MMA869Individual_Michael.ipynb

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    "# MMA 869\n",
    "\n",
    "* **Name: Michael (Yong) Wang**\n",
    "* **Machine Learning and AI**\n",
    "* **Professor: Dr. Stephen W. Thomas**\n",
    "* **Individual Assignment**\n"
   ]
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   "source": [
    "# 1.\tHOW LOVELY!\n",
    "\n"
   ]
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   "source": [
    "[For this question, please answer the parts below in English, and also turn in your code. Your code should be submitted as a notebook – either a Python Jupyter Notebook (.ipynb) or an R Markdown file (.Rmd), along with a PDF rendering of the notebook. Make your code completely reproducible. E.g., set random number seeds.]\n",
    "You work at a local jewelry store. You’ve recently been promoted and the store owner asked you to better understand your customers. Using some sneaky magic (and the help of Environics!), you’ve managed to collect some useful features for a subset of your customers: age, income, spending score, and savings. Use these to segment your customers and create customer personas."
   ]
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   "source": [
    "## 1.1 Coding Part (You can skip this and check the answers below)"
   ]
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   "source": [
    "#### Import Packages"
   ]
  },
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   "cell_type": "code",
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   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "from IPython.core.interactiveshell import InteractiveShell\n",
    "\n",
    "import matplotlib as mpl\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "from sklearn.metrics import silhouette_score, silhouette_samples\n",
    "import sklearn.metrics\n",
    "from sklearn.preprocessing import StandardScaler\n",
    "from sklearn.cluster import KMeans, DBSCAN, AgglomerativeClustering\n",
    "from sklearn.mixture import GaussianMixture\n",
    "\n",
    "from yellowbrick.cluster import (\n",
    "    SilhouetteVisualizer,\n",
    "    InterclusterDistance,\n",
    "    KElbowVisualizer,\n",
    ")\n",
    "\n",
    "import itertools\n",
    "\n",
    "import scipy\n",
    "\n",
    "import pandas_profiling\n",
    "\n",
    "# The following is just to change the display settings.\n",
    "InteractiveShell.ast_node_interactivity = \"all\"\n",
    "pd.set_option(\"display.max_colwidth\", 200)\n",
    "pd.options.display.max_columns = None\n",
    "\n",
    "%matplotlib inline\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
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    "id": "_t1gF0wpEYlk"
   },
   "source": [
    "#### Load data"
   ]
  },
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   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "8rwdWl4UBUM3"
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   "outputs": [],
   "source": [
    "df = pd.read_csv(\"jewelry_customers.csv\")"
   ]
  },
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   "execution_count": 3,
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    "outputId": "8f95b760-f54a-4f6a-bc44-e111bf679aa8"
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   "outputs": [
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Age</th>\n",
       "      <th>Income</th>\n",
       "      <th>SpendingScore</th>\n",
       "      <th>Savings</th>\n",
       "    </tr>\n",
       "  </thead>\n",
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       "      <th>0</th>\n",
       "      <td>58</td>\n",
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       "    <tr>\n",
       "      <th>1</th>\n",
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       "      <th>2</th>\n",
       "      <td>62</td>\n",
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       "      <td>9258.992965</td>\n",
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       "      <th>3</th>\n",
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       "      <td>0.765680</td>\n",
       "      <td>7346.334504</td>\n",
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       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>87</td>\n",
       "      <td>17760</td>\n",
       "      <td>0.348778</td>\n",
       "      <td>16869.507130</td>\n",
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       "   Age  Income  SpendingScore       Savings\n",
       "0   58   77769       0.791329   6559.829923\n",
       "1   59   81799       0.791082   5417.661426\n",
       "2   62   74751       0.702657   9258.992965\n",
       "3   59   74373       0.765680   7346.334504\n",
       "4   87   17760       0.348778  16869.507130"
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      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 505 entries, 0 to 504\n",
      "Data columns (total 4 columns):\n",
      "Age              505 non-null int64\n",
      "Income           505 non-null int64\n",
      "SpendingScore    505 non-null float64\n",
      "Savings          505 non-null float64\n",
      "dtypes: float64(2), int64(2)\n",
      "memory usage: 15.9 KB\n"
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   "source": [
    "df.head()\n",
    "df.info()"
   ]
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     "text": [
      "C:\\Users\\michael.wang\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\preprocessing\\data.py:645: DataConversionWarning: Data with input dtype int64, float64 were all converted to float64 by StandardScaler.\n",
      "  return self.partial_fit(X, y)\n",
      "C:\\Users\\michael.wang\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\base.py:464: DataConversionWarning: Data with input dtype int64, float64 were all converted to float64 by StandardScaler.\n",
      "  return self.fit(X, **fit_params).transform(X)\n"
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       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Age</th>\n",
       "      <th>Income</th>\n",
       "      <th>SpendingScore</th>\n",
       "      <th>Savings</th>\n",
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       "      <th>0</th>\n",
       "      <td>-0.042287</td>\n",
       "      <td>0.062733</td>\n",
       "      <td>1.103593</td>\n",
       "      <td>-1.072467</td>\n",
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       "      <th>1</th>\n",
       "      <td>-0.000821</td>\n",
       "      <td>0.174811</td>\n",
       "      <td>1.102641</td>\n",
       "      <td>-1.303473</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0.123577</td>\n",
       "      <td>-0.021200</td>\n",
       "      <td>0.761727</td>\n",
       "      <td>-0.526556</td>\n",
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       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>-0.000821</td>\n",
       "      <td>-0.031712</td>\n",
       "      <td>1.004705</td>\n",
       "      <td>-0.913395</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1.160228</td>\n",
       "      <td>-1.606165</td>\n",
       "      <td>-0.602619</td>\n",
       "      <td>1.012686</td>\n",
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       "        Age    Income  SpendingScore   Savings\n",
       "0 -0.042287  0.062733       1.103593 -1.072467\n",
       "1 -0.000821  0.174811       1.102641 -1.303473\n",
       "2  0.123577 -0.021200       0.761727 -0.526556\n",
       "3 -0.000821 -0.031712       1.004705 -0.913395\n",
       "4  1.160228 -1.606165      -0.602619  1.012686"
      ]
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     "execution_count": 4,
     "metadata": {},
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   "source": [
    "X = df.copy()\n",
    "scaler = StandardScaler()\n",
    "X[list(X)] = scaler.fit_transform(X[list(X)])\n",
    "X.head()"
   ]
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   "cell_type": "markdown",
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   "source": [
    "#### Normalize data"
   ]
  },
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   "outputs": [
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     "output_type": "stream",
     "text": [
      "C:\\Users\\michael.wang\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\preprocessing\\data.py:645: DataConversionWarning: Data with input dtype int64, float64 were all converted to float64 by StandardScaler.\n",
      "  return self.partial_fit(X, y)\n",
      "C:\\Users\\michael.wang\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\base.py:464: DataConversionWarning: Data with input dtype int64, float64 were all converted to float64 by StandardScaler.\n",
      "  return self.fit(X, **fit_params).transform(X)\n"
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       "      <th></th>\n",
       "      <th>Age</th>\n",
       "      <th>Income</th>\n",
       "      <th>SpendingScore</th>\n",
       "      <th>Savings</th>\n",
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       "      <td>-1.303473</td>\n",
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       "      <th>2</th>\n",
       "      <td>0.123577</td>\n",
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       "      <td>0.761727</td>\n",
       "      <td>-0.526556</td>\n",
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       "      <th>3</th>\n",
       "      <td>-0.000821</td>\n",
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       "      <th>4</th>\n",
       "      <td>1.160228</td>\n",
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       "        Age    Income  SpendingScore   Savings\n",
       "0 -0.042287  0.062733       1.103593 -1.072467\n",
       "1 -0.000821  0.174811       1.102641 -1.303473\n",
       "2  0.123577 -0.021200       0.761727 -0.526556\n",
       "3 -0.000821 -0.031712       1.004705 -0.913395\n",
       "4  1.160228 -1.606165      -0.602619  1.012686"
      ]
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     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
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   "source": [
    "X = df.copy()\n",
    "scaler = StandardScaler()\n",
    "X[list(X)] = scaler.fit_transform(X[list(X)])\n",
    "X.head()"
   ]
  },
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   "cell_type": "markdown",
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   "source": [
    "#### Elbow Method\n",
    "##### find the bestfi k for k-means\n",
    "I first use Elbow method to find the best k (the number of clusters). According to the plots below, 5 is the best cluster number in this case. Here is the reason:\n",
    "\n",
    "In Silhouette score elbow for Kmeans Clustering, when k is 5, we got the highest Silhouette score, which means that, at this point, the cluster is farthest away from the closest neighbouring cluster. So, 5 is the best k number."
   ]
  },
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   "execution_count": 6,
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    "outputId": "5238e334-11f3-4f7c-af09-56b3f5830fb0"
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   "outputs": [
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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "model = KMeans(init=\"k-means++\", random_state=23)\n",
    "KElbowVisualizer(model, k=(2, 11), metric=\"silhouette\", timings=False).fit(X).poof()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "4L20ng-KEygU"
   },
   "source": [
    "***According to the plos above, 5 is the best cluster number***\n",
    "#### Try differnet k with Internal Validation Metrics\n",
    "I use a for loop to try different k value (from 2 to 10)\n",
    "Based on InterclusterDistance plot, we can find that 5 is the best clustering number. There is no overlapping between each two circles. Besides the distance between each others looks good."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 0
    },
    "colab_type": "code",
    "id": "zczBjroeE288",
    "outputId": "6059ea4a-7c7b-4538-8aed-3549d745eaff"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "KMeans(algorithm='auto', copy_x=True, init='k-means++', max_iter=300,\n",
       "    n_clusters=2, n_init=10, n_jobs=None, precompute_distances='auto',\n",
       "    random_state=23, tol=0.0001, verbose=0)"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "text/plain": [
       "SilhouetteVisualizer(ax=<matplotlib.axes._subplots.AxesSubplot object at 0x00000265EC0F3208>,\n",
       "           model=None)"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "InterclusterDistance(ax=<matplotlib.axes._subplots.AxesSubplot object at 0x00000265EC14E588>,\n",
       "           embedding='mds', legend=True, legend_loc='lower left',\n",
       "           legend_size=1.5, max_size=25000, min_size=400, model=None,\n",
       "           random_state=40, scoring='membership')"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "KMeans(algorithm='auto', copy_x=True, init='k-means++', max_iter=300,\n",
       "    n_clusters=3, n_init=10, n_jobs=None, precompute_distances='auto',\n",
       "    random_state=23, tol=0.0001, verbose=0)"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "text/plain": [
       "SilhouetteVisualizer(ax=<matplotlib.axes._subplots.AxesSubplot object at 0x00000265EC1DECF8>,\n",
       "           model=None)"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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R1F8FmDlzJslkoRNDK2PFihXMnr3BA8ENmbhhxm60uGHGDiXuAnc6yorLL2+ceQ4xdk9PD3/605/A19DBqkRRTwEkk0mamgoeI18xjRY3zNiNFjfM2FWP+491AxI2zDzXRuwN6ra2HaXGGFNH7Dh1Y0x+e+0VdgZmEOqqqAfpNK033sRbDxcaJLEy2l55OZS4YcZutLhhxg4l7izXp9x2883liZ1KQSJBxwnHE4kNZkgZU4y6KuoA0Z4e6BloaPfyi/T0hhI3zNiNFjfM2PUwz9GRI2k74nAr6BVWd0XdGFMeyV/7UWndgIQbJOjtpWn+dlbQq8CKujEmr+TDD7sbZSjqkUSS+PT3XTTKVIAd/WKMqaggCIjP2JhI3NqQ1WBF3RhTWX19NC0s5lKjphysqBtjKio2YQKx4f1dOdKUmxV1Y0zFBOk08QkTwk6joVgnlzEmr6AMYzdFW1pJzNumDNmYYllRN8bktfrzp7kbr7wy6PdIzJlNrKOjTBmZYlj3izGmMhJJEnPCGWWxkVlL3RiTV+yFF9yNRKLkaYMgoGnz2cS6u8uclRmItdSNMXm1XHctLdddO7iJ16whMWfz8iZkimJF3RhTVkFfH837foDYCGulh8GKujGmrKLt7TRtsUXYaTQsK+rGmLIJ0mniM2eEnUZDs6JujCmfvj6SO+4YdhYNzYq6MaZsol3DiLW2hp1GQ7NDGo0xeb3z0Y+WPE1syrQKZGJKYUXdGJNXeuIkd6PIM0qD3l4SMzeuYEamGNb9Yowpm/jkyWGn0PCspW6MyavtgvPdjaOK64aJNDcTKcMgYGbDWFE3xuQVSaVKen20q6tCmZhS1GRR7+3t5YwzzuDll1+mp6eHE088kV133TXstIwx/QiCgNi4cWGnYajRon777bfT1dXFhRdeyBtvvMEBBxxgRd2YGhbt6KBpp0Vhp2Go0aK+1157seeee669H4vFQszGGFNIsGYNrYceQrS5OexUDDVa1Nva2gBYtWoVp5xyCqeeemrIGRlj8gmCgOS284iPHx92KsaryaIO8Oqrr3LyySdz2GGHsd9++4WdjjENp2dhEd0pPT0kNt2s8smYotVkUX/99dc59thjOeuss9h+++3DTseYhtSzyBf1AicfxcaOJT7BWum1pCZPPrr88st56623+N73vseRRx7JkUceyXvvvRd2WsaYLEE6TWzKlLDTMDlqsqW+ZMkSlixZEnYaxjS05ptudDcWLMz/gkiE5sU7VS8hU5SabKkbY8IXf/ZZ4s8+m/e5IJ0mPmkiETsyreZYUTfGlK6vj+a99w47C5OHFXVjTMkibW3EOjvDTsPkYUXdGFOSIAiIT5gQdhqmH1bUjTFFC4LAXVh6993CTsX0oyaPfjHGhC81YeJ694MggCBN66GHWNdLDbOibozJ692jj3Y3XnmFoK+P+NRpNO+1B7Fhw0LNyxRmRd0YU1CQThMbNZLWDx9EJGo9trXOiroxJq/E448RpNL07byYtmOOIRKJhJ2SKYKtdo0xeTX98pe0PPoIa7bbzgr6EGJF3RjzPkEQQDxuZ4wOQVbUjTHrCdJpop2dRFpawk7FDIIVdWPMWkEqRWLGxrQfd6x1uQxRVtSNMU5vL/Fx42jZf3/rdhnC7OgXYwwkE7R++GDikyeHnYnZQFbUjWlgQW8v0dGjaN5jT+ITc8ZzWbHC/VetfmJm0KyoG9NAgiCA3l4AYqNG0bLfvsTGjMn/4vb2KmZmysWKujGNIJ0mPlOIjuwmPm48sQnjiTQ1FZ5m5crq5GbKyoq6MTUiCILsO+v+p1Kkk01E2tuIRGMQi0E0CtGo26EZi0LU/4/FiESi/nbcndYfi5KYNYv4uHGlJbTLLu7/LbeUZwZNVVhRNxsk6O2DVJ8rNJlD4GJRIpFMkYlCxBef7EIUibjXRWMQjbjpibjD6DJ/ABHWv0+E3kiE+EYb+bs5ryfImjbzeNZ9WHeoXiSy7v0zwaIRCLLiZk0biUTo6ewkOXOGezwzfd48IkTIngfen1d23Kz7kWgEIv7AtHiM2NSpvPPHP9Ixd24pX41pUFbUTVGC3l5iI0YQaWuD1lairS2sGTGC1gU7Eh09mmhz89oWZKWPb16zdCmtIRW4no52mq24mhpmRd2sFQQBkUSC2ORJRJJJIskmIk1NRJqSRDo7SW666Xqv7126lMTGG4eUrTEmHyvqDSoIAujpIdLRQayrCzo6iHUNI7ntdkRb7fRwY4YqK+oNJOjrI+jtIzZiOMnNNiMxew7R7uF2OrgxdcSKep0K+vqIdnYQHTmKaFcX0fZ2It3DiY0aTXRYp50GbgZ22WVhZ2AGwYp6nQj6+ogkEkRaW4i0tBKfOoWmRYusFW4Gb++93f+lS8PNw5TEivoQFvT2EWlppnfsOFoPPpD41KlWxI1pcFbUh5igt5dIWxuxCRNIbCIkNtmENcuWkZg2LezUTL1ZsMD9v+SScPMwJam/op5OE/T1VT9uKlV63ABIpyAduPNdMiedRMg6WzC29go00TFjSG69FfEpU6xFbirvpZfCzsAMQn0V9UiEd3fZmZac46mr4d3fP0PLrM1KmiYSi7nxN5qbiSYSrnjH4xCP+9O9rXAbY0pTV0U9EomQmjSJpEjVY6dWrQolrjHGZLMrHxljTB2xom6MMXWkrrpfgiBg2aoneOOFN6oe+4VVz4cSN8zYjRY3zNhhxJ2+z/YAPLlq2YCxAwK6kp3MG7ddNVIzBdRXUSfgX33/ovWd1qrHfjP1Bi3vhDNmSlixGy1umLHDiPvap/Z1sV99ZcDYLfEWFk9cXIWszECs+8UYs0HSQYotRm9BIpYIOxVDnbXUjTHls8U3bgTg1UMX9vuaIAiY1DGJjYZNr1ZaZgBW1I0xeU35xSPuRj9FPQgCWhMtzBtr/ei1xLpfjDGDkogl2H3KHrQmqr8Py/TPiroxZlC6mobTnuwIOw2Tw4q6MWZQRraMCDsFk4cVdWNMyYIgoDnWHHYaJg/bUWqMyeudsd39PpcKUtb1UqOsqBtj8rrnurPcjVdfed9z6SBFV1NXlTMyxbDuF2NMyaYMm0pnU2fYaZg8+m2pi8iiQhOq6v3lT8cYUyvG3b8cgFdnjFrv8SAIGNFsO0lrVaHul3MKPBcAu5Q5F2NMDZl37lUAPHHtaes93hf0MaZ1bBgpmSL0W9RVdedqJmKMGRoSkRjDmoaFnYbpx4A7SkVkCvADYCqwELgBOFZVn69oZsaYmjSufTzNcTucsVYVs6P0v4ELgVXA34EfAddUMiljTO0a1Tom7BRMAcUU9ZGq+ksAVQ1U9QrAdnsb04BS6T5Gt44OOw1TQDFF/V0RmYjbOYqILADWVDQrY0xN6kh2WlGvccWcfPSfwB3AdBF5EugGPlzRrIwxobv3qjP8rT4A0kGaGcNnhJeQKcqARV1Vfyci84CZuJa9qmpPxTMzxoRq9STfIs86o3ST7k1DysYUa8DuFxEZBlyA2zl6BfAlEbEBlI2pc/HV7xFf/d7a+93Nw4lFYyFmZIpRTJ/6D4EUcDRwPNABfL+CORljasA++5/OPvufvvZ+t51FOiQU06e+saoenHX/VBF5qlIJGWNqTzpIM7x5eNhpmCIU01JXEdkhc0dEtgCerVxKxphaM6xpGBvbTtIhodCAXn/BHcbYAhwsIn/EdcNsihV1YxpGKkiz1eitiUWsP30oKNT9srhaSRhjalcymmRC+4Sw0zBFKjSg1wsAItIE7AO0AxEgBkwDzqpGgsaYcLVEWohEImGnYYpUzI7SHwHDgY2BB4CdgQcrmZQxJnxPn3wgAK0xO4J5KCmmqG8OzAAuAa4ElgA3VjIpY0z4/nLAIjd2+ls2gNdQUszRL6+pagD8EdhcVVcCycqmZYypBaNbRjMibsenDyXFtNRXiMilwGXA9SIyHte3boypYwtO+SadyU6eO/fCsFMxJSimpX4icJOqPoPbOToOOKyiWRljQjfyD39l2O9Xhp2GKVHRF5729/8N3IIbqdEYU6fSQZpYJG6b5EOQXXjaGPM+AQHJWCLsNMwg1PSFp5cvX85FF13EtddeG3YqxjSUrqZhRKydPiQVs6M0FFdccQW33347LS0tYadiTEMJgoCJHZPCTsMMUjE7SkMxefJkLr300rDTMKbh9AV9TGibALvu6v7MkFLMRTKOr0Yiufbcc0/i8ZrdkDCmbo1tG8uo1tHwwx+6PzOkFNNS/1TFszDG1IRU0MfsEXNsrJchrJim8Isich/wW+DdzIOqem7FsjLGhKIl3sqEDj8i47e+5f4vXBheQqZkxRT1R7Nu2+rbmDqVJs2MrqwLYVhRH5IGLOqqeo6ItAHTgRVAi6qurnhmwMSJE7npppuqEcqYhjeyeQRbjN4y7DTMBipmR+kuwHLgNmA08IKI7FHpxIwx1RMEASNaRoWdhimDYnaUng8sAN5U1b8BiwAb4ceYuhIgw2eGnYQpg2KKetQXcwD8wF7GmDoRBAG7TtmdzqZhYadiyqCYHaUvici+QCAiXcDJwF8rm5Yxplomd05mbNvY9z+RsLFfhqJiWurHA4cDk4DngC2Bj1cyKWNMdUQiEWaPmJP/yWefdX9mSCmmpb6Fqh6a/YCIHAj8pDIpGWOqIRWkmNU9i+Etw8NOxZRRofHUPwI0AeeKyFk505yBFXVjhqx0kGZ8+3i2HL1V/y9aurR6CZmyKdRS7wB29P+zh+HtA86sZFLGmMoKCFgwfgHRSIEe2IMOcv9vuaU6SZmyKDSe+g+AH4jIrqp6b+ZxEelU1beqkp0xpiImtk+kKd4cdhqmAorZUdoqIl8TkXYR+QOwUkSOrnBexpgKCIKA5ngz0r1J2KmYCimmqJ8F3AAcAjwGTMVGbjRmSGqKN7HPtA8wvn182KmYCinqIhmquhz4AHC7qq4C7ABWY4aYdJBml8m70mzdLnWtmEMa/y4ilwLbAEeIyMXYyUfG1LxU0Edboo2upuF0JjsY0zqO7ubusNMyFVZMUT8UOAC4RFVXi8hK4MuVTcsYsyGCIGBC+wR2mbzb4N/kxz8uX0Kmaoop6gf4/zuIyA7A28CBwDUVy8oYU7J0kCYdpJk2bCpdTV3I8E037A3nz3f/7Xj1IaWYop59jHoCWAjcjxV1Y2pCKkgxvWs6Y1rHMrZ1LG3JtrBTMiEq5iIZx2TfF5Fu4MaKZWSMKUoQBPQFfUztnMIO43csf4BZs9z/a6z9NpQU01LPtQp3WKMxJgS96V4S0TibjZzFpI5JjGgeUZlAq6tygTNTZgMWdRH5NRD4uxFgI+AXlUzKmEYVBAGpIEU6SAMBsUiMZKyJ5mgzI1pG0NXUxYT2iYxrH0csEgs7XVODimmpn511OwBetwtlGLPOukKcIhqJEYvGSEYTxKMJYtEYMWJEo1Hi0RixSJxYJOZfFyUWiRGLRElEk8SicZpiSVrjrTQnWmiJtZCMJYlH4yxdvZS5U+eGPatmCCg0SuMifzPIeWqkiCxS1fsrl5Yxta833cvY1rGMbx9Pd3M37ckOWuItJKIJIpFI2OmZBlWopX5OgecCYJcy52LMkJAOUkzqmMTGXTMY1z6+8EiHxlRZoVEa1x7KKCKjVfU1EWkFxqvqn6uSnTE1JB2k6Q162XL01swaMav+W+MnnRR2BmYQBmxiiMingLv83VHAz0TkExXNypgakUr30RpvYWrnVOaNnceCjgXMHjm7/gs6wGmnuT8zpBSzo/R4YDsAVX1BROYCvwW+X8nEjAlbX7qPeWPnsUn3pmuL+KqYHeZnalsxRT0BrMm638P7d54aU1eikQibj9p8vYLecE44wf3/uF1nfigppqjfCtwnIjfhivlBwG0VzcqYEARBQHuynYntExjbPp4J7RPCTilcd/leVyvqQ0oxwwR8QUQOBnYCeoFvq+qtFc/MmCpKBSnGtY5l/vjtaU92hJ2OMYNW1DABqnozcHOFczEmFEEQ0BRrYpcpu9nhiWbIG8zYL8bUlfHtE5g/br4VdFMXbCk2DSkdpIlGooxoHsG8sfNoSbSEnZIxZWEtddOQkrEkB8082AbFKmT27LAzMINgRd00nHSQZvvxO1hBH8gdd7j/duWjIcW6X0xDSQdp5ozanElLVIo2AAASQElEQVQdk8JOxZiKsJa6qXupdB/JWJLOpmGMbRvH5iM3DzuloeGGG9x/kXDzMCWxom7qWjQSZdvxOzK9a3rjnhk6WGec4f7fcku4eZiSWFE3dSUVpIgQoS3RRmuilc26N2NS5+Sw0zKmaqyom7qQCvoY0zqWyR2TmTF8JrGo7QQ1jcmKuhnyopEIiybuysSOiWGnYkzo6q6ot0RbaE+2Vz1uc0hxw4wdZtxM90p7op2Nhm3EmLaxVc/DmFpUV0U9GomyQ+eOzJ1e/Qv0Ln1zaShxw4wdatyN7SLMxuRTV0XdGFNGy5a5/ytXhpuHKYkVdWNMfsOHh52BGYS6K+pr+gLeWdNX9bjvhRQ3zNiNFjfM2GHEjbz0Umixqx23OREjGq2P8xjqqqin0wE/19Us+3f1Nxf/9vfVPBlC3DBjN1rcMGOHEffIo3YB4K4Lr6/beU4FAV2tCQ6ZP4VknRwGW1dFHSAejZBMVH9Im0RIccOM3Whxw4wdRtxMu7Ve5zmVCpg3bQTbTh9RsRhhsAG9jDENJ5VKM2lkK9tM6w47lbKru5a6McYMpLu9iX22GF+X4wFZS90Y03BGdTTVZUEHK+rGmAbT25dm1sRhYadRMdb9YozJ65FPLgk7hYoY1dnEqM7msNOoGCvqxpi8XthxN3fjlVfCTaTMxnW11G3XC1j3izGmgaTSAVNHtoWdRkVZUTfG5PWBzxzBBz5zRNhplNWojiQTR9R3UbfuF2NMXh2vvhR2CmUVj0bYffa4sNOoOGupG2MawsQRrXS2JsNOo+KsqBtj6l5fKs1WU+rv7NF8rKgbY+peZ0uCkR1NYadRFVbUjTF1b1RH/R6Xnst2lBpj8vrLor3CTqEsUumAsV1W1I0xDe63J57ubgzxk4+iEdhsQv0OC5DLul+MMXWtqy1JPNY4pc5a6saYvLa++hIAfr7Hh0POZPDSQcD4rpaw06iqxll9GWNKMvPunzLz7p+GncYGScSibFdnVzYaiBV1Y0zdGtfVTCJeH9ceLZYVdWNMXerpSzNrYlfYaVRdTfapp9Npzj77bFSVZDLJeeedx5QpU8JOyxgzhLQkokwY3hp2GlVXky31e+65h56eHm688UY++9nPcsEFF4SdkjFmiBne3kQsWr/jpvenJlvqS5cuZeHChQBsueWWrFixIuSMjGk873YN7R2MjXbUS0ZNFvVVq1bR3t6+9n4sFqOvr494vCbTNaYu3XbZLe7GEDz5qKcvxeQ6Hze9PzXZ/dLe3s7q1avX3k+n01bQjTFFixBheHv9D7ObT00W9a233pr7778fgCeffJKZM2eGnJExjWf80ocYv/ShsNMYlJZkjKZ4TZa3iqvJ5u/uu+/OQw89xCGHHEIQBHz1q18NOyVjGs6ii84EYOnFN4ScSek6WxJ1fXHpQmqyqEejUc4999yw0zDGDEHpdMCkBu1PhxrtfjHGmMGKAHMmNs6ojLmsqBtj6kYQBGw0up2WpprshKgKK+rGmLrRmwrYrIFb6WBF3RhTRzqa44zubJyrHOXTuNsoxpiC7vzaD8NOoSR9qTRzJg5rqAti5GNF3RiT178nT3c3htAZpVtPG9pDG5SDFXVjTF7R3p6wUyjJ6M5mkg16wlE2+wSMMXn9x1F78B9H7RF2GkVJpdINO4BXLivqxpghb2xXK/NnjAw7jZpgRd0YM+R1tsQbdliAXFbUjTFDWioVMHVU4w4LkMuKujFmSGtviTNtVPvAL2wQVtSNMUNWEATMGNNhXS9Z7JBGY0xeyw4/MewUBhQhwpxJjT0sQC4r6saYvHTfj7gbNXryUTIW4UPbTKK1KRF2KjXFul+MMUNOhIDFm41hWGtjXrKuEGupG2Py2uUrpwJw/XGnhZzJ+priUXadNZYJ3a1hp1KTrKgbY/Ia/cyTYaewnlQqzfD2JraZ1m0FvQAr6saYmhcEAbMmdbFQRoedSs2zPnVjTE0LgoDRnc3Mn24jMBbDWurGmJqUSgckYlHGdzWz06ZjSMRjYac0JFhRN8bUpFFtcY5aMI1o1E4sKoUVdWNMXn+bs01osVPpNGM7YlbQB8GKujEmr9+ccZG7UcWTj3r6Ukwf08GMMR28+dLbVYtbT6yoG2NC15dK09YUZ6dNRjNjrBvLZelLYWc1NFlRN8bktdmt1wHw6ra7VCxGX1+aTScOY8qINqaMbLPuljKwom6MyWvzG38AwL2DKOrpdEBfKk0iFqW1OU4yFiUZj5LI/I9HaYpF2WhMB2OGNZc79YZmRd0Ys0HS6YAgCGhpitOciNLaFGfKiDYmj2ijsyVhre8qq6uiHonARt0JZkzorHrs5LuvhRI3zNiNFjfM2GHETcTduYlThscLxk7GY2w5ZTjxmJ3LWAvqrKhHmD0mydyZ1T+VuOntF0OJG2bsRosbZuxQ4vqiPntMU2iftymdrVqNMaaOVKKlHgPo6empwFsXZ82aNQ0VN8zYjRY3zNhVjztqVHixQ44bRuysmrlB4yFEgiDY8GyyLF26dAHwQFnf1BhjGsfCuXPnPjjYiSvRUn8cWAi8CqQq8P7GGFOPYsA4XA0dtLK31I0xxoTHdpQaY0wdsaJujDF1xIq6McbUESvqxhhTR6yoG2NMHSnLIY0i0gJcB4wG3gY+qqr/yPO6jYFbVXX2BsaLAt8DtgDWAB9T1T9nPf9x4HigDzhPVe/YkHilxPavGQU8DMxR1feqEVdE/hM4xN/9haqeU6W4JwNHAwFwbgifdRT4OXCbql5ejbgi8m1gR9yyDrC/qv67SrH3Br7s7z4BnKyqG3wIW6G4IrIl8K2sl88HPqSqd21o3IFi++c/BxwKpIGvqupPqxT3Cz7uW8DXy7ls+/ffDviaqi7OeXw/4Cxc/bpSVa8o5X3L1VI/EXhaVRcC1wBLcl8gIkcCPwZGliHeh4BmVd0eOB24OCvOWOAU3I9uT+B8EWkqQ8wBY/v4ewK/BMaUMWbBuCKyEXA4sAOwPbCHiGxehbgjgZN83F2By0SknEPyFfysvfOA7jLGLCbu1sCeqrrY/5WloA8UW0Q6gAuBfVV1PvA85fk9FYyrqk9m5hX4LvCTchX0gWKLSBfu97w9sAfrr1wqGXcOcBhuBbYHcK6ItJYrsIicBvwAaM55PAF808fcCfiEr2lFK1dRXwBkvuQ7gd3yvOYNXJJljaeqjwLZF1PcFnhIVdf4H9ufgXIVuIFig2tN7Ab8q4wxB4r7IrCXqqZUNQ0kgLJsIRSKq6qvA1uoai8wFnizHK3GYmIDiMjBuM/7zjLGLBjXt+5mAN8XkYdE5NhqxcatPJ8GLhaRB4C/59sirkBcAESkDTgHV2TLqVDs1cALQJv/S1cp7qbAb1T1Pb+1/SzlrSPPAQfmeXxT4M+q+oaq9gAP4k7mLFrJRV1EjhORFdl/wDAg01p5299fj6reoaqrS43Xj86seAApEYn381zefCoUG1X9lar+s4zxBoyrqr2q+rqIRETkImCZqv6p0nF97D4R+STwKHBzmWIOGFtEZuNaUmeVOWbBuLjCcilwBLAXcFIZt4oGij0S2Bn4ArA3cKqIzKxC3IzjgP/1K/NyGij2i8AzuO6mb1cp7tPAIhHpEJERuBVqW7kCq+otQG8ROZVcv0ou6qr6Q1Wdnf3nk+jwL+kA3iz1fUv0VlY8gKiq9vXzXLnzKRS7kgrGFZFm4Hr/mpOqFRdAVb+DO715kYjsXKXYRwETgPtwffqfEZG9qhD3HeASVX1HVd/28bcoU9yBYv8TeFxV/6aqq4D7gS2rEDfjcFyXQbkVir03btmaBkwGPiQi21Y6rqr+AfgObivwYuC3QLlXZsXkVHL9Klf3y0PAPv723lR+QK+18URkPm6tmvEYsFBEmkVkGG5zZkWVYldSv3F9P/ZtwHJVPV5VyznmTqG4IiI/8fF7cTubyrl53G9sVT1NVbfz/bxXA98oYz9voe94JvCgiMR8/+cCXAuyXArFXgrMFpGRvkU5H9eCrXRc/G+pSVVfLFO8YmO/AbwLrPHdIG8CXZWO6w92GKmqC4BPA5Mobx3pzx+AGSLSLSJJYBHwSClvUK4BvS4D/kdEHgR6cJvFiMjXgZtV9bEyxcn4KbC7iDwMRIBjROQzuL6o2/3RCQ/gVlpnlusIlGJilzFO0XFxAwHtBDT5oyMAvqiqJS0Mpcb1n/Vy3EIXAHeq6v+VIWZRscsYp6S4InI9rrupF7hGVX9fxdhfBO72r71JVctVaAb6rGfidsxWwkDzvBvwqIikcX3Mv6p0XOBnwEYi8jiupn2+zI2l9YjIYUC7qn7f53A3rn5dqaovl/JeNqCXMcbUETv5yBhj6ogVdWOMqSNW1I0xpo5YUTfGmDpiRd0YY+pIJa5RaoYIEfkF8DHcOBOLVfVoEXne336+QjGnAUtU9Th/7PPVqnpAJWINkMeVwGLgTGAT4BjcuCJHqWq/J/SIyJOFni8wXVnmVUTOBlDVszfkfUz9sqLewFQ1c+JFNcNOAab728OBraoZPMvRuMGcekRkJbCbH1rhG4UmGkxB98KcV9NArKg3ABGZiBtCIDMg0imq+mimVZ5nkrNEZCugFddy/a0fY+T7uBERV/v3eFxErsYNfHS1jxWoakRE2nEj+s3GnRz1NVX9EW7sjo1E5Lu4s/TGi8hPVfUAETkKOBXXLbgUN6zseieO+ZM0luBOdnoc+DhuALMrcKfrp4GLVPUaEYnhRjVc7HO4WlW/KSK34042eUxEngAmArf6917m8+8Gfohrxa8BPqOq9w00fyJyNG5MmG5gI+CXqnqSn++185o1P98AXlbVi/39W3DDWD+LG2OmHTek9fmaM7RwJhd/+2jWbW3Nw43014o7tf14Vf2LP6nlo/4zekxVj8/z3ZshzvrUG8NxwB2qug1uAKwFA7z+GVXdCldUPucfuw74tqpuDvwncLMUHtJ4CbBUVefiTnU+U9wQwacAv1PVk/3tV3xBn4Ur0Dv41vBrWbEBEJEJ+GFJVXUWrph+ADgb+Kcfh2gX4Gw/yNbHAVR1a9zonfuLyEJV/aB/fEtVPRZ4BdhHVZ/MCvcV3BmNmwJHAv9V5PyBG/zpINyofvuJG8Z17bzmvM+1uDG7M0Prbo8bH/5juGsBzMMN4nVhgc86+zNK4sZnOczP98XAFX4F90XcSIRzgaT/PE2dsZZ6Y7gH+Ilvff8cN1BRIbf6/78HDvKt0o1V9SfghikVkX8BhfptdgNaZd3QtG3ALNZdWCLXzrghbR/13UFJ3j+myva4YZVf8nkcCSAiS3ArLvxolbfhWueLgC1FZBc/fTswh+LGJtoJP9yFqj7tYxczfwAP+8G+8F073f3Nt6ou8+MUbYxbGfzMdwl9FtjLDwswx+dejJm47q3bs7rVOlU15U+Hfxw3TtDFpZ5+boYGK+oNQFUfEpHNgH2Bj+D6k3cvMElmhLwA102Rb4suglt+Mq/JDPCfEQOOUNUn/HNjcGPM79hPzBhuLJNT/Ovbef/y2evj4V8zyt/MzS+TWww4LbMyEndRj1X9xM+VG2sTIHs44/7m73DWH8t+7edTwHW472UH4AL/2E24wax+hru4zKH5JhSRiLox7DOffQxYmen79y30zAVbPoQbBGxv4C4RObzMY/WYGmDdLw3AD6x2hKr+D/BJ3JV7iqaqbwErReRA/37zcRfFWIHrs820UD+UNdl9uCtiISLjgKdwQ6f2sa5YZ9/+DXCAiIwWN+rjZbj+9WyPA/Nl3ZVgvgns72Md52ON9Hn8xj/+cRFJ+JXEg7iiVoz7WdctsgnuYgrZAyX1N3/9yZ7XXNfjivrGPkdwK92zVPU2XBHOFOhsrwOz/Of1Qf/YH4FuEclcWOFY4Aa/AnwGd4Wys3BX5yrnOPCmRlhRbwyXAgeLyJO4kemOGsR7HAGcIiJP47pvDlR3ZZbLgcUi8hSuFf6qf/05QIu4i6jch2sxP4cbWrRLRK4F/g78VUR+rarL/TT34bp9YqxrtQKgqq/ghkG927/vu8BVwLm4QvY0rhj/l29BX47b4bgM+B1wlar+psj5/TJuCNTluKJ7pK5/Vaf+5q8/a+c19wk/nO3ruBFNMzHOxg3x+wzuyjfP48YUz3Y6cAdulEz177UG+DDu6khP4XaMHqfuCknfBx4XkaW4y6hdWcwHYYYWG6XRGGPqiLXUjTGmjlhRN8aYOmJF3Rhj6ogVdWOMqSNW1I0xpo5YUTfGmDpiRd0YY+rI/wMVU9idt6i/aQAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "InterclusterDistance(ax=<matplotlib.axes._subplots.AxesSubplot object at 0x00000265EC154898>,\n",
