "      <td>-0.098242</td>\n",
       "      <td>-0.062767</td>\n",
       "      <td>-0.694463</td>\n",
       "      <td>-0.895788</td>\n",
       "      <td>-0.328603</td>\n",
       "      <td>-0.362168</td>\n",
       "      <td>-1.298169</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3972</th>\n",
       "      <td>1.706141</td>\n",
       "      <td>-0.381254</td>\n",
       "      <td>-0.186299</td>\n",
       "      <td>-0.043229</td>\n",
       "      <td>-0.098242</td>\n",
       "      <td>-0.062767</td>\n",
       "      <td>-0.463304</td>\n",
       "      <td>-0.479234</td>\n",
       "      <td>-0.328603</td>\n",
       "      <td>-0.362168</td>\n",
       "      <td>-1.297685</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3973</th>\n",
       "      <td>1.707003</td>\n",
       "      <td>-0.012945</td>\n",
       "      <td>-0.186299</td>\n",
       "      <td>0.683121</td>\n",
       "      <td>-0.098242</td>\n",
       "      <td>-0.062767</td>\n",
       "      <td>-0.037430</td>\n",
       "      <td>0.458011</td>\n",
       "      <td>-0.328603</td>\n",
       "      <td>-0.362168</td>\n",
       "      <td>-1.226010</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3974</th>\n",
       "      <td>1.707865</td>\n",
       "      <td>-0.150991</td>\n",
       "      <td>-0.186299</td>\n",
       "      <td>0.683121</td>\n",
       "      <td>-0.098242</td>\n",
       "      <td>-0.062767</td>\n",
       "      <td>-0.106172</td>\n",
       "      <td>2.853193</td>\n",
       "      <td>-0.328603</td>\n",
       "      <td>-0.362168</td>\n",
       "      <td>-1.299622</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3975</th>\n",
       "      <td>1.708726</td>\n",
       "      <td>-0.639851</td>\n",
       "      <td>-0.186299</td>\n",
       "      <td>-0.769578</td>\n",
       "      <td>-0.098242</td>\n",
       "      <td>-0.062767</td>\n",
       "      <td>-0.709992</td>\n",
       "      <td>-1.208203</td>\n",
       "      <td>-0.328603</td>\n",
       "      <td>-0.362168</td>\n",
       "      <td>-1.304950</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3976</th>\n",
       "      <td>1.709588</td>\n",
       "      <td>-0.358081</td>\n",
       "      <td>-0.186299</td>\n",
       "      <td>-0.769578</td>\n",
       "      <td>-0.098242</td>\n",
       "      <td>-0.062767</td>\n",
       "      <td>0.115089</td>\n",
       "      <td>-0.583373</td>\n",
       "      <td>-0.328603</td>\n",
       "      <td>-0.362168</td>\n",
       "      <td>-1.301075</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3977</th>\n",
       "      <td>1.710449</td>\n",
       "      <td>-0.637161</td>\n",
       "      <td>-0.186299</td>\n",
       "      <td>-0.769578</td>\n",
       "      <td>-0.098242</td>\n",
       "      <td>-0.062767</td>\n",
       "      <td>-0.621993</td>\n",
       "      <td>-0.166819</td>\n",
       "      <td>-0.328603</td>\n",
       "      <td>-0.362168</td>\n",
       "      <td>-1.302044</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3978</th>\n",
       "      <td>1.711311</td>\n",
       "      <td>-0.630492</td>\n",
       "      <td>-0.186299</td>\n",
       "      <td>-0.043229</td>\n",
       "      <td>-0.098242</td>\n",
       "      <td>-0.062767</td>\n",
       "      <td>0.437145</td>\n",
       "      <td>0.458011</td>\n",
       "      <td>-0.328603</td>\n",
       "      <td>-0.362168</td>\n",
       "      <td>-1.306402</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3979</th>\n",
       "      <td>1.712173</td>\n",
       "      <td>-0.156886</td>\n",
       "      <td>-0.186299</td>\n",
       "      <td>0.683121</td>\n",
       "      <td>-0.098242</td>\n",
       "      <td>-0.062767</td>\n",
       "      <td>0.157949</td>\n",
       "      <td>0.353873</td>\n",
       "      <td>0.527090</td>\n",
       "      <td>0.428826</td>\n",
       "      <td>-1.304950</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3980</th>\n",
       "      <td>1.713034</td>\n",
       "      <td>-0.443956</td>\n",
       "      <td>-0.186299</td>\n",
       "      <td>0.683121</td>\n",
       "      <td>-0.098242</td>\n",
       "      <td>-0.062767</td>\n",
       "      <td>0.083823</td>\n",
       "      <td>1.707671</td>\n",
       "      <td>-0.328603</td>\n",
       "      <td>-0.362168</td>\n",
       "      <td>-1.304465</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3981</th>\n",
       "      <td>1.713896</td>\n",
       "      <td>-0.720416</td>\n",
       "      <td>-0.186299</td>\n",
       "      <td>-0.769578</td>\n",
       "      <td>-0.098242</td>\n",
       "      <td>-0.062767</td>\n",
       "      <td>-0.709992</td>\n",
       "      <td>-1.208203</td>\n",
       "      <td>-0.328603</td>\n",
       "      <td>-0.362168</td>\n",
       "      <td>-1.305434</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3982</th>\n",
       "      <td>1.714758</td>\n",
       "      <td>-0.616678</td>\n",
       "      <td>-0.186299</td>\n",
       "      <td>-0.769578</td>\n",
       "      <td>-0.098242</td>\n",
       "      <td>-0.062767</td>\n",
       "      <td>-0.695953</td>\n",
       "      <td>-0.791649</td>\n",
       "      <td>-0.328603</td>\n",
       "      <td>-0.362168</td>\n",
       "      <td>-1.304465</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3983</th>\n",
       "      <td>1.715619</td>\n",
       "      <td>-0.470692</td>\n",
       "      <td>-0.186299</td>\n",
       "      <td>-0.769578</td>\n",
       "      <td>-0.098242</td>\n",
       "      <td>-0.062767</td>\n",
       "      <td>-0.697361</td>\n",
       "      <td>-1.104065</td>\n",
       "      <td>-0.328603</td>\n",
       "      <td>-0.362168</td>\n",
       "      <td>-1.307855</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3984</th>\n",
       "      <td>1.716481</td>\n",
       "      <td>-0.726430</td>\n",
       "      <td>-0.186299</td>\n",
       "      <td>-0.769578</td>\n",
       "      <td>-0.098242</td>\n",
       "      <td>-0.062767</td>\n",
       "      <td>-0.687216</td>\n",
       "      <td>-0.895788</td>\n",
       "      <td>-0.328603</td>\n",
       "      <td>-0.362168</td>\n",
       "      <td>-1.307855</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3985</th>\n",
       "      <td>1.717342</td>\n",
       "      <td>-0.144739</td>\n",
       "      <td>-0.186299</td>\n",
       "      <td>1.409471</td>\n",
       "      <td>-0.098242</td>\n",
       "      <td>-0.062767</td>\n",
       "      <td>0.728888</td>\n",
       "      <td>1.395256</td>\n",
       "      <td>-0.328603</td>\n",
       "      <td>-0.362168</td>\n",
       "      <td>-1.307855</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3986</th>\n",
       "      <td>1.718204</td>\n",
       "      <td>-0.390682</td>\n",
       "      <td>-0.186299</td>\n",
       "      <td>-0.769578</td>\n",
       "      <td>-0.098242</td>\n",
       "      <td>-0.062767</td>\n",
       "      <td>0.073097</td>\n",
       "      <td>-0.479234</td>\n",
       "      <td>-0.150036</td>\n",
       "      <td>-0.098503</td>\n",
       "      <td>-1.309308</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3987</th>\n",
       "      <td>1.719066</td>\n",
       "      <td>-0.612013</td>\n",
       "      <td>-0.186299</td>\n",
       "      <td>-0.769578</td>\n",
       "      <td>-0.098242</td>\n",
       "      <td>-0.062767</td>\n",
       "      <td>-0.699680</td>\n",
       "      <td>-0.895788</td>\n",
       "      <td>-0.272176</td>\n",
       "      <td>-0.098503</td>\n",
       "      <td>-1.310761</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3988</th>\n",
       "      <td>1.719927</td>\n",
       "      <td>-0.680818</td>\n",
       "      <td>-0.186299</td>\n",
       "      <td>-0.769578</td>\n",
       "      <td>-0.098242</td>\n",
       "      <td>-0.062767</td>\n",
       "      <td>-0.621993</td>\n",
       "      <td>-0.895788</td>\n",
       "      <td>-0.328603</td>\n",
       "      <td>-0.362168</td>\n",
       "      <td>-1.310761</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3989</th>\n",
       "      <td>1.720789</td>\n",
       "      <td>-0.704418</td>\n",
       "      <td>-0.186299</td>\n",
       "      <td>-0.769578</td>\n",
       "      <td>-0.098242</td>\n",
       "      <td>-0.062767</td>\n",
       "      <td>-0.642698</td>\n",
       "      <td>-0.583373</td>\n",
       "      <td>-0.328603</td>\n",
       "      <td>-0.362168</td>\n",
       "      <td>-1.314635</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3990</th>\n",
       "      <td>1.721650</td>\n",
