Penguins III, K-means model.ipynb

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    "|                    ?                |                 ?                   |                  ?                  |\n",
    "| ----------------------------------- | ----------------------------------- | ----------------------------------- |\n",
    "<img src=\"Adelie.jpg\" width=\"33%\" height=\"33%\"> <img src=\"Chinstrap.jpg\" width=\"33%\" height=\"33%\"> <img src=\"Gentoo.jpg\" width=\"33%\" height=\"33%\">\n",
    "\n",
    "# Penguin Analysis: Part III\n",
    "#### K-means clustering model\n",
    "\n",
    "*Purpose*\\\n",
    "To provide insights into the underlying structure within the penguin dataset and confirm if a clustering by sex and species is most expedient.\n",
    "\n",
    "*Deliverables for Part III*     \n",
    "This notebook contains a K-means clustering model built to identify patterns in the penguin data. These patterns will confirm if there is a standard set of measurements specific to each species. \n",
    "> K-means clustering is an unsupervised partitioning algorithm which clusters unlabeled data. It does this by creating a logical scheme to make sense of that data. K-means works by defining each cluster around a central point (centroid) that is determined by the mean of all points in the cluster. K-means can find patterns not already recognized or confirm those which have been. The identified patterns will confirm the body mass predictions made by the regression models providing further security that they are reliant. Reliant body mass predications mean proper care taking of the penguins can take place with confidence.\n",
    "\n",
    "*Data*\\\n",
    "This project uses the Palmer Penguins data set collected and made available by Dr. Kristen Gorman and PalmerStation, Antarctica, LTER. The data is available at: https://allisonhorst.github.io/palmerpenguins. Here it was accessed through the seaborn library. For this activity, species identification is removed from the observations.\n",
    "\n",
    "*Code*\\\n",
    "All code for this project is located at: https://github.com/izsolnay/Penguins_Python\n",
    "\n",
    "*Report*\\\n",
    "The report for this project is located at: https://github.com/izsolnay/Penguins_Python"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "50e8b910-2c45-424a-a79b-a921472e9ccd",
   "metadata": {},
   "source": [
    "# K-means clustering"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "86dcd437-7141-48b4-9247-336b4c9ac398",
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   "source": [
    "# Import standard operational packages.\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "import seaborn as sns\n",
    "sns.set(style='whitegrid')\n",
    "\n",
    "# Important tools for modeling and evaluation.\n",
    "from sklearn.cluster import KMeans\n",
    "from sklearn.preprocessing import StandardScaler\n",
    "from sklearn.metrics import silhouette_score, davies_bouldin_score\n",
    "from gap_statistic import OptimalK\n",
    "\n",
    "# Import visualization packages.\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "361d9f35-1ba6-4391-9625-151b0ec5b281",
   "metadata": {},
   "outputs": [
    {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>species</th>\n",
       "      <th>island</th>\n",
       "      <th>bill_length_mm</th>\n",
       "      <th>bill_depth_mm</th>\n",
       "      <th>flipper_length_mm</th>\n",
       "      <th>body_mass_g</th>\n",
       "      <th>sex</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Adelie</td>\n",
       "      <td>Torgersen</td>\n",
       "      <td>39.1</td>\n",
       "      <td>18.7</td>\n",
       "      <td>181.0</td>\n",
       "      <td>3750.0</td>\n",
       "      <td>Male</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Adelie</td>\n",
       "      <td>Torgersen</td>\n",
       "      <td>39.5</td>\n",
       "      <td>17.4</td>\n",
       "      <td>186.0</td>\n",
       "      <td>3800.0</td>\n",
       "      <td>Female</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Adelie</td>\n",
       "      <td>Torgersen</td>\n",
       "      <td>40.3</td>\n",
       "      <td>18.0</td>\n",
       "      <td>195.0</td>\n",
       "      <td>3250.0</td>\n",
       "      <td>Female</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Adelie</td>\n",
       "      <td>Torgersen</td>\n",
       "      <td>36.7</td>\n",
       "      <td>19.3</td>\n",
       "      <td>193.0</td>\n",
       "      <td>3450.0</td>\n",
       "      <td>Female</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Adelie</td>\n",
       "      <td>Torgersen</td>\n",
       "      <td>39.3</td>\n",
       "      <td>20.6</td>\n",
       "      <td>190.0</td>\n",
       "      <td>3650.0</td>\n",
       "      <td>Male</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  species     island  bill_length_mm  bill_depth_mm  flipper_length_mm  \\\n",
       "0  Adelie  Torgersen            39.1           18.7              181.0   \n",
       "1  Adelie  Torgersen            39.5           17.4              186.0   \n",
       "2  Adelie  Torgersen            40.3           18.0              195.0   \n",
       "3  Adelie  Torgersen            36.7           19.3              193.0   \n",
       "4  Adelie  Torgersen            39.3           20.6              190.0   \n",
       "\n",
       "   body_mass_g     sex  \n",
       "0       3750.0    Male  \n",
       "1       3800.0  Female  \n",
       "2       3250.0  Female  \n",
       "3       3450.0  Female  \n",
       "4       3650.0    Male  "
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Import data; create df\n",
    "df0 = pd.read_csv('Penguins_clean.csv')\n",
    "df0.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a7fb9054-31b5-415c-aa36-fbb9ba035bbf",
   "metadata": {},
   "source": [
    "## PART I: EDA, check and prepare data for clustering\n",
    "* check for missing values: assumption of k-means is no missing values\n",
    "* drop unneeded variables\n",
    "* encode data: K-means needs numeric columns for clustering\n",
    "  * encode `sex`\n",
    "  * do not encode `species` or `island` because they will not be used in model \n",
    "* any scaling: groups need to be scaled (see below)"
   ]
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  {
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   "execution_count": 3,
   "id": "5c909f49-7253-4f5f-8b81-9470547d8a48",
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     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 335 entries, 0 to 334\n",
      "Data columns (total 7 columns):\n",
      " #   Column             Non-Null Count  Dtype  \n",
      "---  ------             --------------  -----  \n",
      " 0   species            335 non-null    object \n",
      " 1   island             335 non-null    object \n",
      " 2   bill_length_mm     335 non-null    float64\n",
      " 3   bill_depth_mm      335 non-null    float64\n",
      " 4   flipper_length_mm  335 non-null    float64\n",
      " 5   body_mass_g        335 non-null    float64\n",
      " 6   sex                335 non-null    object \n",
      "dtypes: float64(4), object(3)\n",
      "memory usage: 18.4+ KB\n"
     ]
    }
   ],
   "source": [
    "df0.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "b6bbc4d0-3b72-4ead-a3dd-73d6a23e0f73",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>bill_length_mm</th>\n",
       "      <th>bill_depth_mm</th>\n",
       "      <th>flipper_length_mm</th>\n",
       "      <th>body_mass_g</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>335.000000</td>\n",
       "      <td>335.000000</td>\n",
       "      <td>335.000000</td>\n",
       "      <td>335.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>43.856418</td>\n",
       "      <td>17.132239</td>\n",
       "      <td>200.937313</td>\n",
       "      <td>4200.895522</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>5.312037</td>\n",
       "      <td>1.977896</td>\n",
       "      <td>13.981348</td>\n",
       "      <td>800.962616</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>32.100000</td>\n",
       "      <td>13.100000</td>\n",
       "      <td>172.000000</td>\n",
       "      <td>2700.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>39.250000</td>\n",
       "      <td>15.500000</td>\n",
       "      <td>190.000000</td>\n",
       "      <td>3550.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>44.400000</td>\n",
       "      <td>17.300000</td>\n",
       "      <td>197.000000</td>\n",
       "      <td>4050.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>48.450000</td>\n",
       "      <td>18.700000</td>\n",
       "      <td>213.000000</td>\n",
       "      <td>4750.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>55.900000</td>\n",
       "      <td>21.500000</td>\n",
       "      <td>231.000000</td>\n",
       "      <td>6300.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       bill_length_mm  bill_depth_mm  flipper_length_mm  body_mass_g\n",
       "count      335.000000     335.000000         335.000000   335.000000\n",
       "mean        43.856418      17.132239         200.937313  4200.895522\n",
       "std          5.312037       1.977896          13.981348   800.962616\n",
       "min         32.100000      13.100000         172.000000  2700.000000\n",
       "25%         39.250000      15.500000         190.000000  3550.000000\n",
       "50%         44.400000      17.300000         197.000000  4050.000000\n",
       "75%         48.450000      18.700000         213.000000  4750.000000\n",
       "max         55.900000      21.500000         231.000000  6300.000000"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df0.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "4b6a80f6-50c5-462c-9441-ef2ef3a0c436",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(['Adelie', 'Chinstrap', 'Gentoo'], dtype=object)"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Investigate species for later comparisons\n",
    "df0['species'].unique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "39dc0e00-24cb-43b9-831d-bbc6416d3e1b",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(['Male', 'Female'], dtype=object)"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Investigate sex for later comparisons\n",
    "df0['sex'].unique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "acd8fd21-c7a4-412a-9d7f-93af253b4bd7",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "species\n",
       "Adelie       148\n",
       "Gentoo       121\n",
       "Chinstrap     66\n",
       "Name: count, dtype: int64"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Get balances for each species\n",
    "df0['species'].value_counts()\n",
    "df0['species'].value_counts(dropna = False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "f190eb59-c310-4ca1-977b-2e4e477655db",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "sex\n",
       "Female    168\n",
       "Male      167\n",
       "Name: count, dtype: int64"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Get balances for each sex\n",
    "df0['sex'].value_counts()\n",
    "df0['sex'].value_counts(dropna = False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "404b8bc7-b41f-4275-9b37-7d70db57e62f",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "species              0\n",
       "island               0\n",
       "bill_length_mm       0\n",
       "bill_depth_mm        0\n",
       "flipper_length_mm    0\n",
       "body_mass_g          0\n",
       "sex                  0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Check for missing values\n",
    "df0.isna().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "e2da5269-ba26-4e09-aa95-733113222e4a",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Drop for missing values\n",
    "df = df0.dropna(axis=0).reset_index(drop = True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "136b90ec-4c99-4dfe-88d9-5ef3de8a2943",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "species              0\n",
       "island               0\n",
       "bill_length_mm       0\n",
       "bill_depth_mm        0\n",
       "flipper_length_mm    0\n",
       "body_mass_g          0\n",
       "sex                  0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Check that it worked\n",
    "df.isna().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "f24e56cc-4da5-4f9c-b604-c2696c357746",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 335 entries, 0 to 334\n",
      "Data columns (total 7 columns):\n",
      " #   Column             Non-Null Count  Dtype  \n",
      "---  ------             --------------  -----  \n",
      " 0   species            335 non-null    object \n",
      " 1   island             335 non-null    object \n",
      " 2   bill_length_mm     335 non-null    float64\n",
      " 3   bill_depth_mm      335 non-null    float64\n",
      " 4   flipper_length_mm  335 non-null    float64\n",
      " 5   body_mass_g        335 non-null    float64\n",
      " 6   sex                335 non-null    object \n",
      "dtypes: float64(4), object(3)\n",
      "memory usage: 18.4+ KB\n"
     ]
    }
   ],
   "source": [
    "df.info()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "2ec9de4e-dd44-4b81-9e0f-630f2df58f0e",
   "metadata": {},
   "source": [
    "#### Check for outliers"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "434bb938-66af-447f-a09a-a61cf9a959a0",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Drop island variable, because model is clustering based on body features\n",
    "penguins = df.drop(['island'], axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "f392771b-718a-4f91-a7e2-8d6b2b1542f9",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Clean sex column entries to all uppercase\n",
    "penguins['sex'] = penguins['sex'].str.upper()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "26cf6c1a-48dc-4bd3-b160-8262bab419bf",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Encode variable 'sex' because object\n",