       "           embedding='mds', legend=True, legend_loc='lower left',\n",
       "           legend_size=1.5, max_size=25000, min_size=400, model=None,\n",
       "           random_state=40, scoring='membership')"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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4t2wf1yJEWPN69TeU6LnMQTnc8eAjpGcOCnYpYcloMOBnz4oEuRBd8bePUoi+4O/rT4JciC4YjQY8EuYiCDxeL0ajf98GJciF6ILJYPT7660QgeT16q8/f8ioFSG6YDCAFy9er1dGrvTSzLnzmTl3frDLCCterxcvXr8PtEuLXIguGAwGzJEROF3uYJciBhCHy010ZITfjQcJciG6kRhjxupwBbsMMYBYHc4eXZFKglyIbiTGmrE6/L/slhAnyupwkRArQS5EwCTFRtMqQS76kdXhJDk2xu/lJciF6EZybLR0rYh+ZXW4SI6TFrkQAZMUF43dKUEu+o/N4SQxNtrv5SXIhehGfHQUDhm1IvqR0+0mPjrK7+UlyIXoRnx0lLTIRb+yO90kyKgVIQInMymepla7zLsi+oXX66Wp1U5GYpzf60iQC9GNWHMkCTHmHl2xRYjearbaSYgxE2v2/6pUEuRC+CEvPYm6FmuwyxADQG2zlbz0pB6tI0EuhB8K05OolyAX/aDeYqVQglyIwMtJTaTZJl0rou+12OzkpCb2aB0JciH8kJ2SQKNFDniKvuX1emm02MlOSejRehLkQvhBDniK/tCbA50gQS6E34oyk6lutAS7DHESq260UJSZ3OP1JMiF8NPIvEyqG1uCXYY4iVU3tjAyL7PH60mQC+GnYdlp1LVYcbs9wS5FnITcbg91LVaGZaf1eF0JciH8FGuOJD89mUMN0ioXgXeooYX89OQe94+DBLkQPTKmIIvKuqZglyFOQlX1TYwpyOrVuhLkQvSAlpvOkeZWGYYoAsrr9VLT1IqWm96r9SXIheiBtIRYEmLMcrq+CKi6FisJMWbSEmJ7tb4EuRA9NLYwiwO1jcEuQ5xEDtQ2Mrawd90qIEEuRI+NH5xNZV0THo90r4gT5/F4qaprZsKQnF5vQ4JciB7KTIonPy1ZWuUiIA7UNpKXltSj+cePJUEuRC9ML85nX3V9sMsQJ4F91fXMKC44oW1IkAvRC1pOBk6Xm0aLLdiliDDWaLHhcnt6PVqljQS5EL0QYTIyvbiQ3Ydqg12KCGO7D9UyXSvAZDyxKJYgF6KXJg/Noaq+BafLHexSRBhyutxU1bcwaWjvD3K2kSAXopcSY6Mpycug7LD0lYueKztcT0leBomx0Se8LQlyIU7A6aMHs/twHS6ZSEv0gMvtYffhOmaPGhyQ7UmQC3ECclMTKc5Jp7TySLBLEWGktPIIxTnp5KX17JJunZEgF+IEnTV+GPuq63E4XcEuRYQBh9PFvup6zho/LGDblCAX4gSlJ8YxcUgO2ytqgl2KCAPbK2qYOCSH9BM4AehYEuRCBMCcMUOoqm+m1e4MdikihLXanVTVNzNnzJCAbleCXIgASIyNZkZxAdsPHA52KSKEbT9wmBnFBQEZqdKRBLkQAXJaSRG1LVbqZYpbcRz1LVZqW6ycVlIU8G1LkAsRILHmSM6bpLF+b6XMjCiO4vF4Wb+3kvMmab26lFt3JMiFCKAJQ3LIS01ke0V1sEsRIWR7RTV5aYknNFVtVyTIhQggg8HARVNHcrC2SbpYBKB3qRysbeLiqSMxGAx9sg8JciECLCkumvMmSxeL6NClMlkL+AHOjiTIhegDE4bkkJcmXSwDXV93qbSRIBeiDxgMBi6WLpYBrT+6VNpIkAvRRxJjo7loaglf7a7ALqfvDyh2p4u1uyq4aGpJn3aptJEgF6IPjS3KZsqwPNaUHsDjlf7ygcDj9bKm9ABTh+cxtii7X/YpQS5EH5s/fjhZSfFsLKsKdimiH2wsqyIrOZ7544f32z4lyIXoY0ajgYWnjcFqd7KnSi4NdzLbU1WL1eFk4YwxGI192y/ekQS5EP0gJiqSa8+YwJ7DdTS0OoJdjugDNY0W9hyu47ozJhITFfizN7siQS5EP0lPjOPymeNQ1U202CTMTyYWm4N1eyq4fOY40hJi+33/EuRC9KPh2WlMLUrnix3lWGXK25OC1e7k8x3lLJigMTw7LSg1SJAL0c9GZSdzxujBfL5jHzaHhHk4szmcfL5jH3NGD+FULT9odUiQCxEEs0cNZkZxIZ/vKMcmY8zDks3p4vMd5cwoLmTWqKKg1iJBLkSQzBkzhFNH5LNyWxlWaZmHFavDycpt+zh1RH7Ar/bTGxLkQgSJwWBg7tihzBxZxMpt++QycWGi1a6H+MyRhcwdO7TPT7/3R0SwCxBiIDMYDJwxegiRJhMfbd7DtBH5JMX1/SndoncaLTZWlx5g7tihnFZSGOxy2kmQCxECTispJD46imVrtzO2MIu8tKRglySOUVHbyJbyw1x86kjG9dOp9/6SIBciRIwfnE16YiwvfLqBxlY7I/MyQuJr+0Dn9XrZXlHD4YZmvjdvckh+yEofuRAhJC8tiR8umIbN6WJN6QHcbk+wSxrQXG4Pa0oPYHO6+OGCaSEZ4iBBLkTISYwxc7Ov5ffJ1jIsdjkLNBgsdgefbi0jLy2Jm+dNJjHGHOySOiVBLkQIijSZuGTaKE4fPZiV2/Zx4EhjsEsaUA4caWTltn2cPnowl0wbRaTJFOySuiR95EKEKIPBwGklheSnJ/Haqi0crGtiwuBszJHytu0rdqeLDWVVOF1ubjhzEoUZycEuyS/SIhcixBVmJPOTc6czIjuNFVv2SOu8jxw40siKLXsYkZ3GT86dHjYhDtIiFyIsREWYWDBJY1RBlrTOA6xjK/zGMyeHVYC3kVeBEGGkrXX+4abdfLxlL8W5GRRlJsswxV7wer3sq65n58EjTB2Rz7yxQ4mMCO2+8M5IkAsRZqIiTPzPJI2xhYN46+ud7NlcS0l+JjkpCRLofvB6vVTWN7PjQDWpCbHcNHcy+emhOazQXxLkQoSp/PQkfjB/CjsPHuHdb0rZVVXLqPxMMhLjgl1ayKppsrB1/2HMERF8Z9poinPTT4oPPwlyIcKYwWCgJC8DLSedTeWHeO+bUqIiTYzOz5I5WzpotNjYeuAwDqebsyeOYFzhoH69pmZf6zLINU17qKv7lVK/DGw5QojeMBoNTBiczeiCTNbtOsiKzXuIjY5kSFYqg5LjT4pWZ095vV6q6pspq66n1ebkzLFDOWV4bsiPCe+N7lrkkcAdwG8BOVdYiBAXaTIxvbiAycNy2br/MF9sL2dz+SEK0pMYmpVK1AAY5WJ3uth7uI79RxpJiY3hjNFDGF2QRVSYHsj0R5d/VaXUg5qmZQMWpdRv+qkmIcQJioowMXFIDhMGZ1NR28TaXQf4cPMeMhJjGTooLSgXCO5rtc2t7DlUS01TK+OKBnHDnEnkpSUOiG8j/nw83wlc0NeFCCECz2AwkJ+eRH56EudMGMHGsiq+2FmO3ekmIzGW3LRE0hPjMIZh2Hm8Xo40WThY20RNUyvmSBMziguYMDiHuOioYJfXr7oNcqVUE/BiP9QihOhDcdFRzCgpZHpxAZX1zeysqGHr/sOs232QjMQ4BqXEk52SENJ9yE63m6r6Zg7Vt1DTZCE9IZbRBVmcPyVjQA+/7DbINU27HtiqlFrn+/0xYJdS6q99XZwQIvAMBgO5qYnkpiZy5tihNFislFbWsmX/YbaU7yYxxkx8jJnU+BjS4mOIi44KSkB6vV4sNge1LVbqWqy0WO00ttoYnJXKqSPyGZGTRnJcTL/XFYq6G7XyI+Aq4JoON78L/FbTNLNS6tm+LC5cVFRUcOedd/Laa68FZHv33XcfCxYsYNasWe231dTU8Mwzz/Dwww8HZB9CtEmOi2HK8DymDM/D7nSxv6aByvpm9tc0sHZXBTan66hwT4gxExMVgTkyIiBdMh6vF7vThdXhotlq7xDadmKiIihIT2Jknt7izk9PJjrq5D9g21PdPSM3ArN83SsAKKVWapp2DrACkCDvJxkZGRLios+ZIyMYnpPO8Jz09ttabA6q6praw720vpnmVjsWuxNzpInoqAiiIkyYI0xERURgNBowGgwYDAaMBvB49da1x+vF4/HicLmwu9w4XG5sDhd2p5s4cyQJsWbS4mPaQzs7NZH4AdbX3VvdBbmnY4i3UUod0TQtLIcjLlu2jE8++QSbzUZNTQ3XXHMNK1asYNeuXdxzzz04nU6WLFmC0Whk0qRJ3HXXXTz99NOUl5dTX19PY2MjV1xxBR988AFlZWU8/vjjpKenU1dXxy233EJdXR2zZ8/mtttuo6qqigcffBC73Y7ZbOaRRx7B7Xbzgx/8gOTkZGbNmkVsbCxvvvkmRqORiRMncu+99wLw6quvsnjxYlpaWnj44YdJTU1tb/UvWLCAyZMns2vXLpKSknjqqaeIjT35RiGI0BAfHfWtcAf96jmtdgfNNgfNVjstVgctNjtOtwePx4vb68Xt8WAyGjEZDBiNBiJNRuKjzcTHRJEQYyYhOopYcxQRJpmI9UR0F+QuTdMylVLVHW/UNC0LCN0jIt2wWCz89a9/5Z133mHJkiW89tprrF27liVLlrB//36WLl1KTEwMd999N6tWrQIgOjqav/zlLzz//PN89tlnPPfccyxdupR33nmHa6+9ltbWVp544gliY2O58sorOfPMM3nuuee4+uqrmT17NqtXr+bJJ5/kjjvuoKamhqVLlxIVFcV3vvMdHnzwQcaPH8/LL7+My+UCYNSoUdx6660sW7aMZcuWcdNNN7XXb7PZOO+88zjllFP4zW9+w6uvvsr1118flOdSDFwRJiOJsdEkxsoZpMHWXZD/EfiPpml3AxsAGzAZ/QShP/dxbX2mpKQEgISEBIYOHYrBYCApKYnW1lbq6uq4+eabAT3wDxw4AMDIkSPb1xk2bBgASUlJ2O12AIqLi0lISABgzJgxlJWVUVpayp///GcWL16M1+slMjISgLy8PKKi9K+Mjz32GH/961958sknGT9+PF6vF9CDHCA9PR2bzXZU/REREZxyyikATJw4kZUrV/bBsySECBfdnRD0gqZpZuBvQL7v5r3Ak0qpsA3yzo7AGwwGsrOz+etf/0pkZCTLli2jpKSEjz76qNuj9nv27MFisWA2m9m8eTMLFy5kyJAh3HDDDUycOJE9e/awbt06AIzG/36NfO211/jFL36B2WzmxhtvZMOGDV3WCOByudi5cyfFxcWsX7++/YNFCDEwdTdqJQc4A2hCD/O7lVL1/VFYMERERHDddddx9dVX43a7yc3N5ZxzzvFr3aSkJO644w7q6upYsGABw4YN49577+Xhhx/Gbrdjs9l44IEHvrWepmlccsklpKSkkJWVxbhx41i2bFm3+1u0aBGVlZXk5ORwxx139PixCiFOHoa2r/LHo2na+8AW4BPgEsCrlLqhtztbv359EVA2evRozObQvSJ1qJszZw7vvvuuPIdhav369UyaNCnYZYgwYrfb2bp1K8DgSZMm7Tv2/u76yHOVUvMBNE37ANgY8AqFEEKckO7G/DjaflBKOTv+LoLn448/lta4EKJdT0+R6rwf5iTi9XoxGAw0tdpY8skGIiNMqIM1VNY1Y7E7GJWfyfmnlFCcm051o4Ws5PiTcjY5IUR46C7IR2matrfD77m+3w3o/eVD+q60/uH2eNh7qI4t+w+zufww6/ccJDLChNfrZe/h4x/Xraht4v2Nu4+6LTslgTEFWYwpzGLWyCKKMlP6o3whhOg2yEf0SxVB4vF4Of+xl6io1U9ejYuOwmJzYDIamFFcyOShueSmJrBgUjHRkSbio83sq6nnmf+s4aMte5k1shCLzcmMkkLUwSOsKd3PB5t2YzAYKMpMoanVxjdlVcwsKcRklDPXhBB9o7tx5OX9VUh/sDtdfLBpN59tK+M3V5+N0Wjg8tPGsudQHdfNmUh+WiJvfrWTOWMGkxwXw30vvs+/11Vy7RkT28d1D8lK5cnrzuGNtds5raSQzKT49u3f/cJ7fLBxN0s++YbBmSkcrGviV0s/IyclgUumj+bCKSXSBSOECLgBMY1YRW0j//pyG29+tZ0Gi43CjGSqGpr4aPNenvnPGuKio7j7wtMwGo1cfOrI9vU+31HOWeOGfevkHIPBwMWnjvrWfp645myunn2Ih1/9mNsWvcV5k4t55PK5LF+3gz+8s5pn31vLvHHD+OXlZ4b0nM9CiPBy0n/f/3r3Qc791YuQINGdAAAfvklEQVS8+NkGJg3J4flbLuC1ny7kiTe/4Knlq5imFfDaXd8l1vztWdZcHg/mTq5xuPdwHf/6ciuNlqNPnx9bOIhX7lzITXMn8f7GXaTERbP41ot4494ruGTaaKwOV3uIt9qdgX/Ax9i0aRNXX331Ube99dZbLFy4EIAdO3Zw9dVXt/8bM2aMnPIvRJg5aVvktc2tpCXEMmFINnecO52zJ4wgKzket8fDrc+/xZrSA9xz4UyumDm209PhJwzOZsWWPfz0/BlEHnPh1ufeX8eXO8v5n0nat9aLijDxowXTuGrWeFLi9Ynv89OTuO/i/84vvq+6nmv+8Do3zp3MlTPH9cnsb4sWLWL58uXExPx38v0dO3bw+uuvt8/pUlJSwosv6heAevfdd8nMzDxqHnQhROg76YK8rsXKE29+zqqd5bxx75WkJcRy7RkT2+83GY2cOXYoCyaO4IIpJV1u6+rZ47n1+bf4bFsZc8fp85lYHU5eXrmJ9zfu4vo5E4k1R3a6fluIf7R5D3/8zxoW3XohGYlxAMSaI5kwOIenlq/i3fWl3Hn+DHJSE7HYHDRYrDS22miy2rE7XThcbhxOd/sczvo/Fw6nB6fbxbiibK6aPf5b+y8oKODpp5/mnnvuAaC+vp4nn3yS+++/nwcffPCoZVtbW3n66ad56aWX/HiWhRCh5KQK8nW7K7j7hfdpttr53tzJJMQcfdJMq91JrDmSy6aP9mt7M4oLef6WC5gyPA+Au//+HqvVfpptDsYWDiI3NYG/f/INVocLA+B0e7C7/hu8baF7pMlCeU0DFz3+D4oyUvTb3W4cviuv7DhYw/eefVMf09mLx93Z5a7mz59PRUUFAG63mwceeID777//uCcTvf7665x99tmkpqb2ogIhRDCdNEH+2qotPP7G5xRkJLH41gsZNijtqPt3VdVy4zPLeOyqs5hRXOj3dqeOyG//uTAjmfjoKOaOHUphRjItdgcNFhuNrTaaW+3YXe72FrTT/d9WdJw5Eofbzdb91didLlITYnH6WtVx0WaS46KpbrRgAKIiTThdbuxON+4u5sHpqW3btlFeXt4+idfu3bv53//93/aJvN566y3+8Ic/BGx/Qoj+c1IEudfr5es9lUwrzuexK8/6Vkvc5fbw81dWYDQaKcnL7PV+frjg1F6v6/F4+f6f/822/Yf5w03nkp2S8K37nW43LreHrQcOE2uOwmQw0Nhqo9Fio9lmx+bUw9/p8hz1gTEyP6vb/Y8dO5Z33nkH+O81RttCvLm5GYfDQXZ2dq8fnxAieMI6yJ1uN81WB6nxMTxy+ZlEmIzHPfFm+bqdbDtQzRPXnE1qfHCuum00Gnh44Rwue/IVPty0m2tOn/Ct+83GCCJMHh59/VMijCYW/eACRub3/oPHX2VlZeTm5vb5foQQfaPLaWwDLZDT2LrcHu7/x4eUVh7hlTsXdnplba/Xy8KnXsXj8fKvu77b7QUi+lqLzdHtBWXX7a7gR4vfITslnsW3XiQnEZ1kZBpb0VPdTWMbluPIvV4vj77+Ke9v3MWFU0o6DXGAPYfrUAePcNmMMUEPcaA9xLsaQ37KsDye+d65VNY184M/L8dik0knhRCdC8sgf2nlJt5Yu53vzZ3MdXMmdrnssEFpvHHvFZw7KXSmjfnju2v4zhMv4/F0/m1o0tBcnrr+HHYfquX5D7/ux+qEEOEm7PrI15Qe4KnlqzhzzBBuPXuqX+sMyQqtIXW5qYlU1jVTXtPA4KzOZ0mcUVzI0zeey8ShOf1YnRAi3IRdi3zYoFQumFLCo1fMxWjsuqukpsnCPS+8z46K6n6qzj+jfQcwt/tR14ySQmKiImm1O9hRUdPXpQkhwlDYBLnX68Xr9ZKeGMfDC+ccd26UY23bX837G3dhc7r7oUL/Dc5KJToywq8gb3PfSx/wo8Vv0WS192FlQohwFDZB/t6GXdzy5+XfmqSqKzsP1mAwgJaT3oeV9VyEyciInDR2Vhzxe53vn3UKtc1Wfv/Wqj6sTAgRjsIiyOtbrDz+xkp96F5M9y3xNg0WG/HR5i7nQwmWy6aP4azxw/xeflR+FlfPHs/SNdtZt7uiDysTQoSboBzs9I2H9NuiL3fR1GrnjtlZbNywwe/1Kg8dwuD1sH79+p6W2OdyjEA0Part1KxI3o03c/+L7/HLBWOJipA5zcNVKL4mRfgKSpD35ISgzfsOsbpsNd+bN5kL5vbsFPkvquwcsnpC8uSLplYbBoPhW9MJdOfR1Bxe/GwDeUM18tOT+qg60ZfkhCDRUx1OCDqukB9++PqabcSZI7mhm/Hix/OTc6fzk3On90FVJ+7eFz+gwWLln3cu7NF6U4fnMdU3G6MQQkAYBPk9F87kglOK/RqlEk52H6pl8tDez29SUduE1eFkeHZa9wsLIU5qIX+wMz46ikm9DLwjTRZu+tMbfLJ1b4CrOjFHmixUN1p6PSGWx+PlxmeW8dRyGcEihAjhIHd7PPxo8dt8tq2s19tIiotm075DbNhbFcDKTtx234k9o3oZ5EajgYumjuRLtZ/9NQ2BLE0IEYZCNshX7dzPyu37sJ/AyTyRJhMj8zL4Uu2nP2d57M5qtZ8Ik/GExrdffOpIIoxG/rW6ZyOAhBAnn5AN8tdWbSE9IZYzxgw+oe2cf0oJu6pq2bjvUIAqO3ELZ4zh4cvmENfNdLZdyUyKZ86YIby5dgc2hyuA1Qkhwk1IBnltcytf7CznolNHEmk6sbHS50wcQXx0FK+t2hKg6k5cUWYK551SfMLbuXT6aCx2B9+UVQagKiFEuArJUStbyg/j9dKja2t2JtYcyS3zp3R7MYf+4PZ4eGr5KuZPGM7YwkEnvL1JQ3NY9aubiYkKvTNXhRD9JySDPD46ijljhlCSmxGQ7V09e3xAtnOiXvh0Iy+t3MTYokEBCXKT0UhMVEh+qRJC9KOQTIHJw3L53fULurzyT0+5PR6WfPwNb63bGbBt9sTew3X86b21nDlmCGeN83+Ole58saOc7z/3bxyu0JrhUQjRf0IuyN0eD/Ut1oBv14CBz3fs49dvrKSitjHg2++K3eniwX+uINYcyQOXnB7QS87ZHE7WlB5AHfR/JkUhxMkl5IJ876E6Tn/oL3y4aXdAt2s0GvjFd8/EaDBwy3PLqWmyBHT7XVnyyQa27j/Mg5ecHvALKY/xddFsLg+dUTlCiP4VckFeVq2f4FKQnhzwbeelJfHMzedxpLmVG595g6r65oDv43iuO2MCT990LnMD2KXSJis5nqRYM/uq6wO+bSFEeAi5IG9rKWcmxfXJ9scWDuLZ759PfYuVbQf67hJw+2saeOiVFTS12jBHRjBrZFGf7Ss9MY6aptY+274QIrSF3KiVmiYLESYjyXHRfbaPCYOzeedn15Dom0L2061lTB6WG5Ahih6Pl39+sZk/vLOaSJORcyYMZ5pWcMLb7crogiyZm1yIAPJ6vTS12qmsb6ayrom6FiuNrTYaW200W+04XJ72s8VNRgMxUZEkxphJio0mKS6arOR4slMSGJQcjzmy72M25IL8SJOF9ITYgB4QPJ62ED/SZOHuF94jOS6aq2aP58JTSkjq5YfIh5t2t/eHzxxZyIOXnEFWcnwgyz6uX373zE7v27RpE08++SQvvvgi27Zt45ZbbqGoqAiAyy+/nAULFgBQXl7Obbfdxttvv93n9QoRipqtdnZV1bJ1/2H2VTfg8nhIjDGTEBNFfLSZ1PgYclMTiTFHEmk0YjAY8OLF4/XicLmx2l1YHU4aLDYqahtpttpptjpIiY9By02jJC+ToowUIkyB7wgJuSCfM2Yo44qy+21/6Ylx/OW2i3hq+SqeWr6KZ/6zhvkThnP7udO7PTBpdTjZVVnLmMIsDAYD76wvpcFi5dHL53LuZO2EPow8Hi82pxOrw4XV4aLFZqfBYqPJ1yKwOdzYXS6cbg/FuenMHvXtqQwWLVrE8uXLiYmJAWD79u1cf/313HDDDUct9+abb/LCCy9QXy/97GJgabU72VhWxYa9lVQ1tJCeGEtWUjynlRQSExXhx3vYgAl9Xqe440y17fF6abTYqKpvZtuBbbTanRTnpjN5aC7DBqVhNAamwRqCQT6k3/c5tnAQS370HUorj/Dal1t5+2vFjxboVyN6Y+12Vm7fR0pcDBEmI16vlxabA3Wwhr2H6/ECS354MfHRZi6cUozT5abZ5uSVLzZjd+ph63B5cPj+tztdOF0uHG79Z4fTjdOt3293uXG43Dic+v9Od9vvLlyezif9Glc0iL+sWM8LP77kqNsLCgp4+umnueeeewD9EntlZWWsWLGCwsJC7r//fuLj40lKSuKll15i3rx5ffYcCxFKDtY1sbb0ABv3VZEWH0tBRjLjh2RjMga2tWw0GEiJjyElPoaR6MOFK2qbeH31VkxGIzOKC5gwOOeE5l2CEAzyuhYrTpe7X7okjjUiJ52fXXI6d543vf1CFlaHk72H62i2OnB7PBiACKORhFgz4wdnExMVwT8+34QB/ZPV6wWP14PL7cHh+m+QO9t+Piak7U4XXWS0X1rtDg7Vt3zr9vnz51NR8d8LNY8dO5ZLL72U0aNH8+yzz/LMM89w7733csYZZ5xYAUKEicq6Jt7bsIv9RxooSE9mzughRPfjFBfRUZEMy05jWHYatc2tfLO3ig827eG0kgJmlhT1erqNkAvyx5Z+xq6qWt6878qg1dDxakRXzBzHFTPHBXT7bo8Hm0PvT7M6XLRY7TS02mi02Gi22bH7wl7/EHDjcLa16H0tdJcLh9Pj61pxY3O4MPnR7zZv3jwSExPbf37kkUcC+riECFW1za18tGk32yqqGT4ojbPGDQ9Yt0ZvpSXEkpYQi8XuYMeBalarA5w5ZihTR+T1eLLAkAvypLho6i2BP7MzlJiMRuKio07461Sbu/7+Lq0OZ7fL3XjjjTz44IOMHTuW1atXM2rUqIDsX4hQ5fF4WbWznA837aYgI4V544ad8IyqgRZnjmLysDwaW22s3VXB2tIDXDp9NAUZ/p9LE3JBnp4QS4PFhtPlJlKG1PmlpqmV9ITux90//PDDPPLII0RGRpKeni4tcnFSq2my8K8vt9JstTNr1OCQmAG1K0mx0cwoLmD/kQYWf7SeU7V85o0d6lcOhlyQt50IdKS5leyUhCBXEx6GDUolNf74I2zy8vJ47bXXABg1ahSvvPJKp9tZtUquASpODhv2VvLmVzsYNiiVCYOz+3w4cyAVpCeTmRTPhr2VqIM1XHP6BOKjug7zkAvyrCT9IGdlXZMEuZ8evFQOVgoBelfKext38dWuCqZrBb0+JyTYoiMjmKYVsLuqlmfeXcN3p3fdDRpyQT6qIJP/d/Es8tOTgl1KWHC5PZiMhrBqcQjRF6wOfdhvdaOFOaMHE9UPZ1T2tWHZaSTEmFm6ejtnDUnsdLmQm2slOS6G7542lsyk/h9+GI6WrtnGmQ//rU+m/hUiXLTanfzlo/VY7E5mjiw6KUK8TVZyPFNH5Ha5TMgFOegHKT7avCfYZYSFLb7pa/tybhohQlmr3cnij74mwmRk0pAcjCfht9PY45w12lFIBvn7G3bx0yXvUt347ZNcxNE2lx9mTEGWdK2IAcnudPG3j9cTaTIyrmjQgH0fhGSQt13PcvM+uVhCVxotNsprGhhbmBXsUoTod16vl9e+3IrH42V8mI1MCbSQDPLivAyS46J5b+OuYJcS0t7dUArAqVp+kCsRov+t2LKHitpGJg7NGdAhDiEa5FERJi6cUsLHW/ZK90oXxg/O5uZ5pzAqX1rkYmDZduAwn28vZ9qI/IBPdBWOQvYZuGTaaLxeWFN6INilhKzi3AxuO2dqsMsQol81Wmy8/uU2po7I79cJr0JZyI7RyU9P4r0Hrw3KLIjhYOnqbYwuyELLTQ92KUL0G6/Xy7K128lNSyQ1PibY5YSMkG2RA+0h7vZ4glxJaDnSZOFXSz/jra93BrsUIfrVhr2VVNY1MTI/M9ilhJSQDnKAx99Yya3PvxXsMkLK0jXbcXk8XDp9dLBLEaLfWGwO3lqvmDT05BwrfiJCPsjTE+NYU3qAXZVHgl1KSLA7Xby+eiunjsinsAfTXAoR7j7bvo/0hFiS46RL5VghH+QXTx1JYoyZ/136GR7fpXS8Xm/7FawHmkUffU11o4Xr50wMdilC9JsGi5U1ar90qXQiZA92tkmJj+HO82fw8Ksf87N/fkhGYhzVjRY8Xi/RkRHkpCYwbFAak4bmDIhP6kiTiYumjuTUETJ2XAwcH2/ZS15aUq8vhXayC/kgr6htpLy6nsykOFZu38c9F85kyrA8jEYDDpebuhYrqvIIn24rY1zRIBZM1Ig1n7x/7O+fdcqA/TYiBqYmq50NZVXMGzcs2KWErJANcq/Xy8rt+1ixZS8j8zK4/dzpRBiNR80vbI6MIDslgeyUBJxuN1vKD/O7t1Zx7RkTyEs7uabBffOr7aQnxHFaSeGAP4tNDCzf7KkkKymeKLliWKdCto/8k61lrNy+jzNGD6YoM4W0hFiS4qLxeLzsq67/1vKRJhMTh+RQnJfBX1as52BdUxCq7hu7Ko/w2NKVvLpqS7BLEaJfuT0evlTlDB2UGuxSQlpIBvnew3V8tq2MmSWF3+oTe+cbxRNvfsGeQ3XHXTc3NZGSvAxe+mwTdqerP8rtU/UtVn7y1/+QEBPFg5eeHuxyhOhXu6pqMRgMpMjJP10KuSB3uT3868utjCsadNzTb88YPYSU+Biee/+rTi+mUJCeTEyUiRVbwntOc6fbzd0vvEdNk4Wnrl8gF9sQA87W/dXkpHR+ZRyhC7kg31FRA0BO6vH/ePHRUdx69hQcLjd/fHcNFpvjuMuNKRjE2tIKbI7wbZV/tHkP63Yf5KHLzmif2leIgcLj8bJt/2Hy0iXIuxNyQf7V7goGZ6Z0er/H4+H9l/+Cbd3bVNQ08Me3vzjuKI4YcyRJcdHsOFjTl+X2qbPHD+f5Wy7gvMnFwS5FiH53oLaRCJORuG6ujiNCLMi9Xi/lNQ1dTpS1fvUXrPr4A+676w6yG0px7fqq01EcafGxlB/nwGgoc3s8/Hb5F+w9XIfBYGCqjBcXA1Rp5RHSE+OCXUZYCKkgb2y1YUAfVtiZXTu2kZWTy+BhI5hSPJiDW9YC8OnWMiqPGamSEh8dVqNXmqx2frT4bV74dCOfb98X7HKECKrymgYyEmODXUZYCKkgd7o9RJq6LsnaaiHKrI8lN5vNWC0WrA4n735Tyq/fWMmmfVXty0aaTNid7j6tOVD2Vddz1e//xdrSCh689HSuPUNOwRcDl9frpaK2Uaaq9VNIBXmkyYjT3fWUtdExsTgcdgDsdjsxcXHEREVy38WzGJScwLPvfcXrq7ficLpwuNxdtu5Dxc6DNVz5+3/RZLWz6NYLuWSazGooBrZ6iw0vyIUj/BRSQZ4UG40XsHUx/nuoVsLhgxWU793Nto3rGV48CtDnZLnrgtOYObKIDzft4bE3VlLXbCUvLXSPeDvd+reFoVmpnDVuGC/ffikTh+QEuSohgu9wQzNJMdHdLyiAEAtyg8FAUUYyh+qbO11myoxZnDZ3Po/9vztpaW7m8htvab8vKsLElbPG8dPzZzBv7DDqLVby05M6HaIYLC63h79/8g0X/+Zlmq12IiNM/HzhnE6HXAox0DS12sPi23SoCLlnasrwPN5ZX0pRJ0MQjSYT1916O9fdenun2xiRk05empPPtukHQH+65F0WzhjDd6aNIiOIR8H1vvxdvPz5JnZV1TJ7VBEOV3j04QvRn5qsdsyRMreKv0IuyItzM/jPN6UcrG0i9wS6RTaXH2KaVkBRZjJabjrPvv8Viz78mjljhnDZjNFMHprbr5NP1Ta3csGv/0Gz1c6wQan85pr5nDVumEyAJcRxNFhsITtlrbW1lT8/9RjbN20gMTmFq26+jfGnnBrUmkIuyCNMRi6bPpq/ffwNqfExxPRiStp91fU4XG7mjBlCVISJ575/AeU1Dby+eitvrt1BadUR3rz3SkDv5ojoZqRMb7jcHj7bVkZFXRPXnj6BtIRYLj9tLNO0fCYMzu7zAHc6ndx///0cPHgQh8PBD37wA4YNG8Z9992HwWBg+PDh/PznP+eLL75g0aJFgD5SYP369bz99tsMHTq0T+sToitNVjspcaHZR/7BW8so21XKL//vOd567Z8s/v0T/PEfS4NaU8gFOUBRZgpzxgzhs21lnFZS1KP5xQ8caaS08gg3zZ181LSXhRnJ/PT807jt7FOprG/CYDDQandy3q9eJC8tkTGFgxhTmMXYwkEMSo7vVdBuP1DNyu372FJ+mM3lh2iy2inISOKKmWOJNJm47ZypPd5mG6/Xi8PlxupwYnO4sNidNLZaabDYyUqKZ1TB0VdOWb58OcnJyTzxxBPU19dz0UUXUVxczO23387UqVN56KGHWLFiBfPmzWPWrFkALF68mIkTJ0qIi6Bzu92YjCF1CK/dvHMvZMYZ80jPzCIpORm3O/jdoyEZ5ACzRw0mwmjkg027Kc5Npygzpctwdbrc7eF549zJnR44jI6KYEiWPiWm1eHknIkj2FJ+iFdXbeHFzzYCcPcFp3HV7PEcqm/mn19sJj0xjpioCAzo+3e43NS2tFLTaOFIUysPf3cO6QmxfLJ1L89/+DVFGclMGZZLSV4mQwal8O43pVjsThxONw6XC6fbg93l8v2u/7O33edyY/fd5uxwu8Plxun2+JZ1HTVM89zJGv97xbyjHufZZ5/N/Pnz2383mUxs27aNKVOmADBr1ixWrVrFvHn6eocOHeLf//43S5cGt2UhBIDbE7oXT4mNiyc2Lp51q1by3puvc95lVwS7pNANcoAZJYUMzkph6Zpt7KqqIy8tkYzEOJLjovUrBDn1KwQdamjmUEMLE4fkcMOE4X73raUlxHLXBacB+gdBadURNu87zCnDcwGorG/mHys3dTq2PSYqgkiTkZ8ueRej0YDN4WJEdhoer5fSqlq2V9Tg8nhwezw4Xf8N4UC/RI93RfG4OP2gbktLCz/+8Y+5/fbbefzxx9s/DOPi4mhu/u/ooL/97W9cd911REXJvBYi+IzG0D52tPrTFTz/u8eZOvN0Llh4VbDLCe0gB30WxB+ecyoHjjSysayKfTX1HN7VgtvjJSYqktzUBEblZ3H17PEkxva+Ty0ywsSo/CxG5We13zZxSA7rfvMDmlrt2F0u3B4vRoOBCJOR5Lhov7/6Od1ubA5Xe7dIY6vN98+OxeZoD/j2VrirY0tdv91+1H2+lrvTg8Pl6vTSb1VVVdx2221cccUVnHfeeTzxxBPt91ksFhIT9W8tHo+HTz/9lDvuuKPXz58QgRRhNOL2dH1yYLDs3rmdRb//DeOnTOOq7/8Qq9VKXHxwp5gO+SAHfXx5QUYyBRnJQdl30gkedIk0mYiMMZEQYw5QVUfzHOdr6JEjR7jhhht46KGHmDZtGgAjR45k7dq1TJ06lZUrV3LqqfqR9tLSUgYPHkx0dGgeXBIDT1x0VMheGOad11/B7XbzzZpVfLNmFQCLlv4nqN9mwyLIRdeO9zX0ueeeo6mpiT/96U/86U9/AuCBBx7g0Ucf5amnnmLIkCHtfehlZWXk58ssiyJ0JMdFU1nX+YmBwfSTn/0y2CV8i6E/r8i+fv36IqBs9OjRmM190zoVItStX7+eSZMmBbuMkPal2s/GsiqZsqKNx02kpRZg8KRJk/Yde3doju8RQgxoCdHmkO1aCUUS5EKIkJOZFEezNbTmSAplEuRCiJCTkRiH062P1BLdkyAXQoQco9FAdnICdS3WYJcSFiTIhRAhqSAzidrm1mCXERYkyIUQIWlEdjo1TZZglxEWJMiFECFpcFYKLTaHjF7xgwS5ECIkRZpMjMhOp7KuKdilhDwJciFEyBpTmBWyZ3iGEglyIUTIGpmfSbPNgcUuY8q7IkEuhAhZUREmJg/LYXdVXbBLCWkS5EKIkDZ1eD4Ha5tCdlrbUCBBLoQIaRmJcQzOSmHvIWmVd0aCXAgR8uaPH8buQ3W4Orla10AnQS6ECHk5qYmMzMtg58EjwS4lJPX3hSVMAA6HHIEWA5vdbg92CWFn9sgC/rbiG2xZiURH+ndd3pOGp33yMNPx7u7vIM8G/dJiQgxkW7duDXYJYemsIUngaIKB2xbMBvYce2N/B/k6YCZQBcj8lEII4R8TeoivO96d/XqpNyGEEIEnBzuFECLMSZALIUSYkyAXQogwJ0EuhBBhToJcCCHCnAS5EEKEOQlyIYQIcxLkQggR5iTIhRAizPX3KfpCBIWmaUVAKbAd8AJRQCVwvVKqQtO0a4AfAZHoDZzFSqk/+NadAfzOt04tcINSqrzfH4QQnZAWuRhIKpVS45VSE5RSo4DNwBOapt0M3A6cr5QaD8wCrtI07Ubfev8AbvLd9w/gD8EoXojOSJCLgewTYDTwM+AepVQVgFKqAbgW2Kppmhn4mVJqs2+dzUBBMIoVojPStSIGJE3TIoFL0GeTux74puP9SqkdHX59ybeOEXgYeLN/qhTCPxLkYiDJ0TRto+9nM/AVcBd6kNu6WlHTtCjg7+jvmV/1ZZFC9JQEuRhIKn393EfRNG0vMBlY2eG22cA5Sqn7NE2LB5ajH+i8QCnl7K+ChfCH9JELAU8Av9U0bRCApmnpwG+B3b77X/L9vFApJddoEyFHLiwhBgTf8MNPlVJFndz/I+BmwIPewPmzUuqPmqZNQO8/3w60tcQrlVIL+rxoIfwkQS6EEGFOulaEECLMSZALIUSYkyAXQogwJ0EuhBBhToJcCCHCnAS5EEKEOQlyIYQIc/8fsuY+ewEl1qIAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "KMeans(algorithm='auto', copy_x=True, init='k-means++', max_iter=300,\n",
       "    n_clusters=4, n_init=10, n_jobs=None, precompute_distances='auto',\n",
       "    random_state=23, tol=0.0001, verbose=0)"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "text/plain": [
       "SilhouetteVisualizer(ax=<matplotlib.axes._subplots.AxesSubplot object at 0x00000265EC255A20>,\n",
       "           model=None)"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\michael.wang\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\manifold\\mds.py:421: UserWarning: The MDS API has changed. ``fit`` now constructs an dissimilarity matrix from data. To use a custom dissimilarity matrix, set ``dissimilarity='precomputed'``.\n",
      "  warnings.warn(\"The MDS API has changed. ``fit`` now constructs an\"\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "InterclusterDistance(ax=<matplotlib.axes._subplots.AxesSubplot object at 0x00000265ECBE3978>,\n",
       "           embedding='mds', legend=True, legend_loc='lower left',\n",
       "           legend_size=1.5, max_size=25000, min_size=400, model=None,\n",
       "           random_state=40, scoring='membership')"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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WtnuZqqp4gyH6h7XkvqO+FX8ozILCbBYX51KZ78Y6S/EJgvB2t/3nd/j7X/7Mr3/4//jZfcdvnZsaEjaRdw+NcKith/2NXXQMjpDlTCMv3c7lSyswGRMnbEmSsJlN2Mz/Tu6+QIi2fg8v7qnjL5t9lGans6QklwVF2aTbTjxRQRCEqXGlWWjoHJjw+nV/e5K9b27jltu+iMFoIOD3TXgsgD8UxnmSUk0iS5yMiNYLfbC1h82HmugcHCHXZacwy8lZ8wqSqpPRajZSmZ9JZX4m4UiUzsFhdjS08fyuw1TmZXKeXExFXmZSPSZBSDT5GQ6GfUcnvH7Nxe9g75vb+O7nP01Wbi6f/dq3JzxWVVWGvH7y3Y6ZCHXGJUQiHxjxsaOhlW1KKxaTgbKcDJaV5aNLgQ5Eg15HUabWMRqJRGnsGeSJLQfQ6yQuWFDK8vJ8bBaxEqQgTFVhppNQOMqwL4DjBCNN0jPcfP2ueyZ1Xx0Dw2Q7bDiS9LMYt0SuqioNXf28caiZuo4+CjIcrJaLk3Loz2Tp9Toq8txU5Lnp9XjZeaSN9XvqWFqWx7lySUos3iMIs8Wo17O6qgilvZeVFYWnfT+qqlLf2celSyqSdvRZXBJ5e7+HF/cfpXtolHm5bq5cPn/OjfLIcqaR5UwjEApzpKufP764g6qCLK5YXkm28/RnqQnCXHLBwlK217XSMzR62rM7G7sHMBsMLCvLn+boZk9csudfN9dgt5i5dGkFFXnuOZfExzMbDSwsyuGKZZUEQ2F+8/xWntl6AI83OYdBCcJsSjObuOG8at5saGPE97Yd0E6p1+NFae/jxvOXJHUeikvkFy4qpSLPnRI18Omi1+tYWJzDZUvn0e0Z5Wd/38y63XV4A6F4hyYICW1BYTbXrJDZeLCJrsGRSd+usXuAHfWtfOjCpeRnJGcn55i4lFZ0uuT95ptpJqP2E68qP4sDLd1sPdzCO5dXsmp+sRjlIggTWDW/iHSbhSe31NDUPcj8gswTrkkeiURo6/NQ29ZLJBLl/IUltPV5qO/oIxLVlu2IRKNI/HvZDr1OR5rJgMNqji3bYcKeYEt2JMSoFeHtrGYjKysL8Xj9vHLgKHubOrnh3GoyHWnxDk0QElJVQRZfvu58tiotbDrUxIg/CCp4g0GGvQFGAyF8wRAmg4GiTCfFWS7qO/owGQwYDXp0koQkcaxSEFVV1NiyHcFwhFA4SiAUxh/Wlu0IhSOkmY240iwUZDgozk6nIMNBbrodk2F2t5ATiTzBOdMsrF1czuH2Xn7zwhauXD6f1aJ1LgjHRKMqbf0e2vo9NPcM0tw7hC8QwmTQgQppZhvlORlk2KzkZThIt1mmZXRKVFXxB8N4A0EGRv3sqGvF49MW6stypFGS5aI4O52iTCcFGY4ZHREjEnkSkCQJuTCbAreTVw8cZZ9onQtz3NhorwMtPdS2dKPXSaTbLLjSLCwuzsGVZpnxxo5OkkgzG0kzG8kaN9IsEo0yNOqnb8THjrpWXtzjQwKqS3JZWJTDvNwMjNPcYheJPIk4rOa3tM6vOquKVfOLknbsqyBMhcfr53B7H/ubuzjS1Y/TaiHHZeO8BSXYE2gij16nw+1Iwz2uoeXxBWjrG+LZHQcZ9gWYn59JdUkuVQVZ0xK7SORJZnzr/KV9DbT0DfHuVQsx6me3JicIsyEaVVHae3njUBONPYNkOdLIy3BwxbLKaW/VziSn1YyzKIeFQDAUpq1/mNcONPL01lrm55/5sh0ikSepsdb5jvpW7nvpTT5y8fKUnhUrzC3DvgA7G9p5Q2lCJ+koy0nnnWfNR58CI95MRgPluRmU52aMW7ajBr1Od9rLdohEnsQMeh1rqoo50NLDb1/YwkfXnk1RppjmLyQnVVVp6hlk6+EWDrR0k+uyc1Z5wQmHEaaKty/b0c66PXUsK8tjTVUxhW7npEqnIpEnOUmSqC7JoaXXzB9f2sH1axYl9VRjYW5q7B7gn7sP0zPkpTTbxeXLKmd9CF+8vWXZjs5+7t+wk0K3k6vOriLbfvJf2yKRp4jiLBcOq4lnttXSNTjC5csqRSeokPA6B4ZZt7uOxp5B5IJMlpflz/n3rdloYGFxDnJRNkc6+/njSzuoLsyk4iSD1EQiTyHpNiuXVM9j08EmAuEw165YMOc/FEJiGhjxsWFfA/ubuqjIy+DyZRUpUf+eTjpJojI/k/KcDI509p70WJHIU4zZaOCiRWVsPNhEOKLy7nMWislDQsLwBUO8vL+B7YdbKc5K5/JlFUk1+iQe9Hod8/MzYbRvwmNEIk9BRoOeCxeVsvlgE89EVd63ZpFomQtxV9feyxNbanBaLbxjybxZ22t3LhC/ZVKUUa/ngoVl1HX08uyOg6iqGu+QhDnKFwzx1JYD/HnjPhYX57KyslAk8WkmWuQpzKDXcf7CUjbWNmLQ67jmbFm0zIVZNdYKd6VZuHTpPDFxbYaIRJ7ixlrmr9c24rRYuGhxWbxDEuYAXzDECzsPs7+5i+XleeSlJ/d634lOJPI5wGTQc55cwob9DeSm25ELs+IdkpDCugZHeOjV3aSZjaIVPktEjXyOSDMbWVVZyGOb9tE9NPldVARhKg619fC79dspynSyoqJQJPFZIhL5HJLltCEXZPLgK7vFFnLCtFJVldcOHOWxjftYWVFIea473iHNKSKRzzHluW4cVhOPbdpHJBqNdzhCCgiGI/xlcw2bDjazdnE5WU6xTv5sE4l8Dlpalk/fsJd/7j4c71CEJDc06ucPL26nc3CYtYvLsJrFsMJ4EIl8DtJJEmvmF7GzoZ2DrT3xDkdIUn3DXu5dvw2bxczq+UXo9SKdxIt45ucok9HA2eUFPLWlBm8gGO9whCTTPTTCH17cTkmmi0VF2WJ+QpyJRD6HZbtsZDrS+MebSrxDEZJI1+AIf3hxB/Ny3FTkZ8Y7HAGRyOe8JaV51Lb2iBKLMCndQyP88aUdVBVkUZabEe9whBiRyOc4g17HinmixCKcWt+wl/s3vMn8vExKs9PjHY4wjkjkgiixCKfk8fr540s7KMvOEC3xBCQSuQBoJZaDbT0cbj/5AvbC3BMKR3jo1T3kpjuYlycm+iQikcgFQCuxVBfn8vxOhWhULHkraFRV5elttYQjERaKNXoSlkjkwjEFbgf+UJh9TZ3xDkVIEBtrm6jv6OOcyiIxxDCBiUQuHCNJEtXFuazbXUcoEol3OEKcKW29bNjfwLlyiZjsk+DEqyO8RbbLhkGv4836tniHIsRR99AIj23ax6rKItLEtPuEJxK58DbVJTls2NuAPxiOdyhCHPiCIR58ZTdyQaZYACtJiEQuvE26zYrLZmHzoaZ4hyLEwbpdh7GajGIp2iQiErlwQouKc3i9tpFASLTK55K6jj72NnWyrCwv3qEIUyASuXBCdouJdJuFvY1iBMtc4QuGePKNGpaX5WM0iJ19kolI5MKE5uW62XSwCVUV48rngnW7DuOwmsnLEBslJxuRyIUJ5bhsjAaCNPUMxjsUYYaJkkpyE4lcmJAkSZRkudh6uCXeoQgzSJRUkp9I5MJJlee6OdDSzbAvEO9QhBnySs1R7BaTKKkkMZHIhZMyGfTkuuzsamiPdyjCDBgc9bFVaaa6NDfeoQhnQCRy4ZTm5bnZWtciOj1T0IZ9RyjKdGE1idmbyUwkcuGUMmwW/MEw3UOj8Q5FmEZdgyPsa+xgYVF2vEMRzpBI5MIpSZJEtsuG0i62g0slL+6tY16uW3RwpgCRyIVJKchwsL+xK95hCNOkuWeQho5+5ovNk1OCSOTCpOS47HQMjuARo1eSnqqq/HN3HfMLssTytClCvIrCpOh0ElnONOo7+uIdinCGWvs8dA4OUy723kwZIpELk5aXbqemWZRXkt3Wwy2UZqWjEzv+pAyRyIVJK8hwUNfRJ3YPSmKj/iD7mzqZJ1rjKUUkcmHSTEYDFpOBrkExDDFZ7T7aTpbThsloiHcowjQSiVyYEqfVTMeAJ95hCKchGlXZfKiZijyxYUSqEYlcmBJXmoXWPpHIk1F9Zx+RaJRMh9i+LdWIRC5MidueJpa1TVJblGZKs0VtPBWJRC5MSYbNQs/QqOjwTDIj/iB1HX2UZafHOxRhBohELkyJXq8jzWIUHZ5J5nB7L5kOm5gAlKLEqypMmctqER2eSeZAczd56bZ4hyHMEJHIhSmzWUx0Do7EOwxhkkLhCIc7eilwO+MdijBDRCIXpizNbGTI6493GMIkHe0ewG4xYRZjx1OWSOTClKWZjHhGxeJZyaK2pZsclyirpDKRyIUps5oMYhXEJKGqKjXNXRRluuIdijCDRCIXpsxqMuLxBcTWb0mgY2AYFXBYzfEORZhBIpELU6bX69BJEr5gON6hCKfQ1u8h3WaJdxjCDBOJXDgtFqOeEb8oryS6lt4hXGkikac6kciF02I2GRj2BeMdhnAKzb1DuO1ibZVUN2fHI/m8Xv7wix9Tu3c3zvQMPvKpz7H8nDXxDitp6CWdmKaf4EKRCD1Do5xTURjvUIQZNmdb5C8+9zRH6w7z/V//HnnxUu7/1T3xDimpSBKiszPBdQ2OkmYximn5c8CcbZFffu17OP+Sy8nKycWVnk5EtC6nREIiEhWJPJF1DHhwitEqc8KcTeRpNjtpNjs7Nr/Our89yXU3fijeISUVSScRFYk8obX2eURH5xwxp39zbXn1Ze69+y5WXXAx777pI/EOJ6moqookNu9NaO39HjJs1niHIcyCOZvI6w/Vct+v7mb5qnP5yKdvx+fzxTukJKOim7PvnuQw5PVjNRvjHYYwC+ZsaeX5Jx8nEomwa+tmdm3dDMB9T72AyWSKc2TJQVVBJ4lMnqhUVWXYF8QqFsqaE+bsq/zF73w/3iEktWhURa8TpZVE5Q2EMOh1YsTKHCFeZeG0BMIR0szi10uiGvEHsYjW+JwhErlwWgKhMA6LSOSJatgfwGTQxzsMYZaIRC5MWVRVCYYj2EQiT1gjvqBI5HOISOTClAVCYawmIwZRf01YI/6g2BFoDhGfRGHKfMEwzjQxYzCR+YIh8UV7Bl78+zPcct1lBIPJsTCc+MoWpswXDIkZgwkuEo2iExO2piwYCPDUIw+w/u9PxTuUKYlLIj969CihSDQepxamQdugF4tRz86dO+MdSsKK93NTf7SHAW8Qg28wrnEkm4G+XhoOH+KCy97JxhdfoL6uDoMx/pOqjHodVTnOCa+PSyIvLy8HneiISVbexg4WFmazYnF5vENJSDt37mTFihVxjaE9ehDHiJ+qwqy4xpF8qlh97nls3LCejS++QOX8+YkxSTAagdG+Ca8WRTRhyoa9QfIzHPEOQzgpUVaZS0QiF6ZEVVU8Xj95IpEnNJ1OIirWi58zRCIXpmQ0EMJiMoh1rhOcXieJjT/mkJPWyGVZvvNk1yuKIhYsmWP6R7wUZ7riHYZwCnqdTrTIz8CFl13JhZddGe8wJu1ULXIj8J+AHq3odvx/whzTP+KjJFsk8kSXZjYSDItdr+aKk7bIFUX5rizL+cCooih3z1JMQgIb9gYpcE88DEpIDA6LWQzxnUMmUyP/CtAx04EIiW+so1OMWEl8dquJQCgc7zCEWXLKceSKoniAh2chFiHB9Q17cTusOERHZ8KzW0wERSKfM07ZIpdl+WOyLJ8z7u8fy7L88ZkNS0hErf0elpTmxjsMYRIcVjN+kcjnjJMmclmWPw98BvCMu/ifwGdlWf7sTAaWTHq6Ovn+126ftvu775d3s2/n9rdcNjjQz4P3/nraznE6eoZGWVCYHdcYhMmxGA2oQFjUyeeEU7XIbwUuVxRFGbtAUZTXgavQErwwS9Iz3Nxy2xfjdv4RXwCAQtHRmRQkScJhMeMLhuIdijALTlUjj8Zq5G+hKEqvLMtJ+VW/ccN69mzfQjAYZHCgjyuuex+7tr1BW1MjH/j4pwiHw6z725PodDqqFlVz4398kmcefZCujnZGPEOMDA9z6dXv4s03NtLZ3sonv/SfuDLceIaG+OUPvsvw0ADLVq7h3R/4CH093fzpt78kFApiNJr42O1fJhqN8qsffAe7w8nSlauxWCxs+tdLSJJE1aJqPvDxTwPwyrrneeHpv+IdHeWW276I05W72uCNAAAgAElEQVTO7+65izt/9lu+edvHqVq0hLbmRmwOJ7fd8S3MFuuMPm8tfR6qS3KRxIp6ScOVZsYbCIk+jTngVC3ysCzLOcdfKMtyLtrY8qTk8/n46n/9iGuu/wD/+udzfOFb/8XHbv8yr720jmcefZCv33UP37n71wz09VGzW1vFzmQy8bXv/YSV517A3je38eU77+KaGz7Ato2vAhDw+/j0V77Od376a/bt3E7z0QYe/78/cPl17+WbP/o5V73v/Tzx4P0ADA0McMf3f8o119/Exg3r+fAnb+POn/2G7Lx8IhFt7G9Z5Xy+8cOfcfm172HThvVviT8YCHDu2kv5zt2/pqComFfWPT/jz1nP0CiLit72VhASWGGmk/4RX7zDEGbBqVrkvwVekGX5DmA34AdWAj8H/jDDsc2Y0opKANJsNgqKSpAkiTS7nYDfx/DQEL/43rcALeH3dHbEbjNfu43dTmFJKQA2m4NQbOH54rIK0mx2AOZVLaCzrZXWpqP844lHeeGpx1FVMMR2bMnKzTu2NOYnvnQH/3z6Cf76wP1ULlh4bFp1WUUVAK6MDIKBwFvi1+sNLKheCkDlgsVvq6dPt2AozLA/QHluxoyeR5heRZkujnYNxDsMYRacakLQQ7Ism4E/AcWxi48AP1MUJWkT+UTFAQkJd1Y2d/zgbgwGAxs3rKdkXgW7tm4+ZUmho7UZv8+H0WSiQTnI2iuvIb+ohKve+37mL1xMe0szSs0+QFvQaMyr61/gls99CZPJxD13fp36gwe0WE5yukgkTPPRBkrKK6g7WENhSdlUHv6UNXYPsqgoR+wBmWQKMhwM+wKnPlBIeqdaa6UAuARt1MqfgDsURUnZr3i9Qc+V77mBH3/zK0SjUbJycll1wcWTuq3N7uDeu3+AZ2iI1ReupbCklA98/FM8eO+vCQVDBIMBPvKpz73tdkWl5XzvK7fhcKWT4c5inryQjceVUk7k+Scfp6+nm8zsHK6/+WNTfqyTpaoqTb2D3Hzx8hk7hzAzspw2fMEwoUgEo158Cacy6WQrpMmyvB7YD7wC3ACoiqKc9hjynTt3lgFHQ7ZMsbHEGfjqrR/