       "      <td>-0.618196</td>\n",
       "      <td>-0.186299</td>\n",
       "      <td>-0.769578</td>\n",
       "      <td>-0.098242</td>\n",
       "      <td>-0.062767</td>\n",
       "      <td>-0.502065</td>\n",
       "      <td>-0.999926</td>\n",
       "      <td>-0.328603</td>\n",
       "      <td>-0.362168</td>\n",
       "      <td>-1.312214</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3991</th>\n",
       "      <td>1.722512</td>\n",
       "      <td>-0.341984</td>\n",
       "      <td>-0.186299</td>\n",
       "      <td>0.683121</td>\n",
       "      <td>-0.098242</td>\n",
       "      <td>-0.062767</td>\n",
       "      <td>-0.089608</td>\n",
       "      <td>1.707671</td>\n",
       "      <td>-0.328603</td>\n",
       "      <td>-0.362168</td>\n",
       "      <td>-1.312698</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3992</th>\n",
       "      <td>1.723374</td>\n",
       "      <td>-0.619476</td>\n",
       "      <td>-0.186299</td>\n",
       "      <td>-0.769578</td>\n",
       "      <td>-0.098242</td>\n",
       "      <td>-0.062767</td>\n",
       "      <td>-0.671521</td>\n",
       "      <td>0.041457</td>\n",
       "      <td>-0.328603</td>\n",
       "      <td>-0.362168</td>\n",
       "      <td>-1.313182</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3993</th>\n",
       "      <td>1.724235</td>\n",
       "      <td>-0.691000</td>\n",
       "      <td>-0.186299</td>\n",
       "      <td>-0.769578</td>\n",
       "      <td>-0.098242</td>\n",
       "      <td>-0.062767</td>\n",
       "      <td>-0.694877</td>\n",
       "      <td>-0.895788</td>\n",
       "      <td>-0.328603</td>\n",
       "      <td>-0.362168</td>\n",
       "      <td>-1.318025</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3994</th>\n",
       "      <td>1.725097</td>\n",
       "      <td>-0.547079</td>\n",
       "      <td>-0.186299</td>\n",
       "      <td>-0.769578</td>\n",
       "      <td>-0.098242</td>\n",
       "      <td>-0.062767</td>\n",
       "      <td>-0.356960</td>\n",
       "      <td>-0.791649</td>\n",
       "      <td>-0.185750</td>\n",
       "      <td>-0.098503</td>\n",
       "      <td>-1.315120</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3995</th>\n",
       "      <td>1.725958</td>\n",
       "      <td>-0.091465</td>\n",
       "      <td>-0.186299</td>\n",
       "      <td>-0.769578</td>\n",
       "      <td>-0.098242</td>\n",
       "      <td>-0.062767</td>\n",
       "      <td>-0.669367</td>\n",
       "      <td>-0.687511</td>\n",
       "      <td>-0.328603</td>\n",
       "      <td>-0.362168</td>\n",
       "      <td>-1.318994</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3996</th>\n",
       "      <td>1.726820</td>\n",
       "      <td>-0.000043</td>\n",
       "      <td>-0.186299</td>\n",
       "      <td>0.683121</td>\n",
       "      <td>-0.098242</td>\n",
       "      <td>-0.062767</td>\n",
       "      <td>0.343804</td>\n",
       "      <td>-0.375096</td>\n",
       "      <td>-0.328603</td>\n",
       "      <td>-0.362168</td>\n",
       "      <td>-1.315604</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3997</th>\n",
       "      <td>1.727682</td>\n",
       "      <td>-0.185607</td>\n",
       "      <td>-0.186299</td>\n",
       "      <td>-0.769578</td>\n",
       "      <td>-0.098242</td>\n",
       "      <td>-0.062767</td>\n",
       "      <td>-0.689286</td>\n",
       "      <td>-1.104065</td>\n",
       "      <td>0.028531</td>\n",
       "      <td>-0.098503</td>\n",
       "      <td>-1.316088</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3998</th>\n",
       "      <td>1.728543</td>\n",
       "      <td>-0.700508</td>\n",
       "      <td>-0.186299</td>\n",
       "      <td>-0.769578</td>\n",
       "      <td>-0.098242</td>\n",
       "      <td>-0.062767</td>\n",
       "      <td>-0.709992</td>\n",
       "      <td>-1.208203</td>\n",
       "      <td>-0.328603</td>\n",
       "      <td>-0.362168</td>\n",
       "      <td>-1.317541</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>3999 rows × 12 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "            id   Balance  Qual_miles  cc1_miles  cc2_miles  cc3_miles  \\\n",
       "0    -1.735125 -0.451141   -0.186299  -0.769578  -0.098242  -0.062767   \n",
       "1    -1.734263 -0.539457   -0.186299  -0.769578  -0.098242  -0.062767   \n",
       "2    -1.733402 -0.320031   -0.186299  -0.769578  -0.098242  -0.062767   \n",
       "3    -1.732540 -0.583799   -0.186299  -0.769578  -0.098242  -0.062767   \n",
       "4    -1.731679  0.239678   -0.186299   1.409471  -0.098242  -0.062767   \n",
       "5    -1.730817 -0.567483   -0.186299  -0.769578  -0.098242  -0.062767   \n",
       "6    -1.729955  0.112270   -0.186299   0.683121  -0.098242  -0.062767   \n",
       "7    -1.729094 -0.523459   -0.186299  -0.769578  -0.098242  -0.062767   \n",
       "8    -1.728232  3.666042   -0.186299   0.683121   6.675367  -0.062767   \n",
       "9    -1.727371  0.310220   -0.186299   0.683121  -0.098242  -0.062767   \n",
       "10   -1.726509 -0.332566   -0.186299  -0.043229  -0.098242  -0.062767   \n",
       "11   -1.725647  0.227471   -0.186299   2.135821  -0.098242  -0.062767   \n",
       "12   -1.724786 -0.299905   -0.186299  -0.043229  -0.098242  -0.062767   \n",
       "13   -1.723924 -0.302733   -0.186299  -0.769578  -0.098242  -0.062767   \n",
       "14   -1.723063 -0.555296   -0.186299  -0.769578  -0.098242  -0.062767   \n",
       "15   -1.722201 -0.447647   -0.186299   1.409471  -0.098242  -0.062767   \n",
       "16   -1.721339 -0.215469   -0.186299   1.409471  -0.098242  -0.062767   \n",
       "17   -1.720478 -0.591917   -0.186299  -0.769578  -0.098242  -0.062767   \n",
       "18   -1.719616  0.177363   -0.186299   0.683121  -0.098242  -0.062767   \n",
       "19   -1.718754 -0.498668   -0.186299   0.683121  -0.098242  -0.062767   \n",
       "20   -1.717893  0.466189   -0.186299   2.135821  -0.098242  -0.062767   \n",
       "21   -1.717031  1.112309    2.430152  -0.769578  -0.098242  -0.062767   \n",
       "22   -1.716170 -0.526158   -0.186299  -0.769578  -0.098242  -0.062767   \n",
       "23   -1.715308 -0.072708   -0.186299  -0.769578  -0.098242  -0.062767   \n",
       "24   -1.714446  1.310497    0.460058  -0.769578  -0.098242  -0.062767   \n",
       "25   -1.713585 -0.524749   -0.186299  -0.769578  -0.098242  -0.062767   \n",
       "26   -1.712723 -0.546632   -0.186299  -0.769578  -0.098242  -0.062767   \n",
       "27   -1.711862 -0.642828   -0.186299  -0.769578  -0.098242  -0.062767   \n",
       "28   -1.711000 -0.137335   -0.186299   0.683121  -0.098242  -0.062767   \n",
       "29   -1.710138 -0.539685   -0.186299  -0.769578  -0.098242  -0.062767   \n",
       "...        ...       ...         ...        ...        ...        ...   \n",
       "3969  1.703557 -0.606069   -0.186299  -0.769578  -0.098242  -0.062767   \n",
       "3970  1.704418 -0.208582   -0.186299  -0.769578  -0.098242  -0.062767   \n",
       "3971  1.705280 -0.522486   -0.186299  -0.769578  -0.098242  -0.062767   \n",
       "3972  1.706141 -0.381254   -0.186299  -0.043229  -0.098242  -0.062767   \n",
       "3973  1.707003 -0.012945   -0.186299   0.683121  -0.098242  -0.062767   \n",
       "3974  1.707865 -0.150991   -0.186299   0.683121  -0.098242  -0.062767   \n",
       "3975  1.708726 -0.639851   -0.186299  -0.769578  -0.098242  -0.062767   \n",
       "3976  1.709588 -0.358081   -0.186299  -0.769578  -0.098242  -0.062767   \n",
       "3977  1.710449 -0.637161   -0.186299  -0.769578  -0.098242  -0.062767   \n",
       "3978  1.711311 -0.630492   -0.186299  -0.043229  -0.098242  -0.062767   \n",
       "3979  1.712173 -0.156886   -0.186299   0.683121  -0.098242  -0.062767   \n",
       "3980  1.713034 -0.443956   -0.186299   0.683121  -0.098242  -0.062767   \n",
       "3981  1.713896 -0.720416   -0.186299  -0.769578  -0.098242  -0.062767   \n",
       "3982  1.714758 -0.616678   -0.186299  -0.769578  -0.098242  -0.062767   \n",
       "3983  1.715619 -0.470692   -0.186299  -0.769578  -0.098242  -0.062767   \n",
       "3984  1.716481 -0.726430   -0.186299  -0.769578  -0.098242  -0.062767   \n",
       "3985  1.717342 -0.144739   -0.186299   1.409471  -0.098242  -0.062767   \n",
       "3986  1.718204 -0.390682   -0.186299  -0.769578  -0.098242  -0.062767   \n",
       "3987  1.719066 -0.612013   -0.186299  -0.769578  -0.098242  -0.062767   \n",
       "3988  1.719927 -0.680818   -0.186299  -0.769578  -0.098242  -0.062767   \n",
       "3989  1.720789 -0.704418   -0.186299  -0.769578  -0.098242  -0.062767   \n",