    "penguins = pd.get_dummies(penguins, drop_first = True, columns=['sex'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "0e952751-14c7-47d6-8bec-8a77bbbe56dd",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 335 entries, 0 to 334\n",
      "Data columns (total 6 columns):\n",
      " #   Column             Non-Null Count  Dtype  \n",
      "---  ------             --------------  -----  \n",
      " 0   species            335 non-null    object \n",
      " 1   bill_length_mm     335 non-null    float64\n",
      " 2   bill_depth_mm      335 non-null    float64\n",
      " 3   flipper_length_mm  335 non-null    float64\n",
      " 4   body_mass_g        335 non-null    float64\n",
      " 5   sex_MALE           335 non-null    bool   \n",
      "dtypes: bool(1), float64(4), object(1)\n",
      "memory usage: 13.5+ KB\n"
     ]
    }
   ],
   "source": [
    "penguins.info()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "90a9f893-0a14-4d9c-9158-271d2d162933",
   "metadata": {},
   "source": [
    "### Scale features\n",
    "Although this project does not assume knowledge of the 3 species represented in the data set, it is known that Chinstrap penguins account for a smaller portion of penguins in the data set. Because they are less common and therefore in the minority, this will likely affect the K-means clustering since it performs best with similar sized groupings.\n",
    "\n",
    "It is also important to scale because K-means clustering uses distance between observations as its measure of similarity. Different measurement types (for example meters vs grams) could result in significant differences represented as small numbers and minor differences as much larger numbers. By using StandardScaler all variables will have a mean of 0 and variance/standard deviation of 1."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "08fa93fb-af91-4ef6-af5f-cad25912ebb9",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[-0.89674321,  0.79382669, -1.42812676, -0.56378413,  1.00298954],\n",
       "       [-0.82132988,  0.13557931, -1.0699725 , -0.50126586, -0.99701937]])"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Subset X variable, drop species as it's what is trying to be identified\n",
    "X = penguins.drop(['species'], axis=1)\n",
    "\n",
    "# Pass data to fit.transform() function\n",
    "X_scaled = StandardScaler().fit_transform(X)\n",
    "X_scaled[:2,:]"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a957aa60-ec76-4fb7-8a38-f8f9865e74ac",
   "metadata": {},
   "source": [
    "### Start modeling\n",
    "* instantiate model (kmeans3 = KMeans(n_clusters=3, random_state=42))\n",
    "  * use 3 clusters as first attempt\n",
    "* compare the inertias of multiple K values \n",
    "  * fit K-means to return a model object that has \"learned\" the data\n",
    "      * evaluate inertia for different values of k\n",
    "      * location of centroids\n",
    "      * class labels\n",
    "  * write a function called kmeans_inertia that\n",
    "    * takes in num_clusters and x_vals\n",
    "    * returns a list of each k-value's inertia"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "cb2ad62d-7838-4b1a-b55d-6b37c389a118",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Instantiate model\n",
    "kmeans3 = KMeans(n_clusters=3, random_state=42)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "65d71159-b0e7-4d60-9c22-8cc1b7f75776",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<style>#sk-container-id-1 {\n",
       "  /* Definition of color scheme common for light and dark mode */\n",
       "  --sklearn-color-text: black;\n",
       "  --sklearn-color-line: gray;\n",
       "  /* Definition of color scheme for unfitted estimators */\n",
       "  --sklearn-color-unfitted-level-0: #fff5e6;\n",
       "  --sklearn-color-unfitted-level-1: #f6e4d2;\n",
       "  --sklearn-color-unfitted-level-2: #ffe0b3;\n",
       "  --sklearn-color-unfitted-level-3: chocolate;\n",
       "  /* Definition of color scheme for fitted estimators */\n",
       "  --sklearn-color-fitted-level-0: #f0f8ff;\n",
       "  --sklearn-color-fitted-level-1: #d4ebff;\n",
       "  --sklearn-color-fitted-level-2: #b3dbfd;\n",
       "  --sklearn-color-fitted-level-3: cornflowerblue;\n",
       "\n",
       "  /* Specific color for light theme */\n",
       "  --sklearn-color-text-on-default-background: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, black)));\n",
       "  --sklearn-color-background: var(--sg-background-color, var(--theme-background, var(--jp-layout-color0, white)));\n",
       "  --sklearn-color-border-box: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, black)));\n",
       "  --sklearn-color-icon: #696969;\n",
       "\n",
       "  @media (prefers-color-scheme: dark) {\n",
       "    /* Redefinition of color scheme for dark theme */\n",
       "    --sklearn-color-text-on-default-background: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, white)));\n",
       "    --sklearn-color-background: var(--sg-background-color, var(--theme-background, var(--jp-layout-color0, #111)));\n",
       "    --sklearn-color-border-box: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, white)));\n",
       "    --sklearn-color-icon: #878787;\n",
       "  }\n",
       "}\n",
       "\n",
       "#sk-container-id-1 {\n",
       "  color: var(--sklearn-color-text);\n",
       "}\n",
       "\n",
       "#sk-container-id-1 pre {\n",
       "  padding: 0;\n",
       "}\n",
       "\n",
       "#sk-container-id-1 input.sk-hidden--visually {\n",
       "  border: 0;\n",
       "  clip: rect(1px 1px 1px 1px);\n",
       "  clip: rect(1px, 1px, 1px, 1px);\n",
       "  height: 1px;\n",
       "  margin: -1px;\n",
       "  overflow: hidden;\n",
       "  padding: 0;\n",
       "  position: absolute;\n",
       "  width: 1px;\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-dashed-wrapped {\n",
       "  border: 1px dashed var(--sklearn-color-line);\n",
       "  margin: 0 0.4em 0.5em 0.4em;\n",
       "  box-sizing: border-box;\n",
       "  padding-bottom: 0.4em;\n",
       "  background-color: var(--sklearn-color-background);\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-container {\n",
       "  /* jupyter's `normalize.less` sets `[hidden] { display: none; }`\n",
       "     but bootstrap.min.css set `[hidden] { display: none !important; }`\n",
       "     so we also need the `!important` here to be able to override the\n",
       "     default hidden behavior on the sphinx rendered scikit-learn.org.\n",
       "     See: https://github.com/scikit-learn/scikit-learn/issues/21755 */\n",
       "  display: inline-block !important;\n",
       "  position: relative;\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-text-repr-fallback {\n",
       "  display: none;\n",
       "}\n",
       "\n",
       "div.sk-parallel-item,\n",
       "div.sk-serial,\n",
       "div.sk-item {\n",
       "  /* draw centered vertical line to link estimators */\n",
       "  background-image: linear-gradient(var(--sklearn-color-text-on-default-background), var(--sklearn-color-text-on-default-background));\n",
       "  background-size: 2px 100%;\n",
       "  background-repeat: no-repeat;\n",
       "  background-position: center center;\n",
       "}\n",
       "\n",
       "/* Parallel-specific style estimator block */\n",
       "\n",
       "#sk-container-id-1 div.sk-parallel-item::after {\n",
       "  content: \"\";\n",
       "  width: 100%;\n",
       "  border-bottom: 2px solid var(--sklearn-color-text-on-default-background);\n",
       "  flex-grow: 1;\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-parallel {\n",
       "  display: flex;\n",
       "  align-items: stretch;\n",
       "  justify-content: center;\n",
       "  background-color: var(--sklearn-color-background);\n",
       "  position: relative;\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-parallel-item {\n",
       "  display: flex;\n",
       "  flex-direction: column;\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-parallel-item:first-child::after {\n",
       "  align-self: flex-end;\n",
       "  width: 50%;\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-parallel-item:last-child::after {\n",
       "  align-self: flex-start;\n",
       "  width: 50%;\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-parallel-item:only-child::after {\n",
       "  width: 0;\n",
       "}\n",
       "\n",
       "/* Serial-specific style estimator block */\n",
       "\n",
       "#sk-container-id-1 div.sk-serial {\n",
       "  display: flex;\n",
       "  flex-direction: column;\n",
       "  align-items: center;\n",
       "  background-color: var(--sklearn-color-background);\n",
       "  padding-right: 1em;\n",
       "  padding-left: 1em;\n",
       "}\n",
       "\n",
       "\n",
       "/* Toggleable style: style used for estimator/Pipeline/ColumnTransformer box that is\n",
       "clickable and can be expanded/collapsed.\n",
       "- Pipeline and ColumnTransformer use this feature and define the default style\n",
       "- Estimators will overwrite some part of the style using the `sk-estimator` class\n",
       "*/\n",
       "\n",
       "/* Pipeline and ColumnTransformer style (default) */\n",
       "\n",
       "#sk-container-id-1 div.sk-toggleable {\n",
       "  /* Default theme specific background. It is overwritten whether we have a\n",
       "  specific estimator or a Pipeline/ColumnTransformer */\n",
       "  background-color: var(--sklearn-color-background);\n",
       "}\n",
       "\n",
       "/* Toggleable label */\n",
       "#sk-container-id-1 label.sk-toggleable__label {\n",
       "  cursor: pointer;\n",
       "  display: block;\n",
       "  width: 100%;\n",
       "  margin-bottom: 0;\n",
       "  padding: 0.5em;\n",
       "  box-sizing: border-box;\n",
       "  text-align: center;\n",
       "}\n",
       "\n",
       "#sk-container-id-1 label.sk-toggleable__label-arrow:before {\n",
       "  /* Arrow on the left of the label */\n",
       "  content: \"▸\";\n",
       "  float: left;\n",
       "  margin-right: 0.25em;\n",
       "  color: var(--sklearn-color-icon);\n",
       "}\n",
       "\n",
       "#sk-container-id-1 label.sk-toggleable__label-arrow:hover:before {\n",
       "  color: var(--sklearn-color-text);\n",
       "}\n",
       "\n",
       "/* Toggleable content - dropdown */\n",
       "\n",
       "#sk-container-id-1 div.sk-toggleable__content {\n",
       "  max-height: 0;\n",
       "  max-width: 0;\n",
       "  overflow: hidden;\n",
       "  text-align: left;\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-toggleable__content.fitted {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-0);\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-toggleable__content pre {\n",
       "  margin: 0.2em;\n",
       "  border-radius: 0.25em;\n",
       "  color: var(--sklearn-color-text);\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-toggleable__content.fitted pre {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-0);\n",
       "}\n",
       "\n",
       "#sk-container-id-1 input.sk-toggleable__control:checked~div.sk-toggleable__content {\n",
       "  /* Expand drop-down */\n",
       "  max-height: 200px;\n",
       "  max-width: 100%;\n",
       "  overflow: auto;\n",
       "}\n",
       "\n",
       "#sk-container-id-1 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {\n",
       "  content: \"▾\";\n",
       "}\n",
       "\n",
       "/* Pipeline/ColumnTransformer-specific style */\n",
       "\n",
       "#sk-container-id-1 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
       "  color: var(--sklearn-color-text);\n",
       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-label.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
       "  background-color: var(--sklearn-color-fitted-level-2);\n",
       "}\n",
       "\n",
       "/* Estimator-specific style */\n",
       "\n",
       "/* Colorize estimator box */\n",
       "#sk-container-id-1 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-estimator.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-2);\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-label label.sk-toggleable__label,\n",
       "#sk-container-id-1 div.sk-label label {\n",
       "  /* The background is the default theme color */\n",
       "  color: var(--sklearn-color-text-on-default-background);\n",
       "}\n",
       "\n",
       "/* On hover, darken the color of the background */\n",
       "#sk-container-id-1 div.sk-label:hover label.sk-toggleable__label {\n",
       "  color: var(--sklearn-color-text);\n",
       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
       "}\n",
       "\n",
       "/* Label box, darken color on hover, fitted */\n",
       "#sk-container-id-1 div.sk-label.fitted:hover label.sk-toggleable__label.fitted {\n",
       "  color: var(--sklearn-color-text);\n",
       "  background-color: var(--sklearn-color-fitted-level-2);\n",
       "}\n",
       "\n",
       "/* Estimator label */\n",
       "\n",
       "#sk-container-id-1 div.sk-label label {\n",
       "  font-family: monospace;\n",
       "  font-weight: bold;\n",
       "  display: inline-block;\n",
       "  line-height: 1.2em;\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-label-container {\n",
       "  text-align: center;\n",
       "}\n",
       "\n",
       "/* Estimator-specific */\n",
       "#sk-container-id-1 div.sk-estimator {\n",
       "  font-family: monospace;\n",
       "  border: 1px dotted var(--sklearn-color-border-box);\n",
       "  border-radius: 0.25em;\n",
       "  box-sizing: border-box;\n",
       "  margin-bottom: 0.5em;\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-estimator.fitted {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-0);\n",
       "}\n",
       "\n",
       "/* on hover */\n",