mx7/706wseN81OEJDZz9fvu48JElbUc8XDDPiDzDqDxFRo0SjKlFVRVVVdJIOnQQ6nQ6TQY/dYsJuNc2pRJIIG0uM+c0LWyjLziDbZYt3KMKZiEYwahtLlK9YsaLx+KtPVSMvVBTlSgBZll8E9kx7gELCCUWi9A976R/xsrOhnYIMB79btx2PL4DHF0AnaeOUTUYDOklCkiRtFTUJbcU9Vft/JBolEI7gD4YxG/U4rGacaRZcVgsZdgu5Ljt5GXayHLa3lJuE6VOanU7fiFck8hR3qkR+bAtpRVFCsiwnx5bSKe7n//vnabuv8Um7e3CUbs8ow74AaWYjBr2OvhEfq6uKcKVZsJqMWE3GKe/OrqoqwXAEXzCELxjGGwjR1DPIgZZuPF4/oUiUAreD0qx0ijJdIrlPI9HhOTdMdc/OObHAsaqqbK9rZXtdK4Nev1Zj1OnJdtn4zJWrWL+njprmLqJRlcXFuVy8qAybNQH29Zskj9dPa5+Ho90DdA+NHEvQNrOJ0ux0bGYTOh0c7R5gVWUh8/PPbN9HSZIwGw2YjQbST9AwDIbC9I/66Bwc4XBHHx6vH1WFRcU5LC7OoSLPjdkYl+1lk15RppOBUT+qqoo5ACnsVJ+OxbIsHxn3d2HsbwmtXj5v5kKbHZFIhB31bexv6SIYivDZd67mGw+vZ8j79t7+bs8okWiUp7fWHrusvrOfZ3ccRK+TkAuyuP3qNbT3D5PjsiVM8olGVfqGvTT1DtLYNcCIP0SGzUyGw0pZdtEJW75RVaVnyMu5VSUzHp/JaCAv3UFe+r9XVRwNBGnr8/DCrsMMjvopz81gSUkuVQWZpNtmdvJTKsl22rCaDAyO+smwi+ctVZ0q01TNShRxsOVQM09tPcCw/63VonA4wiXV8+gYGCEvw0ZVfhYOqwlnmhmryYSqqvzm1mvoHByhqWeQXUfbae31EAiF8fgCdAwMc9eTrwJg1Os4f0EpN563GH0cOvs8Xj+H2no53N6LJEm40iwUZblwWs0nHd4IaF9G6ba4ffhtZhNVBVlUFWQRikToGBhm6+EWnnvzEDlOG+ctKGFJaZ4YEnkKkiSxpCSXtj6PSOQp7KSjVqZbvEetBMNholFYt/sw/9xdB2jJtijTxfLyPN5RPQ/TGbaiRwNBdjW088y2WkYD/17nYlFRNjdfvBy3I+2M7v9UolGVtn4PtS1ddAyMkOVMIy/Dgc1snPR9RKJRdjZ0cM0KmSznzMY7VVFVpWtwhKNdA3h8flZWFrJ6fjHZzsTpzEukUSsAR7sGeHzzPi6pTvof0HPXGY5aSQlKazcPvbaXvhEva6qKOXteAZcumUd1SS6Liqd32rnNbOLCRWVcuKiMvmEv//fyThq6+qlt7WFHQ5tWgzYZKM6e3lmSvmCIuvY+DrR0gwR5LhvnVBaeVodha5+H4ixXwiVxAJ0kkZ/hID/DwWggSH1HP785vJXiLBcXLCyhqiALvU7sKT5eSbaLQCiMLxDCOoUvdCF5pHQiD4Ui/ORvr9Pap637ZTbqKclysbQ0j6WleTN+/kxHGne850IAdtS3cnZ5Pj948lU6Bkawmgx8/b0XnfFuO75giD2NHSitvaTbLFTkZuBIO/1FkkLhCF2Do7x39cIzims22MwmlpXlUV2SQ0vvEM9uP4SKyjvPqmJZaZ4Y9RKj1+lYUJhNa7+H+fmZ8Q5HmAEpW1oZGPHx3cc2EIpE0eskPnjBUi5cVDaj55wMjy/AD598lcFRPwDnySXccslZU76fYCjCgZYu9jV3kWGzUJqVjsl45s9pfUc/mY40zlsw852cM6HHM8qBlm5Mej1XnV3FgsKsWR+tkWilFYD9TV28uKeO8xeWxjsU4XScorSScr9Bw+Eo3mCQJ7ccIKKqVOS6+e9br0mIJA7gtJr56c1XcsWyCgDeUJr5xsPr6fN4J3X7cCRKbUs3f31jP409gywpyWV+fua0JHF/MEz/iI/l5flnfF/xku20cfGiMkpz0nlqSw2/W7+dxm4xjroy383AqJ9gOBLvUIQZkFKlla1KM3/euI/cdDu3X7Wad69aQI7LHu+wTuj6c6t5x5J5/OzZzfQOe2nuG8RsMmC3nHg8uqqqHO0aYHt9K3qdDrkwa8JjT1dj9yDVJTmkJXkdVZIkCt1OCjIcNHYP8sAruynLTufqFVUJ+36YaVaTkeqSHI529SOLXZ5STsok8v/d8Cbb69sAqMh1J8VY4wx7Gj/88OV0DAxj0On49p9foqogk89euQrduA47rz/IxkNN9AyNUpHnJt1mmfZY+oa9BMLhWek7mC2SJFGem0FJtov6jj5++89tXLpkHhcsLJ2THaJrqop5+LU9VBXMfrlJmFkp8W7+9fNvHEvin71yFR+8cGmcI5qa/AwHdquJdJuZfU1d3PHQesLhKKqqUtfRx1NbDxCJqpxVXjAjSTwcidDQOcDFi8sxpuC4bO0XTDYXLypj6+EWfrduO12DI/EOa9aNzdrtHhqNdyjCNEv6RP6v/Q3UtvQgSXDnDWuTtr5rNRn5zvVrsVtMjPiDfOXBF3j+TYVth1uQC7Mpz0lnphqRDZ0DzM/PPOMRNInOZjFx0aIy0m0W/mfdNl47cJRINCl3LDwtkiRxwcJSjnT1xzsUYZoldSKPRKK8dqCRLIeVH37wMgqzXPEO6YwYjQbuvvlKHFYzgVCEdXvqWFKah2MG13HpG/biD4U5p7Jwxs6RSCRJojI/8y2t8+6hudM6X1aWx+CoH29AbMqcSpI2kT/6+l4eeGU3X77uPL51w1oyE2hm3+kKR6JsOdzMstJc0m0WQpEoB5o7Z/B8qV1SOZnxrfN7123jQEtXvEOaFWajgRUVBTR0ilZ5KknKRP6Xzft5rbaR/c2d2M1mbObkWXlwIt5AiHW7D9MxMMLyefn8xyVncfXZ85ELs2nsHpyRc9bPkZLKRMZa56vmF/PEGzW8vK+B2ZxXES+r5xfT2jdEJDJ3ykqpLukSeUe/h3/t1xZk/Mp152EwJN1DeJtezyjP7jiITqdjcXEOhlgxvKogi9drG3nuzUNsr2ud1nO29w8TjkTnTEnlZNx2K2sXl7O9rpU/v76XQCgc75BmVG66ncp8N3UdffEORZgmSZcF7/7bRgDWVpdRMs3rlcTD0a4Bnt95mIIMB+U56W9blXBFeQGRqMobSguj07SR7uCon46BYa5YXjnnSioTsZiMXLS4jP4RH79fv52BEV+8Q5pRVy6fz5GufkJiglBKSKpE/tLeerzBMHaLiQ+cn1xDDI+nqiq7jrSx8WAjC4uyyZlgK67sdBvzct0APLa55ozP6w+GOdzeyyVL5uFKm/6hjMlMr9NxTmUh6TYLv/3n1pSeEZrjsrO0LJ/a1p54hyJMg6RK5DXNXRj1Or7x3guTekJDNKryhtLMobZelpXln3JUynUrqzDodYz4g2w93HLa541Eo9S29rCiopBCt/O07yeVSZKEXJjNkpJc/vSvXSldfrhs6Tza+obwBcUIlmSXNIn8YGs3H7xgKZ+5chXZSTzNOhpV2XSokZbeIZaU5mKaRI1fkiSuX6OtRritrhWvf+pbp6qqitLWR3GWk8XTvHRvKsrLcLCyspBHXtvDwRRttabbrKyRS6htSc3HN5ckRSJ/4o0afvWPLexr7qK6JDfe4Zy2aFTl9dpGOvqHqS7JPdapORn5GU7WLi5jfl4mFtPUV1Zo6hnCaNBxvlya1L9mZlO208bq+UU8tmkftS3d8Q5nRly8qIwezwgj09T/IsRHwifycDTKy/sakCS4OImX4BxriXcNaklcfxprZS8vz2dFRQGv1zZNaZhcS6+HYV+Ay5ZWotcn/EueUNyONM6tKubxTftR2nrjHc60s1lMrF08j71NnXNi6GWqSvhP9bpdh1GBHKcdsyk5V+VTVZUtSjPt/cMsLsk9ow0P+kd87Gns5IktByZ1fHv/MP0jXq4+uyrpVzWMlwy7ldVVRTy6cS/1nalXMz9/YQk6SaKpZ2bmKwgzL+ET+Uv7GgD4UJIthDXeziPtNPUOsbgk57Ra4uON7fDS3j9MMHzy8c4dAyN0D41w9QoZu/X0dw0StN2exmrmbf2eeIczrYx6PTeev4Ta1h78ouMzKSV0Iu8fHsUfDGMy6FlQlJxrKB/p7KO2pZvqcRN9zoRBryPfrXX2vlpzdMLj2vqG6Roc5uqzZZwiiU+LbKeNxcW5PPTKbjwpVlMudDu5YGEJO4+0ixJLEkroRN7j0SZlXH1WVZwjOT29nlE2HmxmYVE2xmmcgXrFEm13IaXtxD/zW3o99A6Pcs3KBbhmYNnbuaw4y0W2y8Yjr+0hFEmtyTSXVM8TJZYklbCJPBqNUuh28L41i7g0ti1aMvEGQry0r4GynPRp38knw5GG3WIioqrUdfy7A05VVY50DTA46uPalQtES3yGLC7OIRAK8/fth1Kq9SpKLMkrYRP5E1sO8O1HN7CoMAdTkk0jD0eibNhXjyvNMuGMzTN1/ZpFWE0GIlH12DkPxNZlf9c5C6b9y0P4N0mSOKeyiNrWbt441BzvcKaVKLEkp4RN5Lsa2vGHwmTaE3/LtvHGRqiEwhHKstNn7DwZdiufuGwFRZkufMEQexs7KXQ7uXL5fCxJOronmRj0Os5bUMKLe+tTbvbnJdXzsJiMKTsRKhUlbCIf60wyTMPu8LOpvqOPpp5B5MLsty2ANd2e2nKAJ984wM6GdlZWFnKuXDwn96KMF5vZxMqKQh7buDelOj+Nej03X7SMzsFh2vpSa4ROqkrIT/2Q109UVTEZdElVVhmNrYUiF2ad8TDDU1FVlUA4wqDXj91sYkFhtpixGQfZLhv5GQ6e3V6bUqUIZ5qFj649i31NnQyN+uMdjnAKCZnIdzW0A9pwr2ShqiqbDjaR6bTNeH06EApT29JDliMNgKZeMcognhYV59DYPcj+ptTaZago08V7Vy9iy+EWgim+RnuyS8hEPrZGcjKtq1Lf0UffsJfSGayLq6pK5+AIexs7mZfr5sbzlgDQOQd3hE8kep2OFRWF/G17bUqVWEBbFmL1/CK2HG4hmkK/OFLN1FdfmgX5bm3rseXl+XGOZHLGSioLirKZqYpKIBSmvqMfSZK4ZsUCspxpRGMjVnxB0VqKN7fdeqzEstCWWgnvyuXz6RoaYfeRds6eVyBKeAkoIVvkRZlO7rnlnZRmzVzrdrrMdEnl+Fb4e1YtJMuplVR0OomiTG1d8fYUmzaejMZKLEf6UusXkk4ncdP5SwhHVWqaU6t8lCoSLpGHw1G+8chLPPCvXUmxUt+Rrv4ZK6kM+wLUNHfTP+zjmhULOLui4G3PyfVrFnPRolKxIFYCGCuxbGroZvQ01oxPZFaTkU9cuoIRX5ADKbqkbzJLuEzpDWofgF7PaJwjObVwJMr2ujbK8zKmtaTiC4Y42NpDXXsfS0pzec/qf7fCj5fjsrF6fjHptuQab5+q3HYrTouR12ob4x3KtLNZTNx6+UoGRrwcbBNjzBNJXBL54fbeCdep8Aa0qcHJ0GmktPVg0OtIn6a9L4PhCHUdfdQ0d2udmecvYWFRzknHhr+4t45fPLeZ1r6haYlBOHOlbhtblWYGR1NvA2en1cwnLzuHfo+XmmbRMk8UcUnkNouJl/Y0oLT1EI5E33KdP9Zxp5cS7sfCWwRCYXYdbac8J+OM7ysUjnCka4A9RzvIddm58bxqlpfnT2qHe18gTCSqnnQlRGF2mQ16ijJdbNh3JN6hzAiXzcInrziHYa+f/WJDioQQl2x57QqZ2965Cp1OYv2eOnYfaT/WAg/FEnuid4wfaOnCbjFjs5x+bdrjDXCotZfdRztwppm5fs1iVlcVn9YU+0g0euqDhFmzsCibfY0ddA+lVsfnGKfVzKeuOAdvMMyuI+3HRlAJ8RG34Yd5GQ5uvvgsBkZ87GhoZZvSgsVkIMNuxWoyYEjgjk5vIMT+pm6Wlk19nHskGqVrcJTuoVF0EiwuyaUyz33a66MEYmPuTYaEHEk6ZxkNeublulm/p46bLz4r3uHMCJvFxKcvP4e/bN7P67VHOVcuwWwU78N4iPuznmG3csWy+VxSPY+DrT1sPtSE256GPxTG4wsk5FKse5s6cDssWCb5plVVlRF/kK7BEfqGfRS4HVy0qIx8twPdGf70CMRm3E02FmH2zM/P5KW99bT0DlGc5Yp3ODPCYjJw88XL2bC/nldqjrCmSnS8x0PCfPqNej1LS/OoLs7hfLmEAy3d7KhrJRpVyXbZKHQ7yXSmnXHiO1PeQAiltZez5xWc9LhoVGVg1EffsI8hrx+L0UBFnptLqudN67ZrFXluDrX1YkqyxcXmAr1eR2V+Jhv21fOxd6yIdzgzRqeTuGLZfPJcdp7aWkt1SW7KfnElqoRJ5ON99L+fojQ7nb9948O0DwxzqLWHmuYudtS3kuW0kZdhJ89lxxSHVmhdex8ZNusJd/wJhML0j/gYGPHj8QXIcqZRkeemONOFM808IzPiLlpURiSqsqQkZ9rvWzhz5TkZrN9TT9+wl0zHiYeQpoqlZflkOm08+Mouhrx+FhfniFmgsyThErlOp8NuNdHcO8Tuox2cPa+AQreTS5dWMDjq43B7H/ubu9jf1IBBr8NpNeNMs5Bpt+K2W2c0uUejUQ60dlGZl0kgFGbYF8DjC+ILhhj1h5AkKHA7WV6eT6HbMePrggfDEQ60dHNJdbn4OZug9HodBW4H2+tauers5NyycCoK3U5uv/pcHnltD5sONrGiolBMVpsFCZfIAUqy0jnQ0s1WpfktJYx0m5VV84tYNb+IaFSlb8RLx4C2ZnJz7yC7jnag10k4rWZMBj1mo4E0sxGryYjVZMBqMmIy6E/ZSlBVlUAojC8YxhsM4QuE8AXDtPcP0TU4QigcRZK01RnzMxxkOdJw263YLKZZbYG09A7x0Kt7uHTJPG48f8msnVeYmso8N5sPNXPZsgqM+tQvgTmtZj51+TlsOtjEKzVHkAuyKM/NEK3zGZSQifzseQUcaOlmY20Tt1215oTH6HQS2U4b2U4bS0vzAK0u3T/ipWtohGFfEI83wOCon95hL55RrdwRDEcwGvRIsfuQJAlUUFGJRlVUFUKRCBajAYfVjMtmIT3NTH6Gg7b+Ic6VS6guzpn1pH0ijd39ANQ0d3Pj+XENRTgJu9VMmsXIgebupFkI7kwZ9DrWVpezoDCLv75RQ1u/R7TOZ1BCJvK11eU8/NoejnQPTOl2Op1EltNG1knWMQ+FI/hDYVRVJRJVjy3NqddJ6CQJnU6Hxfj24Y+9nlFeO9DIOZWFCbMLz77Y+tcVeWc+KUmYWfNy3Gw+1DRnEvmYvAwHt71zNRtrG3n1wFHROp8hCZnIzy7PpzLPTX1nPwMjPjKmcd9Oo0E/qRmTx9te30ZhpiNhkjhAc682LT+Z1m2fq/LdDvY3d9LW76HQ7Yx3OLPKoNdxyZJ5LCzK5q9v1NDa72Fpaa7o15lGiZOVxtHpdHzr+otZVJRN/0j816uIRlV21LVSkZcZ71COCUUix9alWVIqEnmi00kSRW4Xu460xzuUuBlrnV+4sJQddW1sO9zCSIqtEhkvCZnIARYWZrOsLA9/MBTvUGjuHcSg1834Fm5TYdRrnblmo17M6kwSxVku9jd1zem1SQx6HefKJdzxngupLsllY20ju4604xdbyU3IGwixv7HzpMckbAaobevhsU37Odjaw28+eV1cZ3geaush25V4+4f+5MOX09IvVj1MFs40M+FIhK7BEfIyHPEOJ64sJgOXLq1gdVUxr9Qc4V/7GijOSmdBUdacGNkzGcFQmINtPbT1D3NhVRGoE6/bk7At8pUVhaTbLOxp7OSBl3fGLQ5VVdnf1EVRAtU1D7f3cvczrzMaDCEXZMc7HGGSJEkbaXWorTfeoSQMu8XEdSsX8KXrzseZZubFPfVvWURvLhoc9bGzoY2X9jWQ47LztXedz0WLy056m4RN5AA3xcZGP7pp34Trl8+0vmEvI/7gtHa4nqkXdh6moWuAXQ1t8Q5FmKICt5P9zSf/mTwXue1Wbjp/CV+57nwq8t1sU1rYWNtIS+/QnNj0ORKN0tg9wKs1R9nZ0M6iohzueM+FvHf1IpyT2O8gYUsrAJ+4bAX3vfQmvmCY12qOctmyylmPQWnrJdtpS5jhUkOj/mO7s5Rmi2GHySbbaePNhjY8Xv+kPqBzzYkW0atp7qI4y0VFnhvrDM+Wnm2jgSD1Hf209XkoyXbx7lULqCrImvLouIRO5CaDgTVVRbyhtPDw63viksj3NXdR4E6ceuarB7QNJNLMRhYUibJKstHpJDIdadR19LGiojDe4SSssUX0lpbm0TkwzLa6Vl6pOUqayUCOy06h2zlj6xfNJFVVGRz109bnoWd4lEAozMrKQt63ZhHZJ5n/cioJncgBfn7LVfx5417uXbedjoFh8mexk8gXDNHaO8TSBBmn7Q2E2LCvHoC1i8vjHI1wuvLS7Rxo6RaJfJLyMhy8e9VCrlkh09gzwMHWHnYeaScciZDl1FZGzXbZ4r4y6kQi0SjdQ6O093vo8XhJMxmoLs3j0qUVlGS7pmVuSsIn8jSLiQ9ftIySLBeN3QOzmsg7B0ZwWM1v27k+blQIhaPoJIlrzpbjHY1wmrKc2p6ewtQY9Doq8zKpzMvk2hUyXYMjHGrrZX9TJzvq23DZzDitFtx2K26HlTSTcdZb7KqqMhoI0T/spX/Ex7AvwJA3QEGGg6WleciFWTNSqk34RA7apgnffexlAB758vupys+alfO2D3hwpiXGxhZRVSXNYuSaFTKuNAuGEyyjKyQHm9lIIBRJ2I1TkoEkSeRlOMjLcLC2uhyPL0B7vye2gN4Qh1p7CEUiuNIs2K1m0m0W0kxGrGYjVqPhjBtnkUgUXzCENxjCGwgx6PUz7A3g8QawmAwUZ7pYWJRNfoaDQrcT2wzPQUmKRK7T6bh57Vncv+FN3n/P4zzw+es5axbWrGjuHSLdFv8OqVF/kLv/9jrvP28J152zIN7hCGdIkiScaRY6B4ZFIp8mTqsZZ2E2Cwq1fiNVVRn2BWiPrY7aMThMc+8oHm+AYX8QnSRhNRmOrZKqjy2gJ0kSOkAd+09VUVWVcFQlGA4TCIbxhyJEVVU7Z2xhvYWFWtLOz3DgiMNrmhSJHODzV6/hhZ0K7QPDfPWBF3j+2x+d8R7slp5BlpXFf5GjR17fTefgKC/vaxDrqqQIR5qJ9n4PVQWz8+tyrhn7snSmWY4l9zGqquILhhj2BRnxBxn2BQiEwkRVbRG9sY2kdWML6UkSVpMRu9WE3WLCbjFjNRkSqqM1aRI5wB8/826u+/Ej9A37+MVzm/j29ZfM2Ll8wRBD3gCOOJdW9jZ2suuINu54WVleXGMRpo/bZqW5R8zKjQdJkkgzm0gzm0iVZlFSFVqLs9O59bKVWIwG/rr5AK/WHJ2xc411dMazJ7zHM8r9G94EoCjTydrqeXGLRZhebkcaLX0ikQvTI6kSOWgllle+/3GWl+Ux5PPP2Hk6Bofj3tH53PaDBMMRdJLEl689L66xCNPLZjbiD2rbBQrCmUq6RA6QZjbxnRvW8uOnXuP367fhD07/ymm9Q6PYzPFd7dBsMiIBX7hmDXbRKZZSJEnCZjEykADLNAvJLykTOWjbZ6kq/G79DtZ+9352H+2Y1vsfHPXHZVuqIa+fXzy7ic7BYT580TJ++tErWViUM+txCDPPbDQw7BctcuHMJW0iz89w8NhXbsSo1+ELhbn1f55m86Gmabv/Ia8fq2l2+4J7PV5++OSrKB19vLRXm8HpEutxpCyTwcCIT2ysIJy5pE3kAPNy3XoxjgIAAArMSURBVDzxtQ9iNuqJRFU+d99zPPFGzbQs3O/xBbDOYou8vqOPHz71CkNerYVWkZs4uxEJM8Ns1M/p5VqF6ZPUiRygPDeD5755MwVuB4uLc7nryVd5rbbxjO4zGlUZ8QexGGenRb77SDv3PLsJbyCMBHz2ylWct6BkVs4txI/VZGRwdOY67IW5I6nGkU8kN93OP79zC9Goysv7Grj7mY30ekZZWVFIaXb6lAfue4MhDHrdjG+0HAiFMRsNNPYMAtpSBLdftYb5BaI1PhdYTQbR2SlMi5RI5GN0OokMh5XOwWF+8MSr6CQ4e14Bd33o8ikttjXiC8xoazwYCvP01gPsOtrB9z94Ge8+ZwHhSJT3rlmEYYa/PITEYTUZOeodiHcYQgpIuayxsqKQdd+9hfKcDKIqvNnQzrt+9DD/+/LOSbd+RgJBTIbp3zcwFImw7XAL333sZV450MiQN0Br7xA6nY73n1ctkvgcYzWJzk5heqRk5shx2fnbNz7Ml649F50kEYpE+e/nt/DOHzzI0CRqkpGIik43vTM6B0Z8fO2Bdfzfv3Yx6NViWF6WR1lu8u/yE41G+dNvf8lnbnoX/+/Lt9HZ1hrvkJKCXqcjMge2MRNmXkqVVo73sXes4OqzZQ6395LvdvDK/iNc+YMHuXhRGXaLkcUlubzzrKq3jRePqtEznpofVVVqW7rpG/Zy8eJyhr0B/CFt4lKGzcItl5w1K+PDG5SD/PWB+/jmj39x7LItr77MS//4G3f+7Dc0Hann0fvufcvxX/j291i6YtWkz7FzyyY2/+tFvn33r3nq4f/j/7d3/7Fx13Ucx5/fu95dr7ut19K13UbX/WLfjQ0EEVEmcwiaqBGSCUF00fEjQ/kRQFEEF0SnIkNgIRiFQCbJJiECIYiJAczMJKCbhbEA4yNjc9htbN3649rez+8P/7iDbKztuu3a4+5ej396v77fe3+b9nWf+3y+38/n8Ud/z813/KKox1GJLMvC87xSlyEVoKKDHPIDoS3xGACdBxL4vs9ft7wDwJP/fIu7n97I2adMY+H0VpYsnMm8aZPxfJ/jyfHOg31s6+ziva4+3t7dRSKVIRiwOG/+DKY3x1l8ajuL58+kbXL9CR2T5/lkHIes45LNuURCwSHXf/zLU0/w/LNP09eb74d9ecPfePj+X2NZFj6w7uHfsmzFddx21328ueVV7r3zNs5etPiYQhzgnW1v0jJ1GjPnzOXUT3yS5558/ISOr1pYFrieWuRy4io+yA+1ZOFMXrlrBc9s3sajL3bQeTBB2nF59/0eXnr7PbbvPUhbUz2TorU8v2U7XYkkjbEooWCQUE2AhglR+pJpso6H47oMpLO819XHtxafTm04xPqNr7Nj3+GDV+GaIDv3d5N1POZObWLn/h7M3i5c1yfnuuTc/L5yrl/4mb/vOB6ZXI6c55HJuWRyDlnHIef4uJ6H43t80Jhrb67n9qVLjjjef23cwECiDwpf31/b9DKxiZO49Zf3sPbB+1i24joA1j54Py9veBHXdVm24vpj/r2mkoOEI/kPkkgkQmpw8Jj3UY0sy0I9K1IMVRXkkF+kYuk5C1h6zgJ2dfXwyIsdrLr8QgbTWZau/iMvbH33w9fu7jaHbdsYi9I9xIDprv09hGqC7O1OHP5eFqSzDqufeWlsDqZgf+/QwTn/9DP50sVf55E1q/Fcl60dmwCLVT+8Ec916Dl4gIaTmqiPN/C1S7/JU+vXMrH+2L8t1EbryGbzF7ZkMhmiE45/Edlq4vs+Gt+WYqjqP6P2yQ2suvxCACbUhnniB9/gp5eez2fmtjExGqYmGCBSE+Rnl32BlZcsIZXNHbEPC+hPZ+lLpqmNhKiLhPJLSoVrCAWDhfPRLQJW/rXj6fKrriEUyk/8tfPdd7CsAA0nNXJyezuu63LHjd8FYOmy5by2+ZXjnih/tj2ffbs72bVjO29u6eCUeQuKdgyVzPP9j+2CwVJeqq5FPpL4hFqWfnYBC9tb+PO/DYs+cnXlmiu/ekL79zyPtOMwmM4xmMqSSGXoGUjSm8yQSGVIZrI4rkfOcXFcj6zr4bgenueRcz1cL9+t4vo+npe/7/k+kycdvQU8e+48Vj3wEJNbWuna9z6/ueNWEon8fNjJwQGc3JEfUqP16UWLefuN17nrtu/TMvVkvnfL7ce9r2riez4BNcmlCBTkQwgFg7hjcDZBIBCgLhymLhweVfgW20+uv5pl11zP/NPOoD/RR+uUaQC8v7uTpuYW/vffHce130AwyPJrb2L5tTcVs9yKl/M8wsHiX68g1UdBPoRYNExmDOY4L4V4YyPhSH4u8+XX3sxjv1uDZQWI1tVxw+13AjBr7jxuXPlzll/0xRJWWn1SGafki5dIZVCQDyFWG/7wnO9yZy84jYf/9BwA555/Aeeef8Gwr/3Dsy+MV1lCfl3YegW5FIE66IZQG6rBsvKX1IuMlVQ2R/2EaKnLkAqgIB+CZVlMjEZIVUj3inw8pbMOcbXIpQgU5MOor6sllTn+MzlEjibruEzUWqxSBAryYUyqiwx53rhIsWQch1itglxOnIJ8GPEJUZJqkcsYSuccYtFwqcuQCqAgH0ZrPMZAWnNFy9jwPJ9kOleS6wmk8ijIh9Eaj5FIamFcGRt9yTSNseiYLGAi1UdBPozm+hipbA7H1XzRUnwH+5O0NcVLXYZUCAX5MGqCAVobYlocV8ZE72Ca9hOcl17kAwryEUxvitM9kCx1GVKB+tMZWo9hQXCRkSjIR9DWVE+f+smlyDzPZyCVZYqCXIpEQT4CDXjKWNBApxSbgnwEzfUxso5DWhcGSRHt6xtgVktDqcuQCqIgH0FNMMC8kyezu7u/1KVIBdnfO8j8tuZSlyEVREF+FAvbWtjXO1DqMqRCZHMO/ekMs1oaS12KVBAF+VHMmdJI90ASV+eTSxHs6elnTmuj+selqMZ7YYkgQDZbPpe+B4HZzXEO9CVoiessAzm6UDAA3tBz2XcnBjhnzjQyGQ2iy+gdkplDtgAs3/fHrZiOjo7PAf8YtzcUEaks55111lkvffTB8W6RbwbOA/YCWn5HRGR0gsAU8hl6hHFtkYuISPFpsFNEpMwpyEVEypyCXESkzCnIRUTKnIJcRKTMKchFRMqcglxEpMwpyEVEypyCXESkzI33JfoiJWHb9gzgP8BbgA+EgT3AFcaYTtu2vw3cAITIN3AeMcY8UNh2EXB/YZuDwJXGmF3jfhAiw1CLXKrJHmPMGcaYM40xC4CtwD22ba8AbgIuMsacASwGltm2fVVhu/XA1YXn1gMPlKJ4keEoyKWabQAWAiuBHxlj9gIYY3qB7wBv2LYdAVYaY7YWttkKTC9FsSLDUdeKVCXbtkPAJeRnk7sCePXQ540x2w65u66wTQC4E3hmfKoUGR0FuVSTqbZtbyncjgCbgFvIB3l6pA1t2w4Dj5H/n/nVWBYpcqwU5FJN9hT6uQ9j2/YO4FPAxkMe+zzwZWPMj23bjgHPkh/ovNgYkxuvgkVGQ33kInAPcK9t260Atm03AfcC2wvPryvcvswYozXa5GNHC0tIVSicfvh3Y8yMYZ6/AVgBeOQbOA8ZYx60bftM8v3nbwEftMT3GGO+MuZFi4ySglxEpMypa0VEpMwpyEVEypyCXESkzCnIRUTKnIJcRKTMKchFRMqcglxEpMz9H3XFM2Bxf+pAAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "KMeans(algorithm='auto', copy_x=True, init='k-means++', max_iter=300,\n",
       "    n_clusters=5, n_init=10, n_jobs=None, precompute_distances='auto',\n",
       "    random_state=23, tol=0.0001, verbose=0)"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "text/plain": [
       "SilhouetteVisualizer(ax=<matplotlib.axes._subplots.AxesSubplot object at 0x00000265ECC6A438>,\n",
       "           model=None)"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "InterclusterDistance(ax=<matplotlib.axes._subplots.AxesSubplot object at 0x00000265ECCCC908>,\n",
       "           embedding='mds', legend=True, legend_loc='lower left',\n",
       "           legend_size=1.5, max_size=25000, min_size=400, model=None,\n",
       "           random_state=40, scoring='membership')"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "KMeans(algorithm='auto', copy_x=True, init='k-means++', max_iter=300,\n",
       "    n_clusters=6, n_init=10, n_jobs=None, precompute_distances='auto',\n",
       "    random_state=23, tol=0.0001, verbose=0)"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "text/plain": [
       "SilhouetteVisualizer(ax=<matplotlib.axes._subplots.AxesSubplot object at 0x00000265ECD4DB00>,\n",
       "           model=None)"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "InterclusterDistance(ax=<matplotlib.axes._subplots.AxesSubplot object at 0x00000265ECDB19B0>,\n",
       "           embedding='mds', legend=True, legend_loc='lower left',\n",
       "           legend_size=1.5, max_size=25000, min_size=400, model=None,\n",
       "           random_state=40, scoring='membership')"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "KMeans(algorithm='auto', copy_x=True, init='k-means++', max_iter=300,\n",
       "    n_clusters=7, n_init=10, n_jobs=None, precompute_distances='auto',\n",
       "    random_state=23, tol=0.0001, verbose=0)"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "text/plain": [
       "SilhouetteVisualizer(ax=<matplotlib.axes._subplots.AxesSubplot object at 0x00000265ECE39320>,\n",
       "           model=None)"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "InterclusterDistance(ax=<matplotlib.axes._subplots.AxesSubplot object at 0x00000265ECE398D0>,\n",
       "           embedding='mds', legend=True, legend_loc='lower left',\n",
       "           legend_size=1.5, max_size=25000, min_size=400, model=None,\n",
       "           random_state=40, scoring='membership')"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "KMeans(algorithm='auto', copy_x=True, init='k-means++', max_iter=300,\n",
       "    n_clusters=8, n_init=10, n_jobs=None, precompute_distances='auto',\n",
       "    random_state=23, tol=0.0001, verbose=0)"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "text/plain": [
       "SilhouetteVisualizer(ax=<matplotlib.axes._subplots.AxesSubplot object at 0x00000265ECF24400>,\n",
       "           model=None)"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "InterclusterDistance(ax=<matplotlib.axes._subplots.AxesSubplot object at 0x00000265ECF249E8>,\n",
       "           embedding='mds', legend=True, legend_loc='lower left',\n",
       "           legend_size=1.5, max_size=25000, min_size=400, model=None,\n",
       "           random_state=40, scoring='membership')"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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TEdxTYsZjyQxW03KnxNdZzQiCgN1iYjwaL0r7NotIWPeR6+gsnt7xEB6H7dLf8WQas6hb5DqXYzObGY8UR8it+WgpRVlfVUR0IddZNnpGg/icU4Q8ncGsu1Z0puG2mxkOxorSttFgwGQwFD3/+XKjv9fqLAvZnMJoOMZ1m2ovfZbLqRgMui2xEFRF4dHvfZ3zrxyhpLySN97/SXwVVSs9rILislroLeIKT6PRQLaIOV1WAv0u0lkWoskUotF4aaITQEXVf4ALRD7xCqdffJZ3f/qz2Jwunvjxt1d6SAXHIhpJZrJFc38IAkXNfb4S6PeRzrIQTaaxmi9/AVRUtajV09cj/R0yvvJKKhs20Lh5O/0dbSs9pIIjCAImo5FEpjjuD0EQdCHX0VkMkUQai2m6kAO6ji+IVCKByaxF/ohmC6lEcSYFVxqzyUgilS1K2wZ0i1xHZ1FEk6kZE5sGAVhf91PRMVttZDNpADLpFBabfYVHVBxEU/EmJBXAsM7eBHUh11kWIonUjFBDQRCKtoJvvVLd2MzE8BDDvV10nT9NzYZNKz2kolDMyBJVVXUh19FZDMFYEvu0SkACmnWkM3827zvIzhtu4bt/92ckYlFuf+v7VnpIRUE0GYml0kVpW1XXn0Wuhx/qLAvRRBqb5XIhNxkNqIoCXJux5A/+579y5sXDl332ib/8Mu6S0iseYzAYuOs9H+Gu93yk2MNbUcwmQ9F85DlFwbjOcvzoQq6zLGQUBee0xOM2USSVy2Hj2qzZede7P8wdb38/qqryvX/4C2o3SlcV8WsJgyCQUwr/vqaoKtmcMuPtcK2zvh5LOquWnKLMeJ11WEXSmdwKjWjlEc0WrHYHF44fJRmLctub373SQ1o1FCtEMJnOYreIGNZZNRPdIl8BMrkc0USaaDJNNJkikdYWPyiqeunHaxAE7Z9BwGY24bRacFrNOG1mROPac0Wos8SMO6zmdZnkfyGoisLRxx7k+tfdh2jWa5dOIkBRFgTFUxk8dmvB211pdCEvEulsjuFglIFAhP7xEIFoglA8RTiRJJXJYRVNWEQTFtGIyWhAQEAQBCa1TlU18VPRXgVTmRzJTIZUJodFNOK2WfHYLZQ4bdT43FSVuKgsca2p3CUOa/GSI60VBrs7CI+PsXX/jSs9lFVHMeYjE+kMbvv6e2DqQl4AsjmFwUCEgYkwvWMhuseCjEcSOK1m3DYL7rzgVvvc2CwiFpNx0SsaVVUllc2RSGVIpDPEUhlevthPJJkikkhT6rLRUOalrsxDVYmLKp9rVVjwBoNhxquy3SySWWc5LxZKT+s5/DV12JyulR7KqkKFy9I5FIpEOoN3SgbO9YIu5IsknkrTNjjOmZ4R5P5RrGYRt82C12FlS205HrulKD9EQRCwiiasookSLv9BKopKMJ5kIhLn5Yv9hBMpkukMUo2fbXXlbKoqxWFdmYr1hllixm1mcd0lL1oo4cA4Jf6KlR7GqkNV1aIs+k2ms9SX6Rb5Nc14JM6F/lFO9wzTNxbC57RT4XXy2h0bsK6CWXCDQcDntF2WKjaZztA/EeGpMx386MgZako97KyvQKopo9RlX7ZcJ6LRQHZaFILNbCKTvXYnOwFe964Pr/QQViU5RS1KiGA6m8Nl04X8miOVyXK6e5jnLnQTiCbwux1U+VzsqKtYE7GoVrNIc6WP5kofuZzCcCjKq52D/PLURUrsVm7a0sDOhkosYnF/Cm67lfC0ElsOq5lUNouqFscfqrN2SWcVHJbCG0eJdAaPQ5/svGYYCUU52tbHKxf78dgt1JR6kKpLSWY0//SF/lGyOZWcolyKNjHkJysNBgGTwYDVLGIzm7CZRWxmEatoWtGwJ6PRQLXPTbXPjaqqDIeiPHu+mwdfkdm/sZrrN9VdqnBfaLx2C8PByGWfWUQTdrNIPJXBYV35Nxqd1UM2l8NuKawbUFVVQvEUVd71Nx+hC/kUkuksL7X18uSZTrpGApgMBixmI4OBCPLAGGajEVE0YjIaMRkEjAYDgiBgELRwKZV8Rr+8sGdzCpn8v3QmS05RsIoidqtIicNGuceBz2Wj1Glf9iLEgiBQ6XVR6XURT2VoH5rgnx8+SrXPzU2b69lc48dUwDcOl81CJjvTH+73OIgkkrqQ61xGJqdgMxdWniLJNHaLuGLzRMXkmhby8Uic7tEgPaNBjnX0c6prGJPRQJnbTmO5F7fNikU0YhFNGAtgSSsKpHNZUpkcsaQ2WZrozhBPZXBazZR7nJR7HPjdDkpd9mWz3u0WkR0NFWyrK6dvPMQvjrXy4Csyd+/ZxM6GyoKMw2m1kJrFH17hcdI+PEHlknvQWU9kcrmCr76ciMSpK3UXtM3VwjUl5Iqi0jseQu4f5VT3MOF4EkGAvvEwRoOBm7c2UOYqXlpQgwGsBi3ixDMlllVRtSiYcDxF2+A4JzoHyakqDX4vjX4vVSWuZbHYDQaBer+Xer+XkVCUh4+38vSZTu7Z20JLdemSJkadNjPJWQoF+Fx2zvQML2XYOuuQdDaHrcBCHogm2FpXXtA2VwvrXsjT2RztQ+Oc7R3lbI9mcfs9Dso9DoKxBMFoEqmmjLJljOCYjkFAW7U55ZUvmc4yGo7zYmsv8VSGqhIXjeVe6ko92Jfh1bDc48S/3UH/RJgfvnCaco+De/a0UO/3Lqo9l81Capbl+CVOG7FURp/w1LmEoqrkFBVLgV0r0WSaap9uka8ppk5WTrotbt7SgKKqHG3tZSAQoabUzYaKklVZbsxqNlFX5qauzE02pzARSXC2d4Qjci81pW621pZT7XMVdeyCIFBb6qHa56ZrJMDXnzxGc6WPe/dKlC7wzcVpMZNTFDK53GULlKyiCYdFn/DU+RXJdBaHWSxoqtnJic7qkvU30QnrTMizOQV5YJTnzvcwMBGmptTNoW1N2C0iqqpyoX+Ul9r6Kfc42LuhqigLdoqByWig3Oug3Osgp6gMBaI8c7YTo0Fge30FzVU+rGLxRNAgCGyo8NHg99LaP8aXfnGEe/Zs4vqWunk/SAwGgUqvk2A0id/juGyb3+MgrE946uSJJFKUugvr4lzPE52wToQ8nEjxclsfR+QeRJOJpnIvO+orLk3ShRMpnjvfRSiWZFu9H0eBw5qWE6NBoKbURbXPRTiRonVwnJfb+9lQ4WNbrZ9St2PuRhbdt4EtdeXUlHl46mwnp7qHeesN2+ZtnTeUeRmPxmcIeW2phzM9w1StU2tJZ2FEk+lFu/CuxOBEGKmmrKBtribWtJAn0hmePd/F8+d7KPc4OLCx9rJg/6lWeKXXya7GylXpRlkMggAeuwWPXQvrGwiEefBYK7WlbvY11+At4qIHt83CrduakBdondeWeegcCcz83OfmhQs9KGq+jqfONU08lVmw624uRkJxrttUW9A2VxNrUsgzuRxHW/t44nQ7PqeNW7Y1zrCyo4kUh9eJFT4XoslAg1+bCO0dD/HTo+fYWFXK3qaqok2MCoLA5lo/1aXuS9b5227cfll6gOlUl7iIJmeW77JbzXgdVkKxJCXO9bfqTmf+qKpKLJWh9Cq/o4WSyeYIJ5JsqPAVrM3VxtpwEudRFJVj7f38zU+f5WhbHwdb6jiwsXaGSA8GIvz0pfMYBAO7GivXtYhPxWAQaPB72buhmmAswQ+PnOWVi/0kM8UpmQWadX5oWxOiycA/PXSE9qGJK+5b5nYQT2fI5GZGrzSV+xiLxIo2Tp21QSKd1ZLCFTD0cCAQYUOFr+hpKFaSNXNmXSMBfvziWbJZhZ0NVZTNMhky1ZWyqcpHSQGf6msJ0WSgudJHTWmOnpEg5/pGuG5TDVK1vyiuJYMgsKW2HJ/Tzn8+9Sr37N3EwVlcLSaj4YoTnnVlbk71DOphiNc4kURq1nt7KQwGIty8uaGgba42Vr2Qp7M5Hjt5kZfa+theX0FtqXtWMcopCi/KvXSOBNjRUF7wxQRrEatopKWmlFgyw/GOQTqGg7xmS0PRsr9VeJ3csq2Rx0+1MxSI8IYDW2Ys82+uLGVgPDxDyEucNowGox6GeI0TiifZWFm4uqWKqjIejtNSvb5roa5q10r3aJB/fPAF2gbHee2ODdSVeWYV8UQ6wyPHWxkMRNjVWKmL+DQcVpFdjVUYBYGfvHiOC/0jM3KDFwqn1cyt25voGAnwtcdfmeET31rrZ3QWF4ogCDRVeHX3yjWMqqoEYynqyjwFa3M0FMPvseNeh+XdprIqhTydzfHQMZmvP3GMxvISDrbUXdG/FYgm+NlL5xEEga11hU30tJ4wCFDv97CtrpzjHYM8/Gpb0eplikYjN0r1GA0GvvzQEYYCv8p6WFfmIZXJEk/NXK7f5C9hPJygSM8YnVVOJKHFehdSdHvHguxurC5Ye6uVVad6AxNh/vHBF2idYoVfibFwjF8ck6n0OmlapSs0VxvTrfO2gbGi9CMIAjsaKmiu8PHvj71M33gIyMei15TTPxGecUyF14lZNBKalrdc59pgLBKnqbykYO2lszmGQzH2NutCvqyc7h7m3375Mg1+71WtcNCW4D/8ahsNfg8V3uLk0F6vTLXOX2zr5UhrD4pSnJJr9X4v2+or+I8njtE9GgRgW305I8HojH0FQWB7XTmDgciMbTrrn2AsSb2/cG6VzuEJttdXXJbDaL2yKoRcUVR+ebKNH794loMtdXOu6hoORnn01TY2lHspK+JKxvXOpHXeOxrikRMXZ81OWAhqfG52NlTyjSeP0TUSoLnSRyCWnDUMcUOlj3AiTXqWBFs665dkOoOiqgW7n1VVpXs0xMGWuoK0t9pZcSFPprN85/BJjrcPcOv2pjlDBkfDMX554iIbKn34iphy9lpBNBrYVl+Boij87KULBKKJovRTVeJid1MV33zqVcbCcRrLvQxOzLS8LaKJjZU++nWr/JpiJP+bKFSirOFQFLfNTH0BJ05XMysq5OOROF955CjhRJJbtjZhnSNgfzwS59FX22iq8F51BaHOwjAI0Fzpo9xt5+evXKBnLFiUfiq9LnY2VFzKotg1Ons/W2r9jIViWrUlnXWPqqqMhmK0VBUuF0rHcICbtzReM/NmKxZH3jce4htPHKex3Mum6rm/wGgixSOvtlLv9xQ8D4OORmWJC5vFzFOnO9nfXMO2+sIn4a/2uVFUlcPnushkFcLx5IwohVKXHa/Tyngkhl93na17ArEkNrNIuacw33U8lSEUS7KjoaIg7a0FVsQi7xsP8bXHj7G1vnxeIp7J5vjlyYv43U79xi4yHruFnY0V+dJ3Q0Xpo7bUQ5O/hGgiidw/e9TM9voK+scjeijiNcBgIMK2+vKCWc/ywCgHNtWs6yX501kRIf/RkbPsbqykZh7VOlRV5ZlzXRgEgdpSPc3pcmAVTexsqORk9yCvdgwUZfHQpupSaku9HGntJZ2dmQum0V+CxWTUFwitc5L5NQXNBUpoFUulGQpEObS1qSDtrRVWRMi31ZVTOc/c0yc6hxgNxdhUtbSakToLwyIa2VlfydneEU4UwTIXBIEbNtdhNhl5/GT7jO0Gg8CBTbX0jIZ0X/k6ZjAQoaWqtGA1ac/2jPCarcVLQ7FaWREhn2+IUddIgFM9Q2yt8y9bRXmdX2EWjeyor+BMz1BR3CxGg4G7dm/kZPcQncMzsybWlrrxOqwMB/UIlvWIoqiMhuJINf6CtBeKJQnGEty0zhNkzcaKhx9eiYlogsPnuthSU4Z5GSrI68yOWTSyvb6SE12DnOsdKXj7jeUlNPpLePh4G+OR+GXbBEHgwMZa+sbDKLpZvu7on4hQVeIsWJbSM70j3L6z+ZrMtbQqhTyTy/HEyYvUl3muuVek1Yg1b5m/fLH/0lL7QiEIArdsa8RggMdOXiSTu3yFaYXXSVWJi77xmUv6ddYu2ZxW1Wr/xsJU7RkNx0hnsuu6CtDVWJVCfrx9AKPRqC+9X0VYzSZaakp56kwHwVhhc6FUl7ioL/MSS6V5pb1vxvb9zTUMBiJkc8VJI6Cz/PSNh2nylxRkPYiqqpztHeHuvS2Ixmvz7X3VCflwMMqF/jE2VhYueY5OYfDardSUuHnsZFtBqw4JgjaxaUDgfN8YQ9N84iVOGxurSukYnlnvU2ftoSWzirKnQMnf3Da+AAAgAElEQVSsOkcC2M0iuxoqC9LeWmRVCXkml+OZs500VXgLNoutU1iqfC6soomnz3QU1G/tdzuo83txWkSeOds1w8VyYGMN8VSGiUhxUgjoLB89oyE2V/txF8BtGk9lkPvHeMeN26/pgIhVJeTH2wcwGY36op9VzobKUkLxFK+09xe03f3N1STSGQwCM1wsFtHELVsbaR+eIFukTI06xSeRzhCIJtjVtHTrWVVVjnUMcGhb47zDmdcrq0bIdZfK2sEgwJYaP3L/KO1D4wVr1+uwIVWXISAg94/PcLHUlLrZUFFCx5DuYlmLqKrKxcEJdm+oKkhkSedIAIvJyC3X2OKf2VgVQp5TFA7rLpU1hWgyINX4eeFCD/Fp5dyWwr7mGnKqSqnTyjNnu8jNcLHU6i6WNcpAIIpFNLG9buk5UKa6VPSqYKtEyFsHxlBBd6msMVw2M6VuB89e6C7YMn7RZOTQ1kYmYklUVaV1WgUj3cWyNkmmM/SPh7lla+OSfdm6S2UmKy7kmWyO4+0DNJZfvZiEzuqkoczLWDjOxcHCuVgqS1y0VJVeumEz2cuLTEy6WC4OjutJtdYAqqrSNjjBng1VBVn8c3FwXHepTGPFhfxc3wg2i6gv/FmjGAywqaqUF1t7iRXQxbK/uQaDwUBOUTg7y4rS61vqAYHescIuUNIpPAOBKFbRxPb6pbtUhoNROkcCvPeWXbpLZQoreiWS6Qwnu4Z0a3yNM+liee584V0smZzC8Y4BEunLy9CJRgN37mpmNBxjXPeXr1riqbxLZVvjkqv/RJNpjnUM8N5bdus1CaaxokJ+qnsYr8N6TeZGWG80lHkZixTexbJvQzXRZJpXOwZmbHdaLdyxq5n2oQliqeLUG9VZPNmcwvm+UW5oqcPrWJpLJZPLcUTu4Z49m2iuLEzK2/XEigl5NJnmfN/INVNTb70z6WI52tY3w6e9FHY3VdFSVcoLF3oJx2emBqj0urihpY7zfSMzFhHprByqqnK+b5SNlT5aapZWwk1VVV5q62N7Xfk1U0x5oayYkJ/pHqLMZb+mqnisd1w2M3aLyLne0YK1KQgCt+9sxmk389gsecsBWmrK2FhVyoW+UT13+SqhYziAy2bhuk1LF94zPSM4LGbecGCLXpPgCqyIkGezOVoHx6kunbtCkM7aotHv5WT3IMl04VwdosnIWw9uo3VgjJ7R2Sc3r99Yi9Nm0SNZVgFDgQjxVIbbdmxYcqhh5/AEY5EY7zu0W5/cvAorcmW6x4LYLSJW3Rpfd9gsIh67lZMFLkThdzvYs6Gap860E06kZmw3GAzcvmMDqNAxPKGL+QoRiifpGw9z566NS76/u0eDtA9N8NE79uO0mgs0wvXJigh528A4VXog/7qlwe/lfN8o0eRMwV0Ke5qqcNksPH++m+gsoY4W0cRdezeRzil0jQQL2rfO3EQTKeT+cW7bsWHJ8eK9YyFa+8f4X3ce0BcKzoMVEfJkJkuJw7oSXessAxbRiN/j4HjHYEHbLfc4cNks7Gmq4rnz3cRniVSxiibu2bOJRCpN53BQt8yXiVgyzbm+UQ5ta6S2dGkBDL1jIc73jXD/Hfv0mgTzZEWEvNRl1yct1jn1ZR46hgKECliEQhAEGsq8xNNZ7tjZzLPnumYVc5tZ5N79EslMRnezLAOxZJqzvaPcvKWBxvKlJb3rHg1yoX+Uj96xnxqfPoc2X1ZEyMs9ejD/esdkNFDmtnO+v7B1PpsqSjjfN8LOxkpu39nM4XNds7pZbGaRe/e1kM0pXBzSJ0CLRTSR4mzvCDdvqae5snRJbXUOT9A2MMbH7jxAtS7iC2JFhNxo0DMcXgvUlLpoHRgvaHy3aDJS7nZwumuIm7c0cPfuTTx3vmvWGHOrKHLP3haMBgNne0f0JFsFJhRPcq5vjEPbmpYk4qqqcqpriO7REB+/6zrdnbII9HgenaJhFU3YzCJdI4XNH15d6uZUzzAAB6U63nRgC89f6GFgYmaBZoto4u7dm6gqcXGyc4iEvgK0IAwGIrQOjHP7zg1Lcqdkcjmev9ADqHzy3oP6xOYi0YVcp6hUlbg43T1UsBwsAJUeJ/3joUtJuvZsqOb+2/dxrneE8/2jM/oyGg3ctLme/RurOdUzQiBa2OLR1xKqqtI+NMFYOM4b9m9e0sRmNJHi6TOdNFf6uP92PcRwKeiB3DpFxee00Tmi3fh+T2GsLaPRgM9p5+LQOLsaqwAt5PGT9x7kW8+c4KW2PvY312CcsoBEEAQ215Tjsdt4/NRFKlMuaq+RBWmqqkWKpTJZkpks6UyOZP7/yXSWdDaLoqioqCgqCIAggICA0WDAYjZiFUVEk8BwIIbbbuWWrY1YzYuXj6FghOMdg9y3r4UDG2v14Iclogu5TlERBCh3OzjXO8IhT+HyR1d4nZzpGbkk5KCVivv4667jx0fO8vTZTm6U6rFZLk/IVlXi4k0HtvD4yYu0JjNsrPKtq6K9qgrxdIZIIkU4liIQSxBKpEDVqjqZDAZMRgGDwYDZaMRuMeG2mzEggMAlQVVVFVUFRVXJ5HJEEykGAhG8Dismo8Dhc10kM1kt1NRlp9zrpNRlp9Rpn3HNLx+femmF7odv20tThV7asRDoQq5TdKpK3BzvGOBgpq5guXVqfC6ePN1BJpdDNP5q8txsMvKum3dw+GwXT5zpYHtdOfX+y9Mku+1W3nBgC89d6OZ45wCbqpYWbbFSqKqWYTCaTDMeiTEcimnhnoI20WsxGXHYRJrd3suu0cL6UBmPZogkU1y3sYbaKUnuVFUlmckSSaToHQsh948RT2Uwm4xU+Vw0+kuo8bkw57/zWDLNsfZ+7BYzv3nvQXwFKDKho6ELuU7REU0GHFYzg4HIkuOMJ7GaRexWM92jQTZOi5gQBIFD25vYUOnjhy+cpn8izJ6mKqxT0iWLJiO3bd9A10iA5853k03Gqa5WV5V1PukSicRTxNIZkpkMyXSWZDpDOJ4mnEySTGXIKlp+drPJiGgyYBAE0tkcqgpqUEGFS9tEowmLaEQ0GS9NRltEI7OddSqTpT8QwWwycoNUP8OHLQgCNrOIzSxS7pkcsybuE9EEx9r7efpsigqPE7PJSCSZ5t69Ldy8pQGjQZ+eKyS6kOssCyUOG10jwYIJOWgumwv9ozOEfJK6Mg+fev0NPHGqgyfPdM5qnTeWl1DpdfKz545dss499uVfdTxVtEOJJIFYknAsiaKqWM0mzaJWVSLJDOF4AkEQcFrMVHqcWM3irEI8iaKo5BSFbP5fMp0llkyTySlkclrKYYfFjNNqxm41YxNNeSs/zqYqH43lJfP2YU+Ke41PpMbnJpZMc7JriHQuR4XHyfGOAcwmI7saq7BfxQWzHlBVlUAsSTyVxiAIeB22op2zLuQ6y0Kp287p7iEURcFQIGus1GWne+Tqpd5Eo5G792xiW105P3zhNH3jYfZsqLqsmInVLLKn1ofZ6+e589147BY2VBTfd64oEIwlGAnHGApGSGVy2MwmLCYTNouJ0govRqORcDzJSChGOJbCbhWp9rkxm+bvKjEYBAwGIyKzH5NVcqQyOaLJNKPhOKF4ApPRwOYaP267FVXV5joWgqrCwESEvokwBzbWsr2+HEEQGA3HONrWx8PH29jTVMn1LXXravGPqqp0DAd4sbVXKxwuaGG4iqoST2bw2C3s3lDFgebagtQvnUQXcp1lwZp/nR8JxQpW+dznsvHyxT4UZW6XyKR1/tTpDp463UFtqYcttX7EKYI4aZ0fkXs51jFAbambCq+LQup5JqswHo0zHIwyEophNBhwWMxUep3YpljWmZzCWDjBSCiCoqq4bBZqytwYixDdYTIYwSSQSGdQVAWpqhSvw0Y4meZU9zCZbA6/x0FlfkJzrofIRDRB90gQh9XMmw5svkywyj1Oyj1OkukMHcMB/v2xl/G7Hdy0uYFt9eWL9uWvBsYjcX7y4lmGg1Eayku4bXvTZe48VVUJxpK0D07w3LluDm1r4tC2poKk5xUKGd87F8eOHWsEOs+HIaMvmb7m6BwOUua2c92m2oK1+djJi3zszgMLWg0YjCV4/FQHp7oGaaoooaWqjPb2i7S0tFzaZzgY5eWLfQRjSer9HspcjgVbpZOoKoQSSXpGgwxORLGaTbisZtwOC+ZpwpVTVIZCUYYmolhEI267pajpnhVFJRBLEE9l8LsdVJU4L3u4gbZoJxxPEUlmSKTTVHic1Pu9lDisl7lcIok0XSMBVOD6TbU0+L1zumQUVWVwIkLH8ARZReHuPS3saqhcVXMV8+Hi0DjffuYkjX4vUk3ZnOedSGc41j6A3SLyodv2zulySaVSnDlzBqBp3759XdO360Kus2xEEmk6hyd4x007ChY3fLStj1u2NrKnqWrunacxEory6Ik22gcnsOTiHNq/67ICwaqq0j8R5qXWPlLZHI3lXrwLyNqZVRSGAzG6RgIk0hm8Dis+p21WC0xRYSwco28ijNlopMRpLap1qqoqoXiKSCJFidNGjc81r4iinKIwEU0QjKUQTQYa/V48Dhv94yGS6Sz7NtawqbL0shj++TIainGmd1jLYLm3Bal6bkFcDXSPBvn6E8fYv7FmQStTVVXlROcgCvCxO/df9fvWhVxn1aCq8Ep7P288sGVBgng1zveNUOZ28Ib9mxfdRu9YiP948Cmwuagv87KhouSyV2JFUekcmeDli/0YDQZqy9x4bNYrWuiJVIausSB9Y2EsJiM+lw2XzTLrhKQCBKIJ+sZCIAj4HNailj/M5hRC8STxVAaXzUJtqXtRE3CqqjIRTdIzpq2w3V5fzh27Ni65ur2qqgwEIpzv1b7Xe/a20DBtgno1kcpk+YcHX2BjZSk1i1hgpqoqR+RettWVc9eeTVfuZw4h133kOsuGIIDTamY8EiuYkPuc9iuWf5svdWUe3rCjjsqmTbzU2suTZzrxOW00V/ooc9kxGASaK0tp9JfQOjjGqe5hcrkA5R4nFSUOTPnJ21QmR8fwBD1jIbx2C03l3qv6k+PpLJ1DE6RzCj6n9bIJ2EKiqiqJtBbvnc7l8LscNJZ7F9WfAoRjSQKxBJmcwpZaP9U+F6PhGA++coGNlaXs3lCFw7K45faCIFDjc1Nd4qJrJMA3njyGVO3njQc241iFS/ifOdeJzWxalIiDdr57m6t56nQHe5urF51rRhdynWXFbhEZC8eXnPJ0kskJz5yiLDk2ucbn5s0Ht3HXnhZOdg3y3Plu0tkctaUe6so82C0iW2rL2Vzjz1uNoxy7OIDHYSWXUxiYiOC0mdlY6UO8imtBUbUl6gMTUbx2C2We2a31pZLJ5oim0sSSGcwmA1UlLkqc1kVdp0xOYSySIBxL4rSJtFSVUe51Ysz7sp1WM7WlbnpHQ/zohbNsq/Ozo6Fy0W8XgiDQVOGjvszL6Z5h/u7nz/OWg1vZWlu+atwtmVyOF+U+bpCWVmDaKmoPghdbexf9ZqkLuc6y4rZZGQ5GC9aeaDRiMhqJJFJ4HYUJ57JbRG6Q6jnYUkfHcIBX2vs5fK4Ts8mI3+2gptRDdYmLSo+Tk12DHD7XRSSZwmu3YTWbEK4iy/F0lo6hCXKqQlWJo6B+cFVVSWVyxFJpkpksoMXv11S7cVjEBQugokI4kSQcTxFLZanxudhSU4r7CnH2otHIhkof1aVZekaDnOsbZXdTlRYdtMjzNBoN7G6qYiwc48dHznKqanjVWOcdQwFt4tpmuep+7a0X+Is/+DQf/s3f4TV33DXrPs0VPp473819+6RFPah0IddZVpw2M60DY/MKGZwvFtFINJkumJBPIggCzZU+mit95BQlX4JslNPdQ4yE4wwHI1hMJvZuqMHntDIaiTMwEaFtcByLaMRhNeOxWbGajTOscJd96Va4qqpkcoqWDCutJcEyi0Z8Thv1du0NYqGikMkphGIpIskUyXQGr8NGfZmHqhI3oml+lrxVNNFSXUY8laFtYJzzfaMc2tq4pLDTMreD23c0X2adb6urWHR7haBvPDTn4rFYNMJXvvDn5LLZq+7nsJpR8uGJi4kv14VcZ1kRjQaMRoFwIlkw4bWYTEQSM6sEFRKjwUBjeQk1pW4MBnjiVAeba/woqkrfRIj24QkcFhG33UKl14miKARjSfrGQ2RyCuFEEqNgoMLrxCIu3DpVVJVsXrRTmRwZJUc6k8Ns0h4Yfo8Dj92yYFeGomqhcOFEmngqTTan4Pc4aKkupdTlwDxP8Z4Nu0Vka52fsXCcR09cRKouZd/GmoJY5z964SztGyZ4/T5pxZb7j4RieOxXt8a/+g9f4MBNt/DQT344Z3tOq5mxSFwXcp21gcNiZjwcL5yQi5prpdj0jYf5rxdOo6gq9+5tuSyyJZHOMBFNMBaOMRKKMR5JEEtnMBoNDIc0V5IiaNEvRqMB0TCZ+8SI2WS8FAGjqlrYoqqqZBXl0vJ6FTAbNdH2Oa3YrWbsZnFBi0kmRTuR0vK1JLM50tksDotImdvOxkofJQ5rwWO4y9x2PA4L7YMBfvLiuYJY56/dsYEX2/oYCR3n3TfvXBFXS05VMF+l2tnD//1fhIMBPvmH//+8hNxgEFCUxYXz6UKus+zYLSJjkTjNBco6aDaZiirk2ZzCk2faee58D1tr/bPmi5maX2SS7pEAT5zu4LqWWjw2K+lslmQ6dyn8L5ZOE09mCCeSGAz5FLMGA2ZRy/9tNZuwiSZsFhGzaLzqqk4VyOW0lLOZXI5MViGdU8jlcuQUhUxOvSTaXoeVMo8Ht92Cy2q5NGFZTESjkc21ZQWzzkWTkZs213O6e4h/evgoH7ptz7KXiHNYzFf93T3+4M8IBwP81vveCsADX/kSFVXVtGzbMev+6ayWomExXPUoSZI+d7Xtsiz/6aJ61bmmsVtEgrHCVemxmU0EZ6nZWQgiiRTfOXySaDLFa6ctub4SqqpyomuIk11DbK8rxzXH67eiQCyVJpHKkMpmSeUFP5nOEE2lGYvGyWS15FaCIFw2maqi5ttQEU2adW81i9hEIyVWK1bRiFk05VeTLo9oX41J6/zi4AQDL1/gdbs24pxjsvBKGASBXY1VdI0E+MqjL/HOm3awpdZf4BFfmRqfm5faeq+4/bN//ffk8knJPvPR9/Hm936Qxk3SrPsqqko0kV70w2gu+ReB3wH+Fi2EVEdnyVhNJsbC8YK1Z7OIhOOFt8j7J8I88PSrlOVzgcxn4jCXUzh8vovBQJTdjZXz8ocbDOCymXHZruweUBRNtFVVq+IzmclKAASDgAHWzLJ20Whkc00ZfeMRfvbSee7YtXFJ1nRjeQkum4XvP3eKO3dt5OYtDQUc7dX69fKL4zKKql62IngSX9nlDxWX24PZPPt3PBSIUFUyv9W1s3HVo2RZ/iNJkqqAmCzLX1hUDzo60zCLJuIFLIJsM4vaysgCcrp7mB8dOcO2+nLqy+a3sjCbU3jqdAfBeJJdDRUFFVZtPk/I/1v7CIJAXZkbh8XEI6+2crClDqlm8dZ0qcvOoW1NPHm6nXQ2x23bm4oeb17ucVLlddE3FpqRHnk6//nzx6+6vWM4wGt3bFj0WOYj/78LvGnRPegsiCOP/IyXn3wYg8HA7W//AFv2HVzpIRUc0WQklckWLKWtURDI5grzwqgoKo+fvsgLF3o52FI37wiCbE7hiVMXiSbTbK0tR6+bMD98LjvbzSIvXexnIprguk21i45CsVtEbtnayLPnu8kqOe7cubHoYn7Hrma+9fQJqnyuRfv7+8ZDKKrKjvrFh1POKeSyLIeBby26B51503HuJC888lPe/5k/YaCzjdD46EoPqSgYBDAZjSTTWewFiDYQDAJKAXIGZXI5fvj8aXrHwty6vWneWQcnRTyWyrC5trygaW+vBewWkd2NlZzvGyUYS3LHzuYZGRjni9Wsifnhc10ICNyxs7moYr6xspTdjZW8crGfgy11C+4rmkxzunuYD79276LPGWDOR58kSR+WJOnAlL//SpKk+xfdo84V6Tp/BpvdyaPf+zqvPPUI1Y0bV3pIRUM0GYmnC+NeMaClf10K2ZzCd545yVAwymu2NsxbxHN5d0o0mUaq8esivkhMRgPb68tJZbI8eqKNdH5ydzFYRBOv2drIEbmXp892FnCUs3Pf/s04rWaOtvUt6M0wGEvw7LkuXr+vZcmVs64q5JIk/Rbw60B4yscPA78hSdJvLKnndURwfJQH/vqqAT7zIhmPEQ0FES0WXF4f//P1LzOZnTIaCvLo976+5D5WC6LRQCJ99dVu80UQBBR18a6VdDbHo+cHCMYS3CDVz/vVXlVVnj3fTTCeZHONbokvFUEQaKkuJaeoPPJqK6nM4n8fVtHEa7Y2cPhsF0fkngKOciaiycgHb9tLjc/N46faGZgIc7WssulsjjM9wxxt7eMtB7dy3aal5WqBuS3yjwB3yrIsT34gy/Jh4B40gdcpIBabDYfHg9NTQnXTJqKhIIloBACnx8td714/L0Ki0XApH8hSUVUVg7A4v2oml+NbT58gkVG4rqVu1uiDK3Gya4iBQJittX7dJ14gBEFgU5UPAWHJlrnNLHLTlgYePt5G+9BEAUc5E7PJyNtv3M67bt5Bz1iIx062c7xjgK6RAMPBKEOBCHL/GEdbe3ns5EVcVjOfvu9GdjUuPI/+bMz1/qjkfeSXIcvymCRJazIc8dSRZ7h46jjZTJpoKMj+195N28ljjA30cttb34uSzfLSEw9hMBiobZa49c3v5tkHf0RwdJh4NEIyFmXPoTuRX32JwPAQr//gr+PweIlHw3ztT3+fscE+brr3zbzmDW8nPDHOI9/5GtlsBpNJ5O73fhRFVfjxv3wRq8NJ8/bdmC1WTr94GEEQcLg9RIMBYuEg/e0yBqORH3z589z1nvuxuzz8z9e+zAf+4E/56p/8HnUbJcYG+7DanbzxI5/EbFn+gsFLQRAEFKVAE5SwqPjobE7hO4dPEkkk2VzhWpCI94wGOdk1xK7GtVfNZrWj5bgpoW1wgl+eaOOu3ZsW7T92Ws3sa67mO4dP8Jv3HFxyvvS52FzjR6ouo288TMfwBL1jIQYDEQwC+D1Ottb52VzjnzPR1kKZS8izkiSVy7I8MvVDSZIq4AqVXOfB0NAQiczin7RLITARIBIKcvt7PkbX2RMcffwh7v7QbzHc3c7RJx4hGhjjnvt/G5No5vmffY+XDz9FOBwhlc5w05vfz5kXnuTMy0e45S3vp/3ky7z89GNs3HsDkWCAXFbz+conjuGtbuTM80+yYed+ajZuZrCzjYe++3V233o34eAEd3zgExiNJh76+j9y4K5fw1/TQOuxF9hid3HhpWcxiWZue+dHiIeDvPjYw2y94VbS6TR9ff0kE3H8TRLbD93D8Sce5OkHf8LW6w+tyPVcLBPBGL1qEiEWWHJb0VSG0fEYx44dm/cxqqryROsQ47EU2yo9CIJAa2vrvI6NpDK80DFKvdfB6PDQYoetMwc2VaVvPM73n5zgQEPpgh600xHTcb7w/Yf5tZ11CypcvRScwBYn4JzsLwGhBK2h4YL3NZeQ/xPwkCRJvwe8CiSB/WgLhP5tsZ1WVlauWIWgid6LKJs2U1tbQzo0RnCoibq6WsxkaX35WdLJBM//97cBSCcTmAQFt9tFVU0ttbU1jFZW4bDbqK2tIT42QCI0jttuxWy1svfQG3j+Fz+hfpOESI5oYIy2V57j4vEXABWj0URlVSU+fwUNDdqihTd/5Lc4+vgvOPf849Q0beL1770fg6qwZd9BmrfvpuPsSSJjQ1RWVWI2m6mtrcFsNrPvxtcAEN+1l46zJ6mtrVmZC7pIEsZxqitLaakrX3Jb45E4WesE+/btm/cxT55uR7HEeNPeHRgNBlpbWy+r2XklkukMP3vpAtub66lc5iXh1yK1tSpnekYIYuNgS/2i22kBjrX30xYXed+h3WvuLWpKhaBZmWtB0AOSJFmAbwCTHvkO4IuyLC9ayFeaK32FgiDgLinlXb/9fzAaTZw68gwVtQ20nnzlqmFF7tIyTKKIw60tChjs7mDvoTsprazmujteT21zC+ND/fS0Xcj38yuH6onnn+Tu99yPSTTzgy/9Ff3tbZfGciVyuRzDfd1U1DbQ195KWVXhihkvFwJcdUJoISTSmQW9qp7tHebwuS5u3da0oJhlRVF58kwnDquoi/gyIQgCW2r9nOwaosRpW9KioT1N1Rw+18njpy/yul1XLqu2Fpkr10o1cBta1Mo3gN+TZXnp78KrFIPRyIHb7+W7f/dnKIqCp9Q/7wU5VruTVw9rq7dadu+nrKqW177lPTz6va+TzWbIptPc8Y4PzDiuvLqe//z8H2FzunB5fVQ3NXPqyDNz9nf00Z8TDozhLinjlje+Y2EnugpQKdyS8kQ6S8k8S8cNBSL81wtnuG5T3bzypkzlTM8wkXiSHQ2VixmmziIxGQ1sqfXzYmsvXodt0cv5DQaBg1I9T53uoKWqbMkhf6uJqxZfliTpUeA08BTwNkCVZXnRoRPrvfjyqSPP8NAD/8ZnvvRNTGLx0mr+y2c/xcf++ItF7aPYtA6MI9WUsXkJFtYkp7qG2FRVym1zLHGOJtP800Mv0lRRMqOg71yulWAswU9fusCuhgqsi8xQp7M0JiJxukaCvOm6LYtOtAXaSsqO4QCfvu/GZfOXL5W5ii/P9V5ZI8vyZ2RZ/gXwMeD6wg9R55pEBUOB8oaks1ncc2QY1CJUTuBz2hZclV1RVJ4520Wtz62L+Aric9nxe5w8dqqdTG7xwRK1pR7MJiO/PNlWwNGtLHMJ+aWyK7IsZ6b+rTOTnTcc4g+/8t2iW8qf+IsvrWlrHLSk/KYlVJ+ZSiqbw2m9upAfPtdFOJ5iR8PC81mc6Rkmmc5Q7Vt8MQSdwlBb6kJRVY5dHFhSO3ubqniprZ+ukfXhKV6oebEOHSIzUWYantAAACAASURBVFWV7tEgnSNBEqkMN26uQ+4fp288RCiewmAQMBoMlDisbK3z01DmxbiASi062kIc+wJ91Fcinc3hvEoK2KFAhGfOdnJo28Iz4gVjCY53DrKroYIi51/SmQeTC4Ze7RyiscJLpXdxD1ezaGJnQwU/fOHMmnKxXIm5hHybJEkdU/6uyf8toPnLF593cZWgqloprb7xMCe6hugZDc1IwCTVlHGkdWYC+fFInItDE7zpwGZe7RwkpygYBANSdSlbav26uF+FTFZZ8GTjlUims7iukHwrm1P44QtnkGrKsFsW1p+qqhw+p7tUVhui0ciG8hIOn+3izQe3LjrrYG2ph77xMI+fusi9e2cv+LBWmOvXOXdg7RolFE/yzJkuuseCHGyp40TnILF8jmzRaMBtt7ChvASptgyPzcKn7j2IwSCQzuVIpjLEUhmCsSSj4RgOq0gyk2UkFAOgdzzE4/+vvTePkuyu7jw/L/Y9IiMyM3LfKiujdtVeJVVJQgIkBJLYDDY+dBvaCNxDm9MYQ7cNuNvjdp/B2DN0093AMeM23ficGZqxgYYZsVgbEqgklVR75b6vkbHvES/ee/NHZEZVSZVVmRGRmRGR7/NPVWW+9/JXkRHfd9/93fu9l8dpclh424HeW8Z/qRQoZ6zVzWTFPAqsWX74wrUJsnmJ3hIqFKaWIyTSOfpbKjOSTqVyNDosBGIpzo/Oc9pXulfJ4d5Wnrk0xumBLtwlDD2uFu5WRz61VQvZChRF4frcMi9eny4ONtBoBHQagfef2ovVZMB8lyjRoNVisGhxWEy0NtjZS6Hq4rfvP0QmJ3Jl2s/wfJDlWJLlWIq/f/kav3XmIAa9FofZuOn+yLVAXpYRBCryOBtKpGl322/7ui6E4zx/dbKklIokybwyMkt3s0tNqVQpu1obyk6xmPQ6Optc/OOlMT5034EKr3Dr2BHPi4qikMqKvDg4xfXZAFCY97e7zcPbD/Zi0FXmZTAZ9Bzvb+d4fzuyLDMdiJHOifxqaJoJfwQB6Gx08vZDfTgtteWNUkmyYiE/XombWjiRvm0Viiwr/I8SUyoAI4tBgJqO0uqdm1MsHzi9H12Jqcw97Y38/MIY9+/tpqWhNje06z6JO74U4m+ffYOZYJTh+SBNDiuPHt7FZ95zmseO7K6YiL8ZjUZDT7OLvR1NPHywj36vGwWYDkT5r8+8wa8GpysyDKEWyYlSSeJ6O6KpDG23SV29MTFPKiuWlFIRJZnXx+brqmGkXml0WNBqNFyb9d/94DXQa7XsamngpxdGK7iyraVuI/KMmOfp10eYXI4AoNVo+GcPH8VagYk0G8VuNvL4CR+JTI7v//oKkWSWV0bnGFkI8uEzB+6azqk3sqJYVkPHzcTSWdreFEWJksRPL4xwqLu1pKj/+owfo157x2HIKtVDd7OTixML+NoaSx5e3N/q4ecXx5j0h2vyBl6XEfn4Yohv//x8UcQf3NtNf4t7W0T8ZmwmAx976Chv29eN1ahHXkn5VGreZK2QyOQqYieaFfNIsvKWa50bnsWo19Po2PjPyIp5Lkwu0F2DH+aditVowG42cnmqdFdBraZQbfb0G7XZJFR3EXkuL/GT14eRZAW9VsN7T+yho9G53cu6hcN9bRzobiEvSXz3hUsIQsEE/11Hdu+InGwqJ1ZEyG+30ZnOifzj5TFOD5RWyTC2GMJmMmCtUOpHZWvobnZxaXKJvZ1NWI2lBWzdzQ387MIIc6FYzVWa1U1EHktliaez/OR8QcT7vA383qMnqk7EV9FpNZgMeh4+2Ec8ncMfTfJ3L1xkauUpol5RFEhmRDwVuGEtx5JvyYG/eH0Kt81c0mayoihcnfHTWqMbXjsZk16Hx27mwsRCydfQCAJdjS5evk3PSLVTF0IeSqT5v166zD+cu053o5N3HtrFkyf2bMiidLvo8zbw3hN7gMIA4R+8cp3RheA2r2rzSOVEzEZ9RZqBArHULbamsXSWF69Psa9Ej/OFcIJcPr+jK4pqma4mJyMLQaKpTMnX6Gtxc3FioVieXCtUv9LdheVYku+9dIVUViSdEznU42V/V/nDCraSXm8DH75v/4pHN/z4/PCmzxjcLuLpLE0Oa9nXSWZziHmJTs+NJ65XR2ZpdlpLfrS+NuPH67SrdeM1il6rpdlp5dpM6RUshcjewhsT5Xm5bDU1LeThRJr/59fXikN83310AF2J7brbTZvbwUfOHsRjM+O0GGn31FaObr0k0lmaneUL+Vwwxr7O5qKneV6S+dXQNP2tpXVhZkSJmWAUb0P5a1PZPlob7AzPB8sa2tzX4ual61MVG3yyFdTkZmdelkmkczx9YbQo4p0eB/PhBBP+MJKsoChK8RchCAKCUDC6Mul1mAy6wp96HUa9DqNBi81kQLfNqZhml43fuG8/kqzw0vVpzu7tIpzM1NU0mkptdC5FE7znJn+MwbllDDptyWmRmUgSt8267e8BlfIw6XXYTAbGF0Ps6SjN677RbkGUZEYXQ+wuMTDYaqpeyPOyTDyVI57OEkmmCacypLMiBp0WWSoItctqoq/FjV6rwWUxoNEUnK5XH5EVpWDbKMkyeUlGzMuEc2lkWUaUFPKSRC5faFJxWc24rCYcZiM289aLu9mgZ2o5wuXpJcaWQuTyEr915mBJpXTVRl6SSWVFmstMrYh5iWgyw64Wd/FrLw1OldT8A4VNzplQiqN71Mk/9UBrg42rM0v42htL6iMQBIEOj5MLEwuqkJdDMiOyHE+yFE4QSWYw6LUYdVrMBh1ep5VIMoPbZmJkPohJr+PBfd3r3NhcO+0iK5ARRVJZkdlAlExeQhQlXNaCp0qjw1KxbsS70d3korXBxkI4gU6j4ecXR/nNMwdrbmDsmwnF07Q22NGX6bGyEI7T63UXmz+WIgnmQ3EOdpUmxJFkBlFWsG1zn4FKZXBZTYwvhfFHkyWPhetodPDitSlkWamJz11VCLmiFFqt/ZEkC5E4WVHCZtJjNxtpdlnR3nRXDcRTvDw8S5+3gc4mJ60uW0WqUzQCWAz6WzyyJUUhns4yE4hyfW4Zi6EwdLfJZcVpNm3qptiTx/fw7V+cJy/LLEWTvDY2x8ndtTdk+WaCiRT7S6wouZm5UIyze7uL/z43MkOHx1nyB24mGMVm1KqbnHWCIAg0O21cm/WXLORWowG9Tst0IFITnZ7bIuS5fB5Bq0PMyyyEY0wuR8jlJewmI81OKxaj/rZDwPKSzOvjC+i1Gqb8Ed55z65NjZK1goDLYsJlMaEAqWyOYCLFVCCCUa+jt7mBFpetZLOeO2E2FmrMf35pDJ1Ws3LzctdsikVeuVl3eMqr68/kRMKJDAe6CpN+REni/Ng8D+7vLfmaE0thHCa1AaieaG2wcn5sgayYL7ltv8lhZWguoAr5WkwHoiSyEnOhOBajDo/Ngs1suOsEx2uzyyQyhWlzq4K/VQgU7tJWowEFiKdzjC+FuD67TIfbQWeTs+KP5vu7mpkORBmaD+C2mWs6YowmMzgtprJtEsYWQxzpay3600z6I5iN+pLfC+mcSCiRps2sCnk9odMWChjmQjH6vO67n3Ab2j0OLk0t8sjh/qq3n94WIb8648fjsLOrxbXu6R6BlWk8Oo2GvCyzq2X77pIC4DAbcJgN5CSJYCzNrwancVnNDLR7cFWwoeRdR/ppclo40NlcsYk620Ewniz5A7WKrCjMBKM8dvTGvJPBOX9Zm6ezwRgOsxGNoI6jrTcabCam/JGS33cNVhOJbI5gPEVjBXofNpNtqbXq9DgwG3QbGtFk0GppcljIyzJmg47WEo3kK41Bq6W1wcZAeyN6rcArI3O8Mb5AMlOZzjBBEOhucvHj88MsR5P87OIo4US6ItfeKhQFIqksnWXWxs+HYjQ5rEXbWkVRuDy1VFbN/ZQ/gtumdnLWIx67helAFEkuzZROEASa7FaG5gMVXlnl2RYh72h0Ek5uTIwcFmOxRnigrfpKgjQU3ji7W93kZZmXBqe4Mu0v1rmXg1GnYzYY4+k3RhicDXBxarH8BW8h0XQGg06Lu8z68Ul/5JZNzsVIgrwk4yjREldRFBYjcVzW+jcq24kY9TqMei1LkUTJ1/DYLUz6q9//aFuEfLXgfr0iN74UJpHJkcqK6LUaem4zEaZa0AoCXqeV/lY3yUyWF65OMb4UpsSgACjcxBodFoKJNG1uO9dmlhHL6FzbahZCcQ50esvKM8bTWVJZ8RYflcHZZZoc1pKvm87lyUtyyZthKtWP02Jiejla8vkeu5mZQOnnbxXbIuQaQUN3k4tAPHXXYxOZXGG6fSBKm9vO/s7mmjDD0mk0tDbY6fO6mAlEeHl4mni69Dzs6d0FW9ZUViSXl2ricQ8K04BiqSy7WsvLjw/PBzg90HFLOu7K9NJtpwOtl2A8hdVkqOlNZJU70+SwMLkcLrnd3m42FoOIambbFLG7yUUyI5K9S1Q+vhRGEAq1w+FEmj5v9ZcC3YxBp6WnuQGzQc+vh2ZKjs53tTRg1GkJJdK4bWYuTi7WhBfEQiROf6sbUxlRbyKTwx9LcmbPjbRKJpdnMZKgqQzfllA8teOmM+00LEY9WVEimSktiBIEAafVyGIkXuGVVZZtE3KDTkuft4GlSHLNY/KSzNRyhCa7hXg6h1Ffm4ZYAoVHtJuj841uhgqCwL2+QlTutpnxumwlb+JsFbIC/miSve2leV6scnV6ibft672ldHExEsduNqIpI5xejCTUcW51jiAIWE16gmUUCNhMRuaCsQquqvJsa46iu8lFNp9f87FlNhRDlORijbBY4yPRVqNzk17Pr4emWY6tfRO7HYd7W9nb3kift4F3HNpV9U6PoXgKp8WEp4zSrXAiTSyd5b49Xbd8fT4cL0uEFUVhOZYseaNUpXYwG/QENvhZu5kGq5npKs+Tb6uQ67Qadrd6WIre/kVOpHM02i03moCqvJZzPQhAo8NMu9vOG+OLTCxF2EiG5Ozeblob7EiyzHIsWdXzPhfCcQ6U2ZJ/ZXqJdx7a9ZYNyenlCA1lTBnKinlEdaNzR+AwG/CvoTHrwW0zMx9SI/I70uFxolDwNHkzB7qaeWBfd3GT0GWtn3pfq8lAX4uL8aUQl6cWkeT1qfk/nLvOs1cmGFsM8XcvXNpwVL9VRFIZJFkpa4jxUiSBrCgc73+rx8xMIFrWfNNUVsSoU/1VdgJ2s5HlWLLkPSWLUU80lanqPaltF3KNRsDX1og/uvYLbTLoMOi0dRc9GbRa+loaiKWznBueISPevaTQbjYwHYiSXyk/LKdGdrNQlELN94n+9pJ9aBRF4dqMn8eODLzlGplcnkgyg6OMDtp0TizbhVGlNjDqdSuzYkvb8NTrtMgKZNfx+dwutl3IAVpcNowGHcHEjVl704EoP7s4RjonkpdkGiswjKAa0QoCnY1OdFot54anSefuXMWzOt17OZ7CbNCV9ci4WQQTKQxaTVkt+RNLYWxmQ9Ec62YWI3EclvI2OlO5PPpNMDtTqU7K3fA0G3QkMm/NGlQLVfFOFgSBQ11eArFkcURTOJEuivjJ/nYOdJZX+VDNCECLy4rVZODcyMwda1ZX29EXwgm8TlvVCbmiwLQ/won+jpJtZZPZHINzy3z4vtt7sIcS6bLLBtM5cVNcK1WqE6NOV9xrK+l8fXnnbzZV8062mY30t7qZDcVRFIVwMoPLYmJ0McRLQzPYdkB1QbPDit1k5NzI7JqR+WqVRSydpdllJZhIkZeq55FvKRLHYTXS2ViaXa2iKJwfm+fhg7vW9JKOp7MYy0yLJNM5DLr6StWprI1BpykrojbotMRus49XLVSNkAP0NDdg0GoIxNNEUxlcVhO5vISiKFV9N6wkTQ4LdrORV0ZmbmthYNTr6PA4EIA97U2898TeqrHYlGWFmWCME/0dJa9pYimM2aDj/n3dax4TTWbKdoJMZGu3L0Fl4xj1urKM7Aw6bVVrUFWFJBpB4FC3l2evTCDJCi6rielAFFGSSWRyO2YUV7PDgl9ReHVkjtO+zltyuTqthqN9rUSSWdw2c1mVG5VmJhilxWWnpURnytWUyqcfO31HG4ZIKoPZWN5bN5MTd8z7aT1kM2n+53/9L0wNXcFqd/KOD/8O/QePbPeyKoZBpy0rR27QaUveLN0Kqioih0KKpcfrwm0z47QYi2V55Wxs1SLNTitajcClyUVurkwsVPYI7PI2oCgKI/PBspodKkUyI+KPJrlvT2dJ568npbJKLJUtO0cuy1TNk0w18NozT7M4NcbH//jf07l7Dz/5b9/c7iVVFKNeR6qMiFyrEaq6k7rqhBzgQKeX/lY3OUkuCng1v4ibRZvHQSydZXQhWPxaXpb50auDXJnxA/CT14cZvun724GsFEytTu3uwGYqbS9jeD5w15TKKrF0pmwhlxS5Ot/828Sxhx7ln3z+T3E3t2K1O5GraN+lEhh0WlI5seRacEEQyFexBlXte/lwTwvRVLZYdihXcTH+ZqEBuhqdTC1HWAgVTHuklU7OC5MLCIKATqvZ9u7OmUCEBruZgbbGks5fCMeZDkT56AOH7+psqSgK8XQOs6G81IqiKKjdQDcwmS04PU0Mvn6OV37x/3L84Xdt95IqSqFCSSnZ/llAQJaqV4OqKke+ythiiJ+cH+bx4z6urUSe8jo7H+sNvVZDV6OTy9N+LCYDq9V4WqEgeBpB2NbXZjWl8oHT+0pKVcTSWS5MLPDxh4+uq+V+NSoq18pYuOuE2Nrju3/1vzI7OgiAVqfn81//zobOv/rKS/z4O99g77HTnHn3BzZjiduKVqNBlGVK3RkRSiyn3QqqUsi1Gg0KhdbYvR1NZMQ8LQ13zpvWM2aDjhaXldfH54tlfaaViFSSZbTb9AYrN6Ui5iVeHprm8eO+dU8ql2WlIrltQSNUdcv1RlFkGf/MJO/9xGfo3Xtww08bc+Mj/OQ736T/4FHe+ZsfI5dJY7LUvrfRzQiAUmLQIysK2ip+gqvK1Mpqo0ZekslLMnOhOCMLwbr64G0Ul9WEQavl2nThCaXFZUNRFCRZ2bbGlil/6SkVWVF4eWSWw72tnLiNl8paSLJSkY1vjSCgUD/vp+DSArlshhd+9D2+85UvM3Ht0obOf/lnP0KWJUYuvsZ/+MNP8rXPPUVerN4qjVIQBIFSk5CKolT1QJvqjMhXhEmSZZpXKhjmgnGcFlOxRX0n0ua2E1yZqtTaUCjx+8jZg0Wb363EH00SSaV538nSUiqXpxZpsJp491Hfhs5TFKUiqW2dRli3UVltoHDP2Yc4ePpBxq9d5Md/+w26du/F6lhfY9YHf+9zm7y+6qDUiFySZfS66hXyqlyZdaUiIZ7O0WA1odUIxDM54ukcy7G7j4erV7Qagc5GBx0eB+0eB4Ig4HXZsG9x12sik2PSH+GRe/pLuokMzweIpbJ85OyhDT9NaDTChmx/18JiMhTtIOqBxtYOHv7gR+nYNcD+E/ch5UVCSwvbvayqQqH0PHcuL5VckbUVVKWQOyxG7ulpocFmRhCEYtNLm9tOPJ0lEC+9sL+WyeUlArEUbquZxXCCxUiCS1NLW9qiL+Zlrs8uc3ZvF40l+MOPLQSZC8X4xDuO3zLxZ71oBKEiFUxWo+GuYwZricu/foGvfe4p5sZHGLl4Hr3BSGNr+3Yvq7pQlJIFLytKWx4wbYSqFHJBEHjoQC8dKwZRqwN2w4k0J3d3EEmmCZXRpVWrhBNprs4uo9UKLITjXJ5a4vmrE1vW2CLLcG3Wz76OZna1eDZ8/uRSmKlAhE++80TJQyE0FdqktJoMJZeiVSP7T57h0L0P8n9//X/j4q+e431PfQazrbQO23ql3Ii8mscCVmWOHCgaZzktRo72tjIXijLhD3N2bzendndwbmQWBfBUUYv6ZrMQLtSSB2Mp+lrcvD6+gMdu2bJNmNHFIG6bhaN9bRs+d3wxxIQ/zFPvPI6nDEtinUaDIAhIklzcSykFs0FX86MDb0aj1fLYR5/isY8+td1LqVrykoyhxPGIGTFf1ZYOVRmRA4wshPhvz10gGE/htJo43NOKXqcjI+axmgycGihE5oH4zsiZ5yWZyZW5ga1uOy6riUQmt2UzJ6cDUcS8xNv2927YnnZ0IcjkcphPPXKCZmd5ZaSCIGA3GUjnSm+3BrAY9HUl5Cp3Ji/JaDRCScNEFEUhJ+bVHHkpNDsL+delSMFHpMlhxWkxFjfHrEYDp3Z3EktlWbrDdKF6YSYYRZYVDDotrS47qayIrCjk8hKbXXwxG4gRSWR499EBDBtwDFQUhWuzy8yFYnzqkZMl5dRvh8NivOsAjrthMujrKrWicmey+TyWEm0dsmIeo15X1f7127Ky9eiO02LEqNcWByeIkszwfJCr0/6ivavFqOf0QCdiXmI6GKvr7s9YquCF3OdtQKMRit7IRr2u2L6/GcwH4wQSKd59bADLBh4tJUnm3MgsyXSW33vkZFnplDfjtJrKjsitK1UrVWyfoVJBcqJUcpluPJMra9D3VrAtQj62ELjrB0gQBFob7Ez6w8iKQrvbToPVxHNXJ7k0uVg8zmTQcWqgA4fZyLg/XFclZTfjsprQCAK9zS4A2hrsPH5sgJ4mJ0PzgU25ic0F4yzFErzn6MCGduzTWZHnrk7gddr41KMncVZ4aLbTUr6Q67Ua7GYjyWx9Nb2o3J6smMdqLC3HHYqn6SpxUMpWsS1CbjMZuTbrv6v4HOhsJr5SsywIAod7WwG4MLFwy7lajYZD3V56ml2ML4Xr7sOZyop0N7l499HdmA36YhrJoNNiNxvRamCxwkOYZwJRAvEkjx/zbWjIcSCW4rmrE5zd081vnT2IYRMGHLssprJTK1CYyLT6pKNS32TzUslTxqKpTMkTr7aKbRHy+3xduG1mrsz47+jc1+d18/ixAbqbCi/invZGtBqBVC7P5emlW44VBIE+r5sjfa3MBmN1U54YSWZ4+sIoIwvBoigOLwR5cXC6aO3rtlmY9Icr0iijKDDhjxBJZnj8+MZEfGIpzGtjc/zW2UM8eKB308oibWZjRZ68ml3Wurvpq9weMS9hLTW1ks7S0lDdpZzbIuQarYaHDvTS6XFyYXKR9BrDhjUagf5WT7G8zqjXcbSvDZfVxIvXp4imMm85p8lh5V5fF5FUhtlQrKZ9zGVZ4dWxOQAyKxGooihMLEVu8X5wWIwks2LZMwVlWWFwbhlRknj8+J5179KLksT5sXlmglH++aMn2duxuYOyPTZzRQTYbbOUnaJRqQ0yYr6khh5JkkllxWLxRbWybduwGo2Ge32dnOhv4/K0/44R9Kujc7w6WhC0M3u6ipapU8uR2x5vMxk4s6cLh9nI6GKYeBUPTb0TQ/MB4ukcAhQf7RYjCVI5kb6b3AIFWBmLd/vXYz1kRIkLE4t4bBbec9S37o2hpUiCf7w0jtdl4188dvqu030qQUuDnUQ6V/a+gNtmLlT/1O69XmUdKIpCMivitm98wzKcTNPstKIvsf58q9jWhiBBENjT3ozLYuYXl8ZIZcQVD5Fbj/NHk0wHIhzpbUGn1eIwG7l/X/cdZ+jptVoOdHnxumxcnloims7S6rJVtYPZzUSSGa7PBQDwtTfiWtkwHF8KY9LriqZZq7htZsYWQ+xpa9qwuU8klWF4LsjRvlYOdHnXlRIRJYnLk0tEUml++/5DJQ+VKAWDTovbbiaSypQ1s1S/ssegplfqm6woodNoSio/DMbTdDe5NmFVlaUqVK2lwc57T+4lnskyfJsKjEPdXrKixPXZQPFrgWiSl4dnuTi5cNsUyypNDiv37+vGbqqt6DyWziIADrOBPSsimcjkWIom6Wl2vaUpR6/VYDEamAvHNvRzFkJxRheCvP1QHwe7W9Yl4qtReLPLxr98/MyWivgqXY0uQhVoBmt3OwgndkZT2U4lls7S6LCWtGcTjCfp87o3YVWVpSqEHMBuNvLESl724tQiyZsGpXZ4HLS4bLw0OF2Mws/s7cZhNvLclUmefmPkjrlwvVbLwW4vh3tbWIommQ3Gqr4ZxGLQY9BpOb6rvSjaFoOeve2Nt6RVbsZlNbIQWp+Q5yWZ4bkggXiKJ47vpcNz9135TE7ktdE5rs4s8dv3H+I37t2/LRa6UBiBF0mufQNfL91NlbmOSvWSyOTwujae45YkmVAiTX/rxn2FtpqqEXIoPOq+/VAfR/tauTrjZ3o5gqwUUjCPHO5HlCSeuzoBFB6v33nPLhRgIZzg/3t95K6ueKvRudtuYWwpzEI4UXWboWOLIUYWgjQ6LLzrSH8xpaIoChqNwN6OpuJ0oDdjMxmJpXJkxTvfpELxNG9MLOBxmHnvyb3Fn7EWYl7i0tQiz1wep8fr2rYo/GZaG+zEM+U/XXlddtK5fFUP1lUpj1Q2R6N940K+GEnQ2ejatmBlI1SVkMONvPkHTu9DkhUuThaic7fNzDsO7bplmkxno5PTA4V/jy6GeObS+F1b9fVaLXvaG7l/Xzdmo56RhRD+WLIqukKnA1EuTi0xuXxrVUoik+Pnl8bvWlKpEQrdroE1PNtXo/DpYJS3H9zF2b09GPVrb5NIsszg7DI/vzSG22bhs0+c4fFje6rijb264VnujVin1dDhcRJNq9Ur9Ug5G50L4RgHu7ybsKrKU7Xuh3azkceODjA0v8y54TlaXFZ87U3F4cOFWZUaTu3uoMPjYDoQ5ZWROXqaXet6FDIb9Bzq9tLnbWB4PsDIYpBGuwX3igf6VjO1HOH8eGEQgE6jKc4IVBSF18cXyIr5dU2Ot5kNLEXitHtu3QwNxdOMLYXo8zbwrv7ddxRwWVGY9IcZng/Q53Xz6Xed2pJqlI1g0Gnp8DhYiiSKNsel0tvsYmJOHcJQj8QzOSxGw4Y3OhVFYTmWwte+vU+e66VqhRxuROftbie/vDbJxclFer0u3hibJ5EVefzYAIIg+XBV5gAAGAxJREFU0OFx0u52YNBqWY4l2dXiXrcY20wGjva1EUmmGZwLMLwQxGU147aZtqzkaHQxxKWpQoOT3VwonVyNxseWwgTiKY71tWJex5vRaTYyshBClgupmIwoMekPk8nlefvBXbR71ha9jJhnfDHETCBKS4ONjz98rKp37A92e7k4uVS2kLd7HKRyYvE1U6kfArEUfc0NGw7OQok0DrOxoh5Bm0lVC/kqq9H52GKIV0fnSGZFxhZDXJtdZn9nM1AQ/XQuz/nxeSLJDKIk8+C+nnX7fLisZk4PdBJNZZgJRBlbCmMx6PHYzFiM+k2N0lc321xWE2d8ncUOzlgqy9UZP16Xdd1eDzqtBqNBiz+aIJkVCcbT7O9q5mCX97ZRuKIoBOIpxhdDBBMpDve08uiR3TUxG3WgrZFfXBpbmeNZ+u/HZNDjNBsIJlI0VcihUaU6iKYyHN+18UlJM4EoB3tqI60CNSLkUBDq/lYPPc0NXJ/189+fv8AvLo6h02rwrWy8nd3bBQKcH5tHEArpivv3dnNPz/rK6qBgyOTsMuFra2Q+HGfSH0GSZVwWEw02U8Xq0P3RJLm8RIfHQZOj0GF4eqDjlqeACX8YnVbDkZ7Wda9fUiCbk3h9fIHTA508fKDvtq6FYl5iajnC1HIEvU7L2T3dHO5trYr893ppclixGPREkpmy3el63DaWIglVyOuITC6PJCk0bbArU5Jl5kMxPnTfgU1aWeWpGSFfRafVcLC7hT/+4IN89Ycv8tM3Rokk0xzpacOg13L/3m4arCaevzpJXpJ57uokIwshHrln14Zc+PQ6Ld1NLroanYQSaaaWIwwvhDDptdiMBhwW4x3zzGuRl2QuTy8x4Y9gMehpd9uLP+fNYn2o28vuVs+6xFVWCnnwQDyFxaSn2WHlzN7uW45JZnLMBmP4owli6SwDrY385tmD7PKuPxVVTQiCwIHuFmaD0bKF3Gs3MbOYIp0VMdfQzUxlbQLxJN1NTjQbfG9PL0foaW6ombQK1KCQr+Kymvmj9z/A137ya2wmI29MLOCwGGl129nf6aWrycXlyUWcVhMvXJvi9YkFHjrQu+GfIwgCHrsFj91CfqWudCmSYCoQRRAEbEYDTosRs0F3RzFUFAV/LMnr4wtF576bjepXz03nRC5OLnGo24vFqL+riOfyEoF4mmgyi9tu4tRAO0a9nitTi8iyTDiZYS4UYzmaJC/J7O/y8vhuH31ed0k3ompjX0cTFyfL36jUagT2dDQxH46zq6X6G0BU7k4kmeHk7s4Nnze5HOF9J/dtwoo2j5r+JNvMRv74gw+iEQQyYp4L4/NM+SNIskKz08ZpXxdajUCj3cL/fG0Il9WE3WQgEE9xoMu74Rl8Oq2GZqeVZqeVA0oz0VQGfzTJYjhBWsxj0usw6rWYDbpiQ8+qQMfSWV4anCnaD/S3uNnX0XSL+CczOV4cnCYrSqSy4poirlBwZAvG08X0zP7OZnRagXg6y3I0yXw4zg9euU6z08ahbi+PHt5Np8dZd5t5nY1OcqJEIp0t2aZ0FV9bI1enl+ltVjc9a51cXiKZEWnboGthKJFGUZRiurZWqGkhB4qPTYOzy/z35y/y0QfvoavJxbUZP6+NzuG2m7GbDHjsFp6/OolWIyDJCueGZ+lv9XCo20uHx7Hh1IIgCLisZlxWMwNtjWTFPLF0llgqSySZYWQxRCCWKjg4triLnaQmvY7DPS14XbZbHvkiyQy/GppBVhTO7u26rYdINp8nGEsTSqbRaTSFHLHJQE7Mc23Gj0GnpclhoavJhSAIvP/UPo6tbPR861vf4plnnkEURT7ykY9w4MAB/uzP/gytVovBYOArX/kKjY219eaFghf9qYEORhdDRb/6UnFYTLQ2WPFHE1VvW6pyZ+ZDcXa1ujc8o3N8McgZX3fN3chrXshX8bU10utt4G+ffYOjfW185P5DaAQYng8wuhCi3eOg1WVnIRJnJhBFAaaWw4wsBOnzNvDkiT1AYZfbYTZuWNiNeh35RBp/NMlcKMZssNAqbzMZaHJYyIoSB7qaMep0JLMig3MBNIKATqshns4y4Y+g12rob3UXbwgyIIp5klmR9Ep5XIPNTGuDfUXE9ZgNeuxmAzajAUEQaLCZMep1CAjFaphz587xgx/8ALvdjiRJPPPMM/zwhz/ky1/+MqOjo3zta1/jr//6r/mjP/qjSv5KtowT/R38anCGg11etGXOVdzX6eWX1yfxuuxvMW9TqQ0KNeDJW5oH10NOzLMUTfI7u9o2aWWbR90Iudmo57NP3MfPLozy49eGGJ4P8E8ePMzRvnaO9LYRS2WZDUaZ8IdptFvISRK+tkaCiTRjCyF+emEUj93Mi9en0a+kULwuG+4V4fTYLcTTWfzRJHlZRpJkoqks/miCdx0pNNhcnFxkcC6AduVurhUEOj0O3DYL2byE1WggK+bJ5vOIkkxWLPxpNuhosJrx2M3kRIlYKktWlBDzEoIgYDRoMeh1aAWBVE5kcG6ZKzNLiJJMXpIRJQlJkpEV+ODpfTxyeDd2sxF/rDDv9Hvf+x75fB63200sFsPj8fCnf/qnBINBvv/976MoCkZj9U4Ivxseu4U+bwNTgUjZBkftbgcmnY5gPEWjo3Y2u1RuEIyncVpMeDbYzXltdpljfW0l+ZZvN3Uj5FB4zH7s6ACHelr4zrNvFIc0C4KA02rCaTWxv8tLJicyF4ox6Y+Qy0sksjmWogmuzxbSH6IkE05kWIwkkGSFXV43Pc0uFsNxrs4uv+Xn/sO562g1mqKLnqIoGHQaNIKGsaVQYabmOhwAwsk1WvA3YM43s1x4ErAY9SxGCkOZR0dHkWUZRVEQRZEXX3yRP/iDP+Av//Ivef/738+f/Mmf8LGPfWz9P6QKudfXxd+fu1q2kGs0Aid2d/DC1Qncdgs19oStQsEj5Ujv+kt2oTBOcSEc57fvP7SJK9s86krIV2l3O/hX77+/mIN+aXAKAYETu9vRa7WYDHp2tXjY1eJBURRSWZFALMl0IMroYoi5UAyTXreykZbnxcFpxpZCb/k5GqGQUlmNwO1mI1aTAVlRkGWFvCSTlwEEZEVBkRWkSsxjuxMr712zQVecR6koCjqdjq9//evMzs7y5JNP8vnPf56zZ8/yzW9+k97eXtzu2q7U2N3qQQBC8RTuMsvGOjwOnFYTS5H4W3zfVaqbdE4kncvTs4ZD6Fpcm1kqDKPZwGjDaqIuhRwoNu4oisLLQzMMLwT5+3PXOLuniwf29RQ/7IIgYDUZsJoMdDc3cP++nhWjnRyRZIZUVsTX1kg0nSWRzpERRbKiRC6fRyMIaDQaBEFY1c+CYCsKisJKBYsei0mPIis8f60wns6k1/LAvl76vA2kcyLZvFQQ/WKqRCYvS+Qlhby08j1ZvukYqXjczemVvCTT31oQZLNBTyydRVEUenp6GBwcRK/XY7VayefzDA0NceHCBbq7u5mcnOTP//zP+eIXv7gdv6qKoNEI3Ofr5sLkPCfLFHJBEDjR38FPL4zgddpqbuNrJzMXjLO3o/GW0t67EUtlCCbSnN3bs3kL22TqVshXEQSBzz55huuzyzx/dYKnL4zw9IURPnBqP48c7l/zHJvJeMeZlbKsFEVbUpTC1GJBQIOAoBHQCrAQSeCPJjjS24asKMwEYxzs9nJiVzuGTajhliQZYUV0dFoNkqyQy0t4vV6effZZjh49WkyxBINBjEYjk5OTpFIprly5UvH1bDXH+9t59uo4sXQWR5l5Tq/LRovLzlwoVvUT1FUKZMU8oUSahw72bei8K9N+HtrfV1NdzW+m7oUcCiWK+zub2d/ZTDCe4pfXJotNH4uROC9cm6K70Ul3k4vmN5UFrnlNTUG0AVZ//bFUhgl/mKnlKINzy4wthnBZTNzT3YpGI/B7j57crP8iQFHEoXAzMhl0JDI5vvjFL6LX6zl37hxzc3N86EMf4ktf+hIAr7zyCp/85Cf59re/valr2wosRj0P7e/j/Pgc9/q6yr7eif52fvjqIC0NdvRlVsOobD5Ty1H2dTZtqD8kEEuREUXu9W28caiaqFshz2bS/J9f/9+59No5Wju6+NTn/jUtbR147Bbed+pG19a1mWVeuDqBKBV8rY16LV2NLp56x3GcVhOBWIpkNodeq0Gr0aAoCjlJQszLxNNZpgNRHjuyG71Oy88ujvHzi6MIArQ1OPjg6f3ct6cLQSh4m+QkiZwokcsXGn5S2Vzhz5x4I6UiSW9Ko6ymWm78W1o5RpRk8nn5ptSLxAP7e3nPMR8ABq2WVFbEY4cvfOELXL58mb/4i78oijjA3/zN3/CNb3wDq7U+PEZO+zr55fVJgvFU2S3WDTYzvjZPYRZqjdiZ7lRSWZFoKsOjR3av+xxJlnl9fJ73ndq74XrzaqNuhfylZ3/ByLUr/Ju/+s/8l6/+O/7H336b3//jf/uW4x4+2MeD+3tYCMeZWo4wvRxlNhjFunJX/8WlUZ69MnHHn3V91o9WoyGVFfE6rWg1GiRZ5rmrEzxzeezG5qd8Q5g3i5sHa2gEbpma9K1vfYtPf/rTxX8PDg6STCa59957N209W41Bp+WRw/08e2WcB/Zt3JLhzRzf1c7fv3yVQDxFYw15b+w0ppYj3NPTimkDKcur0376vA01MzziTtStkD/82BOcPPMAJrMFrVaH3rD245ZWU5gS0+FxcmbPrd97YF8PezuaEfMSkiwjCAIGnRa9rtCK3+52FD1LZEUhny9E3Lm8RGa1mWcl6s6I+Vsi7bwk3YjCb9q4vDn6FqVCzXph8/PGMZJ8Y5Pz5tLGW4piBKE4+SgWizE+Ps7p06eL3/7Rj37Ehz70obJf62rjaF8bz12ZYDEcL7tDU6/T8uD+Xn56YRSnxaSmWKqQeDpLOieyr7Np3ecE4ykWI3E++8SZmjSMezN1K+QANoeTL3/mU/gX5/ndz3yupGu0uR3rHlygEQQMet2mbGTejCwX0ju5fCFVkxFFkitpmg73jY05gRsR+auvvsp99913y3VefvllnnrqqU1d63aw2k/ww1eu43XZyv6gtjTYGVBTLFWJoihM+iMc62tf9yAYSZY5PzbP+07tq8nmn9tR9+HF//KFL3H01Bn+w5//m+1eSsXQaARMeh0Os5FGh4UOjxNfWxNHettu8V5WuOFFMzExQUfHrS3Ly8vLNDRsrN62Vtjf2Uyjw8LwfKAi1zu+q52cmCcQ30B3lsqmsxRJoNNq2N22/kn39ZRSWaVuhfzpH3yfr3zp8wgaAZ1eRzZz58HFdYlyw8XvE5/4xFu6N3/5y19uw6K2BkEQ+I17DzC+FCaWzpZ9vdUUy9himKwoVWCFKuWSFfNMB6I8uL933QNf/NEES5EE7z25ty5SKqvUrZCffvBhAL78+59idPAa//wPa7fZpVQkRSl2ne5EPHYL7zqym/Njc7dsApdKS4Ode3pauDa7XNx7UNkeFEVhZCHEoe6WdVcnJTM5zo/N85EHDtVNSmWVus2Ruxrc/Kt/99XtXsa2khXzWI0b81yvN07t7uTS1CLD8wF87evfDFuLwz0thBMphheC+NoaVYfEbWIpkkCrETjU07Ku40VJ4ldD0zx6eDf9LetPw9QKdRuR73QURSErStjMO1vINZrKplgEQeD+fb0owGwwWv4CVTbMRlMqiqLw6sgc+zqaa77xZy1UIa9TsnkJo1677p38emY1xfLa6FxFUiJ6rYZH7tmFP5okGN+Bey/biKIoDM8HN5RSuTrjx2zU8+TJPXWVF78ZVcjrlHRWrLs8YDmcHuiks9HJ6+PzFbmezWTkHffsYmwxRDIjVuSaKndn0h/BatSvO6UyHYiwHEvy0QfuqeugRhXyOiWdE3HVqCXnZiAIAh++7wCiJDFSoZLEFpedM3u6uDrjJ51VxXyzWYokiKWzvP3QrnWlVOZDMQZnl/nYQ0frPqhRhbxOSefyNeutvFkY9Tr+6duOMOEPsxRJVOSa/a0eTg10cHnaTyaXr8g1Vd7Kqq/RI4d3YzLc3aVwMRzn0tQSH3/42I7wlFeFvE6JpjK0uGzbvYyqw2O38NEHD3N+fJ5EBTY/Afa0N3F8VzuXp5dIq2JecbJinutzAR7c33vboeRvZjEc58LkAh976MiOsSBWhbxOSWSy67YW2Gn0ed28+8hufj08gyhVprlnf1dzQcynlkipaZaKIcsK12cDHOry0t3kuuvx86EYFyYX+dhDRzc8JaiWUYW8DlEUhWgyuyMeKUvl1EAnh7pbeOn6FFKF3Cj3dTZz70AnV6b9xNO5ilxzJyMrCtdnl2lx2Tjc23rX46eXI1yZXuJ3335sR4k41HFD0E4mns5iNxtreuLJZiMIAk8c30MuL/HS0DTNmsp0ag60N6LTafjltSl6ml00O+vD532rURSFwdkATquJB/b33LFsUFEUrkz7CcSTPPXOEzsygFEj8joklEjTsUNyg+Wg0Qh88PR+Oj1Ori5GkOTKROZ9XjfvOTbAXCjGhD9CBdwBdhSKojA4F8BmMvDwgb47VqisdmxKssy/eOz0jhRxUIW8Lgkl0nSrQr4uNBqBD913gCabiZcGpyuWZml0WHnfyX3kJYnrs34k1ZtlXRTSKQGsRkOhzPAO/u+JTI7nrkzQ19zAJ95xvO5LDO+EKuR1SDiR3nE5wnLQaTU8NNBCb3MDv7w+iZivzAaoxajn3Ud9NDttXJhYUMsT74IsK1ybWcZhMfKOQ7vQ3UHElyIJXrg6ydsP7uJ9p/bd8didwM7+39chiUwOWVZoVytWNoRGKKRZ9nU289zViYqVJuq0Gs7u7eZIbxuXppZYCMXVVMttyIp5Lk0t0uSw8PDBvjUjcUmSuTCxwKXJRX7noSMrM3Hrs+1+I6ibnXXGXDDK/q7mog+5yvrRaAoboC0uGz8+P8yxvja8FajFFwSB/V3NtDbYeP7aJIF4it1tjZj09dsyvhFiqSyDcwEO97ZyqNu7pjAHYileH5+jv9XDRx88jM20sw3hbkYV8jrDH01yaqA+Hd62AkEQOLm7k0aHle8+f4Ge5gYG2ioz3s1tt/Dk8T1cmfZzYXKBTo+Dlgb7jrbCXYwkmAlEeehgL12Nt68TlySZy9NLLMeSfPDe/ezraFaj8DehplbqiFxeIpbO0ud1b/dSap4+r5tPP3aaUDxVMddEAK1Wwz29LTxx3Ec0neXKDu0GVRSFscUQ/miSJ47vWVPEl6NJ/vHyGA6Lkc8+cYb9nWtH7DuZrY7IC8+SsjoqazPwh6PsaWlAkfJkpZ0nDuWSzd6aF7cZtPzuw0f4yfkhXh6a5FB3S8Ue591WI+89upvhhSBXZ/04zEY63A70uvpPt2TFPBP+MFaTgXce7MGo179FE2KpDMPzQURJ4v0nfexu8SAIylt+RzuFXK7YYHbbN4hQiRFY6+X8+fNngfodFKmioqKyudx/7NixF9/8xa2OyF8F7gcWADUsV1FRUVkfWqCVgoa+hS2NyFVUVFRUKo+62amioqJS46hCrqKiolLjqEKuoqKiUuOoQq6ioqJS46hCrqKiolLjqEKuoqKiUuOoQq6ioqJS46hCrqKiolLjqEKuoqKiUuOoNrYqOwKfz9cDDAPXAAUwAPPAx4eGhmZ9Pt8/BX4f0FMIcL49NDT0H1fOPQP8HyvnBIF/NjQ0NLXl/wkVlTVQI3KVncT80NDQ4aGhoSNDQ0P7gUvAV30+3yeBfwk8OTQ0dBh4APioz+f73ZXz/g74xMr3/g74j9uxeBWVtVCFXGUn8yxwAPgS8IWhoaEFgKGhoQjwO8AVn89nBL40NDR0aeWcS0DXdixWRWUt1NSKyo7E5/Ppgd+g4Cb3ceD1m78/NDR0/aZ/fnflHA3wb4EfbM0qVVTWhyrkKjuJNp/Pd2Hl70bgFeAPKQh55k4n+nw+A/AdCp+Zf7+Zi1RR2SiqkKvsJOZX8ty34PP5xoHjwAs3fe1B4LGhoaF/7fP5bMCPKGx0vndoaEjcqgWrqKwHNUeuogJfBf7K5/O1APh8vkbgr4DRle9/d+Xvvzk0NLQzZ42pVDXqYAmVHcFK+eFzQ0NDPWt8//eBTwIyhQDnW0NDQ//J5/MdoZA/vwasRuLzQ0ND7970RauorBNVyFVUVFRqHDW1oqKiolLjqEKuoqKiUuOoQq6ioqJS46hCrqKiolLjqEKuoqKiUuOoQq6ioqJS46hCrqKiolLj/P+lpqnHFnCFngAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "KMeans(algorithm='auto', copy_x=True, init='k-means++', max_iter=300,\n",