       "3990  1.721650 -0.618196   -0.186299  -0.769578  -0.098242  -0.062767   \n",
       "3991  1.722512 -0.341984   -0.186299   0.683121  -0.098242  -0.062767   \n",
       "3992  1.723374 -0.619476   -0.186299  -0.769578  -0.098242  -0.062767   \n",
       "3993  1.724235 -0.691000   -0.186299  -0.769578  -0.098242  -0.062767   \n",
       "3994  1.725097 -0.547079   -0.186299  -0.769578  -0.098242  -0.062767   \n",
       "3995  1.725958 -0.091465   -0.186299  -0.769578  -0.098242  -0.062767   \n",
       "3996  1.726820 -0.000043   -0.186299   0.683121  -0.098242  -0.062767   \n",
       "3997  1.727682 -0.185607   -0.186299  -0.769578  -0.098242  -0.062767   \n",
       "3998  1.728543 -0.700508   -0.186299  -0.769578  -0.098242  -0.062767   \n",
       "\n",
       "      Bonus_miles  Bonus_trans  Flight_miles_12mo  Flight_trans_12  \\\n",
       "0       -0.702786    -1.104065          -0.328603        -0.362168   \n",
       "1       -0.701088    -0.999926          -0.328603        -0.362168   \n",
       "2       -0.539253    -0.791649          -0.328603        -0.362168   \n",
       "3       -0.689286    -1.104065          -0.328603        -0.362168   \n",
       "4        1.083121     1.499394           1.154932         0.692490   \n",
       "5       -0.709992    -1.208203          -0.328603        -0.362168   \n",
       "6        0.428076     1.395256          -0.328603        -0.362168   \n",
       "7       -0.492582    -0.791649          -0.150036        -0.098503   \n",
       "8       -0.637398     3.269747           2.421330         2.801807   \n",
       "9        0.467168     1.707671           0.492805         0.428826   \n",
       "10      -0.408600    -0.166819          -0.328603        -0.362168   \n",
       "11       1.820451     0.770426          -0.328603        -0.362168   \n",
       "12      -0.248255     0.874564          -0.328603        -0.362168   \n",
       "13      -0.575074    -0.583373          -0.328603        -0.362168   \n",
       "14      -0.709992    -1.208203          -0.328603        -0.362168   \n",
       "15       1.337470     0.353873          -0.328603        -0.362168   \n",
       "16       1.317634     0.458011          -0.328603        -0.362168   \n",
       "17      -0.532296    -0.687511          -0.328603        -0.362168   \n",
       "18       0.425011     0.562149          -0.328603        -0.362168   \n",
       "19      -0.277367    -0.687511          -0.328603        -0.362168   \n",
       "20       1.726281     1.186979          -0.150036         0.165161   \n",
       "21      -0.159220     0.458011           0.957080         2.010813   \n",
       "22      -0.567123    -0.062681           2.135622         2.538142   \n",
       "23      -0.605097    -0.062681          -0.221463        -0.098503   \n",
       "24      -0.543311     0.978703           0.171385         0.692490   \n",
       "25      -0.653051    -0.791649          -0.328603        -0.362168   \n",
       "26      -0.659180    -0.999926           0.547804         0.165161   \n",
       "27      -0.709992    -1.208203          -0.328603        -0.362168   \n",
       "28       0.688553     0.874564          -0.257176        -0.098503   \n",
       "29      -0.517222    -0.375096           0.028531        -0.098503   \n",
       "...           ...          ...                ...              ...   \n",
       "3969    -0.624312    -0.270958          -0.328603        -0.362168   \n",
       "3970    -0.606463    -1.104065          -0.328603        -0.362168   \n",
       "3971    -0.694463    -0.895788          -0.328603        -0.362168   \n",
       "3972    -0.463304    -0.479234          -0.328603        -0.362168   \n",
       "3973    -0.037430     0.458011          -0.328603        -0.362168   \n",
       "3974    -0.106172     2.853193          -0.328603        -0.362168   \n",
       "3975    -0.709992    -1.208203          -0.328603        -0.362168   \n",
       "3976     0.115089    -0.583373          -0.328603        -0.362168   \n",
       "3977    -0.621993    -0.166819          -0.328603        -0.362168   \n",
       "3978     0.437145     0.458011          -0.328603        -0.362168   \n",
       "3979     0.157949     0.353873           0.527090         0.428826   \n",
       "3980     0.083823     1.707671          -0.328603        -0.362168   \n",
       "3981    -0.709992    -1.208203          -0.328603        -0.362168   \n",
       "3982    -0.695953    -0.791649          -0.328603        -0.362168   \n",
       "3983    -0.697361    -1.104065          -0.328603        -0.362168   \n",
       "3984    -0.687216    -0.895788          -0.328603        -0.362168   \n",
       "3985     0.728888     1.395256          -0.328603        -0.362168   \n",
       "3986     0.073097    -0.479234          -0.150036        -0.098503   \n",
       "3987    -0.699680    -0.895788          -0.272176        -0.098503   \n",
       "3988    -0.621993    -0.895788          -0.328603        -0.362168   \n",
       "3989    -0.642698    -0.583373          -0.328603        -0.362168   \n",
       "3990    -0.502065    -0.999926          -0.328603        -0.362168   \n",
       "3991    -0.089608     1.707671          -0.328603        -0.362168   \n",
       "3992    -0.671521     0.041457          -0.328603        -0.362168   \n",
       "3993    -0.694877    -0.895788          -0.328603        -0.362168   \n",
       "3994    -0.356960    -0.791649          -0.185750        -0.098503   \n",
       "3995    -0.669367    -0.687511          -0.328603        -0.362168   \n",
       "3996     0.343804    -0.375096          -0.328603        -0.362168   \n",
       "3997    -0.689286    -1.104065           0.028531        -0.098503   \n",
       "3998    -0.709992    -1.208203          -0.328603        -0.362168   \n",
       "\n",
       "      Days_since_enroll  award  \n",
       "0              1.395454      0  \n",
       "1              1.379957      0  \n",
       "2              1.411920      0  \n",
       "3              1.372208      0  \n",
       "4              1.363975      1  \n",
       "5              1.367365      0  \n",
       "6              1.392549      0  \n",
       "7              1.365428      1  \n",
       "8              1.370271      1  \n",
       "9              1.362038      1  \n",
       "10             1.375598      0  \n",
       "11             1.358648      1  \n",
       "12             1.358648      0  \n",
       "13             1.355743      0  \n",
       "14             1.352837      0  \n",
       "15             1.352837      1  \n",
       "16             1.351868      1  \n",
       "17             1.349447      1  \n",
       "18             1.348478      1  \n",
       "19             1.345088      1  \n",
       "20             1.345088      1  \n",
       "21             1.345088      1  \n",
       "22             1.339277      0  \n",
       "23             1.339277      0  \n",
       "24             1.846813      0  \n",
       "25             1.842939      0  \n",
       "26             1.839549      0  \n",
       "27             1.838096      0  \n",
       "28             1.834706      1  \n",
       "29             1.829379      1  \n",
       "...                 ...    ...  \n",
       "3969          -1.294779      0  \n",
       "3970          -1.297685      0  \n",
       "3971          -1.298169      0  \n",
       "3972          -1.297685      0  \n",
       "3973          -1.226010      1  \n",
       "3974          -1.299622      0  \n",
       "3975          -1.304950      0  \n",
       "3976          -1.301075      0  \n",
       "3977          -1.302044      0  \n",
       "3978          -1.306402      1  \n",
       "3979          -1.304950      1  \n",
       "3980          -1.304465      0  \n",
       "3981          -1.305434      0  \n",
       "3982          -1.304465      0  \n",
       "3983          -1.307855      0  \n",
       "3984          -1.307855      0  \n",
       "3985          -1.307855      0  \n",
       "3986          -1.309308      0  \n",
       "3987          -1.310761      1  \n",
       "3988          -1.310761      0  \n",
       "3989          -1.314635      1  \n",
       "3990          -1.312214      0  \n",
       "3991          -1.312698      0  \n",
       "3992          -1.313182      0  \n",
       "3993          -1.318025      0  \n",
       "3994          -1.315120      1  \n",
       "3995          -1.318994      1  \n",
       "3996          -1.315604      1  \n",
       "3997          -1.316088      0  \n",
       "3998          -1.317541      0  \n",