       "#sk-container-id-1 div.sk-estimator:hover {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-estimator.fitted:hover {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-2);\n",
       "}\n",
       "\n",
       "/* Specification for estimator info (e.g. \"i\" and \"?\") */\n",
       "\n",
       "/* Common style for \"i\" and \"?\" */\n",
       "\n",
       ".sk-estimator-doc-link,\n",
       "a:link.sk-estimator-doc-link,\n",
       "a:visited.sk-estimator-doc-link {\n",
       "  float: right;\n",
       "  font-size: smaller;\n",
       "  line-height: 1em;\n",
       "  font-family: monospace;\n",
       "  background-color: var(--sklearn-color-background);\n",
       "  border-radius: 1em;\n",
       "  height: 1em;\n",
       "  width: 1em;\n",
       "  text-decoration: none !important;\n",
       "  margin-left: 1ex;\n",
       "  /* unfitted */\n",
       "  border: var(--sklearn-color-unfitted-level-1) 1pt solid;\n",
       "  color: var(--sklearn-color-unfitted-level-1);\n",
       "}\n",
       "\n",
       ".sk-estimator-doc-link.fitted,\n",
       "a:link.sk-estimator-doc-link.fitted,\n",
       "a:visited.sk-estimator-doc-link.fitted {\n",
       "  /* fitted */\n",
       "  border: var(--sklearn-color-fitted-level-1) 1pt solid;\n",
       "  color: var(--sklearn-color-fitted-level-1);\n",
       "}\n",
       "\n",
       "/* On hover */\n",
       "div.sk-estimator:hover .sk-estimator-doc-link:hover,\n",
       ".sk-estimator-doc-link:hover,\n",
       "div.sk-label-container:hover .sk-estimator-doc-link:hover,\n",
       ".sk-estimator-doc-link:hover {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-3);\n",
       "  color: var(--sklearn-color-background);\n",
       "  text-decoration: none;\n",
       "}\n",
       "\n",
       "div.sk-estimator.fitted:hover .sk-estimator-doc-link.fitted:hover,\n",
       ".sk-estimator-doc-link.fitted:hover,\n",
       "div.sk-label-container:hover .sk-estimator-doc-link.fitted:hover,\n",
       ".sk-estimator-doc-link.fitted:hover {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-3);\n",
       "  color: var(--sklearn-color-background);\n",
       "  text-decoration: none;\n",
       "}\n",
       "\n",
       "/* Span, style for the box shown on hovering the info icon */\n",
       ".sk-estimator-doc-link span {\n",
       "  display: none;\n",
       "  z-index: 9999;\n",
       "  position: relative;\n",
       "  font-weight: normal;\n",
       "  right: .2ex;\n",
       "  padding: .5ex;\n",
       "  margin: .5ex;\n",
       "  width: min-content;\n",
       "  min-width: 20ex;\n",
       "  max-width: 50ex;\n",
       "  color: var(--sklearn-color-text);\n",
       "  box-shadow: 2pt 2pt 4pt #999;\n",
       "  /* unfitted */\n",
       "  background: var(--sklearn-color-unfitted-level-0);\n",
       "  border: .5pt solid var(--sklearn-color-unfitted-level-3);\n",
       "}\n",
       "\n",
       ".sk-estimator-doc-link.fitted span {\n",
       "  /* fitted */\n",
       "  background: var(--sklearn-color-fitted-level-0);\n",
       "  border: var(--sklearn-color-fitted-level-3);\n",
       "}\n",
       "\n",
       ".sk-estimator-doc-link:hover span {\n",
       "  display: block;\n",
       "}\n",
       "\n",
       "/* \"?\"-specific style due to the `<a>` HTML tag */\n",
       "\n",
       "#sk-container-id-1 a.estimator_doc_link {\n",
       "  float: right;\n",
       "  font-size: 1rem;\n",
       "  line-height: 1em;\n",
       "  font-family: monospace;\n",
       "  background-color: var(--sklearn-color-background);\n",
       "  border-radius: 1rem;\n",
       "  height: 1rem;\n",
       "  width: 1rem;\n",
       "  text-decoration: none;\n",
       "  /* unfitted */\n",
       "  color: var(--sklearn-color-unfitted-level-1);\n",
       "  border: var(--sklearn-color-unfitted-level-1) 1pt solid;\n",
       "}\n",
       "\n",
       "#sk-container-id-1 a.estimator_doc_link.fitted {\n",
       "  /* fitted */\n",
       "  border: var(--sklearn-color-fitted-level-1) 1pt solid;\n",
       "  color: var(--sklearn-color-fitted-level-1);\n",
       "}\n",
       "\n",
       "/* On hover */\n",
       "#sk-container-id-1 a.estimator_doc_link:hover {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-3);\n",
       "  color: var(--sklearn-color-background);\n",
       "  text-decoration: none;\n",
       "}\n",
       "\n",
       "#sk-container-id-1 a.estimator_doc_link.fitted:hover {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-3);\n",
       "}\n",
       "</style><div id=\"sk-container-id-1\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>KMeans(n_clusters=3, random_state=42)</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item\"><div class=\"sk-estimator fitted sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-1\" type=\"checkbox\" checked><label for=\"sk-estimator-id-1\" class=\"sk-toggleable__label fitted sk-toggleable__label-arrow fitted\">&nbsp;&nbsp;KMeans<a class=\"sk-estimator-doc-link fitted\" rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.4/modules/generated/sklearn.cluster.KMeans.html\">?<span>Documentation for KMeans</span></a><span class=\"sk-estimator-doc-link fitted\">i<span>Fitted</span></span></label><div class=\"sk-toggleable__content fitted\"><pre>KMeans(n_clusters=3, random_state=42)</pre></div> </div></div></div></div>"
      ],
      "text/plain": [
       "KMeans(n_clusters=3, random_state=42)"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Fit model to data\n",
    "kmeans3.fit(X_scaled)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "0633aba9-df56-43c0-9ca1-7ae3bca51868",
   "metadata": {},
   "source": [
    "### EVALUATION\n",
    "Good clustering:    \n",
    "* identifiable clusters\n",
    "* within each cluster (**intra-cluster**) = points are close to each other\n",
    "  * $inertia$ gauges how closely related each observation is to the other observations in its own cluster\n",
    "  * $inertia$ is the sum of the squared distances between each observation and its nearest centroid\n",
    "  * $inertia$ should be as close to 0 as possible\n",
    "* between the clusters (**inter-cluster**) = lots of *empty space* "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "60117592-47c0-4bd7-8610-b2caab4e2c43",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Clusters:  [0 2 2 2 0 2 0 0 2 2 0 0 2 2 0 2 0 2 0 2 0 0 2 0 2 2 0 2 0 2 0 2 0 0 2 2 0\n",
      " 2 0 2 0 2 0 0 2 2 0 2 0 2 0 2 0 2 0 2 0 2 0 2 0 2 0 2 0 2 0 2 0 2 0 2 0 2\n",
      " 0 2 0 2 0 2 0 2 0 0 2 0 2 2 0 2 0 2 0 2 0 2 0 2 0 2 0 2 0 2 0 2 0 2 0 2 0\n",
      " 0 2 0 2 0 2 0 2 0 2 0 2 0 2 0 2 0 2 0 2 0 2 0 2 0 2 0 2 0 2 0 0 2 2 0 2 0\n",
      " 2 0 0 2 0 2 2 0 2 0 2 0 2 0 2 0 0 2 0 2 0 2 0 2 0 2 0 2 0 2 0 2 0 0 2 2 0\n",
      " 2 0 0 2 0 2 2 0 2 0 0 2 2 0 2 0 2 0 2 0 0 2 0 2 2 2 0 0 2 1 1 1 1 1 1 1 1\n",
      " 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1\n",
      " 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1\n",
      " 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1\n",
      " 1 1]\n",
      "Inertia:  571.2995770594428\n"
     ]
    }
   ],
   "source": [
    "# Call .labels_ attribute to return a list of cluster assignments\n",
    "# 3 clusters = assigned values to each observation are 0, 1, or 2\n",
    "print('Clusters: ', kmeans3.labels_)\n",
    "\n",
    "# Call .inertia_ attribute to return sum of the squared distances of samples from their closest cluster center\n",
    "print('Inertia: ', kmeans3.inertia_)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "b452138c-9586-47a5-a0a5-a75f1a0297cb",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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vvvpqijgXhSK3IaSqGqVQKBTp5uTJk5w5c4aOHTs6WUD69OlD0aJF3QaZKhSKOyiLiEKhUGSA2NhYXnrpJfr160eHDh2wWq0sX76cw4cPM2bMmJwWT6G4Z1AWEYVCocggK1asYNasWZw+fRopJdWqVWPEiBE0b948p0VTKO4ZlCKiUCgUCoUix1CzZhQKhUKhUOQYShFRKBQKhUKRYyhFRKFQKBQKRY6R62fN7Nu3DyklJpMpp0VRKBQKhULhIRaLBSGEU/HI1Mj1FhEpZYoKk7kZKSVms/mekvl+Rp2P3Ic6J7kLdT5yH/fLOfH0/Z3rLSJJlpDM1u/ILmJjYzl27BgVKlQgICAgp8V54FHnI/ehzknuQp2P3Mf9ck48LcCZ6y0iCoVCoVAo7l+UIqJQKBQKhSLHUIqIQqFQKBSKHEMpIgqFQqFQKHIMpYgoFAqFQqHIMZQiolAoFAqFIsdQiohCoVAoFIocQykiCoVCoVAocgyliCgUCoVCocgxlCKiUCgUCoUix8j1Kd4VCoUiO0iIS2Ddr5tZOWc9N69GUKR0IR4Z0o6WfRtj8lFFNxWKrEIpIgqF4oEn6uZtxrSdwNlDFxCaQOqS0PNhHNhwhCXfrmTyP2/hn8c/p8VUKO5LlGtGoVA88Hz67NecP3oJAKnbqoXq9p/Htp/k69E/5JRoCsV9j1JEFArFA83VM9fZtnQ3ulVPdb1u1Vnz40aibt7OZskUigcDpYgoFIoHmsObj4N03ybRYuXYjpPZI5BC8YChFBGFQvFAI2UaWohCochSlCKiUCgeaGo0r5JmG6PJQJWGFbJBGoXiwUMpIgqF4oHmofJFadT1YTRD6o9DzaDRbkBL8hYMzmbJFIoHA6WIKBSKB57X5oyiVJXiIEAIAYDQbD+rNKzAqC+eyUnxFIr7GpVHRKFQPPDkLRjMtB2TWffLv6yYs57wqxEULlWQR4a2o/UTTVVCM4UiC1GKiEKhUAB+Ab50GdaeLsPa57QoCsUDhXLNKBQKhUKhyDGURURx3xJ5I4pF36xgzS8bkRYoX7sM3YZ3pF6HWo44gAeRmKhYVv2wgTU/byLq5m1KVHqIbs93oEmP+mia+jZRKBTZi1JEFPclZw+dZ0zbidyOiHak7A69eIMtf++ky7B2vPzt8w+kMnL9fBivtBpH2MWbSCRICL1wg90r99OkR33GzXsVo0k9FhQKRfahPn8U9x3WRCtvdZtM9K0YhxICoCfaUngv/24tS2eszinxcgwpJRN7f8LNK+G2JF72Q5OU2nz7kj388t6CHJRQoVA8iChFRHHfsXXxbsIu3nRZO0QIwfypSx64jJrHdpzk5N6zWBNTPy5SSv7+6h/MCZZslkyhUDzIeN0Ge+vWLaZOncqGDRuIjo6mcuXKvPrqq9SvX9/bQz1wmOPNrPttCyvnrOPG5XAKlypI52fbqumFd3Fw4xEMJgNWizXV9VJKrpy6xq3QSEKK5APAarWybfFuls5YzeWTV8lbMIj2A1rRYVArAoMDslH61ImLiWfNT5tYPXcDEaGRFCtXhK7D2tP8sUYYjAaP+ji48SiaQXOpoAFER8Rw4eglKtQt6y3RFQqFwi1eV0ReeeUVwsLCmDp1KgUKFOCnn35iyJAh/PXXX5QrV87bwz0w3I6I5rW2Ezh94DxCE0hdEno+jIMbj7L46xV8uPKdXPHCzBWk09BhMVuY2GcKO5bucbyor50L5b/dZ5j/2RKmbpxE4ZIFs0ZWDwi/FsGrrcdz6eRVBCDtcR371h6ibtsavLvkDXz9fdPu6AGzACkUinsDr7pmzp8/z5YtW5gwYQL169enbNmyvPPOOxQuXJglS5Z4c6gHjqnPfcvZwxcBHHEPuv3nf7vP8NX/zcox2XIbtVpVc2kNAZtr5qHyRchXOC8AP0+az87le4E78RJIm+Uk7NJNJvX5NMtldscH/b/g6pnrdplsy5Lk3L/hCLP/95tH/dRsWc2tNQQgT75ASlUrkSl5FQqFIj14VREJCQlh5syZ1KxZ07FMCIEQgqioKG8O9UAReiGMLQt3unyJ6Fad9b9tJiI0Mpsly5006VGfgiUKuKwdIqWk9+juCCEwx5tZNH2FU1BrcvREnRO7TnN8Z86UgD935CIH1h9xHdehS5Z9t4bY23Fp9lWtSSUq1C2LwZj6cRGaoOeozvj4KjefQqHIPryqiAQHB9OqVSt8fHwcy1auXMn58+dp0aKFN4d6oDi85USagZXWRJ1j2/7LJolyN0aTkfeXvklg3gBHvRDA8QLuPKQt3YZ3AGwv+pjIWLf9aQaNgxuPZp3Abji48ShpzTJOiE3g5N4zafYlhGD8gjHkLxbiNHU5SWFr2OVh+r/TO1PyKhQKRXrJ0oQBe/fu5c0336Rjx460bt06w/1IKYmNdf+yyC3ExcU5/fQGCQkJHre7V45TVlO0QiGm75nMsu/WsPHPrVjNOmVrluKRoW2p274m8fHxAMTFxXvUn9liyZFjazabPQp5SYj37NwHF87DF9vfY+3P/7L+ty1ER8TwUPkidHq2DY2618OSaMGSmLWzZrLiHlFkHHU+ch/3yzmRUnqUrynLFJE1a9YwZswYHn74YT79NHM+dovFwrFjx7wkWfZw7tw5r/VlKigQwn2soWYQiHz6PXecspq6vapSt1dVp2XHjx93/G4RFnwDfUmIca3s6VadwOK+OXJs/YuZ0gy+NfkaSfSPT5d8FduWomLbUk7L/vvvREZEzDDevEcUmUedj9zH/XBOkntIXJElisjPP//M+++/T+fOnfnoo488EsQdJpOJChUqeEm6rCUuLo5z585RpkwZ/P39vdNpVWjUbSc7l+9LNU5EM2i0fLwxDZurKdJ348n56D6iIwumLk01TkQzaJStVYqOfdrmSCbWqlWrsqbFFo5u+8+RkC05QhN0erYNderXyXbZMkqW3COKDKPOR+7jfjknp06d8qid1xWRX3/9lXfffZeBAwfy1ltveeXhLYQgIODemprq7+/vVZlfmzOK19pO5MzBO9N3k35WfLgcL3/z/D13jLITd+fj2fee4sLRS+xcvs8pz4YQgkIlCjBx4esEBgZmp7hOvP37K7zSahxXTl9zTN9NkrN2q+oM/3SQZ9N3cxnevkcUmUOdj9zHvX5OPH3/e1UROXv2LB988AEdOnTg+eef58aNG451fn5+BAUFeXO4B4rg/EF8ue191v26mRWz13HzSgSFShXkkWfb0vrJZmqmQyYw+ZiYtGgsW/7aybKZq7l88hrBBYNoP6AlnQa3JjBvzikhAAWKhfDt3o9Z/eMmVs1dz63QKIqWK0zXYR1o0buRqg2jUCjuabz6BFu5ciUWi4XVq1ezerVzLY9evXrx4YcfenO4Bw5ff18eGdKOR4a0y2lR7jsMBgMt+zShZZ8mOS1Kqvjn8afHyE70GNkpp0VRKBQKr+JVRWT48OEMHz7cm10qFAqFQqG4j1E2XYVCoVAospnDm4+x6OsV/LfrDD7+Jpr3akTX5ztQ8KH8OS1atqMUEYVCoVAosgkpJbPf+o3fP/wLg1FzZE2+cOwyCz5fyocr3qZM7ZI5LGX24tXMqgqFQqFQKFzz74Lt/P7hXwBOpRt0q05CTAJvdZtMXLRniRbvF5QiolAoFApFNjF/yhI0LfVprbouiY6IYdOf27JZqpxFuWYUigecsEs3WfrtKrb8vRNLQiJVG1ekx8hOVGtSOadFUyjSTeiFMJZ8u5pti3dhSUikWtNK9BjZmaqNKua0aFgTrRzb4b6ApmbQOLz5BKWaFM0mqXIepYgoFA8wBzYe4a2uk7EkWByJ3K6fD2XtL//y9ITHGTiubw5LqFB4zr51h3i7+4ckmhOdruc1P23imfeeot//HstR+dIqXmprlPVy5DaUa0aheECJvhXDOz0+whxvdiodkOS3/nHCn+xYtienxFMo0kVU+G3G9fzISamGO9fznLd/Y9eKfTklHmCrDF65QQWXrhkAXepUb/ZgWSOVIqJQPKCs/nEj8dHxqdbYAZuJeP7UpdkslUKRMVb9sIGEWHOuv577vtod3YWMQhME5g2g1RO5M7FiVqFcMwpFMixmCxv+2MqK2esIvXCDgsXz02lwG9r2a46PX+aKN+Y2Dm466na9btU5uOmox6W8FYqc5ODGo25dH0nXc07Tsm8Tnth7hj8+XuQ0fVczaPj6+/D+0v/hn8cvh6XMXpQiolDYib0dxxud3uXY9pNomkDXJdfPh3F483H+nracT9ZNICgkT06L6TU88lcrFPcI98r1LIRg6IcDaPBIXZZ8vZITu0/j6+9D816N6Da8AwWLFyA2NjanxcxWlCKiUNj56sVZnNh1GsBhOk0y8549fJGpw75h/PzXckw+b1OrRTW2/r3L5XrNoFGzRVVlDVHcE9RqWY0dy/e6dc3UbFE1m6VyTe1W1andqnpOi5ErUDEiCgUQERrJul/+dQpyS45u1dny1y5CL4Rls2RZR4dBrfAL9EW4ymlg1ek9uls2S6VQZIxOz7TB18/HpeKsW3X6vNI9m6VSeIJSRBQ5gtVqZfNfO3jzkfcYWH4ULzR6g7+n/UNMVM6YJI9t/88py2FqSCk5vPl4NkmU9QSF5GHSorH4+JrQDHceBQaj7feB4/rSpHv9nBJPoUgXwQWCmLhoLCYX1/PgSU/S8JG6OSWewg3KNaPIdixmC5P6TmH7kj1oBg3dqnPtXCj/7T7D/M+WMHXDRAqXKpS9Qt0b7mWvU6dNDWYf+5wl365m66KdmOMtVG1UkR6jOlOjWZWcFk+hSBcPt6tpu56/WcnWxbuxJFio1qQSPUd1Vgn6cjFKEVFkO7+8u4AdS/cC3HGFSJBIbly6ycQ+U/hqx+RsjU2o2riiUwR7qgiofh++nAuXKsSQD/ox5IN+OS2KQpFpipQuxNAPBzD0wwE5LYrCQ5RrRpGtmOPN/P3VPy4j3K2JOv/tPs3xnaeyVa6QIvlo81RzJ5NucjSDRtMeDShSOpstNQqFQnGfoxQRRbZy/uglYiLdx4FoBo2DG7N/vv8L04ZQ8eFyAI4AzqSfpauV4NVZI7JdJoVCobjfUa4ZRbaSm+f6BwYHMHXTJDb8voV/Zq8j7MINCjwUQqdn2tKuf3N8/X1zWkSFQqG471CKiCJbKVO9JIF5A9xaRXSrTs2WOTPf38fXRMdBrek4qHWOjK9QKBQPGso1o8hWfPx86DGyk8vcFQajRsWHy+aKkt0KhUKhyHqURUSR7Qwc35fTB8+zc9lex/RdsKU+Lli8AOMXvKayed5FXEw8a37axOq5G4gIjaRYuSJ0Hdae5o81wmA05LR49wwndp1i0fQVHNlyApOvkcbd6tF9RCcVhHwPkhCXwNpfNrNyzjrCr92iSJlCdBnSjpZ9m2A0qVfbvYQ6W4psx+RjYtLfr7Plr50sm7mayyevEVwwiPYDWtJpcGsC8wbmtIi5ivBrEbzaejyXTl5FAFJC6IUb7Ft7iLpta/DukjdU/IoH/PHxIr5/42cMRgPWRCsAF09c4a8vlzNp0VjqdaidwxIqPCXyRhRj2kzg3JGLCE0gdUno+TAOrD/Ckm9X8cE/b+Ef+GAVjruXUYqIIkcwGAy07NOEln0erHLXGeGD/l9w9cx1e64VG0lWpP0bjjD7f78x4rPBOSbfvcDeNQf5/o2fARxKCNiOo0WXjO/1MT+f/Zp8hfLmlIiKdPDJM9O5cOIycKceVFJ9qKPb/uPbV+YyesbzOSafIn2oGBGFIhdz7shFDqw/4jLRmtQly75bQ+ztuGyW7N5iwedLXeaIkVJijrewYvb6bJZKkRGunL7GjmV70V3cE7pVZ9XcDUSF385myRQZRSkiCkUu5uDGo6QVLpMQm8DJvWdSLNd1ne1L9/BW98kMLD+KkfVfZ/7UJUTfiskiaXMvBzYcdVnQEGwK3cGNR7JRIkVGOfTvsTTbJJoTOZHNSREVGUe5ZhSK+xCr1crk/l+y8c+td+r5nIVT+84xf+oSpmyYSPEKxXJazOzDg/w1uTjFjSIZ6jzdfyiLiEKRi6nZsmqaD15ffx9HRtgk5n2ymE3ztgI4WQKklERcj2Rcz49ydXI5b1OzVTWXrhmwZdCt3apaNkqkyCg1W6Rd78noY6RygwrZII3CGyhFRKHIxZStUYparao5SpnfjdAEjwxtR0CQv2OZNdHKwi+WuVRgdKvOhWOX2b/+cFaInCvp/XI3l64ZIQQ+viY6Pds2m6VSZITiFYrR4JG6aC7uCc2g0eHpVgQXCMpmyRQZRSkiCoWXsSZa2fDHFl7vMImB5UfxYtO3WDpjNXEx8Rnq73+/vkyRMoVB4IgXSfq6r92qOkM/7O/U/srpa0Rcj3Tbp8Fo4MCGBycmon7H2o7qwsmVOs2gYfQ1MuGv1wkprGbM3Cu8/sMoSlQsZr8nbDeFZk+SWKVRRUZMHZST4inSiYoRUSi8SEJcAm93+5D96w+jaQJdl1w/F8axHf+x8POlTNkwkZAi+dLVZ4FiIXy792NW/7iJVXPXcys0iqLlCtN1WAda9G6kkjd5yJNv9KJ2mxosmv4PR7f+h9HHSJNu9eg2oiPFyhbJafEU6SBfobxM3/URa3/exIo564m4dovCpQvSZWh7Wj3eBJOPKadFVKQD9QRTKLzI92N/4YB99kVSXoOkWIzLp68xeeCXfLxqXLr79c/jT4+RnegxslOabR8qX5R8hfNyK9S1VcSaaKVWq+rpluNep2qjiqp8wH2CX4AvXZ/rQNfnOuS0KIpMolwzCoWXiImMYfn3axwJlu5GT9TZt+YQ549dylI5DEYDj73U1eW0X82gUbLKQ9RtWyNL5VAoFApPUBYRRa7m2rlQlnyziu1Ld2NNtFK9aRV6jOpM5frlkVKyY9lelsxYxYWjlwgKCaRtvxZ0frYtefKlTBN/6eRVlny9kl0r96PrOnVaVaf7yE6Ur13GK7Ke3HsWc7wlzXaHNh2jdNUSqa6TUrJn9UGWfruKMwfPExDsT+vHm/LI0HbkLRjssSyPv9aDU/vOsGn+dud6PpogX+G8TFr0hqrno8gSTu07y+KvV3Bw01E0g4FGXR6m+4iOPFS+aE6LliZR4bdZMWsd63/fQkxkLGVqlKTb8x1p0LmOul+yEKWIKHItu1buZ/yjH2FN1B0v0mtnQ1k1dwNDPxrA6X1nWf/7Fuc8GfvPMW+KLU9GiYp38mRsW7SbTwZ9jUQ6MjJeO3OdZd+v4aWvn6Pb85k373o0HdbNs0zXdT57fgYrZq1DM2oOOc8cPM/8qUv4ZN0EytYo5ZEsBqOBt34fTbsle1g6YxUXj18hMF8A7fq1oNOzbQjOr2YUKLzP31/9w/QXZ2Mwao5swJdPXuXvact5Z96rNO3RIIcldM2F45d5tfV4Im9EOaya18+HsW3xbtoPbMlrc0ahacqJkBUoRUSRK4m4fosJj31Cotnq9IJPerh9P/ZnxzKnPBm65FaYLU/GrCOfARB+5RZTBn2H1Wq9U6wlWV9fjJxJxYfLZjrvQKV65TD5mbC4s4pI13kQln67mhWz1tn2KdF5n25HxPB2t8n8eOorj6vtappG054NaNoz9z78FfcPR7edYPqLswGcShLoVh1dh3cfn8qPp76iUIkCOSWiS6xWK+90n0zUzdtOrtWkZ8uanzZRsW45Hnu5a06JeF+j1DtFruSfWeuwJFgylHRLT9S5ePwy+9YeAmDHgv22flx0ZTBo/DVteWbEBSAwbyBdhrRzTCNMMY5Ro06bGpSuVjLFOikl86cucWkx0a06oRdusH3pnkzLqVBkBQu/XO4y3w3Sdg0vm7k6e4XykN0rD3Dl9HW3ZQDmf7YEXXe9XpFxlEVEkSs5sOGwy6BPTzAYDRzceJQqTStwatc5tw8Ya6LuUFoyy9CPBnD28AUObjzqmL4rhE0HKlauCG/+8mKq24Vfu2WrsOsGg8m2T80ebegVWRWKJK6fDmPjjJ0c2nQMhKB+h9p0H9mJUlWKe9zHvjWHXBZnBJsism/dYQZP8lyu4ztPsvjrlRzZcgKTr5Em3evTfURHCpcq5HknHnBw41EMJgNWi9Vlm7CLNwm7eJMipZ3HTrQksmn+dpZ/v4br58LIXzQfnQa3oW3/FvgF+HpVzvsVpYgociVezT6ejZnM/QJ8+WjVO7YH03eruXY2jHxF8tJpUGvaD2yJfx7/tDtRKLKRtT//y7QRsxCGO3FJV09fY/E3K3njpxdp82Qz7w2Wjhv79w//Ytb/fnWKN7l44goLv1zOu4vf4OF2NXNEruTExcTzVpcPOPTvMae8QUe3/cdfXy7nk3XjyVdIJcpLC+WaUeRKareujnDh4vAEa6KVmi2rAlChYWm3dUYMRo26bb33UDOajLR9qjmfrpvIz2e/5qvtk+k+opNbJSR/0XwULVvYbTCr1XJnnxQKb3D20HmmjZiFlM5xSUkB4h89/SWX/rviUV912lZ37ZrBNm3c0/ts96oDzPrfrw5ZktCtOpZ4C+N7fkTUzdse9eUJNVtWc2sNAShYogAFS+R3WjZzzI8c2XLCJttdeYMuHL/Mx4One03G+xmliChyJV2GtsPkY3SZCwNw+dLWDBolKj9EXfsXU6PH6tqm3rlob7XqPPp/j2RO4EwihKDPK91dWm80g0ahkgVo0r1+9gqmuK9Z9NUKhMG9wr/465Ue9dXrxa6uXTPCloK9y3PtPeprwWdLXX48SClJiDezcs56j/ryhAaP1KFo2cKuP1gE9BndDYPhTqD47YhoVsxZ7zJuRLfq7PpnH5dPXfWanPcrShFR5EqklLTo3TjFeznpi2vIB/1o1bcpgNPDQ2iCfIWCeXfRWMdUu/zF8zHmhxEYDJpToSyDUQMBL341lCoNcz7bZvcRHen0TBsg5T7lyRfI+0vf9HjGjELhCXvWHHSyhNyNNVFnr4fxUzWaVWHk588Ad9XzMWoYjQbe+fNVCpcs6FFfBzcecRvXJXXJfi/WSjLHmWnXr0WKXCFJ92G7fi3o9VIXp3Undp0m0ZyYZt+HNh3zmpz3KypGRJHrOLHrFGM7vktcdHwKC0HxisUYM3skVRtVQtd12vZrztJv086T0fTRBnx3aCqLp69k16r9SKukVqtq9BzVmQp1y2bj3rlG0zRe/X4ELfs0Yck3Kzl3+CL+QX60fqIZXYa1U75mhffxcvxUrxe7UL1ZZRZPX8HBTccwmAw07FyX7iM7OeX1yWax3HLjSjivthrHlTPXUwwcFBLIyzOH06xngxRKSkZm9ClSRykiilxFQlwCb3WdTFx0HLo15Y1+4dhlrpy6TtVGlWx5Mno08DhJUsnKxRn15bPeFtmrCCFo+EhdGj5SN6dFUTwA1G1Xg5VzN7i0imhGLd2lACrVK8+Y2aMyJVetFlXZt+6wS6uI0AS1W1XL1BhJvP/kZ1w7H5aq9hMVHs3RLcdpnspMtcoNymP0MaZpFanRQsV1pYVyzShyFRv/3EbkjahUlRCwPYDmTVmczVIpFPcnPV94BOniXgNA4lGhRW/T+5XubpUQHz+Tw42ZGU4fOMfhzcddKmJSlyydsZq4mPgU64LzB9FxUGuXcSWaUaNB5zrpsgQ9qChFRJGrOLjxqNvIe6lLTu8/R3xsQjZKpcgKEuLM/DNrLS83f5uB5Ucxpu0E1v7yLxZz2vV6FN6hfO0yvDD9WYRwjuswGDU0g8bYuS9QsrLnuUTSwpxgYdXcDbzSehwDy49idKtxrPxhPeYE53PeoFMdBr/7pEOWJDSDhtHHyMS/Xk9X7SVXHNx4NM3ZeXHR8Zw5cD7VdcOnDqJqY1t8WVIiw6TA+BIVi/HaDy9kWsYHAeWaUeQqpIfeYeWfvbeJuRXHa60ncf7IRYQmkLok9HwYBzYcYfE3K/lwxVsq50o20f7plhhCJMdWn+XQxmMIDR5uX5ueozqlmgU4o8RExjC247uc2HXacc6vnw/j8L/HWPTVCj5eM86pWGX/t3pTt11NFk9fwZGtJzD5mhwJzYqWKewVmTL7HPEP9OOTtePZNO9OQrMQe0KzdgNa4B/o5xU573eUIqLIVdRsUY1VP2xwuV5ognI1S6kb/B5nwbvLuXj8MoAjg25SHobjO07y9ctzePX7kTkm34NG0QqFadO9FQEBAVk2xhcjv+fk3rPAnXOe9PP0gXN8MWImb/022mmbao0rUa1xpSyTqVbLamlmcPYL9KVc7dIu15t8TLTr34J2/Vt4W7wHBuWaUeQqWj/RlOACQS6rXEpd0vuV7tkslcKbXD8XxtFNJ13GAOhWnTU/bSLyRlQ2S6bIKm5ejWDjn1vdnvON87Zx4/LNbJWrQt2yVG9a2aU7WGiCbs93VB8+WYxSRBS5Cr8AX95f9iZ+eXydgsCSHhS9XupC+wEtvT6u1Wpl8187eKPzewwsP4oXGr3B39P+ISYq1utjPegc3fZfmm0SLVaO7ziZDdI8WESF32bep4sZUe91nq7wAu/0/JDdKw9kmatTSsmuFfsY1/Mjt3lBwPaRkZSlNDt5+4/RFC5VyCnpYdKzp27bGjzz3pPZLtODhnLNKHIdVRpWZPaxL1g2YzX/LthOfGwCFeqWpcfITtRpUyPFfP7MYjFbmNhnCjuW7kEzaOhWnWvnQvlv9xnmf7aEqRsneZyISZE2Kr4nZ7hw/DJj2oznVliUwx1x/XwY25fsoW6X6oz7vYpXx9N1nSlDvmHV3A0uK1LnBgoWL8C3+z5h1dwNrP5xI5FhURQrX4Suw9rTondjlUQwG1CKiCJXUqBYCE9PeJynJzye5WP9PGk+O5fvBbjz1SZtgbNhl24yqc+nfLXjwyyX40GhWpO0ff4Go4HKDStkgzQPBrqu806PD4m8cdspJiLpet+3/AhLvl7FU68/5rUxF321glVzN9jHT1v5FJqgerPKXhs/PQQE+fPoC4/w6As5W+rhQUW5ZhQPNOZ4M4umr3AZsKYn6pzYdZrjO5WbwFsULVuYKi0quM6/YNBoP7ClyiTrRXavPMCVU9fcukcWTVuB1eq+8Jun6LrO/M+WetxeM2i06N2YgsULeGV8xb2FsogoPEZKyc7le1kyYzXnj1wkKCSQNk+1oPOzbQgKyZPT4mWIc0cuEhPpPg5EM2gc3Hg0V9SjSQ+6rrNz+T6WzFjFhaOXCMzrT4mKDxF64QYRoZEUKV2Izs+2pdXjTTD5mLJVtr7jujD3xQVcOH4ZgUBK6ZjSWblBeUfNEoV3OLjxCAaTwW2F2ZuXIwi7eNPjqbG3wiJZ/t1aNs7bStzteMrXKUP34R2p264mNy6HE3o+zGP5ytYsxcvfPudx+9SICI1k+Xdr2DRvG3HRdnlGdKJuW++7cxXeRSkiCo+wWq18NHAa63/fcieO4iyc2n+O+VOXMGXDxHsyg+D9Gq5gtVqZ3P9LNv651XG+AE7vv5OYKSlvx5JvVvLhyrezNW9HYL4APt04nm1/7WHFnPWEX42gcKmCPDK0Ha2faJrtitH9jpR49WI/tf8sr7ebRHRkjMOaGHohjM0Ld9B9REeeGPuoR/0UeCiEwZOepG2/5vj4+WRSnolER8amkKfHyE68MG2IUkZyMco1o/CIBVOXsv6PLQBO5l2pS26FRvJOjw9dlsPOzZSpXoKAYPe5E3SrTs2W3qlrkV3M+2Qxm+ZtBXA9ZTIpb8fOU3z98pxsky0J3wBfugxrz5db3+fns18zdeMkOgxspZSQLKBmi6pY3VTZBQgI9qdQybRdIxazhbe6TiYmKtbJpZnU/5JvVrFn5QEKl0o7wPulb56j87NtM6WE3JEnLlV5Fn+9khWz12W4f0XWoxQRRZpYrVYWfL7UZUlM3apz6cQV9nlYLjw34evvS89RnVymeTYYNSrWK0eVeyhw0ppoZeEXyzz+AFZ5OxTpYevfuwi/GuG6FoyA+Z8tpffobi770AwaRcoUomGXzBd33PLXzjTkEcybskTN1srFKEXkPuTMwfN8/vwMBld5kWervcw3o3/g0smrGe7v2tlQwq/ectvGYDRwcOPRNPuSUrJ71QEmPPYJT1d4geEPv8bvH/6Voy/BgeP70qBzHQCnAEohBAWLF2D8/DH3jFn3xK5TTHp8ChHXI9O1XaLFyrHt939ArrQcRL/1OnpYe/SwLui3P0FaL+e0WFnO4c3H0NzUcAKIjYoj7GLaCcUObLDFm7hCSrh4/DJt+zV35Py5W9EXAtr1a4E5zuyB9O6x1adyJ4/k4vHL3I6IzvRYiqxBxYjcZyybuZrPR8zEYNAcpsnLp66yaPoK3vp9NC0ea5TuPr31ISGl5PPhM1j+3Vo0o+aoeHnm4HnmTVnCp+vGU7am61TKWYXJx8SkRWPZ8tdOls1czeWT1wguGET7AS3pNLg1gXkD0+4kF/DnJ4v4buzPLmejPOjI6O+Q0Z8ABsAetBlzBhnzI4R8i/BtlpPiZSlS4l1l2oNngqZpvD73BZr3bsRnz80gMuzOx4Zu1fl18kLW/76FqRsnZmq2jLJ03PuoJ9Z9xIndp/l8xEyQOPmD9UQdq9XK+099xrVzoenut1jZwoQUzee2jTXRmmYcxdIZq1n+3VqHTElIXRJ9K4a3uk3Gmuid6YPpxWAw0LJPEz5aNY6fz37N17s+4rGXut4zSsi+dYf4buzPgOuYEHcYjAaqNLp33E/pRSZssSsh4FBCANABMzJiBFIPzwHJsodaraq5nTEDkP+hEI9iRGq2rOb2PhUCSlR+iKD8eRBCcHTrf0TdvO3UxhY8awsofe+Jzzzah8zIU7LyQ/fszL4HAaWI3Ef8PW05Bldfw9L2wl/yzap092swGnjspa64+qDSDBolKhXj4fY1XfYhpWT+lMWOFMp3o1t1wi7eZOvi3emWTwELP1+WYUuIZtBoN6DFfZ23Q8b8gM0SkupaIAFi52efQNlM/U61eah8EbfXSM8XOmEwpJ1FtPljDclfNJ/LvqSEvq90RwhBfGwCS75d6TJPjzVR58jWE5zad9azHUlVnkZpytPn1R73jHv1QUQpIvcR+9YechsZr1t19q3LWEBp3zHdadm3KXBXHIUmyFsomHcXv4HgFjJ6BvqNnuhh7dAjRiITNiOlbWbNldPX3Zp0DSYDBzfuQ8bMRr/Ry9ZH+HPI+PXK/JoG+9cfTrclJMlvX7lBeUZ98WxWiJV7MO/A2RJyNxJp3pFd0mQ7BoOBSYvfILhAkFO8RtK9XKdzNbqP6uRRXyYfE+8v+x8Bwf5OqduTYlC6Pt+BR4a2A2xu17jb8W77E5rgwIYj6dqf5Pj42uUJSl2ebsM78siQthnuX5H1qBiR+4isfFkbDAb+9+tLtO3XnGUzVnPh2GUC8wXQ9qnmdB7SlqCgq8iwXiAjsZm7AesVZMIa8O+Lrr/qyR5A3CLk7TM4NBbrFaR5A/h1hbyfIoSq+5BR/PL40qBTXa6fDyMyLMqWt2NIO1o90RQfXzVl9n6ndNUSzDryGf/MWsf63zYTExlLqeol6Di4FYGlfVxbU1OhQt2yzD76Ocu/W8uGP7cQH5NAuVql6T6iE/U61Mp260OFumWZfexzls1cw8Z5W23y1C5NjxGdeLh99sujSB9KEbmPqNu2Jhv+2OLSKqIZNOq2qZHh/jVNo2mPBjTt0cBpuZSJyLBhIKNwKCGA4ws0bh4hwdUoVq4IV8+6topYLTo1GoXj3MDeR/wyMFaDPMMyLP/9TK1W1di98oDrKYyaoN+bvXnqzV7ZLFkuwacBmLfi2ioiED4Ns1OiHCG4QBBPvN6TJ17v6VgWGxvLsWPH0t1XSJF89H+7N/3f7u2yTblapfHP40dctGuriNQltVplPk9PSJF8DHinDwPe6ZPpvhTZi3LN3Ef0erELVlfmeWGLmu82oqP3B05YB/pV3D3kiZ1D71e6uVRCNIOgYDEzTTpGuBxGxs5BysRMi3s/0vvlbm7zKJh8TXR+gM3TIvAZ3F6f+IK/eoF5G78AX7oP7+g2T0/VxpWo+HC5bJZMkZtQish9ROUGFXhx+jAQths8Cc2oYTBo/O/XlyhWtojbPqT1MvrtT9DDHkEP64B+6w2kxX1ciTTvwr1xTYL1At2G1XW8DJPLJzRBYLCBd386j9Gdh0C/AdZLKRZbE61s+GMLr3eYxMDyo3ix6VssnbGauBj3vun7iYfb12LI5P7AXefeoGH0MTBhwRhCCmc+GNVitrD6p4282mY8A8uPYnTLd1gxZz3m+Mzng8hKhG9zRJ4k92By954BMCFCvkYYVMG1rGDQu09Sr30tIFl8mf3DqFDJgrz9x2iX2x7feZKPB3/FoIr/x9Aao5n15i+EXvC8ho3i3kDIXB4FeOiQ7SVYs6brGRm5iSQzZ9WqVQkIcJ86PKs4tf8si6ev4MCGowiDoEHHOvQY1YmSlYu73U4mbEZGDMf25Zj09WjLuSCCxiICh6S6nR71HsT+Cri3VohCW0AryO6V+1ny7SrOHryAf5AfrZ9oRuenTpHP/4e0+yi4CmEs4/g7IS6Bt7t9yP71h9E0ga5LhBBIJCUrPcR7y9/gaviVHD0f2cmxHSdZNP0fjm79D6OPkSbd6tFtRMc0FVBPiImK5Y1O73J8x6k7x9peqK58nTJ8sna8R1Mkc/IekeYDyNifwbIXMIFfW4T/UwhjyWyVIzeRHefDmmhl88IdLPtuDVdOXSNvoWA6DGxFh0GtCHRRYuH3D/9i1v9+xWC8kxPJplgbeXfxGzzc7t54J2SE3PAe8Qaevr+VIuJl7tULSFpvIsNaA2Zc+U9EyI8I38Ypt41fibz1f256F2AogSi4xmXQmE0JSmPmhlYQUWgTQtyxvkx/aTaLpq9IdXqgZtSo0bwK/T7pcc+dj9zIR4Omse7Xzam6gDSDRrNHGzBu3pg0+7lX75H7ldx4PnavOsCbnd9LdZ0QAl9/H345/w3BBYKyWbLsITeek4zg6fs7y10zM2bMYODAgVk9jCKzxM0DLLieX2tAxroojObbFrSiuMvTIAKecR+57tMUDGXc9CEQAYOclJCYqFiWf7fGZY4CPVHn4IajXD9zw/W4Co+IuH6Ldb+lroSAbWr45oU7Cb2ojrUi8yz4bKmbvCCShHgzK+esz2apFFlFls6a+eWXX/j888+pX79+Vg6j8AK2HAru8lBY7bkYUiKECUK+Q4YPvGvmjD2Vtl8fCOjndnwhNAiZwe2zA1j5K6xbmI/oSAOlKibQ9ekbNOraHBE41GmbU3vPYo63uN8xAWf3XaR1V/fN0sv5Y5dYPH0Fe+2F/h5uV5MeozpTumoJr/RvtVrZumi3PeX8VfIWDKL9gFZ0HNyagCD/9PWVaGXT/O0s/34N186GElIkHx0Htab9wJb4Bfh61Mex7SedsuGmhpSSw5uP0/ap5umST/HgEhV+mxWz1rH+9y3ERMZSunoJug/vxIEN7vPiSF2yf8MR+o7pkY3SKrKKLFFErl+/zvjx49mxYwdlypTJiiEUXidzHjphqgyFVkDsH8j45SBjwFgJEdAPfFp4NI//8lk/Xm1dlYhrkfacKILQy77sXBtM6yfL8MZPkDzxY055FVf/tJFPnpmO0ITj5Xzl9DWWfLuKsXP/j3b9W2Sqf4vZwoTen7Jz2V40g4Zu1bl2LpT/dp9h/mdLmLpxEoVLpl1iHWwxNG91ncyBDUcccR3Xz4VxbMd/LPxiGVPWTyCkSL40+8nlHlzFPciF45cZ02Y8t8KiHFbN6+fD2L5kj8tZNor7kyxxzRw5cgSTycTixYupXbt2Vgyh8DLCpxHuLwcDmNznWRBafkSeEWgFl6AVWocW8i3Ct6VHSoiu64zr+SG3wm7bi+zZttHtMbMb/tjK/ClLnbapWK8cPn5pJOKSULaOd6wUAOeOXOSTZ6YjdelkIdATdaQu+XjwV5w/lnJmT3r4aeI8dv2zz9Zv0lehtCkDYZdu8m7fKR739d3rP3Nok60qsm5/2EspQcLlk1f58OlpHvVTtXHFNFPICyGo0ayyx7IpHlx0Xeed7pOJvHHbybWadL1Le9C5K4QmqO2F3COK3EGWKCJt27Zl2rRplCz54Eai33P49wVMuCwGgxURODjLht+/7jAXj19xbf6XsODzpU7FrQKDA+gytL1TWufkGIwaNVtWpUj5Ql6Tc/H0FS7HA1uBrcXTV2S4f3O8mUXTXdfm0BN1ju88xYldp9LsKyYyhn9mrXUoICn6sursXX2QC8cvp9lX/qIhtHmymUtlRDNoNOvVgMKlvHesFfcvu1ce4Mrp6+7dLy6scEIT+PiZ6PRMm6wST5HN3BOZVaWUxMbG5rQYHhEXF2f7GRuDZlmHwTwfoZ8HkQ+rqStWnx4gcmMVyAC0gKmYYkcDVoR9+q7EgMCKxfclrNY6kEXnYc+aA07T9FIj4totzh49z0MVijqW9RvXi1MHznL43+PJpu/aHE1FyhZm1NeDiUk4hYz+nMTof0GakYYaJPo8gTTWSbecu1cfcCujNVFnz+oDbq/XG5fDWfH9OnYs3YslwUKlBuXp8lx7qjSqwMm9Z4mNcn+MNYPG7jX7KVn9IbftDm09nnYMDbB79X4KlgpJs93QT/pz/tglTu0965i2m4SPn4k67WpwO+o2BqP7NPyOe8T+U5Gz3H0+pJQc2niMf75fy5kDF/AN9KV5rwZ0fKYN+QoHe2XMHf/sSXENuUIzCHTrnXZSSpo91pCY6FhMASlfYWEXb7Jilu3+SjQnUrlRBbo8157KDcp7Rfbs4H65R6R0b9lK4p5QRCwWS4ZSEOcciYjbL+LjvwcpNYTQkfpljNaj6DGzOXljHBZrbvxyzI+P4RMKBq4mr99ehEgkxlyJsOhOxFoqAFl3DsJu3MCTMITTp08TaXHOvvrUx905tLYSO//aT8SVWwTmD6R+t5rU7VIDi3UrVYt8hGa1IIRNgdAtl/FN/IerUb25djt92TTNCWkn7kowm11er2f2XGDOy/NINCcm84vfYOMf22j/XHOqNEv7YSmlJPR6aJr3xPnz59LsC+Datase31+Dp/Vmx4J9/PPVRhLNd3K+xMcm8NWo2az4YS2DPuuLyTftR8u5c57Jp8gezp07h5SSxZ+uYdufexzxSQAXjl5i4efLGfr1kxSvUjSNntwTFXabVXM3eKSE9P/oUVZO38SNC+F3FkpY/+sWti3azdBvnqR45TvynNp1jh9Gz8dqsTrdXxt+20qnkS1p80zTTMme3dwP94iPj0+abe4JRcRkMlGhQoWcFsMj4uLiiL/5CXn99wI4Xn42pVDiY4yg2kPfYg78JWlhLqMq0AqwWRUCgNLZMGp8z0TWz97qtk2+wsE0bdc41S/uGjVr8NTLd9W8kLfxvT0SpBkh7jz0NPs5KRa8gAJFWqKbWnksZ4NOdVg1d6NLF5LBqNGgY12qVq2aYl1MZCwT237upITAHb/4mpmbqdeqDkYfo9NL/m6kLmnzWAsqVXWvtJR6qDRzRy/AkuDeKtKud2tKVnFvXUnOH28uS2lSt+/O6T0X2PnbQYZ+1N/l9nFxcZw7d44yZcrg75++GUAK75P8fGxdsJttf+4BcDrHUpckxJr56dW/+P7YFEyZKJL45kvvY45JW6EvUDyEkKACzkrIXfL8/OpffHdsKiYfI7fDo5nw2mdOSkjy/Vj59SYad2jAwx1qZVj27OJ+uUdOnUrbhQz3iCIihLh3krpIM0F5ViBcJQXDitCP4m86ifCpk72y5WKadG1AiUrFXPqNhbDVUwkK9jyBkYyZj06skxLijIZP4i9oeR/xuM/HXurGyjkbXK7XdUnvl7qmer2umrWR+OgEl75vzaCxfMZaj+Tw8/NL854ICAjgkSFtWfrtqlTjRAxGjVqtqlP5Yc+V/LOHL3Bok2vridQlq+ZsZOgHA9KcZuzv73/v3NcPAH5+fiz+aqUtM3Eq16hu1bkVGsmefw7Stl/GZoadPnCOo1v/86ht75e7sWT6KrfyRFy3y/NUc5Z/u5aEWLPb+2vJ9NU075kyKWNu5V6/RzyteqxqzXgZoZ/BqKUVR6G5zMnhbaSUyITN6BEj0cPaod/ogYz+Fqmn/MrISQTxTPqjJnkL6AgtmfXCHhzZsm9T+r6WvpwBtho47tDBsjtdU1P98/jRoFOdFMsNRg0E9HqpCzPH/szA8qMYWf915k9dQvStGAB2r9qPdDNNWrfqHNp0zK01BGzBeu6UgeQM+3gg1ZtXAXAE2QoBCChWrghv/OQuI25KDm06lqYhLyE2gVP7zqar35xA13W2LdnNW90nM7D8KF5qNobdi97Gcr2v7V65ORAZtxgpc3cdHW8RGxXHucMX3d4PBqOBgxuPZniMQ5uOeTQ19+EOtej0TBvOH/FAng1HADi48ajbtrpV5+CmjMuuyDruCYvI/Uf2uGSklMiodyDuTxzJxQAZ/R/EzIH8P9ryf+Qw0noDGd6f4kXOMnO9kZW/h7BuYQgxURolK/nT/YXRNO7eGE1Lr97s3dwXu1bsY3yvj1MNVq1Uvzx58gWy8LNlyXJ/wKn955g3ZQmvzHyePasPekUkT78ywFb99OPV49g0b5s9oVkY+YrkpZM9oZl/nvSZfZPyu3j72GY31kQrk/t/wcZ529AMGoHBZsZ/d5qyVeORidhvl8vIyB22OkohsxBaYE6Lfc/jqdI/8J0+6b7fVa6be5csV0Q+/PDDrB4iVyG1ciTqAWlYRazg4z4nh1eI+8OuhNjHdKCDjEJGDINCa22ZUXMQGfkqWC8AEBySSN8RYfQdkVRhU4OAdWha+oPMhE9DZMJqNy00MNXz6MUecf0WE3p/SqLZmuoD79j2k47f7879cSsskvG9PsFqdVWGPn3oVp2aLVPGoLjCaDLStl+LDJvTk1OrZdU0H/i+/j5UqFs202NlJX9+sphN87cBtuP56tSLlK4UjxAgHCFI9vNo2Y+8/QEi7/s5Imt2ERDsT+lqJbhw7LLLc2xNtFKzZcbzd9RsWTXNIFW/QF/K1ymDX6AfpaoW5+Lxyy4D2a2JVmrZ84nUalmNHcv3uuxfM2jUbOH5faPIPpRrxtsIH25Ed0K6tHoYwFgNTHWyVAwpJTLme9zlBUG/BgnrslSOtJCJp8C8DWdFKTk6xP6J1KPT37n/o0AAUro6Bjoi8BmPulr+/VosCZYMfXXpibot/4kXPtgMRo1K9ctTuUHOBG+XrVmaWi2r2VxRqSA0QZdh7dOdhj47SbQksvCLZY6XW9FSCTTuEIXB5WeZDnF/IfUIVw3uC4QQ9Hm1h9sYi3yF89KiT8ZjLCrUKUv1ZlXcXj9dn+uAfx5/hBD0fbWHSyUkSZ7mvW3ydHqmDb5+Pi4/LHSrTp9XumdYdkXWoRSRLODa7cfQjUlfn8lneAjQiiDyfZUu83qG0MPtVgZ3bz8j0rzTa0NKPRwZPQP9Rk+bjz1ipK2qrruXd5pxHADxYDmSbnmEFow54At06YN0utTtv4u8yNufokd9gEw877av3Sv3ezTdMCsRQlCweAHGz381668fN/zvt5cpUqYwiDsTv5JieWq3qs6Qye7rCuU0V05f51ZopOPvGg1jEGk+CRPBciBL5coNdBrcmh4jOwE4KQuaQcM/yI/3l72Jj4sZM5f+u8L0l2bzTNWXGFzlRT4fPpOzh1LeV2///jJFSheyXcNJ1489bsTX34etf+/i/X6fcejfY3R6pg3dR3RMKY8mUsgTXCCIiYvGYvI1OSXe0wy2voPy5+HbV+cyY8yPXD1zPaOHSJEFqBiRLEBixOL/GUbDVmTs72A9ByIfwr8n+PdGaNlRujp7X5rS8p+96F0kDpO29QoyYY0ta2vwu7bCdim3zFq5jPU5FjqFKqX2YbRuAP0myFuAZpPVGgmx55GxP0O+zxF+HVP0cWLXKY5u8yzSP6vIExLIwHF96TS4NYF5czZWoUCxEL7d+zGrf9zEqrnruRUaRdFyhek6rAMtejfCaMrdj5UUinFunEWfQwgheGHaEJo92pDF36zk9P5z+AX60rJPE7o+1578RVNPfLfxz61MHvAFEhxT26+duc7y79YweubzPDKknaNtweIF+GbvJ6yau4E1P20k7FI4t0JvIYQgPiaBq2evE3oxjA2/b+WJ13s65FnyzSpOH7DJ06pvU7oMa5dCnofb1WT2sc9Z8s1Kti7eza3QSG6HR6MZNG6HR3M7PJrLJ6/y97TljJs/hibdVUHW3ICQuTzC59AhW3XTmjVr5rAknhEbG8uxY8eoWrVqjk67klIib3QA60XcvexFvi8Rfp0zOVYiMqwd6KG4crGI4Am2Anh3b2s5ibyZVmlcX0ThbQgt/Rlpk58Pf9Mp5M2+pH48BGBAFFqNMBR3LE2IS6B/6ZFE3YzyKOFaliBg1BfP8ugLnk8zzs3k9D2SaEnkyRLPExkWBUCx0gn8sO14GlsZEYU3I7T8WS9gNpPZ83Hl9DWerfqSy4zDQgi+3vMRFeqkjBuKi4mnf+kRxNyKcVmK4O0/XqFV3ybpluvI1hO83Pzt1FcKW+zUT6e/omDxAunuO6vJ6XvEW3j6/laumfsUIQQi8FlcKyEG0IqAbzsX69NBwjrQr+I6zkMgY2an6qIRpopgaoSzCys5GgT0zZAScjcy5kdcX/IS0JGxvzkt3fDHViJvZE4J0QyaY3pvuhHgn8efDgNbZlwAhRNGk5FeL3ZxuLeunvdl++ogrC5nTWvg1/O+VEK8wZJvVrm9PzSDxt/T/kl13frftnA7PNqlEqJpgvlTFmdIrr++WOYyFgVpixlZOsNdMLsiu1CKyP2M/5P2Ynbg/KLXQAQhQr7zyowZW74Od+Z4aYtX0W+kulbkmwKGktje1MK+he3SPLE/hGGNr/LeE1M5sDH9cSJOmLeSXFk6siuAD0eV4pmmVXiuTSXmflyYfSsX8PuHfxF5w/a1fHDTUdcPs2QMfvcJWtq/2pL7p4UmyFcomHHzx2BMowbL3QhN4Ovvy3tL3shxd8y9iky8iH77Y/SwR9DDOqDfehNpOcyTYx+l+WO2mWuaQWPK6FJcPOWHlKA7Puzt59FUCxHs4stawd41B9GtOsXLJTB80mW+33ScWf8e58UPL1GmShzWRCt71xxMdduDm464reqs65LjO09hMVvQdZ2ti3fxVtcPbHl6Goxl4efLiImMSXXbfesOu60LpVt19q8/nL6dVWQJuduZq8gUQmgQ/B74dULG/gqJ/4EIRPh1Af/HEQZvmSQz57MQhsJQ4C+IX4SMW4gl7hon9sSz9KcQNi3OizXxBpdPhbNx3jZ6j+7G858+nelgzZ8+LcLPU4tiMEisVltfF0/6Mf9biTnhV+ZNWcKn6yd4PEum9+ju+PiZaNevBUtnrOLi8SsE5gugXb8WdHq2DcH5g3i4Y212Lt+b5uHyD/InX6EgWj/RjG7DO1K4ZMFM7euDikzYiIwYhU35tCug1kvI+AVoQf/j7T9eYfuSPSz5diWXTlzlszfL8PTYAOo0OQ4iHAzFEAGPg18XhEi7XsaDTMvutxg7/bzNwWl/qxQtncAjA27y+Wsl2LvZxbPGw0dHUt6XfxfsuJOn52wop/aeZd7UJUzdMJFi5Yo4d527ow4UyVCKyH2OEAJ8WyJ8s860L3waIGN/dNcCDCVAc/1CFVogBPTDLHrTv+pIbkfcdqq4mfRls+CzpVR8uBzt+mcgJ4ZPE4hfzraVgfw81VYoK0kJAdB1gSUBkHA7Ipq3u01mwDt9WD13oxu5BeVqlcYvwBeApj0b0LRng1Tb1mtfi13L97nMrqoZNGo0r8KU9RPTv28KJ6Q1FBnxAmDB+W1nT+p3+wOEqbrb86XwjJaPFafvs4vRNEieg8xof7u8/Mklfpme+vOnZouqrP31X5d9a5qgYr3yLPhsGZsX2mb4OdXAkZKIaxGM7/UxM/Z/6vSBUqdNDbYu2unSKqIZNOq0ruHpbiqyEOWaUWQe37agFcV1nIdEBDzjkRVj45/biLwR5aSEJEdognkZ9BmLgKcBnQUzCqEZUu8/KeeI1CWhF27gF+hLcIEgl1kepS49zk3QcVBr/AJ9Xaa41q06vUd386gvRRrEzSOlEpIcAzJmTjYKdP/SfXA4AmclJDm6FXo+m7pbtm2/5uTJF+j6ntAlvV58hL++WOYmyZrO2UMXUqRvf+ylLq5dM8KmiHR9vkPq6xXZilJEFJlGCBMi5DsQQZBqvo4gZOxc9Mi3kRb3NVIObkw9JqNhu0i+XXucJWcO8MXfi0i83gb95lPoYe3Rb3RDRn+FtIal0mMyOX1qQ9A7HNkZiG5NWykSmuDgxqO8t/RN/PL4Ovmyk2R87OWuHltn8uQL5N3Fb2DyMTopZUm/D3inD0172L7OrVYr/y7cwRud32Ng+VG80OgN/p72D6EXwpg3ZQkj67/OwPKjeLvHh+xYtkeZoe9CmrfjmEaeKtZ013uSiWfRo95HD+uMHtYRPXIc0nIiU3LeCyRaEln322bGtJtgr8nzFstmriY+NgGAPAEH3CSDA6MJgoNSjxHxz+PPe0vexC8g9fvr8TE9KF+nLJE3bruV0WDUUtTAqdG8KiM+G+zUX9LvRqOBd/58hUIlct+MmQcR5ZpReAVhqgyFVkDsH8j45bbAVD0cW76O22C9DXGXkHHzIHgiIuDJVPtJzW3xwuRLdHv6pm2cpPe3vAyWy3e2iz5lr5/zA8LkeqqYCOiPZDGeOKelLln362ae+t9jzD72BctmrObfBduJj02gQt2y9BjZiTptaqQvXkXY/ku+n0m/J1ldLGYLE3p/ys5le5PVrQnlv92n+eaVH9B13SF+6IUb7Fi6hzZPNWfsjy9gMKQvIPb+xbuKmYxfgbw12v6XPd4k7iIy7g9bjpyAx706Xm4hPjaB/3V5n0Objjmuxevnwji6/T8WfrGMKRsmEuyyurVnVG9amdnHPmfpjNVsXriDhDgzleqVo8fIztRuXZ1zRy5muO/HXupKjeZVWDx9BQc3HcNgMtCwc116jOpE8QrFMiW3wnsoRUThNYSWH/KMAL/uyBtJicGSf5Xa/fNR48FUDWGqlaKPmi2qseqHDY6/G7WPTKmEpIoOMgYZPhQKb0QIv9RlFIIazapwZOsJJ1+zK+Ji4pnU51O+2vEhT094nKcnZPyFExMZwzs9PsJitji/J+2//zjxTyrWK8fRbSfY9c8+214lr1sDyLtkTlq//vfNVKxblr5j0leh+H5F+DRCmnfj2ipiAJ9GHvUlEy8gb71i7yuVeJOod8BUHWGqngmJcyczxvzIkS02q0/StZZkfbt08iofD/qK935vAomncD193wA+7mtFFSxegMGTnmTwpJQfKCUqFSO4QBBRN11bRayJOrVc1MCpVK88Y2aPcju+ImdRrhmF15Fxv5PyoZ0czZ7TIyWtn2jqFJPxzJtXgbSUkCR0kBEQv9xtqz6vdPdICQGbVeTErtMc33ky7cZpsPrHTcRHx7styvXnJ4tYNH1l+tPJS1jw+VKvFda75/F/HNt3lutaSyJwsEdd2XLLSNxfzz+lV8JcT/StGFbOWefyXtETdXat2M/10LTiLHRE4MAMy3F33pe7MRg1Slcv6Sh+p7j3UBYRRarIxFPI2F8gYattgW9TREB/hNGDYmvxm0jbP7/VaUlcTDxrf/6XVXM3YPI12uuYCIqVNnuohCRhQJp3Ifwfc9miac8G9H+rN7+8v8CjHjWDzf9cpWHF9AiCOd7Mul83s2LOem5eCSf2dpzbWA7dqnN4y/EM17S5eSWCa2dDXZqcpYyHuCXIuIW2LLjaQ4iAPuD3SKrTUw9vOc7ir1dyYucpfPxMNHu0Id2Gd6Bg8QLExcSz5qdNrJ67gYjQSIqVK0LXYe1p/lgjDOnMl5IVCEMRyDcNeesFbNdikoJmAKyIoDcQnlbAviv/TEpSXs/3Ayd2ncKS4DLLm4P9G6Po3O9TWxVtBM7HWkcET0phLYqLjmP1j5tY/dNGboVG8lD5InQZ1oHmvRqmev089WYvTu0/y5a/djpcRGCL4wopko9Jf7+eo/WXFJlDKSKKFMi4xcjI13F6qMResH0Z5v3IVjPH1bbmXWBNXwBfxPVbvNp6PBf/u4JAIKVEaLaf4waV5d0fz+Lr712f/+B3n6R+p9r89O589q5OPZAuM0SF3+a1thM5c/C8bV+yqWDe7x/+zSvfDU/xUJb6LVstoMQT2M6rBOtlZOQOiP0FQubYplDb+WHc7/zy3gIMRs0x8+DC8css+Hwpb/78IjNf/5nLp67aepK2WJV9aw9Rt11N3lvyBj5+OZ93Q/i1gYL/2HLoJKwHEsFUDxE4IFW3oGs8OXcPdrCw8O8Kpir2j5d/AQk+jW0fL6aqTm1vXAnn1dbjuXL6mtP1s3fNIep1qMWkRWNTXD8Go4Fx815l66JdLJu5mosnrhAUkod2/VvQ6Zk2BIVkPvOyIudQiojCCZl4yq6E3G3RsPvDI8fa/OGpWEakfhsZ8bwHoxhsOT3sTB7wJZdPX7PHQUh7X7afB7flYfYHxRjx7hUP98CKMHmWF6JG86pM+vt1Hi/2HLFRsS7b6Vadmi78z66YMuQbR5Cdp0qIZtCo1qQSZw6eJzYqLl3jJbFi9jrqtqtJ26eaOy2XkW/a/fhw56VpP8eWg8io9xH5PgBg66Jd/PKezVqUfPqjbtUxx5mZ2GeKoxuZbB3A/vWHmfXmr47ZCjmNMJZCBL8BvJHxTnyaQOJJMhMDcS9SqX55TL7GNK0iNVvYFA1hLI8IHpdmv+8/+RnXz4Wmev3sXXuIH975nec+eTrFdpqm0bxXI5r38iy2R3HvoGJEFE7I2F9wXxRFIGN+Tn1V3N8gY0j761C35/SA80cvsm/tIUfFzhTy6ILlvxQg5rYnl6oGIgT8u3jQ1oavvy89R3VymcfAYNSoWK8cVRp64JKyc+1cKFsX7/I4DiUJ3arTd0wPeozs7FKetBCaYP6UJU7LZOIlWz0gly9SHeL/RurhAMyfusRl2m1dl+hW3eW+SV2y7Ls1xN7OmCKVG0mtWKMzmYuByK0EheSh0+A2Lq8FzaBRr2NtSlR6yOM+Tx84x+HNx13m95C6ZMmM1cTFxGdIZsW9iVJEcjFSSmT8evSI59HD2qHf6IWMmYXUI7Nu0ARP/OFbnOXUo5ExPyKjv8QjE3XQOFtOD+DgpmNpFoMzx2ucOuSfRqcaiABEyEyXM2ZcMXB8Xxp0rmPrJXmdGCEoWLwA4+ePcbg6rp0L5buxPzOk+ssMrvx/fPrsdE7sPu3U3+HNx9NlqdcMtr6D8ufh+zd+ISEm3vGV6a4OR2pIXXJy7xkS4hLuLLTsIW2BEsF8AF23xamkV4lKTkJsAqf2nc3w9rkNYSyNyDsFW8xD8vgFAyAQwRMRpvszQ+dznz5NtSaVAFuWU7DnvRFQvGIxxv74f263l1IiEzahRwxHD2vHwX9eQaRxScdHx3N6/zlviK+4R1CumVyKlFZk5GsQv5SkADsAefsoxMyG/D8jjCnLanth5PS1tl5BhvcH6xUPtxXOX5ieJuIKHA2G38F6zva3Vtj2T94A4Q++nRABT9rq1qQTk4+JSYvGsuWvnSybuZrLJ68RXDCI9gNa0mlwa0fBuV0r9jG+18dYE+9YBK6dDWXlDxt47pOn6ftqd/suebZPhUrkJyHOTNTNaDSDxu3waG6HR3P5pG2mUN8xPTi9/yyXT17DnGDhVmhkBmNNHuz4BW8g/LuAqbI9BmIzoLuMgbif8A/04+M149g0bzvLv1/D9XNhhBTNR8dBrWk/sCX+ga6Vfil1ZOT/IH4hSc8wmRgH8iEyVopacb+iFJHcSuwPdiUEnC0UEvRwZMRwKPiPrbCdN/FpCnEX8cQfLqVERvwfWK/h2ctOA9PDToGUNVtWS3NTHz8TlZr0QQtO6Tf2FgaDgZZ9mtCyT5NU10dcv8WE3p+SaLY6KRpJJuaZr/1IpXrlqN26OjWaV3HEg7ocz2hg9HfD+V8XW1xGcguEbtVB2MqY//DfNIqULsSulfv53yPvp7kfQhOUr10GX3/fOwtN9UhTIIzgUxtNs8WpHNv2n8vS7Gnh6+9DhbpZoSTnLJ7GQNxvmHxMtOvfIv31nWJ/tishkPQ8qdk42lFGwRV+gb6Ur106A5Iq7lWUayYXIqU1jToYVrCezZIpgyKgP+5fWBIRMMD2q+UgJB7CvSsnOToi8BmnJWWql6R2m+qppnUHmzn4kSHtCAwO8HCMrGH592uxJFhcWjsMRo0Fn9sUx2Jli9CkewO3vvW2/Zuz9ud/MbhyvUhbPMbSGasBqNehFsUrFnO4cVyRWu0bYSwJvm1wXQtIA78etoR02POsuMp1ogk0g+Zy34Qm6DKsPQFBabnSFPczUurImNkplpevEU/1htEuaz0JTdD1uQ7451HXz4OEUkRyI9bLtjwPbjEizTu9PrQwVUTk/RDbpXG3P1xD5J2MMNnzaVh24dklZG8TOArhlzL50Zs/v2Qr4S3uJC5LetHVbFGeIe/cQA97BD2sA/qtN5CWQxnbuUxwYMMRt24Ra6LOgfVHHH+PmTWCUlWL2/Ypybdu/1mpfjle+HIIe9ccdF2UC5tlZN86275qmsa7i8eSt2CwW6v2Yy93pW2/5imWi7wfgLF80l/2n/bzYqqFCH7b0bZYuSKUr1MmRR+aQcPk58M7f75C0bKFUz1ftVtVZ8jktII7FcmxxVFsR4/4P3vtpO7I6K+R1tQLxd0T6NdBT32m2/++OU+REmaEkI5LMen6qdu2Bs++/1R2SanIJSjXTK4kZ336wv9RMFa3+cOTAlN97AnNTJXuNPQ0vsOnKSLPSIRP/VRXFygWwte7P2LNT5tYOXcDt65HUrRsIboMLknzNp9j1K04rC7WS8j4hRA0FhE4JMP7mF7SW1QuuEAQX+2YzNpfNrNyzjpuXomgUKmCdBnSjlZPNMXH15RuGUpWLs73Rz7jxwl/smj6CufLxO55cZXUSWj5ocA8iFuMjFsA1jAwFEME9AW/ro6EZku+WcmXL3yfqqWm2aMNGD51MIVLFqReh1qs/nETq+au51ZoFEXLFabrsA606N0Io0k9VjxFSomMeg/ifsIpFiz6JMTMgvw/3nep4wsWS+Tr1f+x+s+CrFlYnsjwQEdCvBa9G+eKhHiK7EU9MXIjhhKgFbQVjnNJIsLHs3wZGcFmGZngvpFPA9xnUAVEHkTINwjh67aZfx5/uo/oRPcRnQCQ1hvIsDakLOWeFLT7ERirI3wbux/fS9RpXcOtVUQzatRq7ZxrxNffly5D29FlaLvU+2xbg03ztrm0imgGjbptnGdj+Pr7sP63LWia5jyzxS7Wgs+WUql++RR5RACE8IeAJxABT6Q63rEdJ/nyhe9tuc7ulknAtsW7GT5lEGA7Xz1GdqLHyE6p9qXwkPi/7EoIOLs47bWTIoZBoQ2pZr7N1WhFQCsG+tVUVwfk0en5bCiPvjIJ4dc2m4VT5DaUayYXIoQBEfAMrm3wBjCUAZ9m2ShVKphqg7EGrmMPBAQMSFMJSZW4eaRUQpJjQMb+kP5+M8gjQ9ti8jG6TDevJ+o89lLXdPXZ68Wurl0zdpdO1+edXVkb/thK1M3bLqfXppZHxFP+nrbc45gVhXewxVG4us9128dI/IrsFMkrCKEhAp9108IAhuLg2yrbZFLkXpRFJLcS+CwkHoX4ZSQ32YIALQQR8q33Z8ykEyEEhExD3uxv//JJUho0bNMbWyHyvJChvqV5B2nXq9mRob4zQv6iIYxf8BoTHnOevpuUAv25jwdSt23NdPVZtVFFXpg2hK9enIXBYMCaaDvHmlEDaSth/vnwmVw+eZW8BYNoP6AVx3b855R2/W6S5xFxmjnjAfvWHko7ZmXtIUh78s49jdSjIW4hMu5vkJFgKIMIeBJ823n1npN6NCT+l0YrWyyY8M+aqspXz15n4ZfL2LpoJwaDkVotq9FzVGcqPlwOXdfZsWwvS2es4sKxywTlz0O7fraU6nnyBabdecBAsByG+EU4P8M0EMGIfDMRwr0b5vjOkyz+eiVHtpzA5GukSff6dB/RkcKlCmV21xW5CKWI5FKEMEDeKeDXDRn7O1hPgwhC+HcH/z4ILV9OiwiAMBSHgkvuPLj1W2AsbX9wd0jzQeOa3Fffo+EjdZl15HOWfLOSbUv3YLVYqd6sMj1HdU53Qbwkeo7qTPWmlVk0fQUHNx5FGAT12tfi3JGLzPt0saPA17Vzofy3+wy+AT4eh+akF0/6vd8zksjES8jwAc6KtfUK0vwv+LaHfF8gRPrje1yM5qV+MsaOZXuY0PtTp0y5oefDWDlnPc9/+jRHt53g3wU77lyDZ0M5tfcs86cuYerGSbYAczcIoUHej8G/q61OVeJJm6vWrysE9HXM0nLF7x/+xaz//eqkeF88cYWFXy7n3cVv8HC79Cn+ityLUkRyMUJo4NcO4Zd6jEFuQWhBEDgIETjIe336NE7DKmIAn+yvOVGsXBGe++TpVGthZJQKdcvy6vcjHH/PfutXDv17DEiWX8Rehychzux29k6qeUQ8pE7b6vw7f7v7mJV0Wn3uJaSUtmq9+nVSi0siYS0y+htE0IveGVDkAWNFew0gV+c0a2LBbl6NYGKfKVgtiU4KaNK5nzHmR4fHKLkbUEpJxPVbjHv0I2YemJJmxVshBPi2Rvi2Tpd8u1cdYNb/fnWSKUkWS7yF8T0/4pfz3xBcIChd/SpyJypGRJE78e8LmHDtP7ciAgZnnzzZhDnezKLpK10qG0nLXdWiSS2PiKekFbOiaYJuz6ecfn3fYNlnc4e6zIsjIfYnpDR7ZTghBCJgCK6VEA20/ODX2SvjJWf5d2uwJlrdW8FcrLMm6pw7fJGDm456Xa4kFny21GWuGiklCfFmVs5Zn2XjK7IXZRG5B5AyEeJXIeP+AOsl0PIj/B8Dv54ILWcTfaUXKSV7Vh9k6berOHPwPAHB/rR+vCmPDG1ny5FhRxgKQshXXD34Mkvn5mXH6iASEwXVG8TS45kwKjf/P/BpzPale1gyYxUXjl4iKCSQtv1a0PnZtp75sNNB+LUIls1cw7/ztxMfm0CleuXoPrITtVt5d2rl2cMX3VYCBpsSYjQZUo1VcZVHxBOqNa5ki1n5v1kYjM4xKwLBW7+Ppkhp9755mXgRGfcbxK8HEsFUHxHY/96oxWLeiXMsQyrISEg8Dd5K6+7fyxZHEfczKeMoAhEh32Us2DsN9q87nKl6QgajxsGNR71+/SdxcOMRt/JJXbJ/wxH6jkkZOxN6IYwl365m2+JdWBISqda0Ej1GdqZqo4y5TxVZj1JEcjlSJiAjngPzNhxBoNZLSMsBiPnBVnPGcG8Ebkkp+Xz4DJZ/txbNqDkq7p45eJ55U5bw6brxlK15J7Xz7g15Gf9oFayJiej25/P1i76s/jOEYR8X4tTeL1j/+5ZkPmw4tf8c86YsYcqGiZSoWMwrcp/YdYqxHd8lLjr+ji/9Qhgb522jzyvdee6TgWmaqL3N42N6ohk0/l1gU4wqPlyW7iM6UadNjUzJkjxm5cCGIxiMGg0616XHyE5pVlmVCRuREaOwvUyT531ZAEFvpsiqm/vI/pgNIQQEvwN+7ZCxv0LiMVvxRr/O4P/EPXNve5uMnol96w7xdvcPSTQnOu7V6+dDWfPTJp557yn6/e8x7wmp8BpKEcnlyNtTks0OSfpCSAqiu4C89QqiwE+pbZrrWDpjNcu/WwvgUELA9nUTfSuGt7pN5qfT0zEYDbbaLo99Yq/tcqcPa6Lt53ev/+xY5uTD1iW3wiIZ1/MjZh35LNMyJ8Ql8FbXycRFx6FbU9aYmT91CRUfLkvbfumsw+GCMtVLYPQxkmhOdNlG6pLG3etRpWFFnp7wuFfGTc7dMSueIK1hyIgXcJ33ZTKYqiN8GnpNTq/j04A0yxWI4GQZar2DLY6iGcI3+6bj125dncObj6PrGbOKWBN1arWslnbDDFKrRVX2ubHaCE1Qu5Xz+FHhtxnX8yNbKQY95b065+3fqPhwWRp0rptlcisyhooRycVIPRpif8d1wKYVLDuQlpPZKVaGkFIyf8pilyEfulUn7OJNti7eDcA/s9a5re3iDj1R5+Lxy7appplkwx9bibwR5aSEJEdognkZzNuRan9CuMxVkqvxJO9LzA/ZJ09GMNUDYxXc58UZeO8lF0uFLsPaYTBp7ovgulhnMGqUrl6SWq2yThHp/Up3t0qIj5+JTs+0cVq+6ocNJMS6DubWDBrzpy5NdZ0iZ1GKSG4m8RgQn0YjAZaM15yRehQyZjb6jV7oYe3Qw59Dxq9HyrS/lKRMQMbOR7/5FHpYW/SbA5Bxf6cazHcrNJIrp6+7r0hrMnBwo61ey4ENhzNY8t7el9HAwY2ZD6Y7uOmoy4J8YLNOnNp3loS4hJTrnI5PO/vxWeQ22PHs4YtYElxbQ8AWNHpok21WzdWz1/nu9Z8YUv1lBlf+Pz59djr/7Tnt4d55D2neTtp5X7Y7LUmIM/PPrLW83PxtBpYfxZi2E1j7y79YzJasFNUlQghEvumgFcb2Fr6rJo9vG0SekTkim7cpWLwA4+aNwWgyOgWFJl3rz308kGaP2qxXydcLTZCvcF4m/f16lrojG3Sqw+B3n3SSKUkWo4+RiX+97hRTBnBw41G3Hy66Vc/SAFtFxlGumVxN1vqsZeJ5ZHg/eyr55DkTNoBfV8j7qcs8IFK/hQx/GhKP4yh0Yr2CjNwJsb9CyGyEludO+3RaNrIqV0Z68VTuu5s5H5+k2J7L9uPzC4TMQWgZDKi1vwB2/rMvRYK1a2dDWfnDBp7/9OkMz57JGOk7YTG34nit9STOH7mI0ARSl4SeD+PAhiMs/mYlH654K0cqsApjSSi4FOIWIOMWgbyVLKFZ+0zkxcl9NO5Wj1lHPrMlNFu8E4NmS2jWY1RnKtcvT29dZ9vi3SydsYqLJ64QFJLHHgzehqCQPGkPkEn6v9Wbuu1qsnj6Co5sPYHJ1+RIaFa0TOEU7TNiPVXkDpQikpsxVgN8gZRf23eQYEp/ngEpJTJiOOjhpJozIX45GKtCnudS3z7ybVuCoiQZAMcXseUgMupdRL6PHO1DiuSjWLkiXD3r2ipitVgdfufarauzf33GrSLWRCs1W2Z+ZkOtltVZPXejy/VCE5SrVRq/AOeZDTLyf8mOj+7803IQGfUeIt/kFP2VqV6CgGB/YqPiXI6pW3VKVy/BxN5JMTQp/eEzxvxIxXrlsmxWw90In0ZI8y48zfuy4N3lXDx+GbgzJVm3/zy+4yRfvzyHV7/PGeuDLS/OYETg4BwZPzt5qHxRnp38FE2erkPVqlUJCLgzC0/TNJo92tBhGckJqjWuRLXGldJuCNRqWY0dy/e6dc3UbOGl2U4Kr6JcM7kYoeWBgCdwfZoMYGroXBHXU8zbbNla3eRMkLE/2KYO373GehkSVrvZVof4JUjrTccSIYTtC92FXqEZNAqWKECTHrYKvV2GtnNb28UdmkGjROWHqOuFzIutn2hKcIEgNM1FToNU8nbIxEuQsBb3x2cRUg9PscbX35ceIzu7zBNiMGpUql+e//acwWJOdPkVaDBqLPx8mcv98jr+j+Np3pfr58I4uumkyxgA3aqz5qdNRN6IygpJFfcpnZ5pg6+fj0uXkW7Vs9lKqPAUpYjkckTQGHDMNEg6XXb/taEkIt9UR1s9YSv6jUfRr1VHv1YNPawzetzyVPu1fb2mYRDTbyDDOqKHdUKPmohMPGVbbt5L2qb4RLDsd1rSbXgHOg+xVdpM7vcVmiAwbwDvL33TUUI+pEg+xi94DaNP6j7sIZP70+rxpkBKH3axMgFMXV4Rwvvic7sr5Qp8iGZZh5RpzIhIBb8AX95b+iZ+eXxTTbBUqloJSla+a1qrZQ+eHB8ZPhyZsDmFMvH0hL406FQn5b4JQcHiBRg//1W3lYDBZhnZv+5wGjJ4D2EojAiZhu2aSu6+sP0ugsYifG0WkaPb0qqvAokWK8d35M4gbGk5ih75FnpYB/TQTugRL6CHD7bFAd3oiYyeidQjclrMbENar6Lfnooe1gU9rD36rdeQ5v2ebavfQsZ8b3tuhbVDjxhumwaeATdLcIEgJi4ai8nXlOozY/CkJ2n4iJoxkxtRrplcjhB+EDIL4lciY5MlNAt4DPwedcQZ6FGfQuxM542tZyDyZfSENWjJFBYbEo/8+vol28/YC7Z6EXk/wn2ovWs0TeOVmcNp2bsxS75dxdmDF/AP8qP1E83oMqwd+QrldWqfVm0XXddp2685S79dxcXjVwjMF0C3oTXo/NgcNLZCImhIgn2vIuJeQepLId+0dNcKqdqoIrOPfcHM135i3W//Oh22y/9d4YVGb/LcJ0/T99Wkry0PH6KJB5ERz4L/ExA8yfElZ/IxMWnxWDYv3Mny71Zz+eQ1ggsG0X5ASzoNbk1gXu8ma/MWwrc1FPzHlg8jISmhWT1E4ACEqZaj3b3sy5exvyCjJmH7KEhK/X7WuU30cYidbcvxY6yQ7TJmJzJhuy3PEWYcbjnrZWT8IsgzGpHH9TRwmXgaGT4Q9Js4xaglrAO/npD3o3QXGXy4XU1mH7M9M7Yu3o0lwUK1JpXoOaoz1ZpUztA+KrIeIXP5U+HQIdsUzJo1740aF7GxsRw7diyFvzUr0c17IPwp942CP0AL6OP4UyZsQUZkJMGUZlOMIp7F/QvXgCj0ry1DajYhpUTe7OMmTbeAwBFoQS+nu++I67cYUG4UlnjXU4o/XTeB2q2r27KL3mhPegI4RfAkW0Ckh/z87nx+mvinI67ibgxGjYZdHmbS32M97jO7OHPkHM/XfM1tG4PRwO+XZ6RQTnMSad6PDPc0b4sBDEURBdfk+gDXjD6zpH4LGdYaZByurnUR8h3Ct1XKbaWOvNERrJdx5cIUQf97IOJ0UiMn3iNZgafvb+WauR+4/VHabaKnO//t0wQMZXGdM8EVAuJXg287N9tq4Nc9W5UQACwHIfEQadcKcRf8mzrLv1/rNq+Jwaix4HNbjgJhLAm+bfH82ApkzKx0WQoeGdoOo5sYGmuizmMvdfW4v+ykaNnCVGlRwWUtEc2g0X5gy1ylhADI2Ll4fk6ttpdswoYslCiHifvLrRJiyx0zJ/VV5n/BegF3CeRkzByP0ggo7n2UInI/YDmRdhv9itOfQmiIkG9tRbXS5WqxgnkLIu8H4DA737W9CADfZm5jMqS0IuNXoYcPsfvWeyNjfrQlccso5p2keUnL28j41eiRb9t8/GGd0aM+QCaeT725HomMmcX+Vb8i3WShtCbqHFh/xPG37fh4moFTgvW8fQaTZxQoFsK4+WMwuMkDUadN7q3v0ndcF0pUKgYCh0sqKUC3coPyjPw8Y+ngk86XIy9OxHBkwgbvuIPiN5Nm5lUnjEhzxnP85HakI+OzK6z2ezK1bXeSdozaVbBecd9GcV+gYkQeYISxLBRcZs+ZsBjkbbDeIO0kaiC0fFBgHjLmR4j+EpuP2I6MgcjXkPH/QL4vU2SilNKMvPUiJKzDkWODS8jbhyF2DuT/BWFwX9ckdTx82US+glOBsdjzyNifId/nCL+Od3pLPIcM72/Ls6KXBTzPnSC0ECgwH+IWI6O/AD3M4209pVGXh5l99HMWf72S7ctSxtDkZgLzBfDpxvFs+2sPK+asJ/xqBIVLFeSRoe1o/URTTD7pi+MBkIlnkeEDUubFSVgHfj3sMQcZc5PI+BVAZIa2VSgU7lGKyP2AqRJYDrhvo6VeAE5o+SBwCCJwCAB6xGhISGPapzEpN4UvxK8k5Vei/SWQsB4ZPR0RNNp5bfTX9mBGSFk/5xoy4v+gwPz0Z270aYjrPBZ3Y73rd4G89TIUWo0wFLf5sCOed+RZqd0smgPb8uDKUqwZNWq1dk55LYQfBDwOWrBN8XKJbQaUzTqVPoqVK8Lznz7N858+ne5tcxrfAF+6DGtPl2HtM93X3efrDkl5cRbbKubar/N09Z1oq+mUfhIRPunP8XOvIHwaIB33cWoYks34u2tbU30k37kfQCsKBu8UrlTkbpRr5n4gjwcBiXle8LCzdLz8PYrJ+NkpJkPKBIj9CdfWC6utT8tBz+VIwlQbjDVw78d3tX8S0G0zg8CeZ+UsSfvWuV84JpNEiNTl1t3FZPi2A62IG7kkIvDZbK/ge19h3gLWc7iPOfghQ1O4bddEel07ArSHwLd1use7Z/DvDcIPt7ljXAWb+ra0Kd9u7lURODjXB/oqvINSRO4DNN/6EODmS8+vi9OMGbckHvG8jWUXnsRk3MkwCiSesi1zi+bSt+wOIYQtl4VWhOQPRymTZDTh/oWiQ8I22zZ3+bDzF05k3KxzGE0SzXCnD4O9yXMfD6Ru29Qjw4UwIUJmggjC+XjZH7L+fcE/jVlPCrd4FnNw3T5LI52Yt5K+2BAAIyJkJkLcv0ZnoeVD5PsG8CHV3DF5XrJN6U5tW2FAhMwALR+p3hN+3SFgkNdlVuRO7t+75AFDCx6L7tsUbn9ie9kjQSsJQS+i+XdLR0/p+PLLUABg