       "    n_clusters=9, n_init=10, n_jobs=None, precompute_distances='auto',\n",
       "    random_state=23, tol=0.0001, verbose=0)"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "text/plain": [
       "SilhouetteVisualizer(ax=<matplotlib.axes._subplots.AxesSubplot object at 0x00000265ED01EF98>,\n",
       "           model=None)"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "InterclusterDistance(ax=<matplotlib.axes._subplots.AxesSubplot object at 0x00000265ED01E5C0>,\n",
       "           embedding='mds', legend=True, legend_loc='lower left',\n",
       "           legend_size=1.5, max_size=25000, min_size=400, model=None,\n",
       "           random_state=40, scoring='membership')"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "KMeans(algorithm='auto', copy_x=True, init='k-means++', max_iter=300,\n",
       "    n_clusters=10, n_init=10, n_jobs=None, precompute_distances='auto',\n",
       "    random_state=23, tol=0.0001, verbose=0)"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "text/plain": [
       "SilhouetteVisualizer(ax=<matplotlib.axes._subplots.AxesSubplot object at 0x00000265ECE8B8D0>,\n",
       "           model=None)"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "InterclusterDistance(ax=<matplotlib.axes._subplots.AxesSubplot object at 0x00000265EC067BE0>,\n",
       "           embedding='mds', legend=True, legend_loc='lower left',\n",
       "           legend_size=1.5, max_size=25000, min_size=400, model=None,\n",
       "           random_state=40, scoring='membership')"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "for i in range(2, 11):\n",
    "    kmeans = KMeans(init=\"k-means++\", n_clusters=i, random_state=23)\n",
    "    kmeans.fit(X)\n",
    "\n",
    "    visualizer = SilhouetteVisualizer(kmeans)\n",
    "    visualizer.fit(X)\n",
    "    visualizer.poof()\n",
    "\n",
    "    # Instantiate the clustering model and visualizer\n",
    "    visualizer = InterclusterDistance(kmeans, random_state=40)\n",
    "    visualizer.fit(X)  # Fit the training data to the visualizer\n",
    "    visualizer.poof()  # Draw/show/poof the data"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "THNu6P_gE6bE"
   },
   "source": [
    "***According to the plots above, 5 is the best clustering number***"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "kFzF0WwcFHks"
   },
   "source": [
    "#### K-Means"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 0
    },
    "colab_type": "code",
    "id": "1SRtG368FM9U",
    "outputId": "f1051f96-e048-4bdb-e6be-5a5802cace4c"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "KMeans(algorithm='auto', copy_x=True, init='k-means++', max_iter=300,\n",
       "    n_clusters=5, n_init=10, n_jobs=None, precompute_distances='auto',\n",
       "    random_state=23, tol=0.0001, verbose=0)"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "k_means = KMeans(init=\"k-means++\", n_clusters=5, random_state=23)\n",
    "k_means.fit(X)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 0
    },
    "colab_type": "code",
    "id": "UrydEqHqFOWc",
    "outputId": "9470cf1b-2b3a-4afc-8223-0ba80e9b91c4"
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Age</th>\n",
       "      <th>Income</th>\n",
       "      <th>SpendingScore</th>\n",
       "      <th>Savings</th>\n",
       "      <th>group</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>58</td>\n",
       "      <td>77769</td>\n",
       "      <td>0.791329</td>\n",
       "      <td>6559.829923</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>59</td>\n",
       "      <td>81799</td>\n",
       "      <td>0.791082</td>\n",
       "      <td>5417.661426</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>62</td>\n",
       "      <td>74751</td>\n",
       "      <td>0.702657</td>\n",
       "      <td>9258.992965</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>59</td>\n",
       "      <td>74373</td>\n",
       "      <td>0.765680</td>\n",
       "      <td>7346.334504</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>87</td>\n",
       "      <td>17760</td>\n",
       "      <td>0.348778</td>\n",
       "      <td>16869.507130</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Age  Income  SpendingScore       Savings  group\n",
       "0   58   77769       0.791329   6559.829923      1\n",
       "1   59   81799       0.791082   5417.661426      1\n",
       "2   62   74751       0.702657   9258.992965      1\n",
       "3   59   74373       0.765680   7346.334504      1\n",
       "4   87   17760       0.348778  16869.507130      0"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "result = df.copy()\n",
    "result[\"group\"] = k_means.labels_\n",
    "result.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 0
    },
    "colab_type": "code",
    "id": "UrvzPtJqFPo0",
    "outputId": "f51dca97-db1b-4523-d896-a425606abc83"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "Cluster 1:\n"
     ]
    },
    {
     "data": {
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       "      <td>59</td>\n",
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       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>62</td>\n",
       "      <td>74751</td>\n",
       "      <td>0.702657</td>\n",
       "      <td>9258.992965</td>\n",
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       "      <th>3</th>\n",
       "      <td>59</td>\n",
       "      <td>74373</td>\n",
       "      <td>0.765680</td>\n",
       "      <td>7346.334504</td>\n",
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       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>54</td>\n",
       "      <td>76500</td>\n",
       "      <td>0.785198</td>\n",
       "      <td>6878.884249</td>\n",
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      ],
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       "   Age  Income  SpendingScore      Savings  group\n",
       "0   58   77769       0.791329  6559.829923      1\n",
       "1   59   81799       0.791082  5417.661426      1\n",
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       "3   59   74373       0.765680  7346.334504      1\n",
       "6   54   76500       0.785198  6878.884249      1"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "Cluster 0:\n"
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       "      <td>16869.507130</td>\n",
       "      <td>0</td>\n",
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       "      <th>7</th>\n",
       "      <td>87</td>\n",
       "      <td>42592</td>\n",
       "      <td>0.355290</td>\n",
       "      <td>18086.287158</td>\n",
       "      <td>0</td>\n",
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       "      <th>8</th>\n",
       "      <td>83</td>\n",
       "      <td>34384</td>\n",
       "      <td>0.324719</td>\n",
       "      <td>14783.379086</td>\n",
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       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>84</td>\n",
       "      <td>27693</td>\n",
       "      <td>0.367063</td>\n",
       "      <td>17879.558906</td>\n",
       "      <td>0</td>\n",
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       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>87</td>\n",
       "      <td>31481</td>\n",
       "      <td>0.317424</td>\n",
       "      <td>16180.688082</td>\n",
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       "    Age  Income  SpendingScore       Savings  group\n",
       "4    87   17760       0.348778  16869.507130      0\n",
       "7    87   42592       0.355290  18086.287158      0\n",
       "8    83   34384       0.324719  14783.379086      0\n",
       "9    84   27693       0.367063  17879.558906      0\n",
       "17   87   31481       0.317424  16180.688082      0"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "Cluster 3:\n"
     ]
    },
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       "      <th>5</th>\n",
       "      <td>29</td>\n",
       "      <td>131578</td>\n",
       "      <td>0.847034</td>\n",
       "      <td>3535.514352</td>\n",
       "      <td>3</td>\n",
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       "      <td>30</td>\n",
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       "      <td>5706.149573</td>\n",
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       "      <th>26</th>\n",
       "      <td>17</td>\n",
       "      <td>134966</td>\n",
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       "      <td>4128.044796</td>\n",
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       "      <th>42</th>\n",
       "      <td>20</td>\n",
       "      <td>129142</td>\n",
       "      <td>0.887052</td>\n",
       "      <td>5603.121028</td>\n",
       "      <td>3</td>\n",
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       "    <tr>\n",
       "      <th>45</th>\n",
       "      <td>18</td>\n",
       "      <td>130813</td>\n",
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       "      <td>5256.434560</td>\n",
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      ],
      "text/plain": [
       "    Age  Income  SpendingScore      Savings  group\n",
       "5    29  131578       0.847034  3535.514352      3\n",
       "25   30  122788       0.872872  5706.149573      3\n",
       "26   17  134966       0.907242  4128.044796      3\n",
       "42   20  129142       0.887052  5603.121028      3\n",
       "45   18  130813       0.890891  5256.434560      3"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "Cluster 4:\n"
     ]
    },
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       "      <td>85</td>\n",
       "      <td>111389</td>\n",
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       "      <td>16009.237763</td>\n",
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       "      <th>15</th>\n",
       "      <td>92</td>\n",
       "      <td>122879</td>\n",
       "      <td>0.060724</td>\n",
       "      <td>13709.670275</td>\n",
       "      <td>4</td>\n",
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       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>88</td>\n",
       "      <td>120678</td>\n",
       "      <td>0.063273</td>\n",
       "      <td>14264.473847</td>\n",
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       "      <th>21</th>\n",
       "      <td>84</td>\n",
       "      <td>122696</td>\n",
       "      <td>0.082187</td>\n",
       "      <td>13809.734087</td>\n",
       "      <td>4</td>\n",
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       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>89</td>\n",
       "      <td>119697</td>\n",
       "      <td>0.091679</td>\n",
       "      <td>16215.399077</td>\n",
       "      <td>4</td>\n",
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      ],
      "text/plain": [
       "    Age  Income  SpendingScore       Savings  group\n",
       "10   85  111389       0.036795  16009.237763      4\n",
       "15   92  122879       0.060724  13709.670275      4\n",
       "19   88  120678       0.063273  14264.473847      4\n",
       "21   84  122696       0.082187  13809.734087      4\n",
       "24   89  119697       0.091679  16215.399077      4"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "Cluster 2:\n"
     ]
    },
    {
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       "      <td>36</td>\n",
       "      <td>99780</td>\n",
       "      <td>0.265433</td>\n",
       "      <td>16398.401333</td>\n",
       "      <td>2</td>\n",
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       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>30</td>\n",
       "      <td>99949</td>\n",
       "      <td>0.344679</td>\n",
       "      <td>13621.639726</td>\n",
       "      <td>2</td>\n",
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       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>31</td>\n",
       "      <td>107963</td>\n",
       "      <td>0.290509</td>\n",
       "      <td>13407.081391</td>\n",
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       "      <th>20</th>\n",
       "      <td>30</td>\n",
       "      <td>101073</td>\n",
       "      <td>0.314387</td>\n",
       "      <td>14324.555977</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>34</th>\n",
       "      <td>33</td>\n",
       "      <td>101058</td>\n",
       "      <td>0.315082</td>\n",
       "      <td>14911.868398</td>\n",
       "      <td>2</td>\n",
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       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    Age  Income  SpendingScore       Savings  group\n",
       "11   36   99780       0.265433  16398.401333      2\n",
       "12   30   99949       0.344679  13621.639726      2\n",
       "13   31  107963       0.290509  13407.081391      2\n",
       "20   30  101073       0.314387  14324.555977      2\n",
       "34   33  101058       0.315082  14911.868398      2"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "for label in result.group.unique():\n",
    "    print(\"\\nCluster {}:\".format(label))\n",
    "    result[result.group == label].head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "jvS5a5EbFRFl"
   },
   "source": [
    "#### Find Examplars\n",
    "find the observation that is closest to the cluster center"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 0
    },
    "colab_type": "code",
    "id": "XMRaf3eKFTo7",
    "outputId": "05f64e31-1355-4885-89c0-b475a6b9cbed"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "Cluster 0:\n"
     ]
    },
    {
     "data": {
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       "      <th>Age</th>\n",
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       "    <tr>\n",
       "      <th>Income</th>\n",
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       "      <td>0.342991</td>\n",
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      "text/plain": [
       "                        320\n",
       "Age               87.000000\n",
       "Income         12000.000000\n",
       "SpendingScore      0.342991\n",
       "Savings        16180.265252"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "Cluster 1:\n"
     ]
    },
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     "data": {
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       "      <th>Age</th>\n",
       "      <td>59.000000</td>\n",
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       "      <th>Income</th>\n",
       "      <td>56321.000000</td>\n",
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       "      <th>SpendingScore</th>\n",
       "      <td>0.806566</td>\n",
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       "      <th>Savings</th>\n",
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      "text/plain": [
       "                        278\n",
       "Age               59.000000\n",
       "Income         56321.000000\n",
       "SpendingScore      0.806566\n",
       "Savings         6129.288986"
      ]
     },
     "execution_count": 11,
     "metadata": {},
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    },
    {
     "name": "stdout",
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     "text": [
      "\n",
      "Cluster 2:\n"
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    },
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       "      <td>89598.000000</td>\n",
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       "      <th>SpendingScore</th>\n",
       "      <td>0.340152</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Savings</th>\n",
       "      <td>12832.768721</td>\n",
       "    </tr>\n",
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       "</table>\n",
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      "text/plain": [
       "                        339\n",
       "Age               34.000000\n",
       "Income         89598.000000\n",
       "SpendingScore      0.340152\n",
       "Savings        12832.768721"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "Cluster 3:\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>360</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Age</th>\n",
       "      <td>28.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Income</th>\n",
       "      <td>117108.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>SpendingScore</th>\n",
       "      <td>0.857078</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Savings</th>\n",
       "      <td>4749.879237</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                         360\n",
       "Age                28.000000\n",
       "Income         117108.000000\n",
       "SpendingScore       0.857078\n",
       "Savings          4749.879237"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "Cluster 4:\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>368</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Age</th>\n",
       "      <td>84.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Income</th>\n",
       "      <td>110582.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>SpendingScore</th>\n",
       "      <td>0.079049</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Savings</th>\n",
       "      <td>14937.995170</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                         368\n",
       "Age                84.000000\n",
       "Income         110582.000000\n",
       "SpendingScore       0.079049\n",
       "Savings         14937.995170"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from scipy.spatial import distance\n",
    "\n",
    "for i, label in enumerate(set(k_means.labels_)):\n",
    "    X_tmp = df[k_means.labels_ == label].copy()\n",
    "\n",
    "    exemplar_idx = distance.cdist([k_means.cluster_centers_[i]], X_tmp).argmin()\n",
    "    exemplar = pd.DataFrame(X_tmp.iloc[exemplar_idx])\n",
    "\n",
    "    print(\"\\nCluster {}:\".format(label))\n",
    "    exemplar"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "hPo-xc01FVF8"
   },
   "source": [
    "#### Snake Plot"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 0
    },
    "colab_type": "code",
    "id": "O_-mtFPOFYfz",
    "outputId": "315faa06-a450-4e13-8f62-f4aa42d9b76f"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5, 1.0, 'Snake Plot, K-Means, K=5')"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x265ecc2a400>"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "import seaborn as sns\n",
    "\n",
    "dat = X.copy()\n",
    "\n",
    "dat[\"Cluster\"] = k_means.labels_\n",
    "\n",
    "datamart_melt = pd.melt(\n",
    "    dat.reset_index(),\n",
    "    id_vars=[\"Cluster\"],\n",
    "    value_vars=[\"Age\", \"Income\", \"SpendingScore\", \"Savings\"],\n",
    "    var_name=\"Feature\",\n",
    "    value_name=\"Value\",\n",
    ")\n",
    "\n",
    "plt.title(\"Snake Plot, K-Means, K=5\")\n",
    "sns.lineplot(x=\"Feature\", y=\"Value\", hue=\"Cluster\", data=datamart_melt)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "sBV5pMwzFab0"
   },
   "source": [
    "#### Relative Importance Plots"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 0
    },
    "colab_type": "code",
    "id": "A0-4fnimFepF",
    "outputId": "57fb8d3d-7701-44ba-9f41-0c24aa214c06"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Figure size 576x144 with 0 Axes>"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "text/plain": [
       "Text(0.5, 1.0, 'Relative importance of features')"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x265ecc96400>"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 576x144 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "cluster_avg = dat.groupby([\"Cluster\"]).mean()\n",
    "population_avg = dat.drop([\"Cluster\"], axis=1).mean()\n",
    "\n",
    "relative_imp = cluster_avg - population_avg\n",
    "\n",
    "plt.figure(figsize=(8, 2))\n",
    "plt.title(\"Relative importance of features\")\n",
    "sns.heatmap(data=relative_imp, annot=True, fmt=\".2f\", cmap=\"RdYlGn\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "ILKGocQvFxac"
   },
   "source": [
    "## 1.2 Answer:"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "qxtZ9JBoGCr8"
   },
   "source": [
    "### 1.\tDownload the customer dataset: jewelry_customers.csv."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "x-ap0nLlGVSM"
   },
   "source": [
    "I have downloaded the dataset and run the code. Please check the Jupyter notebook."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "i4VtDCMPGeYt"
   },
   "source": [
    "### 2.\tPerform a clustering analysis of the dataset.  a.\tTry different values of parameters (e.g., K for K-means), and describe what you think the best values are and why."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "BvIV8PttGmFk"
   },
   "source": [
    "I used k-means to do clustering.\n",
    "\n",
    "I first use Elbow method to find the best k (the number of clusters). According to the plots below, 5 is the best cluster number in this case. Here is the reason:\n",
    "\n",
    "In Silhouette score elbow for Kmeans Clustering, when k is 5, we got the highest Silhouette score, which means that, at this point, the cluster is farthest away from the closest neighbouring cluster. So, 5 is the best k number.\n",
    "\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 294
    },
    "colab_type": "code",
    "id": "c0Ljfy_xHkRV",
    "outputId": "ab127a03-798c-4ab7-9a9e-d29c214604b9"
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "model = KMeans(init=\"k-means++\", random_state=23)\n",
    "KElbowVisualizer(model, k=(2, 11), metric=\"silhouette\", timings=False).fit(X).poof()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "lcrv05TiHk3s"
   },
   "source": [
    "\n",
    "I use a for loop to try different k value (from 2 to 10). Based on InterclusterDistance plot, we can find that 5 is the best clustering number. There is no overlapping between each two circles. Besides the distance between each other looks good.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 392
    },
    "colab_type": "code",
    "id": "MNZShMgKHvu9",
    "outputId": "0f32cd52-8b63-4ce5-e666-2d454bbe6bc4"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "KMeans(algorithm='auto', copy_x=True, init='k-means++', max_iter=300,\n",
       "    n_clusters=5, n_init=10, n_jobs=None, precompute_distances='auto',\n",
       "    random_state=23, tol=0.0001, verbose=0)"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "text/plain": [
       "InterclusterDistance(ax=<matplotlib.axes._subplots.AxesSubplot object at 0x00000265EE117EF0>,\n",
       "           embedding='mds', legend=True, legend_loc='lower left',\n",
       "           legend_size=1.5, max_size=25000, min_size=400, model=None,\n",
       "           random_state=40, scoring='membership')"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "kmeans = KMeans(init=\"k-means++\", n_clusters=5, random_state=23)\n",
    "kmeans.fit(X)\n",
    "\n",
    "visualizer = InterclusterDistance(kmeans, random_state=40)\n",
    "visualizer.fit(X)  # Fit the training data to the visualizer\n",
    "visualizer.poof()  # Draw/show/poof the data"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "7sLdl6TXH-i1"
   },
   "source": [
    "### 3.\tDocument your findings. Describe and interpret the clusters."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "2fNwG6DGIzVv"
   },
   "source": [
    "I tried three ways to interpret the clusters, which are snake plot, relative importance measures and exemplars. The three methods show the same conclusion.\n",
    "\n",
    "Cluster 0: The customers’ age is highest. The income is the lowest. The savings is the highest and the spending score is relative low. These customers are the senior people whose income is low and savings are high.\n",
    "\n",
    "Cluster 1: The customers’ age is around 59. The income is in the middle. The savings are relative low and the spending score is relative high. These customers are the middle-aged people whose income is about average and love jewelry.\n",
    "\n",
    "Cluster 2: The customers’ age is relative low, which is around 34. The income is in relative high. The savings are relative high and the spending score is relative low. These customers are the people around 34, whose has good income and savings but do not spend too much on jewelry. \n",
    "\n",