       "\n",
       "[3999 rows x 12 columns]"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "air"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## PLOTTING DENDOGRAM"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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X97YcG2n/funAgV6XAgCQGUFWv5makqanpfHxXpdkQxwc30LBleS3rUSQBQBbAEFWPxoflw4e7HUpkMPERK9LAADYILTJAgAAyIAgCwAAIAOCLAAAgAwIsgAAADIgyAIAAMiAIAsAACADgiwAAIAMCLIAAAAyIMgCAADIgCALAAAgA4IsAACADAiyAAAAMiDIAgAAyIAgCwAAIINtvS4AAKDczMyk5uamel0MLSy8VZJ0+PCre1wSaWxsv/btO9DrYgBNIcgCgD41NzelhYVpjY6O97Qcb39774MrSVpYmJYkgiwMDIIsAOhjo6PjuuGGg70uRl84fHii10UAWkKbLAAAgAwIsgAAADIgyAIAAMiAIAsAACADgiwAAIAMCLIAAAAyIMgCAADIgCALAAAgA4IsAACADAiyAAAAMiDIAgAAyIAgCwAAIAOCLAAAgAwIsgAAADIgyAIAAMiAIAsAACADgiwAAIAMCLIAAAAyIMgCAADIgCALAAAgA4IsAACADAiyAAAAMiDIAgAAyIAgCwAAIAOCLAAAgAwIsgAAADIgyAIAAMiAIAsAACCDjoIsM7vQzN5vZl80s/vM7DvN7GIz+7iZfTn8v6hbhQUAABgUnWay/lDS3znn/hdJ10u6T9KvSPqEc+5aSZ8I7wEAALaUtoMsM3uapH8j6R2S5JyrOOdOSrpF0rvCYO+SdGunhQQAABg0nWSyvkHSCUl/YWaHzezPzOwCSWPOuVlJCv/3dqGcAAAAA6WTIGubpG+V9CfOuRsknVYLVYNmdsDM7jazu0+cONFBMQAAAPpPJ0HWMUnHnHOfCe/fLx90zZnZ5ZIU/h8vG9k5N+mcu9E5d+OePXs6KAYAAED/aTvIcs49IulhM3t2+OhFku6V9CFJt4XPbpP0wY5KCAAAMIC2dTj+f5D0bjMbkfSgpNvlA7f3mdmrJD0k6eUdzgMAAGDgdBRkOeemJd1Y8tWLOpkuAADAoKPHdwAAgAwIsgAAADIgyAIAAMiAIAsAACADgiwAAIAMCLIAAAAy6LSfLABAj8zMTGpubqrXxdgwCwvTkqTDhyd6W5ANMja2X/v2Heh1MdABMlkAMKDm5qbWAo+tYHR0XKOj470uxoZYWJjeUgH0ZkUmCwAG2OjouG644WCvi4Eu2yrZus2OTBYAAEAGBFkAAAAZEGQBAABkQJAFAACQAUEWAABABgRZAAAAGRBkAQAAZECQBQAAkAFBFgAAQAYEWQAAABkQZAEAAGRAkAUAAJABQRYAAEAGBFkAAAAZEGQBAABksK3XBQAAoBkzM5Oam5vqdTE2xMLCtCTp8OGJ3hZkg4yN7de+fQd6XYyuI5MFABgIc3NTa8HHZjc6Oq7R0fFeF2NDLCxMb9rgmUwWAGBgjI6O64YbDva6GOiizZytI5MFAACQAZksAADQszZvvW5/lrM9GJksAADQszZvvWx/lrs9GJksAAAgaeu1ecudPSOTBQAAkAFBFgAAQAYEWQAAABkQZAEAAGRAkAUAAJABQRYAAEAGBFkAAAAZEGQBAABkQJAFAACQAUEWAABABgRZAAAAGfDsQgBAR2ZmJrM+ZDeKDy/O/bw5SRob2699+w5knw82NzJZAICOzM1NrQVAOY2Ojmt0dDz7fBYWpjckaMTmRyYLANCx0dFx3XDDwV4Xoys2IlOGrYFMFgAAQAYEWQAAABkQZAEAAGRAkAUAAJABQRYAAEAGBFkAAAAZdBxkmdmwmR02s4+E9880s8+Y2ZfN7L1mNtJ5MQEAAAZLNzJZvyjpvuT970j6A+fctZKekPSqLswDAABgoHQUZJnZlZK+T9Kfhfcm6bskvT8M8i5Jt3YyDwAAgEHUaSbrrZJeI2k1vL9E0knn3Nnw/pikKzqcBwAAwMBpO8gys5dKOu6cuyf9uGRQV2P8A2Z2t5ndfeLEiXaLAQAA0Jc6yWQ9X9IPmNlXJf21fDXhWyVdaGbxmYhXSpopG9k5N+mcu9E5d+OePXs6KAYAAED/aTvIcs79qnPuSufc1ZJeIen/c879mKRPSvqhMNhtkj7YcSkBAAAGTI5+su6Q9Etmdr98G613ZJgHAABAX9vWeJDGnHMHJR0Mrx+UdFM3pgsAADCouhJkAX1lclKamup1KcpNT/v/ExM9LUZN+/dLBw70uhQAsCnwWB1sPlNT1WCm34yP+79+ND3dv8EpAAwgMlnYnMbHpYMHe12KwdKv2TUAGFBksgAAADIgkwUAmUzOzGhqbk6SNL3wLEnSxOH7tX9sTAf27etl0QBsAIIsAMhkam5O0wsLGh8d1fjb75ckTS8sSBJBFrAFEGQBQEbjo6M6eMMNa+8nDh/uYWkAbCTaZAEAAGRAkAUAAJABQRYAAEAGBFkAAAAZEGQBAABkQJAFAACQAUEWAABABvSTBQADZGZmUnNz/kHeCwv+QeiHD09IksbG9mvfvgO9KhqAAjJZADBA5uam1oKr0dFxjY6OS/IBVwy+APQHMlkAMGBGR8d1ww0H130Ws1kA+geZLAAAgAwIsgAAADIgyAIAAMhg67XJmpyUpvq4cei0b9CqiYmeFqOh/fulA9zFBABALVsvyJqa8oHM+HivS1KuX8uVioEgQRbQlMmZGU3NzUmSphcWJEkThw9r/9iYDuzb18uiAcho6wVZkg9kDh7sdSkGV79n2YA+MzU3p+mFBY2Pjmp8dFRSNdgiyAI2r60ZZAHABhsfHdXBG25Yez9x+HAPSwNgI9DwHQAAIAOCLAAAgAyoLgQAbErpcx5bUXwmZCt4fiRSZLIAAJtS+pzHVqTPhGwFz49EUV9ksk6cOaGJd05o+pG3SpIm3vlq7f9f9+vAt/FrAMDWMDM5o4XpXRoZG5G0o9fF2TTKnvOYC8+PRFFfBFmPLz6u+UfmNf4rr5YkTT/if3kQZAHYKuam5vSW+aPaPb5b0g0NhwfQ//oiyJKk8cvGdfAnD0qSJt450dOyAAAAdIo2WQAAABn0TSYLAACg2+rdZVrvTtJu3ClKJgsAAGxa9e4yrXUnabfuFCWTBQAANrVW7zLt1p2iZLIAAAAyIJMF9NLkpDTVJ50XTod0+sRET4uxzv790gG6cgEwmMhkAb00NVUNbnptfNz/9Yvp6f4JQAGgDWSygF4bH5cOHux1KfpPP2XUAKANBFkAAAyodh+CXaaTB2MX8aBsj+pCAAAGVLsPwS7T7oOxi3hQdhWZLAAABthGPgS7GTwou4pMFgAAQAYEWQAAABlQXQgAfSY2Zo5tbWZmJmlEjA3XbqP6ThrQb7YG82SyAKDPxAArNkKmETF6od1G9e02oN+MDebJZAFAH4qNmWlEjF7ayEb1m3FfJ5MFAACQAUEWAABABlQXAui+bjz4ulsPrOYh0wB6hEwWgO7rxoOvu/HAah4yDaCHyGQByKMfHny9yR8ynd5in942v9lugwcGVduZLDO7ysw+aWb3mdkXzOwXw+cXm9nHzezL4f9F3SsuACBKb7GPt81vxtvggUHVSSbrrKRfds591sx2SbrHzD4u6SclfcI59yYz+xVJvyLpjs6LCgAoKt5ivxlvgwcGVduZLOfcrHPus+H1KUn3SbpC0i2S3hUGe5ekWzstJABsJpMzM5peWND0woImZ2Z6XRwAmXSl4buZXS3pBkmfkTTmnJuVfCAmaW+NcQ6Y2d1mdvfy8nI3igEAA2Fqbq70NYDNpeOG72Y2KulvJL3aOfekmTU1nnNuUtKkJO165i7XaTkAYJCMj472uggAMusok2Vm2+UDrHc75z4QPp4zs8vD95dLOt5ZEQEAAAZPJ3cXmqR3SLrPOff7yVcfknRbeH2bpA+2XzwAAIDB1El14fMl/bikz5tZ7HXwtZLeJOl9ZvYqSQ9JenlnRQR6pBu9ljfSrV7N66HHcwDoibaDLOfcnZJqNcB6UbvTBfpG7LW8017H68k5bakaxBFkbQozM5Nr/WLNzExmmX47fWylHaG2io5TsZnR4/tW0q3MTLezL/2caemHXss7scl7PN9q0gAoR4ejsXPT0dHWgv9Wh49icEaQ1bxiIFwW4BK4tqbsx0W31itB1lbSrcxMN7MvZFqAlrQb0LQy/bRz05zoOLV1xUC4uD8QuLau7MdFt9YrQdZW02+ZGTIt2ORmKxXNVSqSfCekB/bt63GJMOjqBcIEru1p9OOi3fVKkAUAGcUAS/IdjxJkDa5GbdaabZtGdd7WQZAFAJnR8ejm0KjNWjNVuVTnbS19FWRN3jOpqc9PafqR6bX3B76NHREA0B86bbNGdd7W0ldBVgywxi8b1/Qj05r6/BRBFrDVdXJXbDfuhO3nu18B9LWuPCC6m8YvG9fBnzyo8csy9x+EzWdy0l9Mp6f938SE/wyDLd4V247x8c7uhp2ezt8hLYBNq68yWUBHil1U0D3E5tGru2K5+3VgNNvXkbRxDc9z9r80iLpx48CgrSuCLGwu6cWYCySaUa86sl51I9WIfaWZvo6kjW14nrP/pUHU6Y0Dg7iuCLIAbG31OumtVdXYwyxppTKrSmVOIyNjGhm5fMPn38+aaZS+0Q3Pc/W/NKg6uXFgENcVQRYAtFod2YUsabGT0ueWDFP2rMJKZU4rK/MaGaHdKtDvCLKQV6M7wxrd/UWVDDapYielZUFW7mcVAsiLIGvQtXJ7e6u3s3cjwGn0vMR6d37RcB0DbrZS0cLKiu47c0ZLq6uSfNYqKuukdGF6QSOzFY1cPiIp/7MKAeRDkDXoWnnocyu3snczwGn3zjAarmPAXT4yoqOLi3pseVmjw8OSfNaqluHdw1qZX1HleDXIAjC4CLI2gxy3txPgAF1V69E6sW3WbGVEuza4TMBmFruMKHYNsZHdQPRdZ6QAsJVcPjKi+ZUVHU/aaLUrNpRfWJjW4cMTa43lga0o7TIiVrsvLExvaPvGwc1ktfuojXYfs0ED7MER9410W7P9sAXEi8fo6PhA9ikEdFuxy4iN7gZicIOsVtoipdp5xAYNsAcLPb9jwLnRE1pYuL+tvrDiRaUf+hTaij18A6nBDbKk9tsitZMFi8/CaxaZk9YVt0tZ1rHZ9UrP7+iGWueKzD3B28IerVz6kCoVDXSHo1uxh28gNdhBVrtazYK1mv0ic9Ke4nYprnfWKzZarXNFmz3Bx0buYyOd3zlYqcyoUjkuyWeM+jUQ2Wo9fAOprRlkSXkfOEvmpH31tks763VykrZZ6Ewr54oG+2js0kEljdzHjq5o5UzzxYoBluQzRv0aZPVascqy1w+N7geNqnGjZqpzpcFdd2V3H5YtS/rkhThMs7ZukIWtIVb1jI+TCUNf23mq9XHoqLSxYpVlKw+NbhSgDWpw0agaN2pm/xrkKt3ieqi1LGU3lDSLIKsdPCpmsMRMBBnGvNLjongMsM+jh9p9SHO9AG2Qgwup/WrcsixY7DIkGqTgM10P9TJ27d5QQpDVjs32qJiyoDFzw15sQulxkR4D/bjPD5hKZVaVypycW9bq6lLD4YsPlh6UC14n4jrq9vLWCka2anuxRpnBQQ8+u61vg6wjc0d6XYT6NtOjYsqCxjYb9mKLKzsuGu3znWaGowEK/hdWVup+75b8cw6dq2hhYVojI2NaWZlvevrFB0vXuuBtpvZKIyOXa3HxKO3TNkC9LNhWDT5r6dsg6+RTJ3tdhK2l2aCxH4NEDLZOMsNRnwf/xZCqfogl2Q7/MA6zEa2szJe1k2+omfY0nbRXAgZNMcO7Efo2yMKA6mZfV9g6Or3bl+C/be22V8olza6VZdZqZdUqldm1cbZKFWlR2V1wg7weup1pnZubknMVra4u6oEHXtNWZ7+t6qsga/bUrOZOz2nyHp63NbDo6wrYECsrC70uQhZlz5uL6mXVKpU5ScNr0+jX4CLnHYub7bFKOTKtZiMaHvb91FUqc1sryLp81+U6+vhRTX1+4x7eiAy62dcVd6wBNTSqdGxPP3Rb0G5j8+Hh0b7p1qLWzQe571jsp8cqdUOOTGuxy4ZmxW06MjLW9Dh9FWQB5+COtfVafSQUD0Svr887q3Wusu7/RugkCBi0hvTNdkbZjno3HxQDh2IV6aD3wzVomt0P5uamtLIyr5GRcUlHm5o2QRb6Xzt3rG1WuR8JJW0rMa8CAAAgAElEQVStALbHndUeOX1a119wQc3vV1d9cGU2Imlxg0rVfiYpR/VOvTZanQYhzXZG2a5ms2plnYOWlaWTNlfttnXbCnLuB1sryIpZgD7+5YpM0ozF5ORgb/Ocj4SSuhfAFo+3Ttd7vSxeJx0Ad9pZba0q7f37pec+t+6oJ8+ebW+efazb1Tu12mh160LYbGeU7Wq2/67ieisrSydtrtpt67ZVxPUfA9mYUWzlETpltlaQVcwCbKVf7JtdGkSNjUmXFxozphfnqSm2+UZIj7fp6c7Xe70sXi87AC6r0o7zTIKs+HDootkm+mfYyOrCflQWuHU7IMrVgWu3++/qpM1VLzpW7VUGrdh+Kr5vdCx18gidMlsryJLWZwG2apXTZpQGUXNz5wZZUntVZ+hMtx9p1E4WbyOO82K5SuY5V6lovqQT0rLAqyhWGyKfZjpwzVl1uVn1KoO2vv1U9f3w8O6G43bz5oG+CLIWlxc1e2pWl+/KeyvlQOpGb9jdqBIdhKrWYhYBG6Pb1YJbTP3e31cK/zdercCiH4OKtIf8djRqQ5W76nKz6nYGbSP2yTSz2Ul/Wn0RZK2srmju9Fyvi9GfOu0Nu1tVJbmrWlu5y6ts2EbDlVUhpsOVBbI8v7E53a4W7IZ67aR6XbaCsvDJjZ5oeTpp9Ug3+/4pCyzaCSqKd3Dl6DDUZ/wWNTIynu3OwY2oukR93donY1A+NLRDq6tL63qBTzObnfSn1RdB1vDQsOaXqs/lmn6kJBPRTE/iUl+eRDvWSUPnblaV5KxqbeUur7Jho5kZaWHBfz47W/28VhVinF5ZIDs+7qdRnMf8fDWYiAZ1v2vl4eA5G493W712UgOwnVZHH2962NiwulKZXVc9Ej+XOg8wmmmU3Uh6YVxYmM7eYWjuOwfb1aghfM5uJXLPayMfTN7pPnn69JG1oFzarZWV+XWBVZyHJJ06dVfbGdK+CLJSu3fsXhdwrWnUk7g0UCdRlGjlQl1r2OPHJed8IFSpSFdd5QOsRu1eagWyExN+/F42rM6p2YeDd7KMvbqzs4l2Un1rxTU9aGxYHXfxeDGIAdbIyJjm5w+pUpnteYDRqK1LuxfpmZnJ0AP++vWW887Bdqus0obwks7J7m1kcNjKvJoJyJp9MPlGKD5mKYrZq1YeuF58hmgrDeL7Lsiqq1FGJ8dJtFu/9MvaNDUap10DVFXSVbOzPosVjYz4AGl+XtrduLFjTb3Y7zZSM5nSTpaxkzs7e7Uvd6kN4pHTp9delzV6r6nJGKv463p4ePe6i0G8eC4uHs36+JBuZUTavUj78VYUH6vTiTTTlCoGgI2qrOJ06mU/yrJ7UuPgsFHP460Eq80Gos0GZI3atW1UG7/iY5aiGDC1Kx5jzeqrIGt+aV47t+1sbaTcj13p1i/9sunkyoAMeFXJmmYyILOzPpAaG/P/V1ak4c5PtHXL1I2gu9lpF23UjQ7FMnV6bLV7Z+dG7ctxXxsbO3e+Hcwzdz9YKyvzWlw89xmGKysLGh4ezTrvVDezL71+LE6aaUoDrmIAKNWvsirLMBYDt3QaxQCnrA1bOv+0argolm9lZV4PPnhHVztX7TQz2Gx7qm487LrsMUudPPNzZWVBQ0MjLY3TV0FWW9p97EorVRj1fukXg7x6v3g3svoi17xy3EmWXuDS7ZK2qaqVAbn8culoc483WBeQtStn9Vozvblv1I0O9crUyjziOpekHTukpaXW95m4L8d9r1KR7rxTuvBC/127QWVxX5ufP3e75mqD2FVlGbKVuheTZjNPtS50Ze2KclTNlWVk6gUfUfEGgFrTiZ/de+8rderUXTIbWZcdKlbt1QsAi/NIxexHsc1PPbWyXM0aHR3X/PwhnT17ckPbSkWVyowOH54onW/ujlelc4MpX304rHbu1K32rdX6uH0XZC2dXWp9pGYeu1IMqrrVOeVW6+C0kzvJat1BODVVvcAV+7tqNgNSqZzb7mphwVcZptMrXkjbUba/1cpwpfthM8FAp7255wgG6gXs6TZ95SvXb980wJJ8gDU/3/7xFve9kZHqtm72eCv7UVXc17olnVc7Xvph6fwzXSjISs0qpWYzT7UudN3uYLOWsurD+sHHioaGdq5VmcYgq9Z0osce+4hWVxclLTZsvtlsWbuhW/019aKtVKVyXJXK8Y5udOhs+X1AdPr0EUnSBRdcr8XF8h/kZR2U+obxSzIb0epqNS5ptc+6vgiyVlar0eGqVtd/2WrmpNbwxaBKWn/3WCcZmYH5xZvopK1Lu3eSxfmNjUmHDtW+EMWqv3i2ixfrycn15Z1Mfi2OjEiLybPddu+uNn6X/Ouc1YiNbszIFXz3+q7bdN4f+Yj/X7zrs6z/snar+dP12so+WOtHVaO+1Rq1zWoUvLXjRZ+Qhppv+B6VZa6KjXVjhiUGTPGRIfExIs1WZ9VqVNxMNw2tVgOVZY/qXXzjRbC4PuLNAGmZ0yCzrK1NupxpZqxSmVGlcvycZWxU1dlJH171FNd7WTcevaiG7Wannu06e/Zkw2His0FPnz6i7dv3qlKZS/aFRa1v69daNqsvgqy6Ws2c1Bs+PUGnVUed/LoeVOl6ikFNDHziRaKVC3OzwXDcBjFzUAwQYlnm56WdO6ufxW1UXIYobfC+siI9+WT1dfp5nE+Ou9zqZaFyBd+1grs0i1TscqLbAVcaqMTqu+lphVRC43LXq4osBtipWv2llQVFaTlTcfoxErnzzvXzapSpbhS8bajaJ/9a1VVl78safherm2o1Kq7XTUPMKMzN+cyB2Yjm5w+13X6r2FnkuVbWBVTxbsuy5Zaq2YzV1TNh+Nl1y5n2lRQDrDitZsoet0FsQ9dKn2ZpI/p0+Lh8xfXear9O3bpxoVKZ1dLSwy2N0w3NVIfGgLlMDMjPnj1ZN+PVjv4PsqTWMyfpiT5eUItaacvTrGbbedULSMoyE5WKb38SxRN4sxfLWuVK1+vcnHTzzevnK1WHPeJPkLr++vJ51Atui8s7Nrb+InTkiM8yzRfu2BgZOfezWO6i4p1brkYmYOfO/gyqW+mMtahWdXlZdWunGbXivpSqVKr9k6V3dBYDmUblTsXjtCw7VKu/tLKgqJb07tNKxe9HxXk1ylT3+eOa0oxOvLAXsxrp+1gV+OCDd+iCC/zxngYlDz/8lrUGwK1kmdKMgs8cSLt331xalZRW8Ujld/wVO4tMlnjdfNPhysrr+xerdv/gXKxNceuW0wcvvhzOVbRr102l00q7zpDKbkJYWXcXaK1gKF3muE2WliprwVacRtxO6XqP/ToVxfVaDKrivOpVHxe3QVlAFsspae3xNfXaqjWjbPxiQFWvOnRoaKdWVxcL+0hRdZ+pBmNp+632n7gwGEFWmVpp/KiserDVaUutVWWU/aotC6jqBSRlmYnpaZ+pGU0O1FYuls20P2vUUP5k45RrzWC4uLwxwxIziem0T52SzOrPJ81C1upgtJZ2G1y0q9muAFrpjLVZtYKYmZnqNqpXrkZVYfF1MWsWxXZylYqvyt292/8/cqQ8WK/Xk//MjO8DTVq/3cv2uXpBUb3MWKuKdyQWpztb+5dzXZc81nnZ1vEXiNhWaWmp0lSW4+zZk+uyLbG6bWnpmKSVtUCpTL3MQgxqdu68Zl0wNjMzqVOn7grVfb7M6fzTBuhR2llkmdHRcVUqM1paOqbV1aW1C2ilMrfWy7cPhtKLqH+9uPjgOctZrUYaXhdQlWXLFhcfWJtezNzVU5atWlmZ1wMPvGYtYIvdDywu+jJI5dVhtbopiMOWZb5ikBa33cLCdGkAFbdBWTYytbIyr9Onj6xr7thsW7V0/0kzUGWZ10ZVtbWqj2upZuK68yirwQ2y0gv37Gy1qivewZRmS9ITdPp9M9OuV5VR9qu++Ks2TmtszP8i/73f8xeIetm5WgFPvSAolqGVqpJO1MpoNJM1u/NOHzSurPjP04ziatIm79Qp/z8GRvHiHLMPe/dKd5WfXGuK02/mAli2jMX3jYLxdPsXq2SLQU3cJxvdPddp31HHj/u/Rt0iNFsVlmaD0nZx8XX8H6t0awXsZYFm3L7HjlWrHh9+eH2QnQY7MfgqHgvpcZ9WPccyxf2xllo/ltIbKY4eld7yFr9u2/kBED12iXTVsfbGrSNebFZXKzpz5r61AGN5+fhaA19pfdXb+h7k55IH7C6e85zANHApXhjPrb5ZOSez8PDDvxcanydD1emmIBXb1JSpVI6vTTcGL4uLR7Wy4jMVZV1gxDLGMsQgrtroeWUto7W6uqivfOV1Wl1dWpfhmp8/tDal1dWKhobS5fLntrTBdQxinnrqjBYXHwhdBVSrKmuVrxllbcFiUHXnnRfp7NlTa583e1df3F927rxOi4tH9cADr1mrao1ioC5JQ0M7SqtIywLyYpayXua1Oty5Qa8XrynNra9uP4x9cIKsWgFNWt0lVe9gkvxJ7sgRf7GODaO3b5eWl6sX7rRxdSoNdIoXtfRXffyV/prXVE/kaZnjtCR/Em73xNuMWu1HiupV+TTKEBa7V5if98seLyrFdVI232Y7ZVwt3ARx8uT6oOrBB5ufVhSrEZvZDsVlGRurvo/BcjPBeNoOLVbJlgU1c3P+r9Hdc436jqpV9Rg/r1Skm25av3+nVevFwG9ubn21e5q5Wl6WzoQTazzuYnu6xcJFr5ltlVb1z85WpzEyUi1LnEel4t/H6sm9e9fvn7G6dHq6el6IQdX0tC97LFNatriPVSrSJZf4cdPpHTpU3R+K0n2ymezvhqpWfayuLq1VV8WMx/BwzJKsDzqKF+iYEYh348UgaHHxqJzzVTNLSxXt2HHVuobm1SqyasDQqFPNeqpVd8vr7v5K+czYUjJOtcF6K1VBq6uLMtu+btgYKEnS8vIJ+erGIVUqs1pePl6YQnEeq2G6S5qfv1OHD0/ozJn7JEnOrUha1erqooaGdmpkZG/IqtXOxlQqsxoZuXxtfRbvlktvgJDWV7fF7NbCwrTuvfeVa9/t2HFl6Q0RsauLNKCM29S55dLyxWWN+9fS0sP6h3+4UNdc82bNzU2tBasPPFC9ZsRAan7+znVB7s6d16yb58zMpE6fPqKzZ09peHh0LftX1epNJN19GHtfB1k/fbfWnzTjBe51r6sGM/HEX7wjrdav5njhLv7Knpvzt6AfO1ZeRVKrei/+up6fl4aGfFXXNdf4gCoNvJbDzlf8xR3bOnWph+l1fQrddZfPiFxySTUrNDKyPqCMv7yLZUgzhOnwcRvEC/XsrF/WM2eq2YV0nRQDsGIbq2YyUcXsyPBw+UW8FXG9N5IuS1qVWayuiooBS6peNdaRI36ffcELqp/t3+/XX+wKIga7afAc95FnP7s6z1pVj3H9F3u/r5WxOnLE30AQj5l43MVjrN76b7Rt7rrLTyfdz2MgE4Omoocf9tNNUwLpHaQPPlitUo8BUQzY0hshytr/1Sr7Y4/5ceLdqVdeWb1zNZ5j7rqrup+srFS/b/du1mseaDxMFxTvpov9KqUXmVjFuLg4r6GhnWvtW1Lxoitp7bvV1cpatUvM9MR5xeBkZeWUHn7498Ln1UCi6Mkn/0lm286Z18jIWMOet4tljVWA7Tg3gEh/BLq1z3w14bnzKJalOo2hsN6Hks+q46wvc3nZl5Ye1sjI5WudlMZsYypdV2VBzcrKvB599ANr6/fRRz+wVuaYqasuhw865+fv1M6d1yQBUTV7F8XsZrGvqXgjRnyGYNzX4ryGhnZoefkx+R8F1WWJWb5Yzrm5qXVVpjGz1i/M1WogvJGF2GdOP3Pu5198m/Ts+JzU7dv9yT6exOLF/rrr/PcPPHDuL+Xh4XNv7a9neNifpGPV3vCwv+jNzvqLalq9F1/Hi0Wc986dPviYnq6eaIvlGhrywddNN/kgRvLLES8MMXC57jrpl3+5mhGKFw1JevWr/Xxf8AJ/4k+DwziNuE5iGYaG/DrcubP6TL+FBf/58rLPBMTg9c1v9hfcQ4fWBzRxGkND0q5d1e1QXIdS9bu47WKGptXsUy5/+qfrA9s3v3l9UPvsZ0v3339uRk2Srr3Wr/O4DWKgMDFR3fa7d/txz5zxgXfch6Rzq4Bj8Bb357QX+7g/XnddNXuWfvalL/npHTrkyxWD5pe+VPrAB3wZ/+t/le64w//oiNsuLm/ap1W8I/DNb5Z+pnBQpvty2X7diuFhn1VcXfXrKd3fak0/7ntF6f55883V4HDXrvWZtnrTaFYaQDWx/PbJTzY96U++sPgiJwt/6boo66hxqDBMK4Y1NDQSLpCNptNeJ5GbQzeWfUjDw7s0MjIWAoxG63tIZsOlmaehoZ1rmapqcFO7jMPDu9fa69ULessC9PXTja+L/8unMTS0U8PDu9YyZFWm1rNXrXvhC3WPc+7GRsNlC7LM7Hsk/aH8mvoz59ybag7bTJCVnhzjL9nVVX+y27Wre6n53bv9BSy9uG7fLp1/vr9ofeQj1SzOZz9bzRBFQ0P+4mHW+sk8DeqOHvUXjPvu8xfNvXur/9Mq0TjP1dXqBfro0foXk507qxfTpaXqcGlQGudz6lR7F6WY1euXgKrMzTevD1hi8BGD2RiUlInLFwOFNCCOmbtDhV91MQDfv99XN87NVQOdGGTtDI+VikHDhRf6dmiHDlUDCKn62e7d1f0yXvzLfljcfPO55bn55mq27MwZH5DEgCUGE7mkQVZc7nRfbEXcDlLnQVSX9W+Qhc2pk6C4ExsT2PSTngZZZjYs6aikfyvpmKR/kfSjzrl7S4cvCbJ++m7pTz/iN13P9dmJu65du84N/FBbrWCwnW0exzGTtm2rVhHXs3279LznnRsANTsv9DWCLGBzajbIGmo0QJtuknS/c+5B5yti/1rSLa1M4M0f75MASxqsixkBVmtiFXTZ5+1MS/JZlWYCLMkP12qAlc4LANC3cjV8v0JS2u3rMUnfng5gZgckxQYwC/oNfSn9/qJMBQOADfPC5rNSLzznBYA+9oxmBsoVZJUlodbVSzrnJiV1oUdAAACA/pOruvCYpKuS91dKmsk0LwAAgL6TK8j6F0nXmtkzzXdm8QpJH8o0LwAAgL6TpbrQOXfWzP5PSf+vfBcOf+6c+0KOeQEAAPSjvuiMFAAAYLPJVV0IAACwpRFkAQAAZECQBQAAkEGufrIaMrMbJX2fpJdJekjSsyTNStoh6TL5BzAtSDpf0i75vrdWJD0paUzSY5JOh//vlfRjYdinSapI+qp8Z2HbJT0laUTSJZLul+8sdUHSV8K8dkvaGeZ9OpTjKlWfUPlImMY3yXdFcV4Y5mmhnLsknZXvQzU8YVmnw7Qrkp6QtByWYa/849G3SfqMpBeEaayGz88P5bgvjHdtmP8OSVer2gfZcpjHA2E5rg6f7ZZ0gXxnsIfD598WxlsOy/4VSXdJ+h+Svj+sm3lJ+yV9XdLTw/A/Eob9Efke+98R1v8zJL04bIePhWleEuY1LOnBsD53huW/JpR1JSzHiKRTYdtdF8r6n8M6uS6sw0tC2a4Kw30qjPvPkv4P+R8IJ8N0vj2Ua1F+Pzoatse3hG11RNJzwvCVMJ+Tki4P8/7GUO5TYTpD8vvJXknPDMvzjLCuvx7KZJL2SLo0DO8k/aP8fnGjpG8Iy3tM0tfC+71h+y6FMsSnoJ4JZX5GmNZo+H4hfH5NeD8Uhj8RxpmT309+UN6c/Pa/LKyz88J6GAnTlPyx9rSwLo/L73c7k/k+FsZZDOW7IPxtD2WYk98/doTyTUu6IXy/rOp+vhSmdVVYNzskfTEMd2kow6lQNpPf/74o6TtDWeYk7QvzeCIMtxCWYbv8dl4KyzAb3m8L6+fJMMwjYbxjki4M2/Qm+e0+GsY/Esq8GL7bFtbvtlDOk2G+p0KZLwzDnwrb9NFQ1qskPR6GeSSs08fDMpwf1uU/yx8HPxDKeH8o59/Ln0NeFt4vJev+rvDdtWEdPSbp2fLnIwvzv1h+v9wjf7xeJL9/uLD87w1lelDSK+WPgb1hOkPy+8zfyh//pyUdDNvim8K2vVLSP8nfyPQf5I/7h8My/EMo9+dDGVfD/J+SP4bjsbY7jHN+WPfH5Pejk2H7fkX++FmR9Lzw/kn5ffk5YRsshOWZDv+vCOV7VP4Y2R1e3yPpe+SP19Gw/s4Ly30qvDZJd0o6FMr/2bC882H468L05lU9Xp8ZtutjYRgXyhQfGbEtbLNHwv/VsHxXhvI+FP7vlT/GzgvTvziUa0dYb6eT9XWp/HXiavnzw7+E9Xp5WD/x2vVEeL09LNtCmN62MM8zoZx/J7/dT8kfM08Ly3JS/lzz9DCfiqR7w7Kfld+/l8IwF4fPHpX03LBNz4Tvvyzp34Txh8Jwy/L75unwWmHdXBjGi+vhWCjLSvj7BklfCuMOhWHOC8tTCcOshu0Qr59x3OmwPZ8mv79eEMq3Pay3J8LyjYf1+3xVr51xejPy55exML2ny+9zpyT9d0n3Oec+rgZ61vDdzL6qJntMBQAA6BNO0qJz7oJGA/ayupDbGgEAQK+0G4ecVZOPV+5ZdaF8Vc4PyKdq98inLlflU6kr8gt/Rj4VWVG12uOkqik8J5/2W5HPio2GYWPV2DFVqxgvCtM4IZ8Kfkw+lX+eqtU2MfU7pmqqM1ZTnpVPd35N0reqWjWxGr4/E5Zrm3w6cSl8thqGi2W7TD6tuSifmozVG09KmpKvlnNhXt+crIcLQ/m+Fj47K58iv1c+jT0W5vVUWKZYHRXTx0+FMrlQlpgWHQ7/485mhf+RUzU9u5xsk93JMg4ly7YtDPNQGG6XfPrXhTLFNPJomFcsx6qq2++xsN1OhfKenwwTy7yclO3rYb5PU7XqeT5Mb2cY7hlhXVbCuGflq5b3hfcXhvGWk7KcCevvYlXT0TvD/Fz4blR+39onX00Uqwh2JOsvru+KfFXA3rBsXwvrcTRMd0H+mIjbbrv8fv9IWAfnh+HmVa0ijtWwcbvcJX+MVVStNjgayj4fyvn1sD5OyO8/FVWrIL4qXw17cSjDtlD+J1WtnlgKf+eF8R8Ny/dIGP/GsExnwrLG6tpYpbI3vH8qjL8UXn8prL8xVavsF5PleCIpV9xv4jH6cBjmkrD9huT3h1gt+0RY/t1h3ccfmg/IH9fnh/GXw/pMzw2zYVtU5Ktc4jESl+HCsAw7VD2uh8L3u8IyjITynhfKE6td9oTvd4XynZGvnjgjfx67OKz3+AT4K8L8V8M0TyfbaFG+qiP+yt4XvovrI5ZpOWyTp8Lw14T18B2qNkdwyXINheUf1vpmC7EcT4Z1/bVQpngcnhfW90IY96yq1c1PT8aP59LlMG4Uj4O4neMxFI/9+TC9Xck63SG/f4/IV6l/g/y55KT8OTiej88L03lUfn98PJR1KLx+KryP5/+nwnyepmq1noVhd4Vl3x6W60Qoa2yKEqu9nwrLvhxe7wvTOhbGnQvbYlco66lQ9h2hXPEY3h5eV8L7beGz3aqebx4K6+TSMM24DuOx+DfyzUFGk20zouq1zal6/J8J01dYluEw31jlGdfBpfJV0xeGYS7S+n3+8WR7nhem97j8PrYnLPNK+Cxebx4Pn1+cbONlVY+JuF+clK8mjOd8hWlI1fPVvPx54jlhuc+qWh25GOZxLHx+RZj2eWE6D4Tt9ZeS/lxN6GV14Qn5jYHeigcRAABozhPOuYsbDdTL6sKviCrDftDrAIt9AECrOG+gl2INU0O9rC78f+TTms+UD/ZWVU1Frib/451bT6qaDo53rsTqq+EwfEw7x8BhMYy7K7yPKdTT8qn0mHqNw58N38c7uWLVyLYwz3hgn69qNUWsToxVeHE50nLEKoSnwv+z4S9W2cVxY7VTTO3X2j6rDb4vqpetauZkFVO7nQZkq6pWL5VVS9aaR7Eqs/i5VK1mOK/w/Vn5lPll8ut+RT4dHlPEq/J3Fn2L/HaL+1tcv3Hbxv0irYuP48dypOPFKotYnTKSTOcpVaujHpNPqw/JV4Nfqmp6Pu5HscosPng97nPxLrRdqlZvx2Mn3qkaq89HwjBLqlbtxurjoTC/x+VT5VL1brptyfLGdRzLvUvrj9l0n3wifH9a1X06zns4rINYnR2rg2K1wN5kmWN1p5J1n/6l1YXx+HfJ+PEOO5dMY0n+uF5Nhl+Qr9aI011OlsXkqyEkv389Jl+duZJ8H/e99JywGNZtrKoeSoaPyvbtOI30v0qGS4dt5rOy8Wtp9Xivd+yuqLou0+EanZfqrada80r31fhZPBbT8eqtj3S9x2rqiqpVk/Hu3OXwP20qMiS/v0WryV98H6e9rPXH1BPyx0o87uMxG6uii+WvqHrti9VrT1O1SjeugyX5c0Ec38nvz6Pyx2esTk+bYcRq5XgcnC18nl7L5uWvZbFazVQ9lw0l48fz6TH5ariTqt5Nn16X4/pwyfyPyd9RGc/v8TiVque9eBxfGL6Lx1v8bqjwP12fcZ0Wr3VpFXU85w5L+oKkH1UTelld+D8l/e/qTjat0Qmh3gmgm5mc4sZRE9MvnkgblTMdrmzjFU9gzZ7U2tHMeq9Xtn5U74LWa/UO1mbKWmvf6WQ5+2Vb9ks5uqGXy9LOvIv7VaPjvJXgaqvYqssdtbv8zf7I6Pb8naRHnXN7Gw3Yy+rC69W9narRdGp93+2d2gqvm5l++susGEjVmm7ZuFH8JRJfF8tQ7FvkEfnof1W+j5PYP9kn5X8hVFTt42pF/hfM2cJ8Xcn/OO/0r2wZ0nFiY/P08/g6LXP6i6s4bCPpeGWfp+WtNWy3NTuP4rqs96u+bPpl26HTY6BfLgz9Uo5W1NrurQTMZZ8X/9IG5fGXefp9cd5xHOnc4yyd5moyTtm+WO+8W5zvauG7WOb496CqjaBX1bl0OeL79P9Ga/YY3qzaPX7Lxqt1HW1mOq2M01RNUi+rCz8nf1wLJsYAAAbsSURBVPeQVPsXUNkvpOJKSC+MKvk+/ax4AkgvrM1OKx2/WNZ62aVGGai0LDGlbKre0RGrW9K71WKa9IykX3fO/enaDM1+Qz6dGTvd3BeG/ZCkQ865P6qzbHWZ2bWSfiL8XRSmOyrpX+XTxOPynRReEL7/xlD+sqq8Vfm7ac46554Vpv9h+RPrbvk71B6VtNM5d1lShudIeoOqHXHWCyzL1LtIqTCNRtuuTLMZvnr7fNnwxXFqTTcOV+/i0WhZapWt3nD1plVv/FrHfKOytDLdRuPVy8TUG7bReapWGR+VPybOl//BW+/cYKpWW6TzSH+AxDvgjskfcx+XPwY/rGrnkH8n6SVhuqvOuT8yswOSfi5MI97lfUbS3fLnnivkOye9T9L3SvqopN+U9HpJPyVfLRzv/DRVq3hiOYvLFu/G3il/B95x+Tu1PhU+f4l8h6R3lp2nzOz6MNyvy3cgGzvrXRskzKMsiRCXLXbc+zr5u26/S9J3y5+v9mj93Y1xmrW2abP7bzvnkVQz+1a9fbA4LRW+L5476+3/rWjmuljr8+LxG8WmRI3UW1dOvknQNlWvq+m1d3ud8Vfkj4eGelld+AlJE+LRPt3yIefcLfFNCISOlgx3r6T702FbZWbvkT+JXx8+igfAqfD6Avme4J8vfyLdXjKZaO2XsnNuOEz/6/JB29NUvYgMO+fWDrJQhnGtv5W+bNqDkuEYpLK2oxfL12ie3ShTraCsmXFiFzXdUHZhjd09/KN8Vx33h3lOh8+f55y7xcxOyx+nxQvqqnyv6M9Xtc3g+fI/fF4i3+bm17tQ7pg5PyN/XpmJ5S07TyXnn+tU/QHaznxN/kfni1V96kgMCKXNfTyic08553Y2GqiXmaxPS7paPjvzDao2xi0qNlJL07vFdHU8ycQGyfH72IfMiKpp6Pgokdg4UKr++knnGV+fTcpY9qu67FdA2TJEaYPmtNFu2nB5VP7XbmwcuBA+mw/lfzC8flL+F1nqDar2WRVvGviIpN9X5/5b+P9WSW+Tf4zFIUnvDOW62Tn3EjO7XT4IeqWqj7aIDTXTPqp+Q75vnmhW/lf0v5VvZPzvJP1SSRmeLenXVO33KfYlFucT+0NKf0nH/UOqXe0wpPX7ltRaBiWdTjrPYlapLHNS3Fdq7Wu1sltFZZmx9H2t7HAsR1kZpfr7e63AY1XnTqdMcbpx+60WhonTLq6X9LPidNNhiusxzSKnZW+UFUizJunNOulyxs9X5PfRxyS9XD7je0a+f6X4oyWdVuxz7pT84z++V76K//LCcI/IN3x+m6QXygcMk5LknPsLM/uWMOxFzrlDkpR89mfh/xVhmneG6V8sf+6Zkw+wdsj3L/QG+UDt+1W9keCUqjcmfVg+ix6rJXeqerPPjlDed8sfv/8sfy34snwWSc65Q0nZysTzz/Vhvm9T9Wah2PA6bWi+pGpDb4V5Pyh/s8vvS/qqc+4XzewXwnT+k/wPvGVVH+lWK1skrT/GisMuqdoQPt4AUGu/i9eedB9Mr2EPhGVeDJ/FxubLWv9DttZ5Jb4uOyfU2s9Xde4xlg5frOYtU8yulZ1Li4Ften2XfCY23pgUP483HsTmKzHzFIdJz79pOVZU7dMvZl2/KB/ov1Y+yB+R3/+Pq3pcLKt6DC+q+gi9+pxzPf+TdEv4f6v8AT4h6Y3h9ZuS4V6XvP4W+Yt5fP8LYQUNSforSW+Pw9WY581x/DCt25Pv3ha+H5I/YXxLOs8wr7+S9Hb5E+WDabmL8y2U8/Zkvren0y4rY1LWmsM1uXxNjdfBNkyXsXRZWl3uVpa32fLJV0O8Qf4Ae5mk300+H5J0QD5wG5I/Gcd96mXyJ/fhMPy7JQ2F1y+L4xfe/4z8sylfK//ct18Lr383DPPaZJw3hf9DYd9/n3zHtPFZem9MvivO6xZJd4RleqOk96fLFIdrY509T9Jvh+n/VdjH3yjpHwtlvSUMG+f/spL5N1p3MVvysuSzW8Mwr5UP4OP3r5X02+G7WK5bJb0/KVM8Dn9QPktxq6rnlHPWYVLe54e/H5L0pjC/5yfTi2W7I8zrtWG+z5P0N2GYqTCfIflzRBx2KG6bGuv7lnS9Ntg2f5i8/qWcx7UK5xGtPy/dnn5X6xguvu92GZPyxHN0S+fLWtMMr3+hzv7+vrBPDMV9tOR4/qtk2/6USq4RTZQhLs9rk+l+uOT7c46l8PkB+XPR81U9/8Tvbgl/6THyTvlj5075Y/edYZp/G46LdcdZrf03bIvSZa+xfDWvW/X2xeI04rpKyvpBST/kkuO9iX0grpehsJ3XzsGt7Es9qy4EAADYzGgPBQAAkAFBFgAAQAYEWQAAABkQZAEAAGTw/wPZWZ2wcq8bNgAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 720x504 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "import scipy.cluster.hierarchy as shc\n",
    "\n",
    "plt.figure(figsize=(10, 7))  \n",
    "plt.title(\"AirLine Dendograms\")  \n",
    "dend = shc.dendrogram(shc.linkage(air, method='ward'))  "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### PLOTTING DENDOGRAM WITH THRESHOLD NUMBER OF CLUSTERS "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Once one big cluster is formed, the longest vertical distance without any horizontal line passing through it is selected and a horizontal line is drawn through it. The number of vertical lines this newly created horizontal line passes is equal to number of clusters. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.lines.Line2D at 0x237759a3d68>"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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IAgAAyAFBFgAAQA4IsgAAAHJAkAUAAJADgiwAAIAcEGQBAADkgCALAAAgBwRZAAAAOSDIAgAAyAFBFgAAQA4IsgAAAHJAkAUAAJADgiwAAIAcEGQBAADkgCALAAAgBwRZAAAAOSDIAgAAyAFBFgAAQA4IsgAAAHJAkAUAAJADgiwAAIAcEGQBAADkgCALAAAgBwRZAAAAOSDIAgAAyAFBFgAAQA4IsgAAAHLQUpBljNlsjLnNGHO/MeY+Y8yVxpgzjDGfM8Z81/8/vV2FBQAA6BWtZrL+VNK/W2v/i6TnSrpP0u9I+oK19hJJX/DvAQAAVpWmgyxjzGmSflzSRyTJWlu01h6VdK2kj/rBPirpulYLCQAA0GtayWQ9U9JhSX9jjNlrjPkrY8ypkoastZOS5P9vbUM5AQAAekorQdYaST8s6S+ttTskHVcDVYPGmF3GmLuNMXcfPny4hWIAAAB0n1aCrIOSDlprv+7f3yYXdE0ZY7ZJkv9/KGtka+2otfZya+3lW7ZsaaEYAAAA3afpIMta+7ikA8aYy/xH10i6V9Ltkm7wn90g6dMtlRAAAKAHrWlx/P8u6ePGmAFJD0t6nVzg9kljzOslPSrpZ1ucBwAAQM9pKciy1o5Lujzjq2tamS4AAECvo8d3AACAHBBkAQAA5IAgCwAAIAcEWQAAADkgyAIAAMgBQRYAAEAOWu0nCwDQIRMTo5qaGut0MZZNoTAuSdq7d6SzBVkmQ0M7tX37rk4XAy0gkwUAPWpqamwx8FgNBgeHNTg43OliLItCYXxVBdArFZksAOhhg4PD2rFjd6eLgTZbLdm6lY5MFgAAQA4IsgAAAHJAkAUAAJADgiwAAIAcEGQBAADkgCALAAAgBwRZAAAAOSDIAgAAyAFBFgAAQA4IsgAAAHJAkAUAAJADgiwAAIAcEGQBAADkgCALAAAgBwRZAAAAOVjT6QIAAFCPiYlRTU2NdboYy6JQGJck7d070tmCLJOhoZ3avn1Xp4vRdmSyAAA9YWpqbDH4WOkGB4c1ODjc6WIsi0JhfMUGz2SyAAA9Y3BwWDt27O50MdBGKzlbRyYLAAAgB2SyAABAx9q8dbr9WZ7twchkAQCAjrV562T7s7zbg5HJAgAAklZfm7e8s2dksgAAAHJAkAUAAJADgiwAAIAcEGQBAADkgCALAAAgBwRZAAAAOSDIAgAAyAFBFgAAQA4IsgAAAHJAkAUAAJADgiwAAIAc8OxCAEBLJiZGc33IbhAeXpz38+YkaWhop7Zv35X7fLCykckCALRkampsMQDK0+DgsAYHh3OfT6EwvixBI1Y+MlkAgJYNDg5rx47dnS5GWyxHpgyrA5ksAACAHBBkAQAA5IAgCwAAIAcEWQAAADkgyAIAAMgBQRYAAEAOWg6yjDH9xpi9xpg7/PsLjTFfN8Z81xjzCWPMQOvFBAAA6C3tyGT9pqT7ovfvkfQn1tpLJD0l6fVtmAcAAEBPaSnIMsacK+mnJP2Vf28k/YSk2/wgH5V0XSvzAAAA6EWtZrI+IOnNkhb8+zMlHbXWnvTvD0o6p8V5AAAA9JymgyxjzMslHbLW3hN/nDGorTD+LmPM3caYuw8fPtxsMQAAALpSK5ms50v6aWPM9yX9g1w14QckbTbGhGcinitpImtka+2otfZya+3lW7ZsaaEYAAAA3afpIMta+xZr7bnW2gskvVrS/7HW/oKkL0p6pR/sBkmfbrmUAAAAPSaPfrJukvTbxpgH5dpofSSHeQAAAHS1NbUHqc1au1vSbv/6YUlXtGO6AAAAvaotQRbQVUZHpbGxTpci2/i4+z8y0tFiVLRzp7RrV6dLAQArAo/VwcozNpYEM91meNj9daPx8e4NTgGgB5HJwso0PCzt3t3pUvSWbs2uAUCPIpMFAACQAzJZAJCT0YkJjU1NSZLGCxdLkkb2PqidQ0PatX17J4sGYBkQZAFATsampjReKGh4cFDDH35QkjReKEgSQRawChBkAUCOhgcHtXvHjsX3I3v3drA0AJYTbbIAAAByQJAFAACQA4IsAACAHBBkAQAA5IAgCwAAIAcEWQAAADkgyAIAAMgB/WQBQA+ZmBjV1JR7kHeh4B6EvnfviCRpaGintm/f1amiAUghkwUAPWRqamwxuBocHNbg4LAkF3CF4AtAdyCTBQA9ZnBwWDt27C77LGSzAHQPMlkAAAA5IMgCAADIAUEWAABADlZfm6zRUWmsixuHjrsGrRoZ6Wgxatq5U9rFXUwAAFSy+oKssTEXyAwPd7ok2bq1XLEQCBJkAXUZnZjQ2NSUJGm8UJAkjezdq51DQ9q1fXsniwYgR6svyJJcILN7d6dL0bu6PcsGdJmxqSmNFwoaHhzU8OCgpCTYIsgCVq7VGWQBwDIbHhzU7h07Ft+P7N3bwdIAWA40fAcAAMgBQRYAAEAOqC4EAKxI8XMeG5F+JmQjeH4kYmSyAAArUvycx0bEz4RsBM+PRFpXZLIOzxzWyK0jGn/8A5KkkVtv1M7n7NSuH+HXAIDVYWJ0QoXxjRoYGpC0rtPFWTGynvOYF54fibSuCLKenH1S049Pa/h3bpQkjT/ufnkQZAFYLabGpvS+6f3aNLxJ0o6awwPofl0RZEnS8NnD2v3a3ZKkkVtHOloWAACAVtEmCwAAIAddk8kCAABot2p3mVa7k7Qdd4qSyQIAACtWtbtMK91J2q47RclkAQCAFa3Ru0zbdacomSwAAIAckMkCOml0VBrrks4Lx306fWSko8Uos3OntIuuXAD0JjJZQCeNjSXBTacND7u/bjE+3j0BKAA0gUwW0GnDw9Lu3Z0uRffppowaADSBIAsAgB7V7EOws7TyYOw0HpTtUF0IAECPavYh2FmafTB2Gg/KTpDJAgCghy3nQ7DrwYOyE2SyAAAAckCQBQAAkAOqCwGgy4TGzKGtzcTEKI2IseyabVTfSgP6ldZgnkwWAHSZEGCFRsg0IkYnNNuovtkG9CuxwTyZLADoQqExM42I0UnL2ah+Je7rZLIAAAByQJAFAACQA6oLAbRfOx583a4HVvOQaQAdQiYLQPu148HX7XhgNQ+ZBtBBZLIA5KMbHny9wh8yHd9iH982v9Jugwd6VdOZLGPMecaYLxpj7jPGfMcY85v+8zOMMZ8zxnzX/z+9fcUFAATxLfbhtvmVeBs80KtayWSdlPQma+03jDEbJd1jjPmcpNdK+oK19hZjzO9I+h1JN7VeVABAWvoW+5V4GzzQq5rOZFlrJ6213/Cvj0m6T9I5kq6V9FE/2EclXddqIQFgJRmdmNB4oaDxQkGjExOdLg6AnLSl4bsx5gJJOyR9XdKQtXZScoGYpK0VxtlljLnbGHP3/Px8O4oBAD1hbGoq8zWAlaXlhu/GmEFJ/yTpRmvt08aYusaz1o5KGpWkjRdutK2WAwB6yfDgYKeLACBnLWWyjDFr5QKsj1trP+U/njLGbPPfb5N0qLUiAgAA9J5W7i40kj4i6T5r7R9HX90u6Qb/+gZJn26+eAAAAL2plerC50v6RUnfMsaEXgdvlnSLpE8aY14v6VFJP9taEYEOaUev5bW0q1fzaujxHAA6oukgy1p7p6RKDbCuaXa6QNcIvZa32ut4NXlOW0qCOIKsFWFiYnSxX6yJidFcpt9MH1txR6iNouNUrGT0+L6atCsz0+7sSzdnWrqh1/JWrPAez1ebOADKo8PR0Lnp4GBjwX+jwwchOCPIql86EM4KcAlcG5P146Jd65UgazVpV2amndkXMi1AQ5oNaBqZfty5aZ7oOLVx6UA4vT8QuDYu68dFu9YrQdZq022ZGTItWOEmi0VNFYuSXCeku7Zv73CJ0OuqBcIErs2p9eOi2fVKkAUAOQoBluQ6HiXI6l212qzV2zaN6rzVgyALAHJGx6MrQ602a/VU5VKdt7p0VZA1es+oxr41pvHHxxff7/oRdkQAQHdotc0a1XmrS1cFWSHAGj57WOOPj2vsW2MEWcBq18pdse24E7ab734F0NXa8oDodho+e1i7X7tbw2fn3H8QVp7RUXcxHR93fyMj7jP0tnBXbDOGh1u7G3Z8PP8OaQGsWF2VyQJaku6igu4hVo5O3RXL3a89o96+jqTla3ieZ/9LvagdNw702roiyMLKEl+MuUCiHtWqI6tVN1KN2FXq6etIWt6G53n2v9SLWr1xoBfXFUEWgNWtWie9laoaO5glLRYnVSxOaWBgSAMD25Z9/t2snkbpy93wPK/+l3pVKzcO9OK6IsgCgEarI9uQJU13Uvq8jGGynlVYLE6pVJrWwADtVoFuR5CFfNW6M6zW3V9UyWCFSndSmhVk5f2sQgD5IsjqdY3c3t7o7eztCHBqPS+x2p1fNFxHj5ssFlUolXTfzIzmFhYkuaxVkNVJaWG8oIHJoga2DUjK/1mFAPJDkNXrGnnocyO3srczwGn2zjAarqPHbRsY0P7ZWR2Zn9dgf78kl7WqpH9Tv0rTJRUPJUEWgN5FkLUS5HF7OwEO0FaVHq0T2mZNFge0cZnLBKxkocuIdNcQy9kNRNd1RgoAq8m2gQFNl0o6FLXRalZoKF8ojGvv3pHFxvLAahR3GRGq3QuF8WVt39i7maxmH7XR7GM2aIDdO8K+EW9rth9WgXDxGBwc7sk+hYB2S3cZsdzdQPRukNVIW6RYM4/YoAF2b6Hnd/Q4O3hYhcKDTfWFFS4q3dCn0Grs4RuI9W6QJTXfFqmZLFh4Fl69yJw0Lr1dsrKO9a5Xen5HO1Q6V+TcE7wpbFHprEdVLKqnOxxdjT18A7HeDrKa1WgWrNHsF5mT5qS3S3q9s16x3CqdK5rsCT40ch8aaP3OwWJxQsXiIUkuY9Stgchq6+EbiK3OIEvK94GzZE6aV227NLNeR0dpm4XWNHKuqLGPhi4dlNHIfWh/SaWZ+osVAizJZYy6NcjqtHSVZacfGt0NalXjBvVU50q9u+6y7j7MWpb4yQthmHqt3iALq0Oo6hkeJhOGrrb+WOPj0FFpbekqy0YeGl0rQOvV4KJWNW5Qz/7Vy1W66fVQaVmybiipF0FWM3hUTG8JmQgyjPmKj4v0McA+jw5q9iHN1QK0Xg4upOarcbOyYKHLkKCXgs94PVTL2DV7QwlBVjNW2qNisoLGnBv2YgWKj4v4GOjGfb7HFIuTKhanZO28Fhbmag6ffrB0r1zwWhHWUbuXt1Iwslrbi9XKDPZ68NluXRtk7Zva1+kiVLeSHhWTFTQ22bAXq1zWcVFrn281Mxz0UPBfKJWqfm/n3HMOrS2qUBjXwMCQSqXpuqeffrB0pQveSmqvNDCwTbOz+2mftgyqZcFWa/BZSdcGWUdPHO10EVaXeoPGbgwS0dtayQwHXR78p0Oq6iGWZNa5h3EYM6BSaTqrnXxN9bSnaaW9EtBr0hne5dC1QRZ6VDv7usLq0erdvgT/TWu2vVJe4uxaVmatUlatWJxcHGe1VJGmZd0F18vrod2Z1qmpMVlb1MLCrB566M1NdfbbqK4KsiaPTWrq+JRG7+F5Wz2Lvq6AZVEqFTpdhFxkPW8uqJZVKxanJPUvTqNbg4s871hcaY9VyiPTasyA+vtdP3XF4tTqCrK2bdym/U/u19i3lu/hjchBO/u64o41oIJalY7N6YZuC5ptbN7fP9g13VpUuvkg7zsWu+mxSu2QR6Y13WVDvcI2HRgYqnucrgqygCW4Y61co4+E4oHo1XV5Z7XWFsv+L4dWgoBea0hfb2eUzah280E6cEhXkfZ6P1y9pt79YGpqTKXStAYGhiXtr2vaBFnofs3csbZS5f1IKGl1BbAd7qx23/Hjeu6pp1b8fmHBBVfGDEiaXaZSNZ9JyqN6p1obrVaDkHo7o2xWvVm1rM5Bs8rSSpurZtu6rQZ57gerK8gKWYAu/uWKnMQZi9HR3t7meT4SSmpfAJs+3lpd79WyeK10ANxqZ7WVqrR37pSe97yqox49ebK5eXaxdlfvVGqj1a4LYb2dUTar3v670ustqyyttLlqtq3bahHWfwhkQ0axkUfoZFldQVY6C7CafrGvdHEQNTQkbUs1ZowvzmNjbPPlEB9v4+Otr/dqWbxOdgCcVaUd5hkFWeHh0GmTdfTPsJzVhd0oK3Brd0CUVwcNHzK/AAAgAElEQVSu7e6/q5U2V53oWLVTGbR0+6nwvtax1MojdLKsriBLKs8CrNYqp5UoDqKmppYGWVJzVWdoTbsfadRMFm85jvN0uTLmOVUsajqjE9KswCstVBsiP/V04Jpn1eVK1akMWnn7qeR9f/+mmuO28+aBrgiyzpko6D3vukvr+gd09IS0+ZRxjV4k/eUVkmZmpJe9bOlIjz8unX229MQT0itfufT7X/s16VWvkg4ckH7xF91ncSr/TW9yr2dmsk/Cb32r9KIXuXFuvLH8u/Fx6cIL3euvfEW6+eal43/gA+7E+/nPS3/wB+XjStIDD0iXXSZ95jPS+9+/dPyPfUw67zzpDW+Q/vEfl37/7GdLa9dKd90lFYvS5s3l3z/nOVJ/v/TYY1Jf39KgI1wQ3ve+pVUt69dLn/2se/37vy994QvS5KQLXgoFyZikOuR735O++tXy5Vu3Lnl/443J9MP3GzYk73ftkvbvL183N97o1p8k3XefNDdXvo2uvDJ5ff310pEj5fM4cSJ5/c1vSgsL5ct+5pnJ66xt/3M/J/23/yaVStK3vrV0mNe+1v01su/F3vQm6RWvcPvAr/zK0u/z2veCD32o+r534oR0yinSJz4h/eVfLv3+ttuks86Sbr3V/cX7hiT92Z9Jb3yj2/cOH3brL97H4n3vjjvKv8va9+JlX7s2ef+Wt5Tve5J07rnS3/2dex32vXj6l17qsp6S2/fi78bHpcHBZFqvec3SY+PKK6U//EP3Or3vjYxI11wjve1t7v03v6kPvOENi1WCa4zRv/zYj+n9r3qVJOmLftsaSVbSsKRDGtGErlPf3IKec6OU3EHo/j/+Eve3dlp69tul/jXRfj84os0vOaSjL96qtY+f0DPedn/Zd5KW7HsXh1/q4fu3vlU6U1r/QEH6LffZnK/ukiTzyyd17DlrtP/WHXrG6IzWpW5/X/+rBc1eNlh935N02p4nFqdf5mMfkyRt/l+HFr8vK2O07138Z+Vlf850Qd9+z1oVNK5zb1+XPX5q34u/u2j+m3roz9wFeejDj0j3jpSPe+aZmnrrERUK47rkb07Tqd8MhR5XqVTQwrb7pH/zwcKNN+riO5NtUzopnXzmfulT7v15v79fejJZ/osL45q9dFAKcd5rXqOL709tmyuvlH7Of3/99br4kdT311wjvdx//9KX6uLD5d9v3XFAh37pPPdZ6px2cWFcR39yi7RDla+5/rzX/9S8Lnzzd5L5Bv68t7jv+e/DOtz49qXnvWMFt/40OJJ53itb/+9+t3TVVdJXvqK5//d1i/vkhb4rk/tvulyn/fgubb/3mdIf/IEuLoyrdNIfI4MjWvebM9Lp0hlfntf5t7nplkoF9fe714/8/n+R5Pa9s26b0NzAZbpw9iFJUp8Z0Pfft0Ol09fq7H+Xzvl8/RmurgiyrKT5Er/UMn35y+7iFZ/8Y+ef7wLOSg4edEFPVmanEeEiOjjoLnThwhIHPK2YnEwu0rfdJj3rWZWrd+IL59q10hlnZA83OiodOyZZKw0MuL9K856aSt4/9pj0yU+6AO348fIATpJuucUFtj/zM/Uv30oW7xuFggvO3vjGzpZpdNTtR4VCsl+Nj7vAucvYrM8GDzc8nbnipE6cOKBisaS1Or31gnnF4pS/GA1qw4ZLZAc3aWbmbhWLa5YEWbXK99C9v6jCWfs1cGJec8X5hsavx8JCUQulE1qz5uLF4DD0J3asMK5jbagGHBwc1tlDV0qDSYB/rDCuhSrjoL3ifbK/310bZ2Ye0OzUmLbrrQ1MyapUKsioT8eP31vWC3wI4iRpoYXqemNt1iG+vNacu8aW3lDSpnWbND03vfhfkuzbffkq9SSergKq1sA1RO/p6sJmqx+aaXzc6LitzKve8ZsZptI4taZVbby4/U66mildFZMeNpiYkB5+2F3wh4bKg6d4uErTS0sHYJI0PS1t2lQ+/HLdQNHq/pBW7eHg9R5bcZnqeV1t/GoaWfasbRrvV1nTrLSv1ZpnlfFG/uRPtGfancs29fdnVhcGX3xh9Oazr5BOqd3Z6KZNVy82rB4YGNLs7H5t2nS1pKTBteQCg6zqrFAdktXgOt0YvNowtT4P34Vqo/C/WvugavPP+vw//mOzD6hKi+sgfcdYpcblWVVD8WeNliX+TJKmp/do06artWPHbn3965epWJzSRRe9d3F7xOOnuxOIt11WeWstT7WyV5tXJdW28cTEqB566M2StLh8tfaJSt9V+77e/TZe7+F9f/8mDQ4O6/jxfTp58pikkvr7N6lUml7cb9KOHbtLxgyUHWM//MN77rHWXp45QqQrMlmxOMAqU6sncYmG7L2ukfY7lYY9dMhlrqanXbbpvPNckFSr3Uultj4jI278TjaszlO9DwdvZRk7dWdnHe2kulap/h+/oWF12MVDY98QYA0MDGl6eo+KxcmOtxmq1dal2cbnExOjPsAqX2953jlYqX1WrSAlbggvqSzIiYOevLqViDUyr3r6kqr3weTLIf2YpSA8dL2RB66nnyHaSIP4rguyqqrV6DWPk2i1X/rp+VXLZmR1H1FrnGZVu6W8FwOBesVVjpKrHpyaSjJPzerEfrec6mlM3soytnJnZ6f25TZ197Lv+PHF19WyWEvUGWOle58Ov8jDxSBcPGdn9+f6+JB2derZ7EXajVdSeKxOK+IuF2LpADCrQXccpMQZxmrljrN78Z1t1YLDWj2PNxKs1huI1huQ1eobrNngtFHpxywFIWBqVjjG6tVVQdb03LTWr1nf2Eh5P3alXb/0s6aTVwak2i3lvRRk1ZMBCdV5oWqwVHIN/vMsUzuC7nqnnVZPD+7tDEDa9eOg2Ts7l2tfDvva0NDS+bYwz7z7wSqVpjU7u7RaMbRXWS7tzL50+rE4caYpDrjSAaBUvW+rrAxjOnCLp5EOcNKBazxu+s65tFC+UmlaDz98U1s7V201M1grOA3a8bDrrMcstfLMz1KpoL6+Cm17K+iqIKspzT52pZEqjGq/9NNBXrVfvMtZfZHXvNrdwWSYRrjAxdtlcjIZplIGZNu25O7EWuKArFl5Vq/V05t7rWCl3QFIqz8O4jZt69a5O0Ub3WfCvhz2vWJRuvNOd0ft8HDzQWV6X5ueXrpd020Qu1JWhqxU9WJSb+ap0oUuq4PNPKrmsjIy1YKPIGR5Qvau0nTCZ/fe+5qydjdBumqvWgCYnkcsZD/iQK2WSlmueg0ODmt6eo9OnjyaWx9g1RSLE4vt8NLzzbvjVWlpMOWqD/vVzDM/k761Gh+364KsuZNzjY9Uz2NX0kFVuzqnXG0dnLbSwWSl58SNjSUXuHR/V/VmQIrFpe2uCoXyOwpD1WGr/WVl7W+VMlzxflhPMNBqb+55BAPVAvZ4m6a7PEjfNDA359Z/s8db2PcGBpJtXe/xlvWjKr2vtUs8r2a8/DPShpk2FKRUsUqp3sxTpQtduzvYrCSr+rB68FFSX9/6xSrTEGRVmk5w5MgdWliYlTRbs/lmvWVth3b119SJtlLF4iEVi4fKtlOj821t+V1AdPz4PknSqac+V7Oz2T/IszooPX58nxYW5mTMgBYWkrik0T7ruiLIKi0k0eFC+kbYRjMnlYZPB1WSu3hMTrphW8nI9Mwv3kgrbV2a7WAyzG9oSNqzp/KFKFT9hbNduFiH/o3ibRsMDEiz0bPdNm1KGr9L7nWe1Yi1bszIK/iudNdtq1WX9Yrnfccd7n/8HMDwPi6b1Hw1f/oO0Xr3wUo/qrLKFqvVNqtW8NaMa74g9TV+13dW5irdWDdkWELAFB4ZEh4jUm91VqVGxVlZpnqzY5VkZY+qXXzDRTC9PsLNAHGZ4yAzq61NvJxxZqxYnFCxeGjJMtaq6qzWjqoV6fUelzXoRDVsOzv1bNbJk0drDhOeDXr8+D6tXbvVdxER9oVZlbf1ayyb1RVBVlWNZk6qDR+foOOqo1Z+XfeqeD2FoCYEPuEi0ciFud5gOGyDkDlIBwihLNPTrmPK8FnYRullCOIG76WS9PTTyev48zCfPO5yq5aFyiv4rhTcxVmk6enmt2s94kAlVN+Nj8unEmqXu1pVZDrAjmVlRuNppz+v1kVHiETuvLN8XrUy1bWCt2VV+eRfqboq631Ww+90dVOlRsVZWaYQgISMwtSUyxwYM6Dp6T1Nt9+Ky5QduJTKAqq436OsTFPIZiwszPjhJ8uW0zViD0HWobJp1VP2sA1CG7p04FZN3Ig+Hj4sX3q9x2WtR7tuXCgWJzU3d6ChcdqhnurQEDBnCQH5yZNHq2a8mtH9QZbUeOYkPtGHC2paI2156lVvO69qAUlWZiLdo3s4gdd7saxUrni9Tk1JV0d9hKQvJvvcCVLPfW72PKoFt+nlHRoqvwjt2+eyTNOpOzYGBpZ+Fsqdlr5zq1L/b+vXd2dQXakqtR6Vqsuzqltbzail96VYsei+C+2bwh2d6UCmVrlj4TjNyg7FGek4E5UVFFUS331aLLr9KD2vWpnqLn9cU5zRCRf2dFYjfh+qAh9++Cadeqo73uOg5MCB9y02AG4kyxRnFFzmwPXzlVWVFFfxSNl3/MVligOodLAZD5dV3mJxsqz7B2tDbYotW04XvLhyWFvUxo1XZE4r7jpDyroJoVR2F2ilYChe5rBN5uaKi8FWmEbYTvF6P3bsrsxuBsJ6TQdVYV7Vqo/T2yArIAvllLT4+JpqbdXqkTV+OqCqVh3a17deCwuzqX0kLdlnkmAsbr/VfCfGvRFkZamUxg+yqgcbnbbUWFVG1q/arICqWkCSlZkYH1/a63sjF8t62p/Vaih/tHbKtWIwnF7ekGEJmcR42seOucf2VBNnIRvtyb7ZBhfNqrcrgKyAodUgsFIQMzGRbKNq5apVFRZep7NmQWgnVyy6qtxNm9z/ffuyg/VKmSnJlfmQzx7E2z1rn6sWFFXLjDUqfUdierqTlX85V3XmkdbLVsZdIEJbpbm5Yl1ZjpMnj5ZlW0J129zcQUmlxUApS7XMQghq1q+/qCwYm5gY1bFjd/nqPlfmeP5xA/QgBAXHjt2VWY7BwWEVixOamzuohYW5xQtosTilvr51WliY88FQfBF1r2dnH16ynEk1Un9ZQJWVLZv1j2SRSouZu2qyslWl0rQeeujNiwFb6H5gdtaVQcquDqvUTUEYNivzFYK0sO0KhfHMACpsg6xsZKxUmtbx4/vKmjvW21Yt3n/iDFRW5rVWVW2l6uNKkkxce54O0btBVnzhnpxMqrrCHUxxtiQ+Qcff1zPtalUZWb/q079qw7SGhtwv8ve/310gqmXnqvVKnf4sVi3wbPev7UoZjXqyZnfe6YLGUsl9HmcU4+cMHjvm/ofAKFycQ/Zh61b37MZGhOnXcwHMWsb0+1rBeLz901Wy6aAm7JO17p5rte+oQ4fcX61uEeqtCouzQXG7uPA6/A9VupUC9qxAM2zfgweTqscDB8qD7DjYCcFX+liIj/u46jmUKeyPlVT6sRTfSLF/v3sm3qFDzf0ACI6cKZ13sLlxqwgXm4WFomZm7lsMMObnDy028JXKq97ibgJCgOEyFLOLnTqG4ePAJX1hXFp9U1qSWThw4P2+8Xk0VJVuCmKhTU2WYvHQ4nRD8DI7u1+lkstUZHWBEcoYyhCCuKTRc2kxo7WwMKvvfe9tWliYK8twTU/vWZzSwkJRfX3xcrlzW9zgOgQxJ07MaHb2Id9VQFJVWal89chqCxaCqjvvPN33fO7Ue1df2F/Wr79Us7P79dBDb16sag1CoC5JfX3rMqtIswLydJayWuY1GW5p0OuEa0p966vdD2PvnSCrUkATV3dJyR1MkjvJ7dvnLtahYfTatdL8fHLhjhtXx+JAJ31Ri3/Vh1/pb35zciKPyxymJbmTcKvPEKymUvuRtGpVPrUyhOnuFaan3bKHi0p6nWTNt95OGdMPdj56tDyoevjhxp9FF6oR69kO6WUZGkreh2C5nmA8bocWqmSzgpqpKfdX6+65Wn1HVap6DJ8Xi9IVV5Tv33HVejrwm5oqr3aPM1fz8+6BslJy3IX2dLOpi1492yqu6p+cTKYxMFD+mKRwU8PUVFI9uXVr+f4ZqkvHx5PzQvwcw/n5pExx2cI+Viy6h4nPzZVPb8+eZH9Ii/fJerK/yyqp+lhYmFusrgoZj/7+kCUpDzrSF+iQEQh344UgaHZ2v6x1VTNzc0WtW3deWUPzpIosCRhqdapZTVJ1N19291fMZcbmonGSBuuNVAUtLMzKmLVlw4ZASZLm5w/LVTf2qVic1Pz8odQU0vNY8NOd0/T0ndq7d0QzM/dJkqwtSVrQwsKs+vrWa2Bgq8+qVc7GFIuTGhjYtrg+03fLxTdASOXVbSG7VSiM6957X7P43bp152beEBG6uogDyrBNrZ3PLF9Y1rB/zc0d0H/8x2ZddNF7NTU1thishkfySEkgNT19Z1mQu379RWXznJgYXXxETn//4GL2L9HoTSTtfb5pVwdZb7hb5SfNcIF729uSYCac+NN3pFX61Rwu3Olf2VNT7hb0gwezq0gqVe+FX9fT01Jfn6vquugiF1DFgde83/nSv7hDW6c29TBd1qfQXXe5jMiZZyZZoYGB8oAy/PJOlyHOEMbDh20QLtSTk25ZZ2aS7EK8TtIBWLqNVT2ZqHR2pL8/+yLeiLDea4mXJa7KTFdXBemAJVatGmvfPrfPvuAFyWc7d7r1F7qCCMFuHDyHfeSyy5J5Vqp6DOs/3ft9pYzVvn3uBoJwzITjLhxj1dZ/rW1z111uOvF+HgKZEDSlHTjgphunBOI7SMMzK6UkIAoBW3wjRFb7v0plP3LEjRPuTj333OTO1XCOueuuZD8plZLvm72b9aKHag/TBum76UK/SvFFJlQxzs5Oq69v/WL7lli46Epa/G5hobhY7RIyPWFeITgplY7pwIH3+8+TQCLt6ae/ImPWLJnXwMBQzZ6302UNVYDNWBpAxD8C7eJnrppw6TzSZUmm0efXe1/0WTJOeZmzyz43d0ADA9sWOykN2cZYvK6ygppSaVpPPPGpxfX7xBOfWixzyNQly+GCzunpO7V+/UVRQJRk74KQ3Uz3NRVuxHBdJRQX97Uwr76+dZqfPyL3oyBZlpDlC+WcmhorqzINmbVu0RUPiDbbjdWvLP38/g9Klz3p36xd60724SQWLvaXXuq+f+ihpb+U+/uX3tpfTX9/8mDh/fvd+xe8wJ30t23LfuhtuFiEea9f74KP8fHkRJsuV1+fC76uuMIFMZJbjnBhCIHLpZdKb3pTkhEKFw1JuvFGN98XvMCd+OPgMEwjrJNQhr4+tw7Xr0+e6VcouM/n510mIASv732vu+Du2VMe0IRp9PVJGzcm2yG9DqXku7DtQoam0exTXj70ofLA9r3vLQ9qL7tMevDBpRk1SbrkErfOwzYIgUJ4MHEIZhYWXAB60UXJPiQtrQIOwVvYn+Ne7MP+eOmlSfYs/uyBB9z09uxx5QpB88tfLn3qU66Mf/EX0k03uR8dYduF5Y37tAp3BL73vdKvpA7KeF/O2q8b0d/vsooLC249xftbpemHfS8t3j+vvjoJDjduLM+0VZtGveIAqo7lN1/8Yt2TXnxAdNmTovNi/F+8LrI6auxLDdOIfvX1DfgLZK3pNNdJ5MrQjmXvU3//xsUHGNde330ypj8z89TXt34xU5UEN5XL2N+/abG9XrWgNytAL59ueJ3+nz2Nvr716u/fuJghSxg1nr1q3AtfqLoeEJ1bkGWMeYmkP5VbU39lrb2l4rD1BFnxyTH8kl1YcCe7jRvbl5rftMldwOKL69q10oYN7qJ1xx1JFucb30gyREFfn7t4GNP4yTwO6vbvdxeM++5zF82tW5P/cZVomOfCQnKB3r+/+sVk/frkYjo3lwwXB6VhPseONXdRClm9bgmoslx9dXnAEoKPEMyGoCRLWL4QKMQBccjc7Un9qgsB+M6drrpxaioJdEKQtd4/VioEDZs3u3Zoe/YkAYSUfLZpU7Jfhot/1g+Lq69eWp6rr06yZTMzLiAJAUsIJvISB1lhueN9sRFhO0itB1Ft1r1BFlamVoLiVixPYNNNOhpkGWP6Je2X9JOSDkr6T0k/b629N3P4jCDrDXdLH7rDbbqO67ITd1UbNy4N/FBZpWCwmW0exjFGWrMmqSKuZu1a6aqrlgZA9c4LXY0gC1iZ6g2y+moN0KQrJD1orX3YuorYf5B0bSMTeO/nuiTAknrrYkaA1ZhQBZ31eTPTklxWpZ4AS3LDNRpgxfMCAHStvBq+nyMp7vb1oKQfjQcwxuySFBrAFPQOPRB/f3pOBQOAZfPC+rNSL1zyAkAXe0Y9A+UVZGUlocrqJa21o5La0CMgAABA98mruvCgpPOi9+dKmshpXgAAAF0nryDrPyVdYoy50LjOLF4t6fac5gUAANB1cqkutNaeNMa8UdL/kuvC4a+ttd/JY14AAADdqCs6IwUAAFhp8qouBAAAWNUIsgAAAHJAkAUAAJCDvPrJqskYc7mkn5J0vaRHJV0saVLSOklnyz2AqSBpg6SNcn1vlSQ9LWlI0hFJx/3/T0j6BT/saZKKkr4v11nYWkknJA1IOlPSg3KdpRYkfc/Pa5Ok9X7ex305zlPyhMrH/TSeJdcVxSl+mNN8OTdKOinXh6p/wrKO+2kXJT0lad4vw1a5x6OvkfR1SS/w01jwn2/w5bjPj3eJn/86SRco6YNs3s/jIb8cF/jPNkk6Va4z2L3+8x/x4837Zf+epLsk/aukV/h1My1pp6THJJ3vh3+VH/ZVcj32f8Sv/2dIepHfDv/bT/NMP69+SQ/79bneL/9FvqwlvxwDko75bXepL+vv+XVyqV+HZ/qyneeH+5If96uSfkbuB8JRP50f9eWalduP9vvt8UN+W+2T9Gw/fNHP56ikbX7eP+DLfcxPp09uP9kq6UK/PM/w6/oxXyYjaYuks/zwVtKX5faLyyU90y/vQUmP+Pdb/fad82UIT0Gd8WV+hp/WoP++4D+/yL/v88Mf9uNMye0n/1XOlNz2P9uvs1P8ehjw05TcsXaaX5eH5Pa79dF8j/hxZn35TvV/a30ZpuT2j3W+fOOSdvjv55Xs53N+Wuf5dbNO0v1+uLN8GY75shm5/e9+SVf6skxJ2u7n8ZQfruCXYa3cdp7zyzDp36/x6+dpP8zjfryDkjb7bXqF3HYf9OPv82We9d+t8et3jS/nUT/fY77Mm/3wx/w2fcKX9TxJT/phHvfr9Em/DBv8uvyq3HHw076MD/pyfl7uHHK9fz8Xrfu7/HeX+HV0RNJlcucj4+d/htx+uUXueD1dbv+wfvk/4cv0sKTXyB0DW/10+uT2mX+WO/6PS9rtt8Wz/LY9V9JX5G5k+u9yx/0Bvwz/4cv9LV/GBT//E3LHcDjWNvlxNvh1f1BuPzrqt+/35I6fkqSr/Pun5fblZ/ttUPDLM+7/n+PL94TcMbLJv75H0kvkjtdBv/5O8ct9zL82ku6UtMeX/xt+eaf98Jf66U0rOV4v9Nv1iB/G+jKFR0as8dvscf9/wS/fub68j/r/W+WOsVP89M/w5Vrn19vxaH2dJXeduEDu/PCffr1u8+snXLue8q/X+mUr+Omt8fOc8eX8d7ntfkzumDnNL8tRuXPN+X4+RUn3+mU/Kbd/z/lhzvCfPSHpeX6bzvjvvyvpx/34fX64ebl987h/Lb9uNvvxwno46MtS8n/PlPSAH7fPD3OKX56iH2bBb4dw/QzjjvvteZrc/nqqL99av96e8ss37Nfv85VcO8P0JuTOL0N+eufL7XPHJP2dpPustZ9TDR1r+G6M+b7q7DEVAACgS1hJs9baU2sN2MnqQm5rBAAAndJsHHJSdT5euWPVhXJVOT8tl6rdIpe6XJBLpZbkFn5GLhVZVFLtcVRJCs/Kpf1KclmxQT9sqBo7qKSK8XQ/jcNyqeAjcqn8U5RU24TU75CSVGeopjwpl+58RNIPK6maWPDfz/jlWiOXTpzzny344ULZzpZLa87KpSZD9cbTksbkquWsn9cPRuthsy/fI/6zk3Ip8nvl0thDfl4n/DKF6qiQPj7hy2R9WUJatN//DzubSf0PrJL07Hy0TTZFy9gXLdsaP8yjfriNculf68sU0siDfl6hHAtKtt8Rv92O+fJuiIYJZZ6PyvaYn+9pSqqep/301vvhnuHXZdGPe1Kuanm7f7/ZjzcflWXGr78zlKSj1/v5Wf/doNy+tV2umihUEayL1l9Y30W5qoCtftke8etx0E+3IHdMhG23Vm6/f9yvgw1+uGklVcShGjZsl7vkjrGikmqD/b7s076cj/n1cVhu/ykqqYL4vlw17Bm+DGt8+Z9WUj0x5/9O8eM/4ZfvcT/+5X6ZZvyyhuraUKWy1b8/4cef868f8OtvSEmV/Wy0HE9F5Qr7TThGD/hhzvTbr09ufwjVsk/55d/k1334ofmQ3HG9wY8/79dnfG6Y9NuiKFflEo6RsAyb/TKsU3Jc9/nvN/plGPDlPcWXJ1S7bPHfb/Tlm5GrnpiRO4+d4dd7eAL8OX7+C36ax6NtNCtX1RF+ZW/334X1Eco077fJCT/8RX49/JiS5gg2Wq4+v/z9Km+2EMrxtF/Xj/gyhePwFL++C37ck0qqm8+Pxg/n0nk/bhCOg7CdwzEUjv1pP72N0TpdJ7d/D8hVqT9T7lxyVO4cHM7Hp/jpPCG3Pz7py9rnX5/w78P5/4Sfz2lKqvWMH3ajX/a1frkO+7KGpiih2vuEX/Z5/3q7n9ZBP+6U3xYbfVmP+bKv8+UKx/Ba/7ro36/xn21Scr551K+Ts/w0wzoMx+I/yTUHGYy2zYCSa5tVcvzP+OnLL0u/n2+o8gzr4Cy5qunNfpjTVb7PPxltz1P89J6U28e2+GUu+c/C9eZJ//kZ0TaeV3JMhP3iqFw1YTjny09DSh0j1LcAAAq4SURBVM5X03LniWf75T6ppDpy1s/joP/8HD/tU/x0HvLb628l/bXq0MnqwsNyGwOdFQ4iAABQn6estWfUGqiT1YXfE1WG3aDTARb7AIBGcd5AJ4Uappo6WV34j3JpzQvlgr0FJanIheh/uHPraSXp4HDnSqi+6vfDh7RzCBxm/bgb/fuQQj0ul0oPqdcw/En/fbiTK1SNrPHzDAf2BiXVFKE6MVThheWIyxGqEE74/yf9X6iyC+OGaqeQ2q+0fRZqfJ9WLVtVz8kqpHZbDcgWlFQvZVVLVppHuioz/bmUVDOckvr+pFzK/Gy5dV+SS4eHFPGC3J1FPyS33cL+FtZv2LZhv4jr4sP4oRzxeKHKIlSnDETTOaGkOuqIXFq9T64a/Cwl6fmwH4Uqs/Dg9bDPhbvQNiqp3g7HTrhTNVSfD/hh5pRU7Ybq4z4/vyflUuVScjfdmmh5wzoO5d6o8mM23ief8t8fV7JPh3n3+3UQqrNDdVCoFtgaLXOo7lS07uO/uLowHP82Gj/cYWejaczJHdcL0fAFuWqNMN35aFmMXDWE5PavI3LVmaXo+7DvxeeEWb9uQ1V1XzR8kLVvh2nE/5UxXDxsPZ9ljV9Jo8d7tWO3pGRdxsPVOi9VW0+V5hXvq+GzcCzG41VbH/F6D9XURSVVk+Hu3Hn/P24q0ie3vwUL0V94H6Y9r/Jj6im5YyUc9+GYDVXR6fIXlVz7QvXaaUqqdMM6mJM7F4Txrdz+PCh3fIbq9LgZRqhWDsfBydTn8bVsWu5aFqrVjJJzWV80fjifHpSrhjuq5G76+Loc1oeN5n9Q7o7KcH4Px6mUnPfCcbzZfxeOt/BdX+p/vD7DOk1f6+Iq6nDO7Zf0HUk/rzp0srrws5L+b7Unm1brhFDtBNDOTE5646iO6adPpLXKGQ+XtfHSJ7B6T2rNqGe9VytbN6p2Qeu0agdrPWWttO+0spzdsi27pRzt0MllaWbe6f2q1nHeSHC1WqzW5Q6aXf56f2S0e/5W0hPW2q21BuxkdeFz1b6dqtZ0Kn3f7p3apF7XM/34l1k6kKo03axxg/BLJLxOlyHdt8jjctH/glwfJ6F/si/K/UIoKunjqiT3C+Zkar4243+Yd/yXtQzxOKGxefx5eB2XOf7FlR62lni8rM/j8lYatt3qnUd6XVb7VZ81/azt0Oox0C0Xhm4pRyMqbfdGAuasz9N/cYPy8Ms8/j497zCOtPQ4i6e5EI2TtS9WO++m57uQ+i6UOfw9rKQR9IJaFy9HeB//X271HsMrVbPHb9Z4la6j9UynkXHqqknqZHXhN+XuHpIq/wLK+oWUXgnxhVEZ38efpU8A8YW13mnF46fLWi27VCsDFZclpJSNkjs6QnVLfLdaSJPOSHqrtfZDizM05h1y6czQ6eZ2P+ztkvZYa/9HlWWryhhziaRf8n+n++kOSvq2XJp4WK6TwlP99z/gy59VlbcgdzfNSWvtxX76n5E7sW6Su0PtCUnrrbVnR2V4tqR3KumIs1pgmaXaRUqpadTadlnqzfBV2+ezhk+PU2m6YbhqF49ay1KpbNWGqzatauNXOuZrlaWR6dYar1omptqwtc5Tlcr4hNwxsUHuB2+1c4NRUm0RzyP+ARLugDsod8x9Tu4Y/IySziH/XdLL/HQXrLX/wxizS9Kv+WmEu7xnJN0td+45R65z0vskvVTSv0n6fUlvl/TLctXC4c5Po6SKJ5QzvWzhbuz1cnfgHZK7U+tL/vOXyXVIemfWecoY81w/3FvlOpANnfUuDuLnkZVECMsWOu59m9xdtz8h6cVy56stKr+7MUyz0jatd/9t5jwSq2ffqrYPpqel1Pfpc2e1/b8R9VwXK32ePn6D0JSolmrryso1CVqj5LoaX3vXVhm/JHc81NTJ6sIvSBoRj/Zpl9uttdeGNz4Q2p8x3L2SHoyHbZQx5u/lTuLP9R+FA+CYf32qXE/wz5c7ka7NmEyw+EvZWtvvp/+YXNB2mpKLSL+1dvEg82UYVvmt9FnT7pUMRy+VtRmdWL5a82xHmSoFZfWME7qoaYesC2vo7uHLcl11POjnOe4/v8pae60x5rjccZq+oC7I9Yr+fCVtBjfI/fB5mVybm7e2odwhcz4jd16ZCOXNOk9F559LlfwAbWa+Ru5H54uUPHUkBITSyj4e0boT1tr1tQbqZCbra5IukMvOPFNJY9y0dCO1OL2bTleHk0xokBy+D33IDChJQ4dHiYTGgVLy6yeeZ3h9Mipj1q/qrF8BWcsQxA2a40a7ccPlQblfu6FxYMF/Nu3L/7B//bTcL7LYO5X0WRVuGrhD0h+rdf/T//+ApA/KPcZij6Rbfbmutta+zBjzOrkg6DVKHm0RGmrGfVS9Q65vnmBS7lf0T8o1Mv5/JP12Rhkuk/S7Svp9Cn2JhfmE/pDiX9Jh/5AqVzv0qXzfkhrLoMTTieeZziplZU7S+0qlfa1SdistKzMWv6+UHQ7lyCqjVH1/rxR4LGjpdLKkpxu230JqmDDt9HqJP0tPNx4mvR7jLHJc9lpZgThrEt+sEy9n+Lwkt48ekfSzchnfGbn+lcKPlnhaoc+5Y3KP/3ipXBX/ttRwj8s1fP6gpBfKBQyjkmSt/RtjzA/5YU+31u6RpOizv/L/z/HTvNNP/wy5c8+UXIC1Tq5/oXfKBWqvUHIjwTElNyZ9Ri6LHqol1yu52WedL+/H5Y7fr8pdC74rl0WStXZPVLYs4fzzXD/fDyq5WSg0vI4bms8paegtP++H5W52+WNJ37fW/qYx5jf8dP4/uR9480oe6VYpWySVH2PpYeeUNIQPNwBU2u/CtSfeB+Nr2EN+mWf9Z6Gx+bzKf8hWOq+E11nnhEr7+YKWHmPx8Olq3izp7FrWuTQd2MbXd8llYsONSeHzcONBaL4SMk9hmPj8G5ejpKRPv5B1vV8u0L9ZLsgfkNv/Dyk5LuaVHMOzSh6hV521tuN/kq71/6+TO8BHJL3Lv74lGu5t0esfkruYh/e/4VdQn6SPSfpwGK7CPK8O4/tpvS767oP++z65E8YPxfP08/qYpA/LnSgfjsudnm+qnK+L5vu6eNpZZYzKWnG4OpevrvFa2IbxMmYuS6PL3cjy1ls+uWqId8odYNdL+qPo8z5Ju+QCtz65k3HYp66XO7n3++E/LqnPv74+jJ96/ytyz6a8We65b7/rX/+RH+bmaJxb/P8+v+9/Uq5j2vAsvXdF36Xnda2km/wyvUvSbfEyheGaWGdXSXq3n/7H/D7+LklfTpX1Wj9smP/1GfOvte5CtuT66LPr/DA3ywXw4fubJb3bfxfKdZ2k26IyhePwv8plKa5Tck5Zsg6j8j7f/71S0i1+fs+PphfKdpOf181+vldJ+ic/zJifT5/cOSIM2xe2TYX1fW28Xmtsmz+NXv92nse1UucRlZ+XXhd/V+kYTr9vdxmj8oRzdEPny0rT9K9/o8r+/km/T/SFfTTjeP5YtG1/WRnXiDrKEJbn5mi6n8n4fsmx5D/fJXcuer6S80/47lr/Fx8jt8odO3fKHbu3+mn+sz8uyo6zSvuv3xaZy15h+Spet6rti+lphHUVlfXTkl5po+O9jn0grJc+v50Xz8GN7Esdqy4EAABYyWgPBQAAkAOCLAAAgBwQZAEAAOSAIAsAACAH/z+mBwZ2SHvPrgAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 720x504 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(10, 7))  \n",
    "plt.title(\"Dendrograms\")  \n",
    "dend = shc.dendrogram(shc.linkage(air, method='ward'))\n",
    "plt.axhline(y=6, color='r', linestyle='--')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "here more clusters are formed .. If we do pca we get accurate results"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### YOU CAN SPECIFY THE NO.OF CLUSTERS YOU WANT"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([2, 2, 2, ..., 9, 5, 5], dtype=int64)"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.cluster import AgglomerativeClustering\n",
    "\n",
    "heirarchial = AgglomerativeClustering(n_clusters=12, affinity='euclidean', linkage='ward')  \n",
    "heirarchial.fit_predict(air)  "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "lables=heirarchial.labels_ "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([2, 2, 2, ..., 9, 5, 5], dtype=int64)"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "lables"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\kumar\\Anaconda3\\lib\\site-packages\\sklearn\\metrics\\cluster\\unsupervised.py:342: RuntimeWarning: divide by zero encountered in true_divide\n",
      "  score = (intra_dists[:, None] + intra_dists) / centroid_distances\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "1.286679517798889"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.metrics import davies_bouldin_score\n",
    "davies_bouldin_score(air, lables)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### DB VALUE IS LOW , IT MEANS CLUSTERING IS PERFECT "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##### HEIRARCHIAL CLUSTERING ALWAYS SHOWS THE POSSIBLE NO.OF CLUSTERS THAT CAN BE FORMED AND LACK  A LITTLE BIT OF PERFORMANCE DUE TO OUTLIERS OR IMPROPER PREPROCESSING"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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