1s4WF4biUHAJxC1Exv2Nbo0gOj4/fiGD8In/EOTNtDtxIWeDtrf5buMJls4twPbVwVgTBdUaxNDjmRtUbek+BkSYqkLBfyDuT2T8clscjbESIqAf+LRQ1pBMk5XXVQasIb6tMpbx+B5D+DaFQitsVqP4NYAZTHURAf0RPg+739ZYAQouh9h5yPilIKPBUAER8BT4tlb3xAOEUkTuIzTfFuDbInOd+DSFuIu4/gI02NoA+DQgzZgMkQeMyQInjRVty6S72TG6ve+MYasVMggROIj42FhOXzpG1cJVQW6y1dBxuW8a+Da29eHTEBnzbYoWxUqbGTbuKsPGXXVaLm9/BMYajuyhqcplKAB5RrhN8qTIGMKnATJmpvtGWmEwFE9/5z5N7FY9T60iEuHbLP3j3KMIQ3FbBuigMenfVguBPM8hXNS0UjwYKNeMwgkR0B/3X4ASETDA9muaMRkCAgYgxJ2CcEL4QsAAXPuVDbZgWFPtdMueFiLgaVwrTgLQEEkuEp+mYCiD5zEzBmTsD5mUUJFhfFqAoTTuYw6eyVDMgQjol47Wms0F59cz3eMoFA8qShFROCFMFRF5P8R2adzt99UQeScjTDYLh6uYDMdl5dMKkUqQrMjzAvi0cm5rWwNaEUTIV27NslJKpHknesRLttoWN7oho79CWtOYIqvldRHFbwAMiHyfIYwl7Ptmz7OCTyrtU8MK5u0etk0bW56Vlejhz3ovz0qG5JDsWrmf8b0+5ukKLzD84df4/aO/ibqZVpxP9nInL04IztdiUsxBV7AX3Ut338bSiLxTSLpOXKOB8EWEzLAVrFQoFB6hXDOKFAj/R8FYHRn7i202AoBPU5vf9y6/990xGei3wFjalq7ct0OqX6BC+EDIN5CwGhn7OySeBy2fbVz/x2yuFRdIKZG3P7TlG0mWC0RGn4KYOZD/B4QpZdCoZlmLjBpLqtYeUwNE3om2vCrJ5TSWQ/q1t7tzsq8SgpRmZMQLYN5A6nlWfkVkw7RGXdf57LlvWTF7PZpRQ0+0WZPOHDzP/CmL+XT9RMpUL5nlcniKMJa35cWJnW+PObgNhvLJYg4y/t0l/LuAqbLtnkjYDOhgKA/SAvo5wA/8OiACnkIYinprlxSKBwKliChSxWYZmeBZ22QxGR73Lwzg1xnh1zl9gsUvsSsh4Oyz10HGIMOHQuGNthwednwMYZjixtrbp6JQWHa4nMkjfJoj49PIqwLYYmdcx4ekBxn9FZg32v9KJc/Krf9DFJjvlbHcsfTb1ayYbcsTkaSEAEhdcjsihre7TWbuyWkYjLlniqUt5mAYIs8w7/dtLI8IHuf1fhWKBx3lmlHcU8iYWbiO29BBRtgtGHcoGLjGts6lVUNDxvyU+ir/LiDuNvenhhXhhemGtjwrP+M2z4rlIDIjeVbSJYdk/tTFLndbt+pcPx/G9qV7slQOhUJx/6MsIg8YUpoh/h9kzO9gPQPSDMLHPpX0SfDrlGtzH0gZB4nH0mhlQJp3IfwfcyzJ43sY4XZ2jxXil6LfLo4IeNLJtC6EP4TMtFUqljGkVBAEIBFBYxG+TdK5R6mQ+B/IaKxW2LYyL8t/ys/ls74E57fSvncEHZ4IJyAPYN4FplqZH88F4dducfVMqNs2BpOBgxuP0uzR1LNn3utIPRxi/7CVKpAxYKxin27dNFdOLT285TiLp6/gxK7T+PiZaNarId2e70DB4gVyWjSFwi25842jyBKkHo2MeBYs++9aEQOWHcjIHRDXGEJmOrk2cg0ZLlzmyXZWiPkWGTsHQr53Ss0tfGpDwRW2eJb4pfZaJrptGrJPE0TAAFsbL2ExCyYNKcPOtcFoBoluFVy7KDl5wJ8FMwvy6cKzFKniteFSxStF4u5hpOUYMnwQyCgc7jHrFWTCalvyueAJuUoZmfP2b/z6wUIMRg2r3Y124fhlFn6+jA/+eYsazbL4glEoMoFyzTxAyKj3006dbt6JvP1J9giUToQWAMYquL9srQiTcw6S6IQaSI8udR1kAjLieaTuHDMiDIXRgl5EK7QKrchetCL70QpvRsv3iVeVEIwV+XlqSXatswXs6lb7y04KpBSEXfHhvedKgqm+98ZMhfxF81GkTCG3bawWKzVbVs1SOXICKS3IiGH2uKHkljR7TFLcbxD3Z06Ilir/LtzBrx8sBHAoIWBzn8XHJvB298nE3o7LKfEUijRRisgDgtTDIX4RaReF0yH2z2yfJuoptuJ8rvZBs8Vz+HdxWnojpr1tnUc5QWxBr8T9nSk5M4olQWPxDwWRMnVZdavgxL5ATuwPyFI5NE2jzyvdXa83aBQqWYAm3bNWIcoREtaAHorrBGYCGTMr11iN5k9dgqalfr1IXRITGcvaX/7NZqkUCs9RisiDgvkAkOhh4wSwHM5KaTKOX49kNSiSz9bQQATY63s4u5XM1sJY/FPLjeIaad5l+6lHImNmod/oZcvnEfE8Mn59lr2Ezh6+SGyU+wyemkFwaFNasTKZp8fITnQc1No+5p1HhdAEefIF8v7SN3PVjBlvkXbdGmkrsCcjskki11itVo5uPYGuu74eNSE4uOloNkqlUKQPFSPywJA7vt4yixACgv4Hvm1tOR0Sj4LwB99O9kDTwqlup5vaIwKXIWN+gTgXM2TuQiaeQ4b3t8eEJE2fvWIrfe7XFfJ+mkPVQbMnNkHTNMbMHkmrx5uy5JuVnDt8Ef8gP9o82ZxHhrYlX6G82SJH9nN/3CtJ3F97o7gfUYrIg4KpNskTgLnHF0zVs1igjCOEAN8m6Z6lIozlEHnfQU88Bpa9uHVT+dRDRjwPejjOj3L78YtfZjtGgUPTK75bylQvQUCwP7FRrn36ulXPttgMIQQNH6lLw0fqZst4uQFhaoDkV3ctbOnkRUi2yeQKg8FA1SaVOLHjpEuriJSSWi3uv1gexf2Dcs08IAhDAZtbI81TrkFAX7fZTe91ROCzuI8zCQDDQ2A9izvFTcbMQcr0lod3j6+/Lz1Gdka48PkbjBqV6pencoMKXh1XkQy/DqAVwrUbTyICn801s2b6vtLdpRKiaYLA4ADaDWiZzVIpFJ6jLCIPECL4HaT1DFgOuG5kqo8Iei3FYmneaXNrJB4B4WfLiOr/BMLgfmZFbkT4tUcGjoSYr3G2EmmArVaITNiC7fZwE1ejh4H1EhhLe1W+pyf05cyBc+z8Zx+aQUO32pQmIQQFixdg/PxXvfIStOXJ+BMZt8i2L1hBBIKpESJwAMInZ6wgV25H8euhg6w+cwqz1UqdosUYWKsODxd7yCv9SynBvMPu2jsGwh/h9wj4P44wFLSXIPjOPn03+cwZ+7Xi/4Ttn7sx9FiI/xsZ95fNqmYoiQh4wl72wLuP3eaPNeLJN3rx+4d/OU3f1QwaPn4m3lvyBgFB/pkeR8p4iFuMjFtgc1dqDyECHrdlSBamdPUVYzaz8PhR/jp+lJuxsZTOm5cna9SiY/mKGDX1ffygoRSRBwih5YH8v0D8smQJzSz2hGYVbMma/Do5PVRstV0+gtjZpF7bZQ4iCxNrZRVa0MtI3+a2l5HlAOCbrFZIMWTC5hyTzeRjYtLisWxeuJPl363m8slrBBcMov2AlnQa3JrAvIGZHkNajiPDnwYZiZPrScZAwlJkwhLI82KqRQuzkh2XLvLM4oWYrVZ0e0DwpahIFp04xujGTfm/hplLGme7nt+H2B9xvp5PQswsyD8XYaqBMFWDgisg7g9k/HKQsfakf/3Bp7n7oozWUGT4ALCeT1oC1stI81ZbVeeQGU4VqTOLEIIhH/SjQec6LP56JSd2ncLX34dmjzak2/COFCqR+YRmUg+3XS+J/5GUxA/rZVvuodhfIGS2bXq9B1yPjubJBX9wIfKWrW/g8u0oNl+8QItSpZnZ7VF8jerV9CChzvYDhhA+4N8L4d/Lsw3il9qVEEi9tsuwFLVd7hWET32ET+rTT4VPQ2TMt+470AqCoXgWSGbz/bfq24RWfb2QrfUubHkynrMn60rNpG9bJqO/BGN1hF8br8uQGlEJ8Qxd8reTEgJgtf/+2fatVC9UhLZly2V8kPi/7UoIpHo9RwyDQhsRwsfmzswzEpFnZLqGkLdGg/UizsfWblUxb0fenooIfjPj++CCWi2rUatlNa/3CyAj34DE00l/2X/a98myH3l7MiLvux719X8rlnIpKtL56NjP8eYLF5i6fQtvNm+V+saK+xJlA1O4xaPaLnHLXay/h/FpCoYyuI4TEIiAwbk2Hb5bEtaBfo20c8oYbJlms4kFx44SazE7KSFO0gjBrH27MzVGmtezfhPi/8l4/5bjYNmF69giHeJ+R+oxGR4ju5GJFyBhI+73aSFSv5VmX8fCQtl95bJDuUwxFpJfDh0g1mLJsLyKe4978Cn64CJlAsQtRcbNB/263UfbB/y62CwdXh8v3jY91i0GpGUngsfSaJf9SD0cQ8KvVC60CJ/bVmJulmXdwuL8NSMSXZfUaVWd7iM7Ub52mRTbCqFByLc2E7t+kxSWA60I0lQzQxNpwy7dZOm3q9i6aBfmeAtVG1ekx8hOVGtSOSO7mW7u5MlIK6+MFcy7kFJmS2DmjssX3UsjJTsvX8qwPFKPtrsW3GFAmnci/Humu3/AVgMoyXXhUhB7zSQX1rhch3k3aU8CttiyNvu6D4rdeeVSWkeHWIuFo2Gh1H8oa6yNityHUkTuEaQeiQwfCInHueOjvYKM3Gn30c6xxYB4d1Qvtcl+pOUEMvxpjDISo0lHSDCJS3R9AoQlP1+8XoJrZ66z7Ps1vPT1c3R7vkOKPoSxHBRcjoyeZjvGyS0IehhEDEIPeNZW8M7DF+OBjUd4q+tkLAkWRxDq9fNhrP3lX54e/zgDx/f1xu6nQe48Z7lVrPRxX+xElpFLktEqchlZ4prRdZ0vv/ySFi1aUKdOHYYNG8bFi+6/dhTukZFvQ+LJpL/sP5N8tIeQUZO8PqYQ/qAVS6OVDsY6Xh87M0iZaI+BiESgk6QjJMW/dRkQTteBN22zCyR8MXImJ3adSr0z4QdxS1NZYTdTx862xdF4QExkDO/0+AhzvNmhhABYE219/TjxT7Yv3eNRX5nBVtDPkyy7GvjUz7Zpqg2Kl3C73iAEDR4qkWF5hJYHjBVxnxDO6lTwMN34NCRtZcTPXjPpHsEjy40RTDXTbNWweIk0j46/0UTVgvfebDxFxskSReTrr7/m119/5d133+X3339H13WGDh2K2WzOiuHue6T1CiSswq2PNn4J0nozC0b34BLJJfkUHCSsBf0qrmIgdB16Dw8j6YVhMGj8Nc1FnEvccnsqb9d5R2xxB2mz+sdNxEfHI13lfDBozJ+6xKO+MoVve9AKk/a51REBz2S9PHb6VK1OgMmEcKEoWKVkyMP1MjWGCBiCa0VBAy0/+D2S8f5NVewFCV3FFmkQ8GQWWC+zDmEsBT6tcLtP/r0QWtoJ3qoVKkz9Yg9hcPHM0ISgX81aBPp439WsyL143TVjNpuZPXs2Y8aMoXXr1gB89tlntGjRglWrVtGtWzdvD3lPI62XkbG/Qvw6IBFM9RCB/ZynxJr3kvZXlhUs+8DQ3jtyJZ5CxvwI+uW0G9+eikQHv54eT+HzXI5LyLjf7jo+/RFuvr5sdWJcx0BoGhQva6Zhuyja9LpF5bqx6NYT6Ld1e5r4onf6suzEfUZaHRKPImW8Y+aQlIkQvxIZ96ctz4hWAOH/GAc2nHfRh70nq86hf49leUyGECZ7noynXcycsbv+Al9A+LXNMjnuJq+fHzO7PcqQJX9hsVodAY0GIbBKyUuNmtCubPnMDeLfCyxH7Gn+k59XYf9nsrlA/R8D/542q2A6Efk+s5UGsCZZgSU2pU8Hn8aIoFcztw85gMj3kS1eKvEUd2Jg7Ptkqo0I+p/HfX35SDeeXPAHFyMjIaknIdClpGmJkoxp0tyrsh8Jvc5PB/ez8/IlDJpGmzJlGVCrDqXy5vPqOIqM43VF5Pjx48TExNCkyZ1ph8HBwVSrVo1du3YpRSQZMmEzMmIEthem/YFovYSMXwhBryMc6cOz17Eq4xYjI1/H45omMgIZNR5ifoD8P3styZlM+Nd+fKykPD5j7ZV4U93So/7f/ekciRYwmgDMEPOtbZZIyPd3zPMeO7XtU15lvM0tZN6O40FtvYS07EeaqwG540tPmKra82TcndAsIFlCs8xZHzJCk5KlWD3wGX45eIA1Z06RkCyhmTeCF4UQEPw2+LW1fQBYjtrT+MdhmzVzHfRQpGU/xMy1X8/py8MhDEWgwN8Qd3dCs6TkX/deaJ7Q8kOBBRC3yJbQzHoDDMVsSdr8HklXsHzRPEEsfeppFh47wsLjRwiPi6NkcF6eqlGLzhUqeTWh2dwDe5m4cb1DmQU4dyuCuQf28XWXHrQrl0nFVuEVvH5HXLt2DYBixZxjCwoXLuxYpwBpvYmMGAmYSa2Wibz9sS2Hg28T8HmYNCPxMYCpTublSjxlV0LSmtqZCtYLyFuvIgr8mHbbtOSw3rAfHwupH5+P7MencYpthU9DZKzrwna6brOKQJIS4lgDMsFWY6bQRoQWZOsr/i83kmpgrOz4cpa3PwHzzjv92ZYCULPRDbYudx1zoxk0ajSvkm0xGbY8GSMQeUZky3ieUjwomNebteD1Zi2ypH9braJmCN9m6JETIe43+xrp/NN6Dhn5GiL/7NS6cT+GlgcCByACB3hF5tyAEP42t1LAk5nuK4+PD0/XrsvTtbMue++eq5eZuHE9gNN0YauU6FIycvkSNg4eQtE89285i3sFrysicXG2Yl0+d/n4fH19ibSb4tKLlJLY2NhMy5YdJO1/0k9XGBJ+w4gZ4UK5kBiw3p6FxVobCMFkbIOWuBGRiotAoqEbO2NJCAAyd5yMcXMxuPTSp4UVLNuJu30Iacjcl4Yh4VcPj08qWV1lY3xFEZBhiFQUKk1zVkac0ZEyBkvUPKw+T4Jsja/IBzIq1b5Ax2wcgB4bC/I2vrF/umgHHfreYO5HhYmPM6YaJ6JbdbqN7HDPXOsZxdN7JMuRUfjGzXN5vmzTlzcTd/so0lAmOyXLVnLN+fAy3+/e5WQJSY4ErFLnx317eKFew+wXLg3ul3PiqZvZ64qIn5/NT242mx2/AyQkJODvn7F6BxaLhWPHjnlFvuzi3LlzbteXL7CBIF/dpfdDYAXzDo6dt+23QTxFhUKn8DdeAARCSKS0/Yw1l+XUlcfQZfqPkUHEkD9wA/n9N2PQYhFaJELLeCE3KSH08jJuxKScDpseeYoFzQch3R4fYfkXEd4RXfpyK64RN2LakajnA8DPOJqKBd/FoNkSRwlxR/mQLpWQpJ2A6Ij1nAuvDUCA6VUqFPwATSQghG7fTw0hdEKjH+Hy5bLAMfL4HKFioQSX3ebJqzPhh7O8M7AiFrNIoYw8VKUIMdbb99y1nlHSuke8iZ/xEgUDVxHkexiQxCeWwGS4ifBxH0AvJVy/tJSbse2yR9AcJDvPR3aw+cJ5l4nTwJbNdf2pk7QLyL0Wkaw8J5djY1l57TIHboUjJdTIF0Lnog9RKtC7gdR3GyVSw+uKSJJLJjQ0lFKlSjmWh4aGUrlyxhI2mUwmKlS4N6qNxsXFce7cOcqUKeNW8TLFBLgr7AqApmlUrZqsfLf8k0TLPxgsfyP1MKShCFZTLwxBnalcMP2xB0K/gE/MiyBvADJN549nnQqKFi1KIZ/0lx0X1vP4xP4fyJsOedwPpeNrDAXAz3SZosErMAfOQBqqI6xGDDH2KH/pEM3+S1odQ3BQEFWLJO1DVSx6Y4zm+WiJa0DGI41VsPo8QXBQA4IfsnWoJd5O0yBVu1kM3+5sy3dv32LbYueputdPhfFF/9mM/GIwnZ7NnrTqOYGn94i30MyLMcVPAITDouhnuubZtS5sz7TCGbie7xWy+3xkByvOnCLGmvYU9YCAAOdnbC4hq8/JP6dPMXbrLgR33Fah1+NZc+0Kk1q2oVcl70wvP3XKRVqEu/C6IlKlShXy5MnDjh07HIpIVFQUR48eZcCAjPlLhRAEBHh3NkZW4+/v71ZmqTdBRu/EdSyGAeHT6K4+AoD+9n820lfzMtn4UiJvjAYZTnL1I7PRCQKJT2AzhCl950tK3S5PBJ6qQ8Lpdx2IxTf2BSi0Fm68CEQ7NRROP12rXQIw+jfG5HTsSwOv2v/ZuPvYS70eMtYeoOpGZqNfPXb+MyWFCElVU79+6QeqNa5C5fr3dyBdWveIN5CWE8ioCaR2Tjy51gXgk6cpwnhvPX8yQnacj+zg3K0Ixq5fk2Y7TQialSqTq/c5K87J2VsRvLFhTYpSCkkKybhN63m4eAmqFiqc6bE8jXfzeh4RHx8fBgwYwKeffsratWs5fvw4o0ePpmjRonTs2NHbw927+PfF9ipzdaKsiMBBWTe+eRtYT5OmWSYjZCTY0rzNVg04U/LYa99ET3WbR8SGm1wSIsA2zTOLWDZzh80t40IEg8HA367ymijShYz9hYyr1wbwaYow3t8K4f3GL4cOeNTOIARP1bj3Kodnlp8P7ne7XhOCuQf2ZY8wdrJkHtmLL75IYmIib7/9NvHx8TRo0IBZs2ZhMmX0+/3+QxgKQsh0+8yQZNNT7bkNRJ4xCN9mWTZ+Wrk2UmLA9mL3wFph3gnG/2/vzsOjrM7Gj3/PTCYrgQSSsIU1bIEkhH0TQRBUFARc2VTEpYttrW21tfvrr33baq1vra2l4MYmKojIImJxAwQEsrET9oQtkI3sycz5/TFJyJDMlsxkstyf6/JCMs88z5l5SObOOfe57+tLabr8iPUDoWw3YICAm1HBc1F+PW8Yj6N6Ha4yQuk3uP7aal7TAASgwv+NMtRj3bj8IK7sNtr/WYpNZdUbmSvMJP03zf3ri9rKdlLvf1PG7qh2L3l0OML7dpw76zA3pMpr06bTObTp5od4i7P3x6w1O86dbcQReSkQMRqN/OxnP+NnP/uZN07fYqiAmyFic42CXeVgGooKno/yH+zlq2tcCioMXUCZrN1oTfGQ76xwke1vn7roXWuNEQxcL4t+Bl20HMJeQQVWzZLpWs/1urB/Wcuzl6cAARA4BRU8B2V0VtbeHmmk0fTU456o9qjQH3mlQJ/wPu1CEBIVHMKtvZtH3qGnufL+NLbmV1mnhVF+3VChz0Los417YdNw4J+Oj1HBqMjPqgswaUsBOv/3gP2dIaDBZC0GpstTK4MQje1vpWZAoXOfhohPUX7RKP8R6MJ/OR6PIcL6X8Ux7M88mMF/NBSfcPTCrAWmAiahAj24G8IUh7VgmeOdGImTEjiRtt3urIjRz0DipDjPjas18x8DxedxZ1ZEtf05Kmim14YkvGtst+6kZ1+1+1u/USlu7tGzcQfVhIzr3oOTOdkO359x3brX+Zi3eKXXjGiZlKENBN+P/X82RmtVTlNfAHThMgfHasBinQ0C64yLsSf2+1koVPDDldVU7fd9QYVD6DNg6OTgXBoVstDjhcOUIdSF92c00793v/Xadi5vNluY9YNpHh1ba6WC5+N6cb6G95oRvjc/IdHh4xatediLhdSauvnxjmfbLVp7tdBcXSQQaa3K92H/g7qSLqrRL8NKhf4MqruTVv3zqezTYeyGCvvr9YOdrs9boHAVlqzJ6Kv3QeA0UO2w/YSuHGPAHRDyGATOgOCHbR+rGosKRoUvxmAIRYUvBhWKxlCjSnvl8UH3QdAcx6+9nlTozypnmyrHZP0qNd+fzr078vzKH2E0GjD6Xf8WNPoZQMEP/lxI314/wnLtFbRZqhE3hDINQLX9X6z3wNG/dwOoEFT4f1AqoJFGB7osBUvus1iybsWSNQ3LtZfQZhf6O/lASUU5qw+kcu97q5jw1hLmrnmPj44eptzshYT3BugVFs7Lt03DoJRNcz2jshZq/OOkKQyK6ui7AfpY7/D2/HXqHfbfn8lTGeiBHTPukKWZVqt+64RKBUL4G9ambkWrK5u6tUcFz4bAmShDiJvXKABzAZABFQesMxnB34HSL0BfA2NvVPAcCLgFpSo/tEOfh4BJ1gTYikOggiDgtsqGddZvIGUaAJGfUJ63nIqCjwjwN2PwH4AKngv+471WRl2pIGj/ZuX78y6YM8FobXpnzTmwvj833zuGXvHdWf/aFr79NBldnkPCmExmPJJDTFyhNc+24kTt3jfCbSp4NpjirHlJZTut/yxNfUCXgPksqCBU4B0Q9IDH+iS5QhcsRhe8hE3CdOFJdOHbEP66V5PV3ZVdXMTcte9z7OqV6l3nmdfy2ZV5juWpybw9816Cm9BmhOn9BhAbEcmy1GS+Pnsara1LNvMTEomNaLx73FTN6B/LwMgo6/tz5jQa374/SjfFzJUa0tKsuwfi4+13W21KioqKOHz4MLGxsU16f7ou3YHOcdLi3RCBivyq3k26LLk/gZJNuLdrwQh+A1EdPvBIsNAc7ocu+Qyd+z07j1bO9ER+1axaxzvSHO6Jtzn+/lNAICrqc2uzOS9z5X48tv5Dvjxzqs68AoNS3D8wjj9OlvIMntJSvkdc/fyWpZnWyn8MGHvhNCejAZ1CVfBDuN88zwwVaVCeWu/rNje68E3sfytaQBdC8bpGHJHwNus9t5/DBCVQtKYRR2Tf2bxcPj990m5yo0Vr1hw+SG5J8+6LInxHApFWSikDKvx1a3JenTkZt0PIooZdw38wqu1vcb4+fyNDjQ62vqUteejCpViuzMKSNRlLzpPoks89tgVOawuU78VZwKbLv/XI9eo8t/kilmt/s+YoZN2KJfen6LLGLWjU6pTtwfFMoUaX7W6s0Ti0JzPD6SJrucVCinRXF/UkOSKtmPLrBREboXgNunh9jZyMByFg0vWcjIZcI3gumBLRhcuhfA/o8sqqp02frjiNzp4HFmsvHgDM59Gln0PgndDuJZRyJ8BqenTZHnT241i3ZFcGQ+ZMdMl6aPNDVJunfDm8FqxJr4gL0agkEGnllCEMQhZVbov10jVMA1FhfwRA61L05TGgCxw8w1JjZ45vaG1B5zwJFttePNW/xZZsBNMg606eBlDKgDYNhfIkHPaoqd6J4znakm99jTWDEKDqNeqCv4PfIFRgy23A5zP+I53sKlMo/6bRnn5El2inx/gZDCR07NQIoxEtkSzNiEalVAAEz8d+FVUj+A0Ck7cryzpR9g2YT+Fo+lwXvonWDd+6qEIexXFtlGDwRoGt4nXWLdoOGi/qojc9f12BClmIoyAEAiD43kYckX09wsK4pWcvm62eNRmUYvaAgYS3kM69ovFJICJsaHMmlmsvYsm6A0vWFCy5P0eX1933ROtSdNEHWK7OwZI1CcvV+ejidWjtuLKoavMU+E+o/FvNf4IKDB1R4f/w2vZaV+myPTidMLRkWbcvN5AKnAIh9nbN+KPCF9ev940T2mkejhnKvq0zH0ZbitBFq7Bcvc+aO5P9MLp4E1q72rvIO2rn9HwHXfpFo5W11roMXfwhlqvzKr8n5qKL1qK1bTViFXATqs0zlX+rubxnxHrP/9koO2Zc9Zdbb6d3uHU8Vd+Zhsrv0cROnfn1zTJrJupPlmZENV26HZ3zHWya8Jkz0CVrIfQ5m+UbbclFZz8EFUeo7mdvPo/O2wNFKyH8DbvbTZXyh/B/QelWa62NijNgCLOW1Q6a7ZUPXfc19hq+qvGnvuFr3lK/16jNWdbcGfNprt/7THTZN1A8FsL/3ahFwarHVXEKnT2/jpyebdZCeO3+7NWcHm0pQGc/AhWpWANsi/V9Kd8LRe9A+3dQhrbVx6s23wH/0db6JuX7AX8InIwKmoPyc74c0pg6BAez7oF5rDt6mPcPHSCrsJAuoW2ZExfPtL798Tc271wp4VsSiAgAtPkqOue7QDl15UToa3+25gsEjLb+Pe9XUHG86tmVf1ZO8ZenovNfQIX92e71lDJC4O2owNs9+TI8RvmPRBe+7vggQwQYuzb4WrrkMyh8repvNR8Byqx5HF6oI6L8R6BLP3NwhAH8h9eandK5z9SouHvDvS/bhb72V1RbZ80RPct5Ts96MMU2eCeYwzHk/85alA+4vtxVOZaKo+i8X6PC/8/mOco/EeWf6LUxeVKQycScuATmxCX4eiiihZGlGWFV/D61g5CarucLaHMmlG7F/hq3BUo+Rpuven6cjcWl3jePNKjOShWf1REJmmWtSmt35sWCCrYtuqXLj0H5bhze+6J30RZHycheULajcobGUU7PWx7J6anz3OYsaydnR80YS7dIyX4h6iAzIgKgsmaBo1oWZqiqa1C2H+fT+hVQngzGyejyI9Zy7GW7AQME3IwKnovy61m/sWoN5d+iC1dAxUFQgdaZFQ+W6FbKAOGvV071X+X6660sxx1wh0d+u75eR8Tx+6nLv0Uxv8HXq0kZ2kHY6+icJ7AGoVUf0pWvMeQpVOAk2yeVf4vt8lFdSqDicKPufLqe0+MgR8VyyVpy38/znUV12T6cF++zQNk+CLrT49dvKc7m5bIiLYVtp05itlgY2TWa+QmJxLXi3jCtgQQiopI7+QKuH6uL3kXn/xbrb/yVH3RFZ6zr4mGvoALdKwuttUZf+xMUVVWmrNpqmg6Fb0L7t1Amz7QDUH69IWITFH+ALv64ss5KTGWdlVs8UmfF11TAaIj8BF20Cko+A0rBlIgKnofyH1b7CU22I4TvxqV1GThbxhNObTt1ku9u/AiL1tVVXM/l5/HeoQP8+uZbWJg41McjFN4igYgAQPmPQpftwtFWTkyVdQ38h+H8t2IjGhPk/7byuJpT4mZAoXOfhohP3UvMK/m4MgipOk8V6xKGzn4Mor60NufzAGudlcdQDawXYvf8PqwjUn1uYxdU6E8g9CfOD/YfgfMP/UDwi/XE0Fym/EegCxc7PsgQ5ZGcnhvpgr9bZ4CcUuAvH6Z1uVRQwPc2rafCYrHN8KkMSF746nPioqJcqmkimp/m/yud8Iyg+wAT9vMFzKiQRwBQxq4QMBn7+RMGCJxuDRrs/hPTgAVdvMqtYerCpQ7GaAGdU9lor/nwWR2RelCm/mAagcN7H/xA4zfo8x8Pxh44aiWgQhZ6fNeM1iVQtALnwZkBAqaijJ09ev2W4t2DqbWCkJqMSvFW8v5GHZNoPBKICACUMQIV/hrWYOTGugag2vwMFTD2+vHt/gh+far+Vvln5T8nvzhU2984qRwJYIHSb1weo9bFlb95OpmJKfNeXxZvsK0jUvO9NwABXqsjUl8q7G9gjMZ632+49/4jrbMrjT2myt5JeRVRLDkymLs/nc2kjQ/yxNe388WFbuiAOyH4Ec9fuPyoNZnYGUMEqt3/a9CltCUbXfAvLFdmWHsC5Xzf2sW3yS6Xue6bc+ewOHgdZq3Zce5sI45INCZZmhHVVMDNELHZOktRsg0oB9NQVPB8lL9tpVNlCIMO70PxenTxGjBfBmNnVPC9EHgXSvmjPb1u3wJ+4NpjCH0aHTAOXbQSylOAAAicggqe0+R+i1bGKOiwDkrWoYvWWrfMGqNRwQ9A4G0oZfLJuE5dC2fOJw9wpai48t+eIrOoLdsu9ODuywN4aYrC6JM6eQqCH7ImB9eTLj9srdujr2HTE6h0KwTNgba/83kRwIbw+M8K0axIICJsKL9uqNBnIfRZ58eqQAi+HxV8f90H+I+pXCaxNytihIAxro/NEIz2GwAVx3C0TVKZfNunpr6U/wiUj3vsuEoZQiB4Hip4nq+HAlhb0T/28YdkF5dUfqRZP5TN2vrn+qNHGBTZkceGejjXxtQPVIiTWRGNcuPfea1n6zJ0zuOV/Zlq9wSieJW175G978NmYHR0N/ZdOG93VsSoFGOjPb/bSTQNsjQjvEYFP4T9gME6ra+CHnTvnCGLHJzTACocgqa5dU7R/G0/e4bTubnVyY030sDSpH2YLc622LpHqSAInov9H6VGMCU0bCdXyWdguYyj3jS6cGmzXqKZE5eAURnsZ6hpzSOya6bFkhkR4TXKfzC0/S06//fYbN/FCChU2N9Qft3cO2ngDCg/AEVvU3P7blVSpwpf7LEdM6L52J15Dj+DgQoHgcalwgIyr+XTvV2YR6+t2