    "Cluster 3: The customers’ age is lowest. The income is the highest. The savings is the lowest and the spending score is the highest. These customers are the young people whose income is very high willing to spend on jewelry. \n",
    "\n",
    "Cluster 4: The customers’ age, income and saving is relative high. The spending score is the lowest. These customers are the old people whose income is high but spend little on jewelry. \n",
    "\n",
    "Obviously, the best customer is cluster 3. These young people has the highest income and spend a lot on jewelry. The store can be more focus on these customers.\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "n8EQRt3TJKUK"
   },
   "source": [
    "**SnakePlot**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 297
    },
    "colab_type": "code",
    "id": "cN0xMnulI3L9",
    "outputId": "b8f4584e-1574-4a65-abfa-560a7e1b955d"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x265ee17a3c8>"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.lineplot(x=\"Feature\", y=\"Value\", hue=\"Cluster\", data=datamart_melt)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "ewYzE726JWPE"
   },
   "source": [
    "**Heatmap**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 283
    },
    "colab_type": "code",
    "id": "zZ-yp5cQI8e8",
    "outputId": "32bd0315-2b09-437b-d17b-dee4489d33cc"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x265ee207b00>"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.heatmap(data=relative_imp, annot=True, fmt=\".2f\", cmap=\"RdYlGn\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "Uc8ZsAn6JgpM"
   },
   "source": [
    "**Examplar**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 977
    },
    "colab_type": "code",
    "id": "806nUbgGJCHW",
    "outputId": "85680688-3124-41f7-db71-e6cfdfdef25a"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "Cluster 0:\n"
     ]
    },
    {
     "data": {
      "text/html": [
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       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "\n",
       "    .dataframe tbody tr th {\n",
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       "\n",
       "    .dataframe thead th {\n",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>320</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Age</th>\n",
       "      <td>87.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Income</th>\n",
       "      <td>12000.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>SpendingScore</th>\n",
       "      <td>0.342991</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Savings</th>\n",
       "      <td>16180.265252</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                        320\n",
       "Age               87.000000\n",
       "Income         12000.000000\n",
       "SpendingScore      0.342991\n",
       "Savings        16180.265252"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "Cluster 1:\n"
     ]
    },
    {
     "data": {
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       "      <th></th>\n",
       "      <th>278</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Age</th>\n",
       "      <td>59.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Income</th>\n",
       "      <td>56321.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>SpendingScore</th>\n",
       "      <td>0.806566</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Savings</th>\n",
       "      <td>6129.288986</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                        278\n",
       "Age               59.000000\n",
       "Income         56321.000000\n",
       "SpendingScore      0.806566\n",
       "Savings         6129.288986"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "Cluster 2:\n"
     ]
    },
    {
     "data": {
      "text/html": [
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       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "      <th>339</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Age</th>\n",
       "      <td>34.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Income</th>\n",
       "      <td>89598.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>SpendingScore</th>\n",
       "      <td>0.340152</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Savings</th>\n",
       "      <td>12832.768721</td>\n",
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      ],
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       "                        339\n",
       "Age               34.000000\n",
       "Income         89598.000000\n",
       "SpendingScore      0.340152\n",
       "Savings        12832.768721"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "Cluster 3:\n"
     ]
    },
    {
     "data": {
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       "      <th>360</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Age</th>\n",
       "      <td>28.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Income</th>\n",
       "      <td>117108.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>SpendingScore</th>\n",
       "      <td>0.857078</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Savings</th>\n",
       "      <td>4749.879237</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                         360\n",
       "Age                28.000000\n",
       "Income         117108.000000\n",
       "SpendingScore       0.857078\n",
       "Savings          4749.879237"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "Cluster 4:\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
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       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
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       "      <th></th>\n",
       "      <th>368</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Age</th>\n",
       "      <td>84.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Income</th>\n",
       "      <td>110582.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>SpendingScore</th>\n",
       "      <td>0.079049</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Savings</th>\n",
       "      <td>14937.995170</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                         368\n",
       "Age                84.000000\n",
       "Income         110582.000000\n",
       "SpendingScore       0.079049\n",
       "Savings         14937.995170"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "for i, label in enumerate(set(k_means.labels_)):\n",
    "    X_tmp = df[k_means.labels_ == label].copy()\n",
    "\n",
    "    exemplar_idx = distance.cdist([k_means.cluster_centers_[i]], X_tmp).argmin()\n",
    "    exemplar = pd.DataFrame(X_tmp.iloc[exemplar_idx])\n",
    "\n",
    "    print(\"\\nCluster {}:\".format(label))\n",
    "    exemplar"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "YeBdOASkJGzl"
   },
   "source": [
    "### 4.\tHow good are the results?"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "HvU3MEvsJovN"
   },
   "source": [
    "I think the result is very good. First of all, the Silhouette score is over 0.8, which is very high. It means the clusters has good distance between each other. The snake plot shows that lines are not overlapping and have different combination of low, medium and high value on the features. The InterclusterDistance plot clearly shows 5 area without overlapping. The exemplars give us a sample of each cluster, which is easy to understand. Overall, the result is satisfied."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "c1eou23rJq20"
   },
   "source": [
    "# 2.\tCATCHING RECIDIVISTS BEFORE THEY STRIKE"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "dHVTMlwbrHEQ"
   },
   "source": [
    "A recidivist is a criminal that was released from prison, but commits another crime. You are a warden at a maximum-security prison in Kingston, and you want to determine which prisoners will likely become recidivists. Luckily, you have a Queen’s degree, so you are going to take a data-driven approach. You have collected some historical training data that include some basic metadata, and whether the prisoner ended up becoming a recidivist or not.\n",
    "Given the training data below, use the ID3 algorithm and entropy-based information gain to construct a decision tree by hand to predict which prisoners will become recidivists. Show all the steps. Use the resulting decision tree to predict the class of the following prisoner: Good Behavior = false, Age < 30 = false, Drug dependent = true.\n",
    "\n",
    "| id | Good Behavior | Age < 30 | Drug Dependent | Recidivist |\n",
    "|----|---------------|----------|----------------|------------|\n",
    "| 1  | False         | True     | False          | True       |\n",
    "| 2  | False         | False    | False          | False      |\n",
    "| 3  | False         | True     | False          | True       |\n",
    "| 4  | True          | False    | False          | False      |\n",
    "| 5  | True          | False    | True           | True       |\n",
    "| 6  | True          | False    | False          | false      |"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "pZX33p3trSUY"
   },
   "source": [
    "## 2.1 Answer:"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "PE_fVoLSJvUt"
   },
   "source": [
    "* Calculate information gain\n",
    "*\tE(Recidivist)=E(3, 3)=E(0.5, 0.5)= -0.5 log2(⁡0.5)-0.5 log2(⁡0.5)=0.97\n",
    "* E(Recidivist, GoodBehavior)=P(False)E(2,1)+P(True)E(1,2)=0.5*0.92+0.5*0.92=0.92\n",
    "* Gain(Recidivist, GoodBehavior)= E(Recidivist)- E(Recidivist, GoodBehavior)=0.97-0.92=0.05\n",
    "* Gain(Recidivist, age)= 0.97-2/6*0-4/6*0.81=0.43\n",
    "* Gain(Recidivist, DrugDependent)=0.97-1/6*0-5/6*0.97=0.16\n",
    "* Because the Gain(Recidivist, age) is the highest, we first split data from this one.\n",
    "* Since the prisoner we want to predict is older than 30, so we filter the data and get the following:\n",
    "\n",
    "  \n",
    "| id | Good Behavior | Age < 30 | Drug Dependent | Recidivist |\n",
    "|----|---------------|----------|----------------|------------|\n",
    "| 2  | False         | False    | False          | False      |\n",
    "| 4  | True          | False    | False          | False      |\n",
    "| 5  | True          | False    | True           | True       |\n",
    "| 6  | True          | False    | False          | false      |\n",
    "\n",
    "* Gain’(Recidivist, GoodBehavior)=0.81-1/4*0-3/4*0.92=0.12\n",
    "* Gain’(Recidivist, DrugDependent)=0.81-1/4*0-3/4*0=0.81\n",
    "* Because the Gain(Recidivist, DrugDependent) is the highest in this level, we split data from this one.\n",
    "* Since the prisoner we want to predict is has drug dependent, so we filter the data and get the following:\n",
    "\n",
    "| id | Good Behavior | Age < 30 | Drug Dependent | Recidivist |\n",
    "|----|---------------|----------|----------------|------------|\n",
    "| 5  | True          | False    | True           | True       |\n",
    "\n",
    "* So we can stop here. I will predict Recidivist as True."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "uWymD5u7J0rd"
   },
   "source": [
    "# 3.\tMORE CLASSIFICATION MEASURES\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "Wspla4-xrdr_"
   },
   "source": [
    "In class, we talked about several classification measures, such as accuracy, precision, recall, AUC, etc. There are many other measures out there, each with its own pros and cons. \n",
    "Do a bit of research of your own to find at least two classification measures that we did not discuss in class. For each measure, describe what it is, how it is calculated, how it is different from accuracy, and in which scenarios it is best used. Show some example datasets/confusion matrices to illustrate your points."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "3MoMR89irY-H"
   },
   "source": [
    "## 3.1 Answer:"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "Ed8kYOezLcWt"
   },
   "source": [
    "1.\t\n",
    "\n",
    "* classification measure name: Diagnostic odds ratio (DOR)\n",
    "\n",
    "* The Diagnostic odds ratio =TP x TN/(FP x FN)\n",
    "DOR is utilized for estimating the discriminative ability of the test and also for comparing between two diagnostic tests. The cons is that if there is a zero in FP or FN. Then DOR cannot be applied in this case.\n",
    "* Here is an example:\n",
    "\n",
    "| Confusion matrix 1 |       |\n",
    "|--------------------|-------|\n",
    "| TP 60              | FN 20 |\n",
    "| FP 10              | TN 10 |\n",
    "\n",
    "\n",
    "| Confusion matrix 2  || \n",
    "|-----------|-----------|\n",
    "| TP        65\t|FN   15 |\n",
    "| FP        15\t|TN   5  |\n",
    "\n",
    "* Accuracy_1=(TP+TN)/(TP+TN+FP+FN)=70/100=0.7\n",
    "* DOR_1= TP x TN/(FP x FN)=60x10/(10*20)=3\n",
    "\n",
    "* Accuracy_2=(TP+TN)/(TP+TN+FP+FN)=70/100=0.7\n",
    "* DOR_2= TP x TN/(FP x FN)=65x5/(15*15)=1.44\n",
    "\n",
    "  \n",
    "\n",
    "* In this example, the accuracy is the same for both confusion matrices. But the DOR_1 is larger than DOR_2. Because the formula of DOR use TP x TN comparing to Accuracy using TN + TP. As we all know, the product of two real number is maximum when the numbers are equal give sum is constant, So, if two models have the same accuracy, the model with higher DOR should be a more balanced model to give the TP and TN. \n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "wvPVe7tRLhL8"
   },
   "source": [
    "2.\t\n",
    "* classification measure name: Youden’s index (YI)\n",
    "* Youden’s index=TPR+TNR-1=TP/(TP+FN)+TN/(TN+FP)-1\n",
    "YI evaluates the discriminative power of the test. The YI metric is ranged from zero when the test is poor to one which represents a perfect diagnostic test. It is also suitable with imbalanced data. One of the major disadvantages of this test is that it does not change concerning the differences between the sensitivity and specificity of the test.\n",
    "* Here is an example:\n",
    "\n",
    "| Confusion matrix 1 |       |\n",
    "|--------------------|-------|\n",
    "| TP 60              | FN 20 |\n",
    "| FP 10              | TN 10 |\n",
    "\n",
    "\n",
    "| Confusion matrix 2  || \n",
    "|-----------|-----------|\n",
    "| TP        65\t|FN   15 |\n",
    "| FP        15\t|TN   5  |\n",
    "\n",
    "\n",
    "* Accuracy_1=(TP+TN)/(TP+TN+FP+FN)=70/100=0.7\n",
    "* YI_1= TP/(TP+FN)+TN/(TN+FP)-1=60/80+10/20-1=0.75+0.5-1=0.25\n",
    "\n",
    "* Accuracy_2=(TP+TN)/(TP+TN+FP+FN)=70/100=0.7\n",
    "* YI_2= TP/(TP+FN)+TN/(TN+FP)-1=65/80+5/20-1=0.8125+0.25-1=0.0625\n",
    "\n",
    "* In this example, two models have the same accuracy. But if the accuracy is the same, the Yi score is quite different, the one with higher YI score has overall better balance of specificity and sensitivity.\n",
    "\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "MZKKfwZONXNq"
   },
   "source": [
    "# 4.\tTHE INTERN\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "ZFehBkPCrjTe"
   },
   "source": [
    "You are an in-demand, world-traveling, work-all-night consultant who specializes in designing supervised machine learning solutions for clients in a wide-range of industries. You have seen it all and you know what to do. To help you get more done in less time, you have hired an intern from Ivey, who, unfortunately, needs some handholding. Your intern does not understand when to use which classification measure. Your intern keeps getting it wrong. To help your intern learn from your experience, you have decided to look at some previous projects and describe which measure you used, and more importantly, why. \n",
    "For each project below, describe which measure(s) are best, and why. Also, give an example of a measure which would be horrible to use, and why. List any assumptions you are making, about the dataset, problem, or business priorities that were involved in the project.\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "xncl15Rrrl64"
   },
   "source": [
    "## 4.1 Answer:"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "wuQOC_N4N7Zh"
   },
   "source": [
    "#### a)\tThe fraud department at a bank wanted to predict which transactions were fraudulent. The training dataset had 100K credit card transactions, of which 97K are legit and 3K are fraud."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "YuJp58WhN_BD"
   },
   "source": [
    "1.\tI will use recall to measure in this case. \n",
    "2.\tAssumption: A fraud will cause a huge loss and the cost of following up potentially fraudulent.\n",
    "3.\tBecause the huge loss and not much on following up the case, so we do not want to miss the true positive, which means when the Fraud is actually true our model should predict it as true. Also this may cause more false positive. Since in my assumption that the cost of following up potentially fraudulent is not high, Recall is a good measurement to use.\n",
    "4.\tIt is horrible to use accuracy to measure in this case. Because the data is imbalance, the model will predict all the result as legit and get 97% accuracy. So, it is not wise to use accuracy in the case.\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "GxURfhJjlzuY"
   },
   "source": [
    "#### b)\tA hospital wanted to predict whether a MRI scan contained cancer."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "4_o3EQXYl5DH"
   },
   "source": [
    "1.\tI will use F1 score to measure in this case.\n",
    "2.\tAssumption: Most of people who have MRI scan do not have cancer. So, the data may not imbalance.\n",
    "3.\tIn this case, because we need to deal with the imbalance data, we want to balance both recall and precision. Since F1 score equals 2 * precision * recall / (precision + recall), F1 score is a good way to measure the model.\n",
    "4.\tIt is horrible to use accuracy to measure in this case. Because the data is imbalance, the model will predict all the result as no cancer and get high accuracy. So, it is not wise to use accuracy in the case.\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "PzkzRZ27l7Q6"
   },
   "source": [
    "#### c)\tAn IT team wanted to filter spam from email inboxes."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "fxWSn2q8l_M4"
   },
   "source": [
    "1.\tI will use precision to measure in this case.\n",
    "2.\tAssumption: We need to avoid predicting an important email as spam (Avoid FP)\n",
    "3.\tIn this case, if the model predict the spam as true, we want to have high confidence to make sure that it is actual spam. Precision equals to TP/ (TP + FP), which is what we want in this case.\n",
    "4.\tIt is horrible to use specificity to measure in this case. The model will only predict a mail as non-spam when it is very sure, which means it will cause more normal mail be treated as spam.  \n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "TxQ6kYzgmBdQ"
   },
   "source": [
    "#### d)\tA sports analytics department wants to predict which team will win the match."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "Lv-6uQfVFzsO"
   },
   "source": [
    "1. I will use ROC/AUC to measure in this case.\n",
    "2. Assumption: The department wants to know some of which matches they have high probability to win and some of which mataches they will probably lose.\n",
    "3. In this case, we will choose the model with highest AUC score. Then, we can check the prediction result to tell which part of prediction we have high confidence to believe the result.\n",
    "4. It is horrible to use specificity to measure in this case. The model will only predict a game as lose when it is very sure, which means that the accuracy to for predicting winning will be sacrifised."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "n7gbrMxNmEZg"
   },
   "source": [
    "#### e)\tA city government wanted to build a system to monitor Twitter to see if any local residents were tweeting about emergencies that needed quick response from the police department. They don’t trust Twitter that much; they only want to send police in true emergencies."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "mS9IMiOwhQ63"
   },
   "source": [
    "1.\tI will use precision to measure in this case.\n",
    "2.\tAssumption: We need to avoid wrong emergency alert (Avoid FP).\n",
    "3.\tIn this case, if the model predict the emergency as true, we want to have high confidence to make sure that it is actual emergency. Precision equals to TP/ (TP + FP), which is what we want in this case.\n",
    "4.\tIt is horrible to use specificity TN/(TN+FP) to measure in this case. The model will only predict non-emergency when it is very sure, which means it will cause more emergency alerts.  "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "SnNg_wghmHki"
   },
   "source": [
    "#### f)\t [Describe one more project, whereby the best measure is one that you have not yet listed in parts a-e above.]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "BTPQrIDdx53q"
   },
   "source": [
    "1. Project: A company is preparing an event, it has invited a lot of guests. Since they have the history data of previous events, they want to have a model to predict who will come on event day.\n",
    "2. I will use log loss to measure in this case.\n",
    "2. Assumption: They only care about how many people in total will come.\n",
    "3. In this case, since I conly care about the overal situation instead of individual, I want to use log loss to penalize models being very wrong.\n",
    "4. It is horriable to use precision to measure in this case. The model will make sure the precise of prediacting people who will come. But it will sacrifice the overall accuracy. So, it is better to use log loss in this case."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "-qJuBhuCmLWw"
   },
   "source": [
    "# 5.Uncle STEVE’S GROCERY STORE\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "VW3Tngchr2TA"
   },
   "source": [
    "Uncle Steve runs a small, local grocery store. Looking for some customer insights, he has hired you to do some data science. He has given you a few years’ worth of customer transactions, i.e., sets of items that customers have purchased. You have applied an association rules learning algorithm to the data, and the algorithm has generated a large set of association rules.  \n",
    "For each of the following scenarios, provide an example of one of the discovered association rules that satisfies the following conditions. (Just make up the rule, using your human experience and intuition!) Also, describe whether and why each rule would be considered subjectively interesting or uninteresting for Uncle Steve."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "koWJMRdKr39_"
   },
   "source": [
    "## 5.1 Answer:"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "VHzMFoFemPA5"
   },
   "source": [
    "#### a)\tA rule that has high support and high confidence."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "fhgpyPi2mbJx"
   },
   "source": [
    "{egg} → {milk} \n",
    "\n",
    "* These two items are both high frequency purchasing items. It is uninteresting for Uncle Steve because it is a common sense that people will often buy egg and milk together (High support). Besides, Uncle Steve will not be surprised that people will buy milk when they buy eggs (High confidence).\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "RBaR0lVsmdHQ"
   },
   "source": [
    "#### b)\tA rule that has reasonably high support but low confidence."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "fvHYHGGvmhWZ"
   },
   "source": [
    "{egg} → {bacon} \n",
    "\n",
    "* Most people will buy eggs when they shop at a grocery store. But, bacon is not as popular as eggs. \n",
    "\n",
    "* This rule has reasonably high support since they are both daily food which has a relative high purchasing frequency. However, the confidence is low because when people buy eggs, they do not always buy bacon.\n",
    "\n",
    "* I think this is interesting to Uncle Steve because he will realize that giving a discount to eggs will not influence the sales of bacon much.\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "JpM5b3iZmkDg"
   },
   "source": [
    "#### c)\tA rule that has low support and low confidence."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "sRj7-X70moli"
   },
   "source": [
    "{Christmas tree} → {Halloween pumpkin} \n",
    "\n",
    "* These two items are both low frequency purchasing items. People probably buy once a year. Furthermore, since Halloween is in October, there is very low possibility that people will buy Halloween pumpkin when people buy Christmas tree. So, this rule has low support and low confidence.\n",
    "\n",
    "* This rule is not interesting to Uncle Steve because it is a common sense and he must know this result.\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "YX7T04bYmryA"
   },
   "source": [
    "#### d)\tA rule that has low support and high confidence."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "UPOW6XS9mw9p"
   },
   "source": [
    "{outdoor grills} → {barbecue charcoal}\n",
    "\n",
    "* Since outdoor grills are very low frequency purchasing item, the support should be very low. But, when people by outdoor grills will always buy barbecue charcoal when they buy outdoor grill, which will give this rule a high confidence.\n",
    "\n",
    "* I think this is interesting to Uncle Steve. He will realize that giving discount to barbecue charcoal will not increase the sales of outdoor grills. Instead, he can give a bundle promotion of these two items since the confidence is high. This may help to increase the sale of outdoor grills."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "9fT1WYN0mzdg"
   },
   "source": [
    "# 6.\tASSOCIATION RULES, TWO WAYS\n",
    "\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "1WWS1laushxX"
   },
   "source": [
    "Consider the following table of customer transactions.\n",
    "\n",
    "| Customer ID | Transaction ID | Items Bought |\n",
    "|-------------|----------------|--------------|\n",
    "| 1           | 0001           | {a, d, e}    |\n",
    "| 1           | 0024           | {a, b, c, e} |\n",
    "| 2           | 0012           | {a, b, d, e} |\n",
    "| 2           | 0031           | {a, c, d, e} |\n",
    "| 3           | 0015           | {b, c, e}    |\n",
    "| 3           | 0022           | {b, d, e}    |\n",
    "| 4           | 0029           | {c, d}       |\n",
    "| 4           | 0040           | {a, b, c}    |\n",
    "| 5           | 0033           | {a, d, e}    |\n",
    "| 5           | 0038           | {a, b, e}    |"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "RoKXEsQUsjrH"
   },
   "source": [
    "## 6.1 Answer:"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "wfZK3HmlnRcW"
   },
   "source": [
    "#### a)\tCompute the support for itemsets {e}, {b,d}, and {b,d,e} by treating each transaction ID as a market basket (i.e., the normal way)."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "jO88RCg_nc0B"
   },
   "source": [
    "| Customer ID | Transaction ID | Items Bought | a | b | c | d | e |\n",
    "|-------------|----------------|--------------|---|---|---|---|---|\n",
    "| 1           | 1              | {a, d, e}    | 1 | 0 | 0 | 1 | 1 |\n",
    "| 1           | 24             | {a, b, c, e} | 1 | 1 | 1 | 0 | 1 |\n",
    "| 2           | 12             | {a, b, d, e} | 1 | 1 | 0 | 1 | 1 |\n",
    "| 2           | 31             | {a, c, d, e} | 1 | 0 | 1 | 1 | 1 |\n",
    "| 3           | 15             | {b, c, e}    | 0 | 1 | 1 | 0 | 1 |\n",
    "| 3           | 22             | {b, d, e}    | 0 | 1 | 0 | 1 | 1 |\n",
    "| 4           | 29             | {c, d}       | 0 | 0 | 1 | 1 | 0 |\n",
    "| 4           | 40             | {a, b, c}    | 1 | 1 | 1 | 0 | 0 |\n",
    "| 5           | 33             | {a, d, e}    | 1 | 0 | 0 | 1 | 1 |\n",
    "| 5           | 38             | {a, b, e}    | 1 | 1 | 0 | 0 | 1 |"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "S6euLmZBniwP"
   },
   "source": [
    "* S{e}=8/10=0.8\n",
    "* S{b,d}=2/10=0.2\n",
    "* S{b,d,e}=2/10=0.2\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "nRbWM2g6nlUX"
   },
   "source": [
    "#### b)\tUse the results in part (a) to compute the confidence for the association rules {b,d} → {e} and {e} → {b,d}. Is confidence a symmetric measure?"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "bjY21BbEnrCb"
   },
   "source": [
    "* C({b,d} → {e})=0.2/0.2=1\n",
    "* C({e} → {b,d})=0.2/0.8=0.25\n",
    "* Confidence is not a symmetric measure.\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "GOXblC_nnwUx"
   },
   "source": [
    "#### c)\tRepeat part (a) by treating each customer ID as a market basket. Each item should be treated as a binary feature (i.e., 1 if an item appears in at least one transaction bought by the customer, and 0 otherwise). "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "b4Rj5ZXPnzEg"
   },
   "source": [
    "| Customer ID | a | b | c | d | e |\n",
    "|-------------|---|---|---|---|---|\n",
    "| 1           | 1 | 1 | 1 | 1 | 1 |\n",
    "| 2           | 1 | 1 | 1 | 1 | 1 |\n",
    "| 3           | 0 | 1 | 1 | 1 | 1 |\n",
    "| 4           | 1 | 1 | 1 | 1 | 0 |\n",
    "| 5           | 1 | 1 | 0 | 1 | 1 |"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "QDsEQq8Vn5KZ"
   },
   "source": [
    "* S{e}=4/5=0.8\n",
    "* S{b,d}=5/5=1\n",
    "* S{b,d,e}=4/5=0.8\n",
    "\n",
    "* C({b,d} → {e})=0.8/1=0.8\n",
    "* C({e} → {b,d})=0.8/0.8=1\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "mGpOpHg8n9nA"
   },
   "source": [
    "# 7.\tVIVA LA VINO\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "FCjzX2SOsrFm"
   },
   "source": [
    "Some Smith faculty have started a wine club. At each meeting, members of the club perform blind taste tests of different wine varietals. Members indicate how much they enjoy each varietal, using an integer scale of 1 (worst) to 7 (best). After the most recent meeting, here are the ratings.\n",
    "\n",
    "|  |Zin    | Pinot Noir | Chard | Merlot | Cab | Pinot Gris |  \n",
    "|--------|------------|-------|--------|-----|------------|---|\n",
    "| Yuri   | 7          | 6     | 7      | 4   | 5          | 4 |\n",
    "| Steve  |            | 7     | 6      | 4   | 3          | 4 |\n",
    "| Gary   | 3          | 3     | 3      | 1   | 1          | 5 |\n",
    "| Qurat  | 2          | 2     | 1      | 3   | 7          | 4 |\n",
    "| Brigid | 5          | 6     | 7      | 2   | 3          | 3 |\n",
    "\n",
    "Unfortunately, the club ran out of Zin before Steve had a chance to try it. Luckily, the club has you, a data-driven, clever, and charming Queen’s student. Use your skills to predict what Steve would rate Zin. Use user-based collaborative filtering with cosine distance. To predict the rating, find the two nearest neighbors and take the weighted average of their scores of the item in question.\n",
    "Hints:\n",
    "\tRecall that the cosine distance is calculated as  〖dist〗_cos (A,B)=1-(∑_(i=0)^n▒〖A_i B_i 〗)/(√(∑_(i=0)^n▒A_i^2 ) √(∑_(i=0)^n▒B_i^2 ))\n",
    "\tWhen comparing two users, only include items that both users have rated.  For example, to compare Yuri and Steve, the calculation would ignore Zin (since Steve hasn’t rated it) and would be 1-(7*6+6*7+4*4+5*3+4*4)/(√(7^2+6^2+4^2+5^2+4^2 ) √(6^2+7^2+4^2+3^2+4^2 ))=0.021.\n",
    "\tTo find the weighted average of two ratings R1 and R2, which have a distances D1 and D2 respectively, use the formula: Weighted_Average = ((1-D1)*R1 + (1-D2)*R2) / (2-D1 - D2).\n",
    "\tRound the predicted rating to the nearest integer."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "p2N1rEz73K2O"
   },
   "source": [
    "## 7.1 Answer:"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "5ipm7B_D3TWR"
   },
   "source": [
    "Here is how I caculate this manually."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 0
    },
    "colab_type": "code",
    "id": "RVHiNvAhoRRc",
    "outputId": "1f5c7e51-350c-4b8f-ee3d-4c38036adb00"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "6"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# import a package to find the smallest number\n",
    "from heapq import nsmallest\n",
    "\n",
    "# create the table\n",
    "dict_df={\"Zin\":[7,np.nan,3,2,5],\n",
    "        \"Pinot\":[6,7,3,2,6],\n",
    "        \"Chard\":[7,6,3,1,7],\n",
    "        \"Merlot\":[4,4,1,3,2],\n",
    "        \"Cab\":[5,3,1,7,3],\n",
    "        \"Pinot Gris\":[4,4,5,4,3]}\n",
    "df_club=pd.DataFrame(dict_df)\n",
    "df_club.index=['Yuri',\"Steve\",\"Gary\",\"Qurat\",\"Brigid\"]\n",
    "\n",
    "# slice the table to remove the column Zin\n",
    "df_without_zin=df_club.iloc[:,1:]\n",
    "\n",
    "# define a function to caculate the distance\n",
    "def dist_cos(A,B):\n",
    "  '''Caculate the cosine distance'''\n",
    "  result=1-sum(A*B)/((sum(A**2))**0.5*(sum(B**2))**0.5)\n",
    "  return result\n",
    "\n",
    "# create a dictionary to save the distance value\n",
    "dict_dist={}\n",
    "for i in ['Yuri',\"Gary\",\"Qurat\",\"Brigid\"]:\n",
    "  dict_dist[i]=dist_cos(df_without_zin.loc[i],df_without_zin.loc['Steve'])\n",
    "\n",
    "# find the dictionary key of the 2 smallest value\n",
    "two_smallest_distance=nsmallest(2,dict_dist,key=dict_dist.get)\n",
    "\n",
    "# caculate the rating\n",
    "rating=int(round(((1-dict_dist[two_smallest_distance[0]])*df_club.loc[two_smallest_distance[0],'Zin']+(1-dict_dist[two_smallest_distance[1]])*df_club.loc[two_smallest_distance[1],'Zin'])/(2-dict_dist[two_smallest_distance[0]]-dict_dist[two_smallest_distance[1]])))\n",
    "rating"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "nybiwo7e498b"
   },
   "source": [
    "So, my prediction is 6."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "HUIjHIxToIbY"
   },
   "source": [
    "# 8.\tYUM, ORANGE JUICE!\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "8pdmb0hqsxZB"
   },
   "source": [
    "[For this question, please answer the parts below in English, and also turn in your code. Your code should be submitted as a notebook – either a Python Jupyter Notebook (.ipynb) or an R Markdown file (.Rmd), along with a PDF rendering of the notebook.]\n",
    "One cup of fresh orange juice has 124 mg of vitamin C, which is 200% of the recommended daily intake of vitamin C for an adult. With this as (completely unrelated) motivation, build a model to predict whether a grocery store customer will Purchase Citrus Hill (CH) or Minute Maid (MM) orange juice.\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "j3mMl1upoqyu"
   },
   "source": [
    "## 8.1 Coding part"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "40M-E1EyvRnN"
   },
   "source": [
    "### Load data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 0
    },
    "colab_type": "code",
    "id": "-4IFPH1DovGH",
    "outputId": "a088c61f-d799-4233-e243-f6da1e9cdc99"
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Unnamed: 0</th>\n",
       "      <th>Purchase</th>\n",
       "      <th>WeekofPurchase</th>\n",
       "      <th>StoreID</th>\n",
       "      <th>PriceCH</th>\n",
       "      <th>PriceMM</th>\n",
       "      <th>DiscCH</th>\n",
       "      <th>DiscMM</th>\n",
       "      <th>SpecialCH</th>\n",
       "      <th>SpecialMM</th>\n",
       "      <th>LoyalCH</th>\n",
       "      <th>SalePriceMM</th>\n",
       "      <th>SalePriceCH</th>\n",
       "      <th>PriceDiff</th>\n",
       "      <th>Store7</th>\n",
       "      <th>PctDiscMM</th>\n",
       "      <th>PctDiscCH</th>\n",
       "      <th>ListPriceDiff</th>\n",
       "      <th>STORE</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>CH</td>\n",
       "      <td>237</td>\n",
       "      <td>1</td>\n",
       "      <td>1.75</td>\n",
       "      <td>1.99</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.500000</td>\n",
       "      <td>1.99</td>\n",
       "      <td>1.75</td>\n",
       "      <td>0.24</td>\n",
       "      <td>No</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.24</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>CH</td>\n",
       "      <td>239</td>\n",
       "      <td>1</td>\n",
       "      <td>1.75</td>\n",
       "      <td>1.99</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.3</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0.600000</td>\n",
       "      <td>1.69</td>\n",
       "      <td>1.75</td>\n",
       "      <td>-0.06</td>\n",
       "      <td>No</td>\n",
       "      <td>0.150754</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.24</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "      <td>CH</td>\n",
       "      <td>245</td>\n",
       "      <td>1</td>\n",
       "      <td>1.86</td>\n",
       "      <td>2.09</td>\n",
       "      <td>0.17</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.680000</td>\n",
       "      <td>2.09</td>\n",
       "      <td>1.69</td>\n",
       "      <td>0.40</td>\n",
       "      <td>No</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.091398</td>\n",
       "      <td>0.23</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4</td>\n",
       "      <td>MM</td>\n",
       "      <td>227</td>\n",
       "      <td>1</td>\n",
       "      <td>1.69</td>\n",
       "      <td>1.69</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.400000</td>\n",
       "      <td>1.69</td>\n",
       "      <td>1.69</td>\n",
       "      <td>0.00</td>\n",
       "      <td>No</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.00</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5</td>\n",
       "      <td>CH</td>\n",
       "      <td>228</td>\n",
       "      <td>7</td>\n",
       "      <td>1.69</td>\n",
       "      <td>1.69</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.956535</td>\n",
       "      <td>1.69</td>\n",
       "      <td>1.69</td>\n",
       "      <td>0.00</td>\n",
       "      <td>Yes</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Unnamed: 0 Purchase  WeekofPurchase  StoreID  PriceCH  PriceMM  DiscCH  \\\n",
       "0           1       CH             237        1     1.75     1.99    0.00   \n",
       "1           2       CH             239        1     1.75     1.99    0.00   \n",
       "2           3       CH             245        1     1.86     2.09    0.17   \n",
       "3           4       MM             227        1     1.69     1.69    0.00   \n",
       "4           5       CH             228        7     1.69     1.69    0.00   \n",
       "\n",
       "   DiscMM  SpecialCH  SpecialMM   LoyalCH  SalePriceMM  SalePriceCH  \\\n",
       "0     0.0          0          0  0.500000         1.99         1.75   \n",
       "1     0.3          0          1  0.600000         1.69         1.75   \n",
       "2     0.0          0          0  0.680000         2.09         1.69   \n",
       "3     0.0          0          0  0.400000         1.69         1.69   \n",
       "4     0.0          0          0  0.956535         1.69         1.69   \n",
       "\n",
       "   PriceDiff Store7  PctDiscMM  PctDiscCH  ListPriceDiff  STORE  \n",
       "0       0.24     No   0.000000   0.000000           0.24      1  \n",
       "1      -0.06     No   0.150754   0.000000           0.24      1  \n",
       "2       0.40     No   0.000000   0.091398           0.23      1  \n",
       "3       0.00     No   0.000000   0.000000           0.00      1  \n",
       "4       0.00    Yes   0.000000   0.000000           0.00      0  "
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 1070 entries, 0 to 1069\n",
      "Data columns (total 19 columns):\n",
      "Unnamed: 0        1070 non-null int64\n",
      "Purchase          1070 non-null object\n",
      "WeekofPurchase    1070 non-null int64\n",
      "StoreID           1070 non-null int64\n",
      "PriceCH           1070 non-null float64\n",
      "PriceMM           1070 non-null float64\n",
      "DiscCH            1070 non-null float64\n",
      "DiscMM            1070 non-null float64\n",
      "SpecialCH         1070 non-null int64\n",
      "SpecialMM         1070 non-null int64\n",
      "LoyalCH           1070 non-null float64\n",
      "SalePriceMM       1070 non-null float64\n",
      "SalePriceCH       1070 non-null float64\n",
      "PriceDiff         1070 non-null float64\n",
      "Store7            1070 non-null object\n",
      "PctDiscMM         1070 non-null float64\n",
      "PctDiscCH         1070 non-null float64\n",
      "ListPriceDiff     1070 non-null float64\n",
      "STORE             1070 non-null int64\n",
      "dtypes: float64(11), int64(6), object(2)\n",
      "memory usage: 158.9+ KB\n"
     ]
    }
   ],
   "source": [
    "df8 = pd.read_csv(\"OJ.CSV\")\n",
    "df8.head()\n",
    "df8.info()\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "PTCkEB8SVVlW"
   },
   "source": [
    "### preprocess the data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 467
    },
    "colab_type": "code",
    "id": "uQDXC4YqIuf-",
    "outputId": "57978c8b-55c0-4adc-c45a-60600a46f4de"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 1070 entries, 0 to 1069\n",
      "Data columns (total 20 columns):\n",
      "Unnamed: 0        1070 non-null int64\n",
      "Purchase          1070 non-null object\n",
      "WeekofPurchase    1070 non-null object\n",
      "StoreID           1070 non-null object\n",
      "PriceCH           1070 non-null float64\n",
      "PriceMM           1070 non-null float64\n",
      "DiscCH            1070 non-null float64\n",
      "DiscMM            1070 non-null float64\n",
      "SpecialCH         1070 non-null int64\n",
      "SpecialMM         1070 non-null int64\n",
      "LoyalCH           1070 non-null float64\n",
      "SalePriceMM       1070 non-null float64\n",
      "SalePriceCH       1070 non-null float64\n",
      "PriceDiff         1070 non-null float64\n",
      "Store7            1070 non-null object\n",
      "PctDiscMM         1070 non-null float64\n",
      "PctDiscCH         1070 non-null float64\n",
      "ListPriceDiff     1070 non-null float64\n",
      "STORE             1070 non-null object\n",
      "Store7_dummy      1070 non-null int32\n",
      "dtypes: float64(11), int32(1), int64(3), object(5)\n",
      "memory usage: 163.1+ KB\n"
     ]
    }
   ],
   "source": [
    "df8.WeekofPurchase=df8.WeekofPurchase.apply(str)\n",
    "df8.StoreID=df8.StoreID.apply(str)\n",
    "df8.STORE=df8.STORE.apply(str)\n",
    "df8['Store7_dummy']=np.where(df8.Store7=='Yes',1,0)\n",
    "df8.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 576
    },
    "colab_type": "code",
    "id": "ZEvs0vmmUUZu",
    "outputId": "32e3b78c-88d2-4ad0-f369-7b53e1918d27"
   },
   "outputs": [
    {
     "data": {
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th>Pearson Correlation Coefficient</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Col1</th>\n",
       "      <th>Col2</th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th rowspan=\"2\" valign=\"top\">PriceCH</th>\n",
       "      <th>PriceMM</th>\n",
       "      <td>0.616402</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>SalePriceCH</th>\n",
       "      <td>0.586716</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"2\" valign=\"top\">PriceMM</th>\n",
       "      <th>SalePriceMM</th>\n",
       "      <td>0.532859</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ListPriceDiff</th>\n",
       "      <td>0.665187</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"3\" valign=\"top\">DiscCH</th>\n",
       "      <th>SpecialCH</th>\n",
       "      <td>0.534775</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>SalePriceCH</th>\n",
       "      <td>-0.711274</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>PctDiscCH</th>\n",
       "      <td>0.999022</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"3\" valign=\"top\">DiscMM</th>\n",
       "      <th>SalePriceMM</th>\n",
       "      <td>-0.846868</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>PriceDiff</th>\n",
       "      <td>-0.823908</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>PctDiscMM</th>\n",
       "      <td>0.998793</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"2\" valign=\"top\">SpecialCH</th>\n",
       "      <th>SalePriceCH</th>\n",
       "      <td>-0.508586</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>PctDiscCH</th>\n",
       "      <td>0.549015</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"2\" valign=\"top\">SalePriceMM</th>\n",
       "      <th>PriceDiff</th>\n",
       "      <td>0.852798</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>PctDiscMM</th>\n",
       "      <td>-0.856749</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>SalePriceCH</th>\n",
       "      <th>PctDiscCH</th>\n",
       "      <td>-0.722776</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>PriceDiff</th>\n",
       "      <th>PctDiscMM</th>\n",
       "      <td>-0.828097</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                           Pearson Correlation Coefficient\n",
       "Col1        Col2                                          \n",
       "PriceCH     PriceMM                               0.616402\n",
       "            SalePriceCH                           0.586716\n",
       "PriceMM     SalePriceMM                           0.532859\n",
       "            ListPriceDiff                         0.665187\n",
       "DiscCH      SpecialCH                             0.534775\n",
       "            SalePriceCH                          -0.711274\n",
       "            PctDiscCH                             0.999022\n",
       "DiscMM      SalePriceMM                          -0.846868\n",
       "            PriceDiff                            -0.823908\n",
       "            PctDiscMM                             0.998793\n",
       "SpecialCH   SalePriceCH                          -0.508586\n",
       "            PctDiscCH                             0.549015\n",
       "SalePriceMM PriceDiff                             0.852798\n",
       "            PctDiscMM                            -0.856749\n",
       "SalePriceCH PctDiscCH                            -0.722776\n",
       "PriceDiff   PctDiscMM                            -0.828097"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "corr=df8.corr()\n",
    "corr_triu = corr.where(~np.tril(np.ones(corr.shape)).astype(np.bool))\n",
    "corr_triu = corr_triu.stack()\n",
    "corr_triu.name = 'Pearson Correlation Coefficient'\n",
    "corr_triu.index.names = ['Col1', 'Col2']\n",
    "corr_triu[(corr_triu > 0.5) | (corr_triu < -0.5) ].to_frame()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "ffZZaQOvWESB"
   },
   "source": [
    "Because the correlation between DiscCH and PctDiscCH and the correlation between DiscMM and PctDiscMM are extremely high (almost 1), so I deceide to drop PctDiscMM and PctDiscCH. Also, I will drop PriceDiff, SalePriceCH and SalePriceMM, since the correlation coefficient is high."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "GPWI6oNhWpfi"
   },
   "outputs": [],
   "source": [
    "features=['WeekofPurchase','StoreID','PriceCH','PriceMM','DiscCH','DiscMM','SpecialCH','SpecialMM','LoyalCH', 'Store7_dummy','ListPriceDiff']\n",
    "X=df8[features]\n",
    "df8['Purchase_dummy']=np.where(df8['Purchase']=='CH',1,0)\n",
    "y=df8['Purchase_dummy']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 173
    },
    "colab_type": "code",
    "id": "D3AEAEm0pBU2",
    "outputId": "fb2444f7-08b3-426a-fd62-918b1bf0c2e0"
   },
   "outputs": [
    {
     "data": {
      "text/html": [
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th>Pearson Correlation Coefficient</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Col1</th>\n",
       "      <th>Col2</th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>PriceCH</th>\n",
       "      <th>PriceMM</th>\n",
       "      <td>0.616402</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>PriceMM</th>\n",
       "      <th>ListPriceDiff</th>\n",
       "      <td>0.665187</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>DiscCH</th>\n",
       "      <th>SpecialCH</th>\n",
       "      <td>0.534775</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                       Pearson Correlation Coefficient\n",
       "Col1    Col2                                          \n",
       "PriceCH PriceMM                               0.616402\n",
       "PriceMM ListPriceDiff                         0.665187\n",
       "DiscCH  SpecialCH                             0.534775"
      ]
     },
     "execution_count": 37,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "corr=X.corr()\n",
    "corr_triu = corr.where(~np.tril(np.ones(corr.shape)).astype(np.bool))\n",
    "corr_triu = corr_triu.stack()\n",
    "corr_triu.name = 'Pearson Correlation Coefficient'\n",
    "corr_triu.index.names = ['Col1', 'Col2']\n",
    "corr_triu[(corr_triu > 0.5) | (corr_triu < -0.5) ].to_frame()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 420
    },
    "colab_type": "code",
    "id": "yzQlHpjSUXy7",
    "outputId": "36923e3e-8a3d-4437-9534-c792911494b3"
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\michael.wang\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\ipykernel_launcher.py:4: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy\n",
      "  after removing the cwd from sys.path.\n",
      "C:\\Users\\michael.wang\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\pandas\\core\\indexing.py:543: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy\n",
      "  self.obj[item] = s\n"
     ]
    },
    {
     "data": {
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       "\n",
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       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>WeekofPurchase</th>\n",
       "      <th>StoreID</th>\n",
       "      <th>PriceCH</th>\n",
       "      <th>PriceMM</th>\n",
       "      <th>DiscCH</th>\n",
       "      <th>DiscMM</th>\n",
       "      <th>SpecialCH</th>\n",
       "      <th>SpecialMM</th>\n",
       "      <th>LoyalCH</th>\n",
       "      <th>Store7_dummy</th>\n",
       "      <th>ListPriceDiff</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>237</td>\n",
       "      <td>1</td>\n",
       "      <td>-1.152062</td>\n",
       "      <td>-0.710313</td>\n",
       "      <td>-0.441664</td>\n",
       "      <td>-0.577188</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>-0.213788</td>\n",
       "      <td>0</td>\n",
       "      <td>0.204766</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>239</td>\n",
       "      <td>1</td>\n",
       "      <td>-1.152062</td>\n",
       "      <td>-0.710313</td>\n",
       "      <td>-0.441664</td>\n",
       "      <td>0.826428</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0.111205</td>\n",
       "      <td>0</td>\n",
       "      <td>0.204766</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>245</td>\n",
       "      <td>1</td>\n",
       "      <td>-0.072806</td>\n",
       "      <td>0.034162</td>\n",
       "      <td>1.006140</td>\n",
       "      <td>-0.577188</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.371200</td>\n",
       "      <td>0</td>\n",
       "      <td>0.111730</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>227</td>\n",
       "      <td>1</td>\n",
       "      <td>-1.740747</td>\n",
       "      <td>-2.943741</td>\n",
       "      <td>-0.441664</td>\n",
       "      <td>-0.577188</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>-0.538781</td>\n",
       "      <td>0</td>\n",
       "      <td>-2.028100</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>228</td>\n",
       "      <td>7</td>\n",
       "      <td>-1.740747</td>\n",
       "      <td>-2.943741</td>\n",
       "      <td>-0.441664</td>\n",
       "      <td>-0.577188</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1.269920</td>\n",
       "      <td>1</td>\n",
       "      <td>-2.028100</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  WeekofPurchase StoreID   PriceCH   PriceMM    DiscCH    DiscMM  SpecialCH  \\\n",
       "0            237       1 -1.152062 -0.710313 -0.441664 -0.577188          0   \n",
       "1            239       1 -1.152062 -0.710313 -0.441664  0.826428          0   \n",
       "2            245       1 -0.072806  0.034162  1.006140 -0.577188          0   \n",
       "3            227       1 -1.740747 -2.943741 -0.441664 -0.577188          0   \n",
       "4            228       7 -1.740747 -2.943741 -0.441664 -0.577188          0   \n",
       "\n",
       "   SpecialMM   LoyalCH  Store7_dummy  ListPriceDiff  \n",
       "0          0 -0.213788             0       0.204766  \n",
       "1          1  0.111205             0       0.204766  \n",
       "2          0  0.371200             0       0.111730  \n",
       "3          0 -0.538781             0      -2.028100  \n",
       "4          0  1.269920             1      -2.028100  "
      ]
     },
     "execution_count": 38,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "features_to_be_scaled = ['PriceCH', 'PriceMM', 'DiscCH', 'DiscMM','LoyalCH', 'ListPriceDiff']\n",
    "# Separating out the features\n",
    "\n",
    "X[features_to_be_scaled] = StandardScaler().fit_transform(X[features_to_be_scaled])\n",
    "X.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "rNsxVJP50kSJ"
   },
   "source": [
    "### Split data into test and train"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 35
    },
    "colab_type": "code",
    "id": "nMXXBdTWYUt2",
    "outputId": "0f0512cc-7f93-48f6-911e-5ea0880a2797"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.6102803738317757"
      ]
     },
     "execution_count": 39,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sum(y)/len(y) # check the ratio of True in target"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "sr3zS8Km0qjh"
   },
   "outputs": [],
   "source": [
    "from sklearn.model_selection import train_test_split\n",
    "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=1120)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "umOTMtAECu3G"
   },
   "source": [
    "### Define a function to measure the score"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "_TWnUYTtC39i"
   },
   "outputs": [],
   "source": [
    "from sklearn.metrics import accuracy_score, cohen_kappa_score, f1_score, log_loss,roc_auc_score,precision_score,recall_score\n",
    "def score_list(predicion,y_test=y_test):\n",
    "  '''this function will print Accurarcy, Kappa, F1 score, Log Loss and AUC'''\n",
    "  print('\\n ****************************')\n",
    "  print(\"Accuracy = {:.2f}\".format(accuracy_score(y_test, predicion)))\n",
    "  print(\"F1 Score = {:.2f}\".format(f1_score(y_test, predicion)))\n",
    "  print(\"AUC = {:.2f}\".format(roc_auc_score(y_test, predicion)))\n",
    "  print(\"Precision = {:.2f}\".format(precision_score(y_test, predicion)))\n",
    "  print(\"Recall = {:.2f}\".format(recall_score(y_test, predicion)))\n",
    "  print('**************************** \\n')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "Opf-10cg1sgZ"
   },
   "source": [
    "### Five models"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "2CjIhL0o1yXP"
   },
   "source": [
    "#### Decision Trees"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 557
    },
    "colab_type": "code",
    "id": "hJqm5KOq3eWn",
    "outputId": "e8515916-f605-4273-c1a4-04e6efbc6602"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "GridSearchCV(cv=5, error_score='raise-deprecating',\n",
       "       estimator=DecisionTreeClassifier(class_weight=None, criterion='gini', max_depth=None,\n",
       "            max_features=None, max_leaf_nodes=None,\n",
       "            min_impurity_decrease=0.0, min_impurity_split=None,\n",
       "            min_samples_leaf=1, min_samples_split=2,\n",
       "            min_weight_fraction_leaf=0.0, presort=True, random_state=42,\n",
       "            splitter='best'),\n",
       "       fit_params=None, iid='warn', n_jobs=None,\n",
       "       param_grid={'criterion': ('gini', 'entropy'), 'max_depth': [2, 4, 6, 8, 10], 'min_samples_split': [2, 10, 50], 'min_samples_leaf': [1, 5, 10], 'max_features': [None, 'auto'], 'max_leaf_nodes': [None, 5, 10, 50], 'min_impurity_decrease': [0, 0.1, 0.2]},\n",
       "       pre_dispatch='2*n_jobs', refit=True, return_train_score=True,\n",
       "       scoring='roc_auc', verbose=0)"
      ]
     },
     "execution_count": 42,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      " ****************************\n",
      "Accuracy = 0.76\n",
      "F1 Score = 0.80\n",
      "AUC = 0.75\n",
      "Precision = 0.79\n",
      "Recall = 0.82\n",
      "**************************** \n",
      "\n"
     ]
    }
   ],
   "source": [
    "from sklearn.model_selection import GridSearchCV\n",
    "from sklearn.tree import DecisionTreeClassifier\n",
    "\n",
    "# build model using grid search with 5Fold Cross validation to train and test\n",
    "treeclf = DecisionTreeClassifier(splitter='best', presort=True, class_weight=None, random_state=42)\n",
    "parameters = {'criterion':('gini', 'entropy'), 'max_depth':[2, 4, 6, 8, 10], 'min_samples_split':[2, 10, 50], 'min_samples_leaf':[1, 5, 10],\n",
    "             'max_features':[None, 'auto'], 'max_leaf_nodes':[None, 5, 10, 50], 'min_impurity_decrease':[0, 0.1, 0.2]}\n",
    "cv_clf = GridSearchCV(treeclf, parameters, scoring='roc_auc', cv=5, return_train_score=True)\n",
    "cv_clf.fit(X_train, y_train)\n",
    "\n",
    "# predict on test data\n",
    "y_pred_dt_cv = cv_clf.predict(X_test)\n",
    "\n",
    "# print the score with the function defined myself\n",
    "score_list(y_pred_dt_cv)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "s61gsWx25g8Y"
   },
   "source": [
    "#### Naive Bayes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 0
    },
    "colab_type": "code",
    "id": "z-Pv0oeL5mS3",
    "outputId": "ff847af9-6763-4598-9b90-ba34b923ce50"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "GridSearchCV(cv=5, error_score='raise-deprecating',\n",
       "       estimator=GaussianNB(priors=None, var_smoothing=1e-09),\n",
       "       fit_params=None, iid='warn', n_jobs=None,\n",
       "       param_grid={'var_smoothing': [1e-07, 1e-08, 1e-09, 1e-10, 1e-11]},\n",
       "       pre_dispatch='2*n_jobs', refit=True, return_train_score=True,\n",
       "       scoring='roc_auc', verbose=0)"
      ]
     },
     "execution_count": 43,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      " ****************************\n",
      "Accuracy = 0.74\n",
      "F1 Score = 0.78\n",
      "AUC = 0.74\n",
      "Precision = 0.80\n",
      "Recall = 0.75\n",
      "**************************** \n",
      "\n"
     ]
    }
   ],
   "source": [
    "from sklearn.naive_bayes import GaussianNB\n",
    "\n",
    "# build model using grid search with 5Fold Cross validation to train and test\n",
    "gnb = GaussianNB()\n",
    "parameters = {\"var_smoothing\":[1e-7,1e-8,1e-9,1e-10,1e-11]}\n",
    "cv_gnb = GridSearchCV(gnb, parameters, scoring='roc_auc', cv=5, return_train_score=True)\n",
    "cv_gnb.fit(X_train, y_train)\n",
    "\n",
    "# predict on test data\n",
    "y_pred_gnb_cv = cv_gnb.predict(X_test)\n",
    "\n",
    "# print the score with the function defined myself\n",
    "score_list(y_pred_gnb_cv)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "GYPN1day5vAQ"
   },
   "source": [
    "#### KNN"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 0
    },
    "colab_type": "code",
    "id": "bCg4Wa5T6bzv",
    "outputId": "d6a8389d-e366-4c3c-b1d7-167dde2570ef"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "GridSearchCV(cv=5, error_score='raise-deprecating',\n",
       "       estimator=KNeighborsClassifier(algorithm='auto', leaf_size=30, metric='minkowski',\n",
       "           metric_params=None, n_jobs=None, n_neighbors=5, p=2,\n",
       "           weights='uniform'),\n",
       "       fit_params=None, iid='warn', n_jobs=None,\n",
       "       param_grid={'n_neighbors': range(2, 11)}, pre_dispatch='2*n_jobs',\n",
       "       refit=True, return_train_score=True, scoring='roc_auc', verbose=0)"
      ]
     },
     "execution_count": 44,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      " ****************************\n",
      "Accuracy = 0.76\n",
      "F1 Score = 0.80\n",
      "AUC = 0.74\n",
      "Precision = 0.77\n",
      "Recall = 0.84\n",
      "**************************** \n",
      "\n"
     ]
    }
   ],
   "source": [
    "from sklearn.neighbors import KNeighborsClassifier\n",
    "\n",
    "\n",
    "# build model using grid search with 5Fold Cross validation to train and test\n",
    "knn_clf = KNeighborsClassifier()\n",
    "parameters = {\"n_neighbors\":range(2,11)}\n",
    "cv_knn_clf = GridSearchCV(knn_clf, parameters, scoring='roc_auc', cv=5, return_train_score=True)\n",
    "cv_knn_clf.fit(X_train, y_train)\n",
    "\n",
    "# predict on test data\n",
    "y_pred_knn_clf_cv = cv_knn_clf.predict(X_test)\n",
    "\n",
    "# print the score with the function defined myself\n",
    "score_list(y_pred_knn_clf_cv)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "Yvm8tdZm6fZH"
   },
   "source": [
    "#### NN"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 0
    },
    "colab_type": "code",
    "id": "Mz3T550W6zJP",
    "outputId": "82f81033-6176-4da1-e236-9ac2df129558"
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\michael.wang\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\model_selection\\_search.py:841: DeprecationWarning: The default of the `iid` parameter will change from True to False in version 0.22 and will be removed in 0.24. This will change numeric results when test-set sizes are unequal.\n",
      "  DeprecationWarning)\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "GridSearchCV(cv=5, error_score='raise-deprecating',\n",
       "       estimator=MLPClassifier(activation='relu', alpha=0.0001, batch_size='auto', beta_1=0.9,\n",
       "       beta_2=0.999, early_stopping=False, epsilon=1e-08,\n",
       "       hidden_layer_sizes=(100,), learning_rate='constant',\n",
       "       learning_rate_init=0.001, max_iter=200, momentum=0.9,\n",
       "       n_iter_no_change=10, nesterovs_momentum=True, power_t=0.5,\n",
       "       random_state=1, shuffle=True, solver='lbfgs', tol=0.0001,\n",
       "       validation_fraction=0.1, verbose=False, warm_start=False),\n",
       "       fit_params=None, iid='warn', n_jobs=None,\n",
       "       param_grid={'activation': ['logistic', 'tanh', 'relu'], 'alpha': [0.001, 0.003, 0.01, 0.03, 0.1, 0.3, 1, 3], 'hidden_layer_sizes': range(2, 11)},\n",
       "       pre_dispatch='2*n_jobs', refit=True, return_train_score=False,\n",
       "       scoring='roc_auc', verbose=0)"
      ]
     },
     "execution_count": 45,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      " ****************************\n",
      "Accuracy = 0.59\n",
      "F1 Score = 0.74\n",
      "AUC = 0.50\n",
      "Precision = 0.59\n",
      "Recall = 1.00\n",
      "**************************** \n",
      "\n"
     ]
    }
   ],
   "source": [
    "from sklearn.neural_network import MLPClassifier\n",
    "\n",
    "\n",
    "# build model using grid search with 5Fold Cross validation to train and test\n",
    "nn_clf = MLPClassifier(solver='lbfgs',random_state=1, verbose=False)\n",
    "parameters = {'activation':['logistic','tanh','relu'],'alpha':[0.001,0.003,0.01,0.03,0.1,0.3,1,3],'hidden_layer_sizes':range(2,11)}\n",
    "cv_nn_clf = GridSearchCV(nn_clf, parameters, scoring='roc_auc', cv=5, return_train_score=False)\n",
    "cv_nn_clf.fit(X_train, y_train)\n",
    "\n",
    "# predict on test data\n",
    "y_pred_nn_clf_cv = cv_nn_clf.predict(X_test)\n",
    "\n",
    "# print the score with the function defined myself\n",
    "score_list(y_pred_nn_clf_cv)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "4Ie0Vry88TuO"
   },
   "source": [
    "#### Random Forest"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 503
    },
    "colab_type": "code",
    "id": "JrGf9TqL662d",
    "outputId": "c4f75013-3779-4c3d-8146-2ed2bec88ed3"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "GridSearchCV(cv=5, error_score='raise-deprecating',\n",
       "       estimator=RandomForestClassifier(bootstrap=True, class_weight=None, criterion='gini',\n",
       "            max_depth=None, max_features='auto', max_leaf_nodes=None,\n",
       "            min_impurity_decrease=0.0, min_impurity_split=None,\n",
       "            min_samples_leaf=1, min_samples_split=2,\n",
       "            min_weight_fraction_leaf=0.0, n_estimators='warn', n_jobs=None,\n",
       "            oob_score=False, random_state=1120, verbose=0,\n",
       "            warm_start=False),\n",
       "       fit_params=None, iid='warn', n_jobs=None,\n",
       "       param_grid={'n_estimators': [50, 100, 200, 300], 'max_depth': [2, 5, 8, 11]},\n",
       "       pre_dispatch='2*n_jobs', refit=True, return_train_score=False,\n",
       "       scoring='roc_auc', verbose=0)"
      ]
     },
     "execution_count": 46,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      " ****************************\n",
      "Accuracy = 0.80\n",
      "F1 Score = 0.83\n",
      "AUC = 0.80\n",
      "Precision = 0.85\n",
      "Recall = 0.81\n",
      "**************************** \n",
      "\n"
     ]
    }
   ],
   "source": [
    "from sklearn.ensemble import RandomForestClassifier\n",
    "\n",
    "\n",
    "# build model using grid search with 5Fold Cross validation to train and test\n",
    "rf_clf = RandomForestClassifier(random_state=1120)\n",
    "parameters = {\"n_estimators\":[50,100,200,300],'max_depth':[2,5,8,11]}\n",
    "cv_rf_clf = GridSearchCV(rf_clf, parameters, scoring='roc_auc', cv=5, return_train_score=False)\n",
    "cv_rf_clf.fit(X_train, y_train)\n",
    "\n",
    "# predict on test data\n",
    "y_pred_rf_clf_cv = cv_rf_clf.predict(X_test)\n",
    "\n",
    "# print the score with the function defined myself\n",
    "score_list(y_pred_rf_clf_cv)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "Sh_ra80fo6Op"
   },
   "source": [
    "## 8.2 Answer"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "9ww-6PLIp4c5"
   },
   "source": [
    "#### 1.\tDownload the file OJ.csv. The target feature is Purchase. The rest of the features are self-explanatory, hopefully."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "Pu6iRid9qLsX"
   },
   "source": [
    "I have downloaded the data and loaded in the coding part."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "N0ETWIRGqQUX"
   },
   "source": [
    "#### 2.\tPreprocess the data however you see fit. Describe what you did and why."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "kQoIcVnwVNL2"
   },
   "source": [
    "* I set WeekofPurchase, StoreID and STORE to object in pandas dataframe.\n",
    "* I made a correlation table to check the correlation between each two features. Then I list the correlation coefficients which are larger than 0.5\n",
    "* Because the correlation between DiscCH and PctDiscCH and the correlation between DiscMM and PctDiscMM are extremely high (almost 1), so I deceide to drop PctDiscMM and PctDiscCH. Also, I will drop PriceDiff, SalePriceCH and SalePriceMM, since the correlation coefficient is high. \n",
    "* Also, I drop PriceDiff since the correlation coefficient is high.\n",
    "* I set the target to 1 and 0.\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "lv3Lg5PTqT0H"
   },
   "source": [
    "#### 3.\tSplit the data into training and testing sets. Describe what you did and why."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "tGW5baE9YE_9"
   },
   "source": [
    "* I check the ratio of true and false in target. Since around 60% is true and 40% is false, I consider this is a balance data.\n",
    "* I just randomly split the data into train and test. The train data is 80% and the test data is 20%\n",
    "* I will use 5-foldcross validation to tune the hyper parameter with train data and test the model with test data."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "PzyvATC1qVlH"
   },
   "source": [
    "#### 4.\tChoose an appropriate metric to analyze a model’s performance. Justify."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "gvpmqK-4Zsv4"
   },
   "source": [
    "* I use AUC score to measure the performance\n",
    "* The target is not 60% true and 40% false, I do not want to use accuracy as the measure.\n",
    "* I do not care too much about either specificity or sensitivity.\n",
    "* I may want to know top 10 percent of situations that customer will buy CH.\n",
    "* So, AUC is a good choice in this case."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "RyqyV4NlqaC4"
   },
   "source": [
    "#### 5.\tBuild five different models, using five different classifier algorithms. (Any five will do.) a.\tTune each model. What were the best parameter values for each model?\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "vsZY6DZChvoo"
   },
   "source": [
    "* I have build 5 different models to test, which are Decvision Trees, Naive Bayes, K-nearest neighbors, neural network and random forest.\n",
    "* For each model, I use grid search with 5-foldcross validation to tune the hyper parameter.\n",
    "* Here are the best parameter values for each model, please check the output below."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 701
    },
    "colab_type": "code",
    "id": "JJIDyDwBf3R6",
    "outputId": "7fafee78-ecce-4fe6-897a-a92290ead5c3"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      " **** Decision Trees ****\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "DecisionTreeClassifier(class_weight=None, criterion='entropy', max_depth=4,\n",
       "            max_features=None, max_leaf_nodes=50, min_impurity_decrease=0,\n",
       "            min_impurity_split=None, min_samples_leaf=10,\n",
       "            min_samples_split=2, min_weight_fraction_leaf=0.0,\n",
       "            presort=True, random_state=42, splitter='best')"
      ]
     },
     "execution_count": 47,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "\n",
      "\n",
      " **** Naive Bayes ****\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "GaussianNB(priors=None, var_smoothing=1e-07)"
      ]
     },
     "execution_count": 47,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "\n",
      "\n",
      " **** k-nearest neighbors ****\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "KNeighborsClassifier(algorithm='auto', leaf_size=30, metric='minkowski',\n",
       "           metric_params=None, n_jobs=None, n_neighbors=5, p=2,\n",
       "           weights='uniform')"
      ]
     },
     "execution_count": 47,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "\n",
      "\n",
      " **** neural network ****\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "MLPClassifier(activation='tanh', alpha=0.1, batch_size='auto', beta_1=0.9,\n",
       "       beta_2=0.999, early_stopping=False, epsilon=1e-08,\n",
       "       hidden_layer_sizes=5, learning_rate='constant',\n",
       "       learning_rate_init=0.001, max_iter=200, momentum=0.9,\n",
       "       n_iter_no_change=10, nesterovs_momentum=True, power_t=0.5,\n",
       "       random_state=1, shuffle=True, solver='lbfgs', tol=0.0001,\n",
       "       validation_fraction=0.1, verbose=False, warm_start=False)"
      ]
     },
     "execution_count": 47,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "\n",
      "\n",
      " **** random forest ****\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "RandomForestClassifier(bootstrap=True, class_weight=None, criterion='gini',\n",
       "            max_depth=5, max_features='auto', max_leaf_nodes=None,\n",
       "            min_impurity_decrease=0.0, min_impurity_split=None,\n",
       "            min_samples_leaf=1, min_samples_split=2,\n",
       "            min_weight_fraction_leaf=0.0, n_estimators=300, n_jobs=None,\n",
       "            oob_score=False, random_state=1120, verbose=0,\n",
       "            warm_start=False)"
      ]
     },
     "execution_count": 47,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "print('\\n **** Decision Trees ****')\n",
    "cv_clf.best_estimator_\n",
    "print('\\n')\n",
    "\n",
    "print('\\n **** Naive Bayes ****')\n",
    "cv_gnb.best_estimator_\n",
    "print('\\n')\n",
    "\n",
    "print('\\n **** k-nearest neighbors ****')\n",
    "cv_knn_clf.best_estimator_\n",
    "print('\\n')\n",
    "\n",
    "print('\\n **** neural network ****')\n",
    "cv_nn_clf.best_estimator_\n",
    "print('\\n')\n",
    "\n",
    "print('\\n **** random forest ****')\n",
    "cv_rf_clf.best_estimator_"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "ogulDkNjqdLQ"
   },
   "source": [
    "#### 6.\tDescribe and compare the performance of each fine-tuned model."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "G7W9s6R306ON"
   },
   "source": [
    "* Random forest gets the highest score on  AUC, accuracy ad f1 score. \n",
    "* Here are the scores, please check the table below."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 204
    },
    "colab_type": "code",
    "id": "z9QSTrzIqldf",
    "outputId": "4d222f81-d16b-45bc-f494-3053477cc9a3"
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>accuracy_score</th>\n",
       "      <th>f1_score</th>\n",
       "      <th>roc_auc_score</th>\n",
       "      <th>precision_score</th>\n",
       "      <th>recall_score</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>random forest</th>\n",
       "      <td>0.804</td>\n",
       "      <td>0.829</td>\n",
       "      <td>0.802</td>\n",
       "      <td>0.850</td>\n",
       "      <td>0.810</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Decision Trees</th>\n",
       "      <td>0.762</td>\n",
       "      <td>0.802</td>\n",
       "      <td>0.750</td>\n",
       "      <td>0.786</td>\n",
       "      <td>0.817</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Naive Bayes</th>\n",
       "      <td>0.743</td>\n",
       "      <td>0.776</td>\n",
       "      <td>0.741</td>\n",
       "      <td>0.798</td>\n",
       "      <td>0.754</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>k-nearest neighbors</th>\n",
       "      <td>0.757</td>\n",
       "      <td>0.803</td>\n",
       "      <td>0.739</td>\n",
       "      <td>0.768</td>\n",
       "      <td>0.841</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>neural network</th>\n",
       "      <td>0.589</td>\n",
       "      <td>0.741</td>\n",
       "      <td>0.500</td>\n",
       "      <td>0.589</td>\n",
       "      <td>1.000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                     accuracy_score  f1_score  roc_auc_score  precision_score  \\\n",
       "random forest                 0.804     0.829          0.802            0.850   \n",
       "Decision Trees                0.762     0.802          0.750            0.786   \n",
       "Naive Bayes                   0.743     0.776          0.741            0.798   \n",
       "k-nearest neighbors           0.757     0.803          0.739            0.768   \n",
       "neural network                0.589     0.741          0.500            0.589   \n",
       "\n",
       "                     recall_score  \n",
       "random forest               0.810  \n",
       "Decision Trees              0.817  \n",
       "Naive Bayes                 0.754  \n",
       "k-nearest neighbors         0.841  \n",
       "neural network              1.000  "
      ]
     },
     "execution_count": 48,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dict_score={}\n",
    "for i in [accuracy_score, f1_score,roc_auc_score,precision_score,recall_score]:\n",
    "  dict_score[i.__name__]=[]\n",
    "  for k in [y_pred_dt_cv,y_pred_gnb_cv,y_pred_knn_clf_cv,y_pred_nn_clf_cv,y_pred_rf_clf_cv]:\n",
    "    dict_score[i.__name__].append(round(i(y_test,k),3))\n",
    "\n",
    "df8_6=pd.DataFrame(dict_score,index=['Decision Trees','Naive Bayes','k-nearest neighbors','neural network','random forest'])\n",
    "df8_6.sort_values('roc_auc_score',ascending=False)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "s-7G71bIqhF_"
   },
   "source": [
    "#### 7.\tSelect the best model. Justify."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "IWtQ4cdU7yec"
   },
   "source": [
    "I select random forest as the best model in this case. Because it has the highest AUC, F1 score and accuracy."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "oCHSJrEfqjWg"
   },
   "source": [
    "#### 8.\tIs this model good enough to deploy today? Justify."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "fJy0WhcG35TU"
   },
   "source": [
    "* Since the AUC is over 0.8, I think it is good enough to deploy today.\n",
    "* Besides, I think the model will definitely help the business. At least they can have the support to predict the demond of the CH and MM orange juice. \n",
    "* If the merchant can collect data in more dimensions such as weather, temperature, holiday, and season, I think we can make a better model for the business."
   ]
  }
 ],
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