vwIXX4Eyr6murR71UeqsTMq7NUGnV+XO66RciinPcvTu7EnewlGgx8Te/ZifnwiPcLCGnRdZ64WFfHuwTQ2HT9KYXkZsRFRzIsfzLhu3d1eJurUJpR/TpvOdzetx1wjabVqb96vxk9keJeufHYynZVpqZzIyaZdYCAz+8dy78A42gY0fksB4TkSiAivUsFzwZSILlwO5Xuw7h4Yb61T4dfb/fMpBaHPQ8Aka5G0ikPW6qABt6GCH7TmL4hWx5eTAdbeSYuhZAu6eDVUnK3snTSrsndSA3cQOXhty44P4n+SxmFQGrPOB+B0bg7vpCTx2rTp3Nq7j/0nN8ChrMvMW/s+18pKq5dTMvPz2XLiOPPjB/P7iZPdDkaUUrVupK7+U/PUpo/55MRxjEph1ppz+XkcvHyJJUl7efeeBzweYIrGI4GI8DplGogK+6PnzqcUBIxp0Lq7aFlGdo3m9X2OuwlHhYTQNbStw2PqSykjBE1DeWFZUPmPQBevrPX1/Vc68vukm4DruTDW/9dYtOb7mz7m84cX0cXDr7nMbObR9WspqBGEVF0XYHlaCoOiOvLAINeXo6rqiJh13Wmrf/j6y+plm5rLbxrIKizkyQ0fsWnuQ806Ybc1kxwRIUSzd3OPnvRoF4bRwQfRoiHDMBqa4Y+8wClgiOTGGilvH4/DqOpeitJYP7DfPZDm8eFsPZHO5cJCu/k4Cliyf69bOSvO6oiA/Ykhs9YcvXqFPZkZLl9PNC0yI9LKaF0GJZvRRe+D5QIYolBB90DQdJ+0bhfCEwxKsWT6TOasfY+r1dt3qZ7Gn95vAI8m1lGyvhmwLv38B539sM323Z2XumLW9gMri9ZsP3uGnmFhvHfwABcKrtGpTRvuGxjH9H4DCPCr+8e/1ppvMs6xIi2Zg1mXCfYzMa1vfx6MSyAiOJhdTvJxNHAiJ5vckhLCg4Jceo3O6og4Y1SK3ZkZjIp2M+dMNAkSiLQi2lKAznnU2oyuKqnOnIku3wdF70D7d6z1QYRohmLad2DLvEd471Aa648e4VpZKX3COzAvfjC39OqNoRlP2yvTQIjYDMXvoUs2gS4C5e/0eceuXuGnWz/BoBQWrcnIz+fb85m8nZLEitn30TbANrFba83vv9zGO6nJ1UEcwLHsqyxJ2svyWfd55fVJHZHWrRnOU4r60vl/gPLUyr9ZbP+sOI7Oe94XwxLCY8KDgnhy2Eg2zn2Irx55nDfuns3k3jHNOgipoowRqDbfwxCxAUPkNsZ2j3W4FAVQXFEOUD3bUPWBf+RKFr/ctrXW8WsOH+Sd1GTANhfDojUFZWU8un4tQzt3drg7SQG9w8IJC3R999ro6G4NukdmrRnZVfrQNFcSiLQS2pINJR/hqBAYpf9FV8g6qxDNwSOJQ+3maVRxlFexOf04FwuuXT9Wa5Yk7bPfyUlrrhQVYdEQGRxiN3DQwGNDh7uVOOqsjgjY7zBlVIq+7TswSgKRZksCkQbQFWew5P8RS9btWLKmYMn7Fcp81NfDqltZCvbqEFynoXxf3Y+U7cGS8yNrj4srd6EL/oE2Z3l8mEI0R2aLhc3px3ho3QdMeGsJM1ev4J2UJArKyrx2zWGdu/LbCbcA2MyMGJVyaXbBojVPfLyOr86dQWtNQXkZx65ecbhI4mcwsCvjHDP71+6uXPVhMjcuwa0dM3C9joifwVDrtQA8f9PNTInpY/M1sAYnEcEhLL5rpuyYacYkR6SedMlWdO6PsGlxX5yBP+8TEbIQaNw26M7Vbw1Wa42+9mcoeoOaBcR0QToUvgnt30SZEjw3TCGamTKzme9s+IgvzpyqkYuRR9qli7yRtI9V9zxA51DvNC18ePBQhnXuyrLUJHZnZmBUBib07EWPdmH8/sttTp9/MOsy39uyiZsjO/K/Mc7r+lgqA67i8vJaP1E6BIfwp8lTmdizV72Cgsm9Y9gy/xGWp6bwxemTVGgLI7pEsyAhkYSOnXhUa7aeTGdVWionc7NpG2AtaHbfwDjaubEMJJoeCUTqQZvPVwYhZmw/4M0oILrdG5SZbwFG+WR8dTINxrYSaV0UmG7YWVCyoTII4YbnWkAXorMfh6gvpZqpaLVe2bWTL8+cAmrmYlhlXsvnqc0fs+b+uV67flxUR/586+02X7tcWMALX33udCdK1aNfZV1i3dEjRIWEcLnQft8cC1BSUVHnrzVXigpJu3yJW3q5X6iwSs+wcH5180R+dfPEWo8ZlOK2mL7cFtO33ucXTZMszdSDLnoX67ekvW9yA36ltQsQ+ZIydrCWR3fUEyNgEsrPdp1VFy7F/uqsBXQOFG/y4EiFaD5KKspZnpbsMBcj6eIF0i5fatRxRYW04a6+/Z0ms9b09oEUlMMsDSt7wY0G3k7ZT5nZ0S87QtQmMyL1UbYT+0mfoJQFg3l3443HRartr9Hmk5Vt5mv2xNDg1wfVzrb6qdYl1hLqDhnR5XtQzPboWLU5C4pXo0u2gC4Gv0GokHko/5EevY4QDXH0yhWneSAGpdidcY74qI5eH4/Wmq/OnGblgRQOX8nCZDBiNjvLDbO6XFTU4OvnlJRwIieb2IjIBp9LOJd88QLLUpPZf+E8/kYjt/aOYW78YK9VEPYWCURaEWVoA+1XQMlGa0Ez8wUwRqKC7oWgGXUsr/hmb78uT0VnL6xsrV4Z8Jkz0aWb0cELUaE/l8Q00SQ0peoXFq35+Wdb+ODwQZsaIHC9eZxoOV77djd//Wa7zb0+mZPNm8n7WTJ9FmO7dffxCF0nSzP14T+aG8st16S1AYuxCeWH1KCUPypoFoYOKzFEfY6hw3uo4PvrzPFQKgj8BmB/aQbAjDKN8Nj4tC6x5p3UDEIqrwNA0ZtQ8rHHridEQ/TvEEGIyeTwGEsj1bhYlprEB4cPAtTa1utK4B4ZFEyf8PYuLM7YFxYYSEx4+wacQbjiy9On+Os32wHbe23WmtIKM09sWEduSbGvhuc2CUTqQQXPwfrhXPe3rFIWKvznNOqYvEWFLMJRLgwqHILu9NwFSzZZ807sLn2pyrwVIXwvyGRifkKi3e2yRqUY3LETCR07eXUcFq1Zsr/urfdVj4PjH/gPxw/m8WEj6j1zooCHEobgb7T/S5rwjKXJ++zm/2g0xeXlfHDoYCOPqv5kaaYelLErhP0NnftjbLbvYkRjISP3ESLb+n5Lqy5LQhetqCzp7g+BU1DBc1BGN34oBs6A8oNQ9Ba2u24MoIJR4Ys9umNGl32L4909GioOo3WxdcZGCB97etRYDmdl8dXZ09Xbd6s+IrqEtuW1adO9PoZLBQVkXst3eIxRKUxGI6UV5lq9eG6KjGJBXAJtQkLYfuY0Hx93vx5SZHAIiZ06o7WunoGxWCz8c+9u3klNJqe4GD+DkTHR0fz65kn0Cg93/4UKAPZkZjgsZqeBXZnneGzo8MYbVANIIFJPKvA2iNiILloJpV8CFjCNpMx4L1cyNb5O1dIFr6ILXsXmQ73wJLrwLWi/BOXv2nKKUgpCfwEBt1iDmopDoIIg4DZU8IMoY5SnR+7iYdrxipEQjSTAz4+lM2bxSfpxVh1M5UxuLuFBQdwTO5DZAwYRGuD9ZpKu9GpRSnFv7CC6tm1b3Yunb/sO3NNvAFEFRRgNBpRSfH/E6HoFIleKCnl0/Vrmxw/m9xMnY9aaKcve5ExebvUxZnMFX5w5zZfL3uDfd83k1t4xbl9H4FZn4+ZAApEGUH69UG1/Cfyy+mu6qAg47LMxAeiSzyuDEKhV+4NSdM4TEPkVyuBakSWlFASMQQWM8fRQa1/LNAJdvNbBEQbw64cyBHt9LEK4ymgwcGe//tzZr79Prt+pTShdQkM5f+2a3WMqLBZu7tGTW3v34clh13efFRUVcfjw9Z9ZvcLDCQ8MIsfNHIOqxdTlaSkMiurIF6dP2QQhNWnguxs/4uB3f4i/nS7Awr6RXaPZlXHO7qyIgmZV8l5yRFogXfgG9m+txdq5s/jDxhyS64KmWfNOHIxfhTzamCMSoskzKMVjQ+xPwxuVomtoW27p6bzYmMloZGHi0HpPOCrgP/u/ZeuJ4w6PM2vNP/Y2vTIHzcGiIcMdBiGBfn7cGxvXuINqAAlFWxitNZTvxVGdE7D2jlEhDzXOoNygVBCEL0bnLLQGTNWvo3KJKfhhCLzbhyMUTVleSQnvHUqzWXqYGz+YiT3qV3Yc4Py1fFampbL1ZDplZjNDOnVmQUIiQzp38fDoG+ahwUNIu3yJD48cstnSaVCKdgGBLJ0xC6PB8e+eKZV1KfZeyCTYZKKwvNztrb8aOJmT49KxX54+xTOjx7lx9qbDojXbTp1gZVoqJ3KyaRdoLTl/78A42np5OW5iz148M3ocL+/aYXOvjUrhZzCyePpMwoOaTw6dBCKiyVH+gyHiE2sF29KqgmYDUcHzwH+01BARdTqZk82cNe9xpajweon1/Hz+e+okM/vH8uKU251+EN9oV8Y5Hl2/ljKzuXrnSUZ+HuuOHuaZ0eN4auRoD7+K+jMoxUtTbueufv1ZkZrCsewrtDH5c1e/ATwYF0/7IMfLmUtS9vPKt7ttgxiUS/knrU2FxcIPNm9gy4nj1e/Xufw8Dl6+xJKkvay+50G6tWvn1TE8NXI0Y7t1552UJJIuXqguaDY/PpGubaWgmfAhpRTaNLSyi66D6q8uJqv6ijJGoUJ/CKE/9PVQRDNg0ZrHPv6Q7OIi2+5PlR+oHx09zMDIKLd2EeSVlPD4xx/aBCE1z/nyrh0MiopyabmjsSiluKVnb7fHlJKbzSsHUwHbuhSWegQhCmvPmNO5OU6fPaFHT7fP3xS8vncPn1YuPdV8vzSQVVjIkxvXsXHOQ17/pWlo5y4MbWIzc/UhOSItkDWHwl4QYrDuegma1ZhDEsKrtp89w+ncXLvr5hpYmrQPs8XxkmVNaw4fpKi83G5vFaNSLN2/tz7DbXI2ZGY47Uvj6keqBh4fOpwpMX0cHmdUiqdGNJ0ZJVeVm828mbzfYX+hI1eu8O35zEYdV3MmMyItkAqcjA75PhS+hm1NDiNgQoX9G2VoXlN3QjiyO/McfgYDFQ4CjUuF1lob3duFuXxOR8xas+d8pk3djKbCbLHw6cl0Vh2wbiduHxTErAEDmR07iDb+/rWOP5CX47AuBViLtxU5yBmpWqKYG5fAA4Pi6RkWztdnTlNcUXevm/sGxvHg2ve4WlREj3bteDAugakxffFzc/mssZ3KzXG6o8ioFLszzzmtqKu15sszp1mZVrmU5h/AjH4DmN6rdW1rlkCkhTKE/ggdMK6yoFkKEACBt1YWNGv+U3lC1OSNsgrNtVRDaUUF39n4EV+euV5gLSM/j9RLF3kjaR+r7nmAzqHXt+6vO3aEChde7LBOXbi9bz/eP3SArMJCIkNC6BTShpM52RRXVDAgIoL58Ync1L0Hf/1mB//cu7vOWZaBERHklpay+mAaYA1qMq/ls/3cWcZ378Hiu2YS0IS39Hrqn4VFa57d+glrb0guPpyVxeJ93/Lr2DhiPXStpq7p3m3RYMp/OMq/eVTWE6IhRnaN5vV9exweExUS4lZX0pFdo/nvqRN2HzcqxYguXZvcbMgru3fy9dkzwPXS7tXJu9fy+cHmDXxwv7UFxZErWfz6q8+dnlOhGNu9O3PiEpgT57hq9Ob0Y/yzclvujbMsBqU4evUqWtumwFaNc/vZs7y8awe/uGmC0zH5Sq+wcMIDA8kpKbF7jFlrRnZxPBvydkoSa48cqj6+ikaTV1rCnw6lMWnIUM8Muolr2nNgQgjhgpt79KRHuzCHeQ6Lhgxza9fMvQMHEWQyoexkR5i15tEhw9weqzcVl5ezPDXZbl6LWWv2XzzPgcuXAFiWmmy3T05NAX5Gl+tSLN2/z+45LVpj1tpuBptGsyIthaLycpeu5Qv+RiMPD7ZfZ8WoFP06RDhclrH2BrKfX2TWmkulJWzPcLw82FJIICKEaPYMSrGksnZCzcChKjCZ3m8Ajya6FzSEBQZVLhMY6wxw4qM6ujXD4innr+Xz0s7t3Lb8LW55eynPbNlE0oXzABy9eoVCJx/iBqXYnZkBwNdnTzvNDVHAv++6mw7BzqsZV1gs7L943m4g5Iqi8nIOZV2u9/NdYbZY2HT8GA+t+4AJby1h1uoVvJOSRGFZmUvP/+7wkdza25qMW/PfhsLac+ffd97tcKbsQsE1LhTYr4Jbdd69lfe1pZOlGSFEixDTvgNb5j1iU9CsT3gH5sUP5pZevV36zf9GY7t1Z+uChfzPl5+z9WS6zWOHsi5z58p3+OOkKTzgZLnCU745d5ZFN2wprlnXZFy37h6/Zo92YYzv3tPj5/WV0ooKntzwkU2TwuocmuT9vHvP/XRq47j9hclo5F93zmDryXRWpqVwKjeHtgHWgmb3D4qjbYCTRqDNNP/IWyQQEUK0GOFBQTw5bKRNL5WGqjBb+Pz0yVq7RapmEp7ftpVBUR2Ji+rosWvWJa+khCc2rLPpnltzHC/v2kG/9h0IrtzdYo9F6+plg7HR3Vlz+KDdWRGjUkzs2cvlMfoZDCR26kzqpYv1nhUJ8jMRG+G9tqF/272T7fZyaPLz+MHmDbx/3xyn5zEoxW0xfbktpq/bY+gcGkqnNm24WFBg9xiz1gzr3NntczdHsjQjhBAOrDyQUiu5siaDUrydkuT1cXxQWdfE3kiMSrEsLZn5CYkY7GQwGJUisVNn4iuDpocHD3EYMGhgfkKiW+N8bMhwu+c0KIVRKbsfPAalmBufQEgdW4w9oTqHxs57aNaafRfOc7Ayh8ZbDEqxaMhwh3kmUQGBjI/2/AxXU9TqAhFtzsRy7UUsWdOwZE3BkvscujzV18MSQjRRO86ddZhHYdaaHefOODzH5cIC/m/3Tu5Y8TYT317CDzZvYE9lnoardjtJXDRrzZ7MDH48aizjuls/wKqWo1Tlf11C2/KPO+6qfk5sZBS/Hz8RhW2ug1EpDErx16l30Du8vVvjvKNPX75TOSNV85wGpQj08+Mfd0wnul276jHVHOfY6G78dMxNbl3PHUeuZDlNhK2ZQ+NNCxOHMrO/dYPuje9Tu4AAfj4w3u2WBM1Vq1qa0aXb0TnfBcqprjxqzkCXfAihz6JCHvPl8IQQTZBuYEGR5IsXeGjdBzZVWjPz89l4/ChPDBvBc2PHe3QLcICfH0tnzGZL+nFWHbxe0Gx27EBmDxhE6A0N2Wb3jyX4WgG7S4rYc+E8Cripew8WJAyhb4cObl9fKcWz48YzsWcv3klNIu3SJQL9/Lgtpi9z4hLoHBrKuO49WHv4IGuPHCS7uJhubdsxJy6B2/v082pBs6aUmmFQipem3sGd/QawIi2Z49lXrxc06x3DxVOnfT3ERtNqAhFtyUbnfA8o44aVXuvj1/5ibawWMNYXwxNCNFFju3Xn2NUrDvMoxtqZQi8uL+fR9WtrlYqvOtfifd8SFxnFXf0GOB2Hq3VNwJqrcWe//tzZr7/T8wL0CGnD7cNHEOzCzhhXjewabXcLaxt/fx4aPISHBg/x2PVcMSAi0q0cGm9TSjGpV28m9bLtDVRUVMTFRhlB09A65n0Aij6gdhBSkxFd+FbjjUcI0SzMix/s8HGL1jycWHfhqfXHjpBbUuIwZ8JRPYmaXKtrIgUMHQk2mZgXP9juDiqjUgzt1MXricfCVuuZESnbhaNutGCG8t2NNRwhhAeUmc1sPHaU1QfTuFBwjY4hbbhvUBwz+g3wWJnwnmHhvHLbnTy9ZSNwfTbDWLn18/9NmsLAiEg2px+r7u0SHhTE7AED2Xsh06Z8940sWpN6+RKlFRVOx1tV12TR+g+psJhtxmHWmqdHja31m7Wo7ZnR4zhyJYuvz56p3r5bFZZ0CW3LqzVyaETjaDWBiBCiZblWWspD6z4g5dLFGvUg8tl7IZN3UpJYPus+2gU6qefgojv79WdARATLUpP5+uwZLFozOrobDyUkEtO+A49/vI4vzpyyqUuRdukiQSaTR/MSxnbrzmcLFrIiLYWtJ9MpM5tJ7NSZhwYnMqxzVw9eqeWqyqH5pDJwPJuXV51Dc09sXJ1NAYV3tZpARPmPRJftxP6siBFMnqs9IITwrt998d/qUuXX60FY/zxyJYvnt33Ka9NmeOx6Me078LuJk2t9/S87vubLM6duGIdVcXm5w0DEoBSDIqPcmr3p2rYtz44bz7Pjxrv8HGHLz2Dgrn4DXMrNEd7XenJEgu4DTGB357YZFfJI441HCFFvWUWFrD92xO6Sh1lrPkk/zvlr+V4dR0lFOcvTku0GG1Vft/dTx6I1i5pYvxohGlvrmRExdoDwf1Zu3zVTtVsGjIAZ1eanqIBxvhugEMJlSRfOO+2RooF9F87TxYv9YI5kZVHgpD+JwloSvMJiqZW02q9DB3qGhXttfN50taiIdw+msen4UQrLy4iNiGJe/GDGdeuOUopvz2ewPDWF1EsXCfTzY2pMH+bGDaZjmza+HrpoYlpNIAKgAsZDxGZ08Soo2QaUg2koKng+yt9xZrwQouloCvUgLFrz6re7nB6nlOLJoSM4nZfLx8eO2Dx2IjubmatX8Oubb2GhnZ03TdGhrMvMW/s+18pKbWqjbDlxnPnxgwkNCOBfe/fYJOoez77K0qR9vD3zHslnETZaVSACoPy6oUKfhdBnfT0UIUQ9De3UpTox1B4FDOvcxWtjWJ6azOenTzk9zqI1Qzp3YfH+b+32q3nhq88ZFBnVaPUrGqLMbObR9WspqBGEwPXXsjwtpdbXwPo+lFRUsGj9h+xY+ITXyriL5qf15IgIIVqMyJAQpvfrb1MauyZjZUMyby3LWLR2qf6HQSkGd+xE6qWLlFvsdTixjvfN5H2eHaSXbD2RzuXCQqdLY3WxaE1+aSkfHT3shZGJ5qrVzYgIIbznXF4eK9KS2XbqJBUWCyO6dmV+wpDqJmue9PuJt3IqJ4fUy5dQKDS6esahX4cI/nfyVI9fs8rlwgIyXEiEDTGZeG3adH7y6WaHszdmrfnGSS8ZbziXl8eb+79la/pxjGlJjIqOZkHCEIcFvXZlnsPPYKDC4qguk30K+OrsaeY6KRQnWg+vBiK/+c1vKCsr409/+pM3LyOEaAK+OH2K72z8CLPFUv3b8rn8PN4/dJBfjp/o8d0hbQMCWH3vg2w4fpTVB1K5UFBQXdDs7v4DCPQzefR6NbkyGWBQipkDBtIltK1Lxzd24su2Uyf57saPsGhtvV/FkHk4n/cPHXSas9KQ/jsa67+V41ev1quXjWh5vLI0Y7FYePnll1m9erU3Ti+EaGKyCgv57saPKDebbabsq/7/D19/4bR7bH0E+PlxT+wg3rtvDl8vfJwP7p/DA4PivRqEAHRs04auTpZ9LFpzU7ceAIzp1s1uWXGwLs2Mju7m0TE6crmwgO9tWk9FjaARbHNWvj1fdwfaoZ271GtZpqZys4XHPv4Qcz1nVUTL4vFA5MSJE8ydO5f333+fLl28lygmhGg6Vh9Mc5oD8UYzyYFwhUEpHhtqf4bHqBRdQkO5pbLk+oODEvAzGOxXMdK6UXfNvHsgjQpnOStJ++t8zP6rcJ1Gcy4/jy/OOE/2FS2fx5dmdu3aRUxMDK+99hpPP/20p08vhGiCvsk42yRzIOrjWmkpaw4f5MMjh8gtKaFXWDhz4xO4tXcfm1mNBQlDSLt0ibVHDtlsUzUoRbuAQJbOmF3d0r5jmza8Nm0639u4/vpSCNf7xDx/0wRGOZkRqV23I5J58YnVdTtqMlssfHoyvbr3TfugIGYNGMjs2EG08fdn5znn92tnxtk6H3PWP8dVfgYDuzPOMblXTIPOI5o/jwci8+bN8/QphRBNXFOo6+EJGfl5PLhmNReuXat+Teev5fPV2dNM7d2HV++4C5PRCFgDjhen3M6d/fqzMi2Fo1evEGLyZ3q/ATwYF0/7oGCbc0/uFcOn8xeyPC2Zz0+dpEJbGN65KwsGD2Fwx04Ox3U46zJz66zbkc68+MH8z8TJ1cFIaUUF39n4EV+eOW3T+yb10kXeSNrHqnseaPD7pJRyLVFGCBe4FYhkZGQweXLtXgtVvvnmG9q3b9/gQd1Ia01RUZHHz+sNxcXFNn8K35L70TiGRXViT2aG3d+yjUoxolMXioqKmuw90Vrz5MfruFRQUGetj60n03ll53a+P2yEzfNGRXVi1OQbAglNnT+zIv39+fGwkfx4mG1fK0c/38rNZhZ+tMYmCKk5rhVpKfRtF8a9AwYC8Lc9u/j6zGmgdu+bzGv5fH/jR4zp2o29FzId3q+RnbvUOa7BEZGscJDboYDubdtRZjZzobDA7nEVFguJEVHN5md7Y2qq3yPu0lrXmq2ri1uBSMeOHdm0aZPdx9u1a+fO6VxWXl7O4cPNa9/56dOnfT0EUYPcD+8abPTDiMJe1oFZaya0bWfzfdzU7snR/DwOX71i93ENvJOWzPjAYEyGxivBtPPKZS47+bBevG8PAy1QZrGw4mCq3daeZq1JvnyJaR2iMGC/BahZa8a3aVfnz91oi4Uwkz/55WV1Pl8Dt0d2pNRi4a1T6XWe3wB0CAgkorCo2f1sb0xN7XukPvxdKFznViBiMpmIiWn89TyTyUSfPn0a/br1UVxczOnTp+nZsydBQUG+Hk6rJ/ej8fxf+3B+tPWTOnMgfjpyDPcmJAK+uScncrJZdegA32RmoJRibNdoHhwYR+8afV62J+93Wq21sKKCn6Ul4WcwMLJLV+YMjKNf+/pvQU27fIlVhw+QdOki/gYjvcPCySkp5mRuLqXmCvwMBoxKOR3X+eJistqEsOJgKiVms93jwLqklBsYwJReMWw+aRsoVN2vZ0eNZVZcAl+dPcPqIwc5lZtL24AA7orpy8x+A/hPxygWbfqYgvKyWuPq3rYd4wcOIiGqI1e/NPBx+jGbnBJVOQaD0cBfThxjdv8BTIvpi3/lkpdoOT+30tPrDkRv1CwKmimlCA4Odn5gExIUFNTsxtySyf3wvtv7x/Jpx87XC5o5yYForHuy5vBBnvtsCwps6pu8e/ggL0+9gxn9YwHwM5lc2g+SWXANgIxr+Xxw5BB/nDSFB+IS3B7X63v38JedX9t8SJ/IzXH7PFW+/+kmh1uEq2itWZKaVGdBspj2Hfjz5KkMiurIDzdv4JMTx6+P7xocvpLFOwdSeffeB9j60EJe3PE1aw4ftJkHy7yWz/yPP+QnY8bxyh13MfPMKVakpnDkahbZxcWUVFRg1ppLRUVkFRez/9IF3j1ykOWz7qNtQGC9X39L1Nx/brmyLANS4l0I4UE9wsJ4fvxEPnvoUb54+DFemnqH00RMbzpyJYvnPttiM0sD1oDEojU/+XQzJ7KvAjCya1e3doKYtUYDz2/byoHLl9wa1/azZ/jLzq+rz+MpjmZNqmigwly7EzDAsatXyMjP59/79rDlxPFa49NAVlEhT2z4CD+l2JR+DG4I36qO/+s3O/jyzGlu6dmbJTNmMbxzV8orZ2uqzlg1hsNZWfxq22fuvVjRYkggIoRosZalJjud5ahq0ja8c1diIyLt9q+xx6AUb6ckufWcpUl73b6OI66eqeo4e7k8BqX4z/5veTN5v92dUGatOXb1Cq/s2klxeTnazpFGpXgjydqP53JhARuOH7UbdJm1ZlP6MS5WzjaJ1sWrgciyZcukvLsQwmd2nDvjcMbBrDXbz54BrNPIr995N1Ehbdwq2WXWmh3nzrg1rt2ZGQ2eCakZyLhyJgUEmUwOl28sWpN6+RLZTnZrGJVix7m664xUMWvN7kxrddZ9F847na2xaM2+8+cdHiNaJpkREUK0WO5+1ndr147N8x7msSHDPTpjcaOGxCAdQ9oQ3bYtk3r15o0Zs116TruAAH4z4RYm9ujlgbqoQnhWs0hWFUKI+hjXrTvvHzpgd/bBqBTjunW3+VqQnx/rjx1x+RpGpRgb3d35gTWM6hrtdLbGnhWz76N3+PV6TUM6dSbl0kWHMw4/GXMT8xMSCTD6VeZ11M2gFIMio8jIzyenxP6siFlrxnTrzikHybVGpRjZNRqAoZ26ON35Y1CKYdIWpFWSGREhRIu1YPAQhx9+Gphfua24ytaT6VwqLHA5SLBozcNu9olZNGSY20GIAbipWw+bIATgsaHD7b5Gg1KE+vszs7LY2Yz+sbQLCLC7PGPRmseGDmdh4lC7MydGpejbvgPPjB5LoJ8fys6RZq15tLLjcsc2bbizb3+7s0xGpbg9pi+d2oTauapoySQQEUK0GHklJfxn/7dMX7WMiW8v4eVvtvNIZZBQ80OwqjbHS1PuoM8NdUB2Z2ZU94hxxdhu3WnrH+DWOMf36MlPx9xUa1yO9AwP56+33VHr67fH9OW7w0fWOpdBKQKNfiyZMYs2lUWlgk0mls6YXRlA1PbE0OHc1bc/3xk+kqkxfWqdUwERwcEsvmsm4UHBLJ4+E3+jsdZ7C/Dj0WO5pWfv6q+/cMtkYiMjK89jPaaqgV7/iEj+MGmKS++DaHlkaUYI0SKczMlmzpr3uFJUeL2keX4+Zq25pWdvokJC2JVxDgXc1L0HCxKG0LdD7WJk2s2Zil0Z55i6/E1emzadW3u7XnjxeyNGMSa6G++kJrP/wnlMRiN9wtuTU1LMiZxsSiqsBc26tW3Hg3EJzBowkGCTqdZ5lFL8bOx4JvToxbLUZFIvXSTQz4+pMX2YGzeYzqG2swwKa45KXa+yqu6Dn8HAa9Nm8NnJdFampXIyN5u2AYHM7B/LfQPjaBdorfcxrlsPPluwkBVpKWw9mU6Z2cyQTp1ZMDiRYZ272py7bUAg7987h/XHjvD+wTQuFBTQqU0b7hsYx939Ywnwk4+j1kppd7/rGllaWhoA8fHxPh6Ja4qKrCWLY2Njm3UhmpZC7kfT4417YtGaW5e9wbm8vDqXPBTwi5sm8NjQ4U7PteHYEX74yUa3rq+wfnh//vAiuoS2deu5jamovJxxbyyu1bempldvv4s7+/Vv5JGJmlrKzy1XP79laUYI0extP3uG07m5dvMuNLA0aR9mB83aqkyN6UtUSIhbu2Y01pyIVQdSXX6OL3x89DB5pSUOc0r+U1n7Q4jGInNhQrQAx69eZVlqEjsrazvc1L0H8xMSa+U/NCcnc7JZnpbCV2dOo7VmdHQ35ickEhsRWevY3Znn8DMY6ixbXuVSYQGZ1/Lp3i7M4XX9jUbemDGbeWvfdzhzcCOL1uw8d5afjHHpcJ/YnZlhU1L+RhatSb10kTKzWXq/iEYjgYgQzdzawwd59oZeKmfyclmelsJLU+5g5oBY3w6wHjanH+OHmzcA11/T2bxc3j2Qyh8mTeHBG3q7eHqBeWBkFFsXLOTdA6lsPH6UwvIyLhUUUO7CjEpT1qTX4UWrJUszQjRjx65e4VkHvVR+uvV6L5Xm4mxeLj/6ZGOdr0kDv6yjt8vIrtEOZ0MAokJC6OpG/kZEcDBPjRzN5nkP89UjjzM1pq/D5RqDUoxxs55IYxvVNdrhtmGDUiREdZTZENGoJBARohlz1ktFVR7TnKxMS0Frex1MrB+WbyXvt/nazT160qNdmMNAYdGQYRjd2JZ7o4WJQ+1+iCus21bnxLvfhbcxuVJHZJELCb1CeJIszQjRjG0/63ovleZix7mzTl/TjX1ODEqxZPpM5qx9j6tFxdWN2KqqebYx+bMyNYX07KvMTxhCfFRHLFrz+amTrDyQQnp2Nu0CA7m7fyz3DRxUZzv6oZ278LsJk/jdl9ts8iyMSqGU4tU77iIiKJi1hw/y3sEDXCi4Vr09dXq/ATbbU49dvcKy1GR2njtbYztxIjH1yOk5lHWZd1KS2JOZgdFgYGLPXsyPT6RHWFitY6vqiDy87gOKKyqq818MgIXrdUSEaEwSiAghmpT65jHEtO/AlnmP8N6hNNYfPUJWUSFXi4owAAXlZRSUl5FxOJ/3Dx3k+ZtuJvnSRTYdP1YdVJzLz+Pg5Uss3b+XVfc8UOcH+UODhzC0cxeWpSZVJn4amNCzFwsSEokICuaBNatJvXSxOgDKyM/n2/OZvJ2SxIrZ99E2IJA1hw/yXB05PSvSUnj5tmlM7zfA5df8TkoSv/9yG4YagdHp3BzeSUmyW9dkaOcubF2wkFUHUvkk/TjF5WVE+wfyxKgxTOzTtz5vvRANIoGIEM3Y2G7dycivu3YGVPZS6d6jkUfVMGOju3H0SpbD1zS2W925GOFBQTw5bCSzBgxkwltLAOtv+lWqzvnH7V/V+hpYg6CsokKe2LCOT+Y9XF3gq6a4qI78+dbba339mS2bOFiZu1I101A1M3PkShbP/3crT40czbNbP6kVbFWN4ZktmxgUGVWrjHtd9l84z+++3FbrNVTlB31/08d265p0ahPKj0eP48ejx12vWdGla63jhGgMkiMiRDP20OAhDmcQNDA/PrGRRuMZ85yM16I1Dw8e4vCY1QfTKLdY6jW7Ytaa49lX2ZVxzuXnZBUWsv7YEbvBk1lrPjlxnH/v22M3P6PKchdzet5M3m83J6aqrsm7B9JcOpcQviQzIkI0Y/07RPDnW2+rNdVvVAoNvHjr7XWWMW+oovJyPjxyiDWHD3K1qIhu7doxZ1ACt/Xp61aflrr0CAvj/26/kx9VVjet+ZosWvPCLbeS0LGTzXOuFBXxP19uY9vpk5RWVAC4XP+jLkal+Pz0KfZfvMCmyu27sRGRzItPJDwwkGWpyTY5GT3ahTm9nqUyX8dZ/svKtFTaBwXzQFw8kcEhdo/9xkkuTVVdk2fGjKv12OXCAlampbLlxHGKysroFhDAE2HtuDnG9RL1QniKBCJCNHP3xA4iLqqjNfmxMjF1XGXyY78OER6/XlZhIQ+sWc3p3Bxr3xIg81o+O8+dZVy37iyZPqvBfUOm9e1P/w4R1QXNLFozqms0Dw0ewsDIKJtjD2VdYubqlU6377pDa83y1GTKLObqACMzP58tJ9IBbJJVT+fmuH5eF44ps5h5ZfdOluzfy/LZ9xEX1dHOueoXaCVdOM9D69ZQXFF+/bVdU+zc+BFPDhvBs2PH17kkJYS3SCAiRAvQv0ME/++WWxvlWj/6ZCPn8nKB6x+sVR9o32Sc4y87v+bXN9/S4OvEtO/AbydMcnrcnDXveTQIAWteSZnZTM3FnRvzMOr6f0cUMK5bdzYdP+b0ORatKSgv49H1a/n6kcfrDOzGduvOJ+nH7Z7LoBRjunWz+VpReTmPrv/QJggBql/nv/d9S1xkR+k1IxqV5IgIIVx29OoVdmWec1gi/N0DqRSUlTXKeDanH+Oah69V9UOxPhkm9uYRjEpxW0xfvjN8lFsl468UFbE5/Xidjz/iQl2TGyvQSq8Z0RRJICKEcNmezAyHBdQAiisqeHLDOvZkZnh9PBuPHfXo+RQQ4OfnVsO7mgL9TJXnUdXnA+gfEcH/Tp5KbEQkf7r1tupAwRk/g4E9mXUnzQ7r3JXfTqg986SwBhT/uGN6rUqyVb1m7KnZa0aIxiJLM0IIl2kXf5vfnZnBg2tW8/jQ4fx83M1eyznwRO+UID8THYKDaBsQyMz+saRnX2XN4YP1Ole/Dh2YFz+Y9w6mcaGgoLqg2d39Y6uXV+4bGEd8VEfeSNrHB86u4+QF1gx4ah6qlKKut1x6zYimSAIRIYTLRnaNdunDrGrq/z/79zIoMooZ/b3TeO/Ovv3ZnH6s3s83KsV9Awfxu4mTq7/20ZFDvHfoQL3Ol9CxE/cOjOPegXEOjxsQEcmfb72NlEsXSc++avc9rdAWRnaNrvOxmnVEaj5fA2aLpc46IqO6RvPR0cN2x2VQirjIKOk1IxqVLM0IIVw2ICKSkV2jXV66MCjFEi/mHPQOD2/Q8zUwPyHR5msNmb1x55lKKR4fOtxhT50OQUHc0adfnY/Xp46I9JoRTZHMiAgh3PL32+/kgQ9WczYv1+nsiEVrDly+TElFeXX+hKtO5mRXb9/VWjM6uhvzExKJjYisPqYqZ8XZOKryJm6ss/LSlDvoc0N/l30Xzttsz3XHh0cO8fnpU4QHBTF7wEBmxw6ijb+/3ePviR1E2uVLLEtNtrmmQSlCTP4snTG71o6Z89fyWZmWyub0Yw4TX+uqI+LNXjMnc7JZnppsvV/A6OhuLEhIZECN+yVEXSQQEUK4JSqkDRvmLGDtkUO8smsHOSUlHr/G5vRj/HDzBuD69tizebm8eyCVP0yaUms3iDN/ufU29l+8cEOTuSF1FnvTWltnReoRiFwrK+NaWRkZ+XmkXbrIG0n7WHXPA3QODa3zeKUUv5swiSm9+7AiLZlDWVkEmUxM69OPOXEJRIbYFjT75txZFn38oXVrcT0Ltnmj18zGY0d5eottAbqq+/XHyVN5YFB8vc4rWgcJRIQQbgvx92dBQiL+RiO/+O+ndo8zKEVsRKRbsyFn83L50ScbsWjbkl1VH3C/3LaVuKiOxEV1ZIQLOStBfn7c1qcf9zjJ26gysms0y9NSXB5vXarGlHktn6c2f8ya++faPVYpxU3de3CTk55AeSUlPLFhHaUVZpeKmdVVR6SKJ3vNnMnN5ektG2vNIFX9/fn/fkp8VMdaheiEqCI5IkKIepvRbwBhgYEOcw4eczPnYGVaClrb/6g1KMVbyfsBiI2IZEQX+zkrhspaGo6WR240NaYvUSEh9d7CW5NZa5IuXiCtshleQ3xw+CBF5eUuBSH26oh4w4q0ZIePG5SBt1P2e30covmSGREhRJ32ns9keWoyKZcuEmA0MjWmL3PjE+jU5voyQ5DJxNLps2qVDK/Sr30HeoW5l1C6w0kPFbPW7Dh3tvrvf7/9Th5cY81ZAetshKGyL83ortH8bOxNdZ7HojWfnzrJygMppGdn0y4wkLv7x3LfwEG8MWM289a+z7Wy0urXZJPDgW1XX0cMSrE74xzxdZRqT7l4gWWpyey7cB5/o5HJvWKYFz+Yrm1rd8zd7WITPqNSKDt1RLxhu9P7ZWH72bN2H3ekzvcnYXCjvC7ReCQQEULU8rddO3h1zy6bD9/0nGzeSN7Hm3fPZkSX61tKh3TuwmcLFvK/279k/bEjNudJz77KzNUr+OX4iSwaMsyla7ub+dCxjTVnZc3hg6w9cpCrRcXWJnxxCdzRp1+dTfgqLBae3rKRTcePVb/Gc/l5HLx8iaX797LqngfYumAh7x5IZWNl07sBHSKZn2Btevfz/37K4StZbo7U1ut79/CXnV/bvMcnc7J5M3kf/5k+y+lSjT0LBg9hQUKi2wFgU/Ovvbt5cef2Ot6f/SyZMZNx3er3/oimRwIRIYSNrSfSeXXPLsC2j4pFa0oqKnhs/YdsX/gEoQEB1Y8ZlGLLieO1drBUzRr84esviIuMYlR03TkLNY2N7sbRK1l2f8s2KsXYbt1tvhbi789Dg4fw0OAhrrxEFu/7ls3HrfVHal5HA1lFhTyxYR2fzHuYp0aO5qmRo22eu+XEcbeCEIvWtWqBbD97hr/s/LrW9c1aYzGbeXLDOr5e+Djtg4KrHxvZNZr/njph9zpGpRjVNZrfeKDPjzvGRnfn+NUrDu/XuBvulzNfnznNizu3A3W9PxU88fE6ti98gvCgoPoPXDQZkiMihLCxJGmvw5yPgrIyPjxyyObrqw+mUW6x353FqBRvJO9z6frz4hMdPm7RmoddDDjqUm4282byfrtjNWvN8eyr7LKzFLJkv/3350ZGpRjcsRMJHTvZfH1p0l6HNUBKKsy8f0NRtXsHDiLIZKquplrXuB8d0vg1QOYlDHb4uEVrlwPEKkuT9jl5fyr44HD9is6JpkdmRIQQ1Sxas/d8psPlEYW1fkfND5dvMs463E5q1ppvXMxx6BEWxv/dfic/+sR2O6ixMu/jhVturfXB7o6zeblcLS5yeIxRKT4/fYr9Fy+wqXJpJjYikjlxCey7cN6l6yigS2hbXps2vdZjuzMzHOZVaDS7MjJ4ctjI6q+FBQax+K6ZLFr/IRUWs837Ytaa+wfG8dHRw7zw1ef4G430ad+eK0VFXCwooH1QELNcqGtSH73CwvnbbdP48ZZNQO379YdJU4irIz/GEefvD+zKOMfjQ0fUe9yi6ZBARAjhlro+Hjzdw2Ra3/707xBRXdDMojWjukbz0OAhDd4G6spYtdYsT02mzHK9Xkdmfj5bTqS7dI12AQE8PXosswcMslnCun5+d0Z83dhu3flswUJWpKWw9WQ6ZWYziZ06YzIaeO/QAZt8iuPZV6ufl5GfR6oLdU3q665+A4iNiGRZajJfnT2N1jCmsqBZbL3ul3TFaU0kEBFCVDMoxdDOXUi6eMHhDMeNOQ+ju3ZjT2aG3ecYlWJ0V+f5ITXFtO/AbydMcus5rujRLoz2QUFkFxfbPcYC1qJh2OYnVHFWzfWZMTex4IbS8TWN6hrNjnNn7P7Wr7Dme9Sla9u2PDtuPM+OGw/AJ+nH+d6m9bXGWFPNuiY/2LyBD+6f42D09RPTvoNNz56GGNk1mp0OduMoYJSb/55E0yU5IkIIG48NHW43oDAoRYi/P7MGDLT5+oNx8ZgMBru9Vqz5C67tmvE2k9HIwsShdsda9UPRfsaL/SDEoBSh/v7MdNLkb9GQYQ4/ZAP8jNznYgE2Rzk9NzJrzf6L5znggbom3rRoyHAn748f9w4c1LiDEl4jgYgQwsZtMX35/ohRADYJgwalCDAaWTJ9VvVyQ15JCf/Z/y2L1n9I24BA63E1zlX1/OdvmsBoF3bMNJYnh43kthhrOfOar7HqQ87VYmY3vj+BRj+WzJhV53JMTeN79OSnY2rXN1GAyWDg33feTYfg4NpPvIHZYmH/hfNulXs3KMXuzAyXj/eFm3v05CeVPXJqvsdGpfA3Gvn3XXfb7CgSzZsszQghavnJmJsY370ny1KTSb2hoFlVW/mTOdnMWfMeV4oKq2cIqgqJhZhMtA8KYkSXaBYMHsLgBiSXeoOfwcA/pk3nvydPsCIthZO52bQNCGRm/1jSs6+y9sghp4kci++ayUdHD5N66SKBfn5MjenD3LjBLudfVM1i1FrmqSxI1tp9f8RoxkZ3553UZPZXFTTrHcP8+MFEt23n6+EJD5JARAhRp5Fdo2vlglSxaM3jH68ju7jItm5I5Yd3YXk5Pxw1pknvajAoxZSYPkyJ6WPz9Q3HjvDeIftbQxXWnT2Te/Xm1t4x9bp2zToiNd8/jXV7cV11ROpiNBgY0qkzKZcuujwrUlddk6ZqSOcuDOncxdfDEF4mSzNCCLftOHuGU7k5DrdYvpG0D7PF1ULoTYezXjMaeGzI8AbNWtSnjog9jnJ6bmRUisROnessNy+Er8iMiBDCbbszM/AzGKhwEGhcKizklreXYjIY6NuhAyUVFZzOyyXQaGRIm7Z8t0cPuruQB+EJx69eZVlqkrVHjdb07dCB4ooKzuTlEuxnYlrf/jwQF09kcAj+RqPDXjNz4hKY08BmcvWpI2LP7TF9+e7wkfxr7x6b7bs1VYU8XULb8o877qrvsIXwCglEhBBuc6UDLEDGtXwATlU2pKtyPDubTR+cZ/ns+73+2/mawwd57rMtKK5vb71xPMeyr7Jk/16Wzb6vumW9vV4z47v3aHAOR33riNRFKcXPxo5nQo9eNjk9MeHtuVJ8vaDZ7NiBduuaCOFLEogIIdw2sks0/9q7p97P11jzSB79aC3bFz5OgJ93fhQduZLFc59tcbp0YdGagvIym/FEBAfX2WvGExpSR8QeRzk9QjRlkiMihHDb+B496RkW5vI217pYtOZqcRGbKpvPecOy1GS79ULsjWdzuvfGU8WTdUSEaO5kRkQI4TaDUiyZPosH16zmalGxy0s1N/JTBnZnnmNW7EDnB9dgtljYevIEqw6kcDo3l/CgIGYPGMitvWLYmH6UtYcPcbHgGvmlpW6NzKgM7M7MYOYA98bjqnN5eaxIS2bbqZOEBQaSW1KCgetdio1K4edGHREhWgIJRIQQ9dI7vD1b5j3Ce4fSWH/0CNfKSrlSVERJRYXL56hPAFNmNvPdjR/x+elT1XVLqnqp/M9Xn7tV3KuuEXnL56dP8p0NH2HRuno2pGr8YQGBdAgOljoZolWSQEQIUW/hQUE8OWxk9e6OX27bynsH0xzuCKnJXI+aFv+3eydfnD4FXK9bUnW1hgUhlePp4vk8i8uFBXxv43oqLJY6667klpbw+l13S46HaJUkR0QI4TELEhJdnlMwKEWHoCDu7Nvf5fOXVJSzLDXZK/MWVeOZ1refx8/97oE0yi32u9cYleLN5H0ev64QzYHMiAjRROy7kMmylGRSbiip3qmNZ1u223MqN4flqclsPZFObkkJKAgLDGRq777MTxhMz7Bwp+cYEBHJnyZP5bnPtmCwU9MCrL8BBfuZWDJjtls7Zo5evUpBWZnLx7vKoBQhJn+WujkeV3155pTD2Rqz1nyTcc7j1xWiOZBARIgm4G+7dvDqnl02BanSc7J5I3kfb8yY7fUp+83px/jRJxsx3/Bbe0FZGW+l7GdZahKv3nEXUysbxTly78A44jt2YllqMjvOngGt6VNZ0OxsXh4BRgND27Tju+NvpkdEpHsD9WQBDqB9UBARQcFM69ufOXEJRIaEePT8AMkXL5B66aLzA72XniJEkyaBiBA+tvVEOq/u2QVgM4Ng0ZqSigoe+/hDdix8wmuFqM7l5fGjTzbarZJq0RqL1vxg8wa2PbyIrpVN7xzp3yGC/3fLrXU+VlRUxOHDh4kMdv9Dv39EBCEmE4Xl5W4/ty55JSWsvucBYtp38Mj5blRcXs6j69c6zV0xKtWkuhML0ZgkR0QIH1uatK+6E+uNLFpTWFbGh0cOee36Kw6koF2YaTBrzaq0VK+NwxWBfibmJyTafb/qY3laisfOdaP1x46QW1LidLLDrDULE4d6bRxCNGUSiAjhQxat+fZ8htPfmPdkZnhtDDvP2q/wWZNFa3acO9Pg65VbzOzIusSiTeuZ8NYS7nlvJasOpFLs4izH06PGMr57DwCbgKQ+oYlZa7afbfhrsmdPZoZLRd+eHTueUTIjIlopWZoRosnz3G//dWnM1ITi8nIe37yBvRfO29QASbp4gTeS9rHqngeIcFLIK8DPjyXTZ7HlxHFWHkjlTI2CZt9knOWzkyeaTLqFKzNNgMyGiFZNAhEhfMigFMM6d2X/xfMOZkXcr7XhjrHdunPkSpbTWRGDUozt1r1B1/rzjq/Yf/ECULsGyOncHJ7Zsol3Zt3r9DxGg4Fpffsz7Yatv0F+fmw9ecLl8RiVYlwDX5Mjo7pGs+7oYbuPG5RiUGSU13rtCNEcyNKMED722NBhdoMQg1IEm/y9VnIcYF78YJe6yRqVYm7c4HpfJ7+0lHcPptl9rWat2X7uDCeyr9b7GtP7x9IuINDlHBINzE9IrPf1Gjoei9YsGjLMa9cXojmQQEQIH5sa05enRlg7vNbMJzAoRYDRyNIZs2hbjx0zFq3576kTLFq/lglvLWH6qmUs2b+X/NISm+O6twvjldvuxKhUnYtAhsr+J3+/4y66tnW+Y8aeA5cvUWY2Oz1uz/nMel8j2GTijRmzCPIzOQxGjEphUIqXptxBHzd3zBzKuszPP9vCpLeXMmXZm/zh6y84k5trdzxL6xiPUVl/9D4+dDjT+w1w6/pCtDQyHyhEE/DMmHHc1L0Hy1NrFzTr4sJ22RuZLRae3rKJjceP2tQmOZR1maVJe1l1zwM2Bcqm9e3HgIhHWFZZ0CyvMlgJCwxkSkxf5scPpnd4+wa9xvo2xnPXkM5d+GzBQlYeSOGT9OOUVFTQo10Y/kYjJ3KyUcBN3XuwIGEIfTu4F4QsS03md1/816ZY2+ncHN5JSeK1adO5tXefWs8ZWsd44qKimB+fyBgvLgsJ0Vwo7Wo2lY+kpaUBEB8f7+ORuKaqRkJsbCzB0j3T51rr/fjX3t28tHN7nR/9RqXoFRbOlvmPuLQk4yn5pSWMXPK601mRT+c/4vYsRWNIunCee95fVedjCvAzGPj84UX1Chx9qbV+jzRlLeWeuPr5LUszQrQwFRYLbybvtzv/YNaa9JzsRi8p3jYgkPsHxtldMqlKHG2KQQjAm8n77W7F1VTWWTng2zorQjRHEogI0cKczcvlSlGRw2P8lIHdmQ0PRPJKSvjP/m+ZvmoZE99ewuMff8jnp0/a3bb6i5smMKRjJ+B6DRBV+V+PsDBevm1ag8fkLTszzjrcWWTRmp3nzjbiiIRoGSRHRIjWyAMrMidzspmz5j2uFBVWz75k5ufz31Mnubv/AF6acgdGg+3vOkEmE0umTefN7V/zzbU8zhcUEBEczL0D45g1YCDBJlPDB+YtTXoRW4jmSwIRIVqY7u3C6BAUzNVi+7MiFRZLg2qTWLTm8Y/XkV1cZPP5XDVj8NHRIwyMjOLxoSNqPddkMDI+siNP3DyxWa1/j+nWnU/Sj9nvKKwUY6Il+VQId8nSjBAtjJ/BwMLEoXYnPYxKERPenrEN+NDccfYMp3JzHC5VvJG0D7OdRnrN0cLEoXZfr8L6vs6JT2jcQQnRAkggIkQL9MSwEdzepy9gW5tEAR2Cglk8fWaDdszszszAz+D4x8elwkIy8vPrfQ1HzBYLn6Qf5+F1HzDhrSXMXL2Cd1KSKCgr88r1wLoN93cTJgG276lRKYwGA6/ecZdLnYmFELZkaUaIFsjPYODVO6bz35MnWHkghRM52YT6BzBzQCz3DYwjLDCoQedvrJogdSkzm/nuxo/4/PQpm341aZcusjRpH+/e8wCdQ0O9cu2HBg9haOcuLEtNYndmBkZlYELPXixISKRXjbosQgjXSSAiRAtlUIopMX2YElO7yFZDjewSzb/27nF4TFRwCNENqMRqz//t3skXp08BtfvVnL+Wz/c3f8za++d6/LpV4qI68udbb/fa+YVobWRpRgjhtvE9etIzLMxhi/tHhwyrtWumoUoqylmWmuywRkryxQukXrro0esKIbzH44HIhQsXeOaZZxg3bhwjRoxg0aJFHD9+3NOXEUL4kEEplkyfRXhQEDU71FQFJtP7DfBKM7ejV686zQMxKMWezIx6X+NaaSlvJe/n7neXM+GtJTyybg2fnjjuoDuyEKIhPLo0U1ZWxhNPPEFYWBivv/46gYGBvPrqqzz88MNs2LCB9u0b1qtCCNF09A5vz5Z5j/DeoTTWHz3CtbJS+oR3YF78YG7p1dvlDrhu8XIwkJGfx4NrVnPh2jWb5Z6vzp5mau8+vHrHXZiMRq+OQYjWxqOByN69ezl27BhfffUVHTt2BODFF19k1KhRbNu2jXvvvdeTlxNC+Fh4UBBPDhvJk8NGNsr1+kdEEGIyUVhebvcYi9aMqEeNFK013924nksFBXXWRtl6Mp3Xvt3N06PHun1uIYR9Hl2a6du3L4sXL64OQgAMlWvE+V7axieEaD0C/UzMT0h02K9mcMdODK4sI++O/RfPczDrst1aIRp4O2U/pRUVbp9bCGGfR2dEIiMjmTBhgs3Xli1bRklJCePGjfPkpYQQjUBrzddnz7AiLZmjV68QYvJner8BPDAonvCghm0Brq+nR43lyJUsvjxzunr7LlhrpHRuE8o/pk2v13l3Z2RgVMphkba80lJO5mQTGxlVr2sIIWpzKxDJyMhg8uTJdh//5ptvbPJAtm7dyl//+lceeeQR+vfvX+9Baq0pctLEq6koLi62+VP4ltyP+rNozW+//oIPjx2x+YA+ciWL/+z/ljfvvJs+4e7nfXninvx98m18dvoU7x85yNn8fMICA5nRpx8z+w2gjdGvXj8vyspdK4ZWXFLSbH4euUK+R5qelnJPtNYuFU5U2l6bzDqUl5dz9qz97pI9e/bEWJnItWrVKl544QVmzJjBH//4x+olGnelpaVR5sVqiUKIum25kMmSk3XveDMAHQICeHXYKIyqZVQBOJyfy2/Skh0eE2L04z8jx2Ly8LZkIVoqf39/4uPjHR7j1oyIyWQiJibG6XEvvvgiS5YsYeHChTz33HMNKiVddd0+fTxflMkbiouLOX36ND179iTIR1PX4jq5H/WjtebHqftR1N101gJklZZyKTiYyT17uXXupnpPBmjNisxzpOdk17k8o4AFCYNJGDSo8QfnRU31frRmLeWepKenu3ScxyurVgUhzz33HI8++qhHzqmUalZdOgGCgoKa3ZhbMrkf7rlcWEDGNccJ5n4GA8lXLjN9YP0+mJviPVk8fRYPrlnNxYJrgDUIq8pDmdwrhh+PHd9it+82xfvR2jX3e+LqJIRHA5Hdu3ezZMkSFixYwPTp08nKyqp+LDg4mJCQEE9eTgjhJa21dle3du3YPO9hPjh0gA+PHCKvtISeYeHMiUtgau8+Hq8UK4TwcCCyYcMGwLpTZtmyZTaPPfXUU/zgBz/w5OWEEF4SGRJCdGhbh7MiFRYLI+tRr6OpaxsQwKNDhvGoFyrDCiFq82gg8sILL/DCCy948pRCCB8wKMVjQ4fzuy+31fm4USmiQtowuZfznDEhhHBE5hmFEHWan5DIPbHW/I+aze0MStE2IIClM2bhJ0sVQogG8niyqhCiZTAoxV9uvY07+/avs6BZh2acRCeEaDokEBFC2KWUYmLPXkx0c4uuEEK4SuZVhRBCCOEzEogIIYQQwmckEBFCCCGEz0ggIoQQQgifkUBECCGEED4jgYgQQgghfEYCESGEEEL4jAQiQgghhPAZCUSEEEII4TMSiAghhBDCZ5TWWvt6EI7s378frTX+/v6+HopLtNaUl5djMplQNRqFCd+Q+9H0yD1pWuR+ND0t5Z6UlZWhlGLo0KEOj2vyvWaa201QSjWboKk1kPvR9Mg9aVrkfjQ9LeWeKKVc+gxv8jMiQgghhGi5JEdECCGEED4jgYgQQgghfEYCESGEEEL4jAQiQgghhPAZCUSEEEII4TMSiAghhBDCZyQQEUIIIYTPSCAihBBCCJ+RQEQIIYQQPiOBiBBCCCF8RgIRIYQQQviMBCJecuHCBZ555hnGjRvHiBEjWLRoEcePH/f1sESl3/zmN/z85z/39TBaFYvFwt///nfGjx9PYmIijz/+OOfOnfP1sESlf//73yxYsMDXw2jVcnNz+c1vfsPNN9/M0KFDmTNnDnv37vX1sLxOAhEvKCsr44knniArK4vXX3+dlStXEhISwsMPP0x2dravh9eqWSwWXn75ZVavXu3robQ6//znP1m5ciUvvPAC7777LhaLhccee4yysjJfD63VW7FiBa+88oqvh9HqPfPMMyQlJfHyyy+zZs0aYmNjWbRoESdPnvT10LxKAhEv2Lt3L8eOHeOll14iPj6evn378uKLL1JUVMS2bdt8PbxW68SJE8ydO5f333+fLl26+Ho4rUpZWRlvvPEGP/zhD5k4cSIDBgzgb3/7GxcvXuTTTz/19fBarUuXLvGd73yHl156iZ49e/p6OK3amTNn2LFjB7/73e8YPnw4vXr14te//jVRUVF8/PHHvh6eV0kg4gV9+/Zl8eLFdOzYsfprBoP1rc7Pz/fVsFq9Xbt2ERMTw4YNG4iOjvb1cFqVI0eOUFhYyJgxY6q/1rZtWwYOHMi3337rw5G1bgcPHsRkMrF+/XoGDx7s6+G0auHh4SxevJj4+PjqrymlUEq1+M8NP18PoCWKjIxkwoQJNl9btmwZJSUljBs3zkejEvPmzfP1EFqtixcvAtC5c2ebr0dFRVU/JhrfpEmTmDRpkq+HIbAG5jd+bmzZsoUzZ87w/PPP+2hUjUMCkXrIyMhg8uTJdh//5ptvaN++ffXft27dyl//+lceeeQR+vfv3xhDbHXcvSeicRUXFwPg7+9v8/WAgADy8vJ8MSQhmrT9+/fzi1/8gqlTpzJx4kRfD8erJBCph44dO7Jp0ya7j7dr1676/1etWsULL7zAjBkzePbZZxtjeK2SO/dENL7AwEDAmitS9f8ApaWlBAUF+WpYQjRJn332GT/96U8ZOnQoL730kq+H43USiNSDyWQiJibG6XEvvvgiS5YsYeHChTz33HMopRphdK2Tq/dE+EbVkszly5fp3r179dcvX74ss4RC1LB8+XL+8Ic/cPvtt/PnP/+51ixiSyTJql5SFYQ899xz/PznP5cgRLRqAwYMoE2bNuzevbv6a/n5+Rw6dIgRI0b4cGRCNB1V29vnzZvHyy+/3CqCEJAZEa/YvXs3S5YsYcGCBUyfPp2srKzqx4KDgwkJCfHh6IRofP7+/syfP5+XXnqJ9u3b07VrV1588UU6derE1KlTfT08IXzu1KlT/PGPf2TKlCk8+eSTXLlypfqxwMBAQkNDfTg675JAxAs2bNgAWHfKLFu2zOaxp556ih/84Ae+GJYQPvXDH/6QiooKfvWrX1FSUsKIESNYunQpJpPJ10MTwue2bNlCeXk5W7duZevWrTaPzZo1iz/96U8+Gpn3Ka219vUghBBCCNE6SY6IEEIIIXxGAhEhhBBC+IwEIkIIIYTwGQlEhBBCCOEzEogIIYQQwmckEBFCCCGEz0ggIoQQQgifkUBECCGEED4jgYgQQgghfEYCESGEEEL4jAQiQgghhPAZCUSEEEII4TP/H8t/EHq/1VivAAAAAElFTkSuQmCC",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Plot first attempt with scatterplot\n",
    "plt.scatter(X_scaled[:, 0], X_scaled[:, 1], c=kmeans3.labels_, cmap='viridis') # cmap = sets colors\n",
    "plt.title('K-means Clustering K=3')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "13b442ae-7e91-490d-9872-57aaf845f989",
   "metadata": {},
   "source": [
    "Results  \n",
    "3 clusters isn't great\n",
    "* create function to compare inertias of multiple K values\n",
    "* find # of clusters by examining inertia values for different values of k   "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "3e9562d9-a1dc-4337-9a7e-6c2a8d14327d",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Create function to compare inertias of multiple K values                                                                                                                      \n",
    "num_clusters = [i for i in range(2, 11)]               # generate a list starting from 2 up to 10 (inclusive)\n",
    "\n",
    "def kmeans_inertia(num_clusters, X_vals):\n",
    "\n",
    "    inertia = []\n",
    "    for num in num_clusters:                          # Create iterative for loop\n",
    "        kms = KMeans(n_clusters=num, random_state=42) # Different k values to try\n",
    "        kms.fit(X_vals)                               # Training data\n",
    "        inertia.append(kms.inertia_)                  # A list of inertia scores, 1 for each value of k\n",
    "\n",
    "    return inertia"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "45ae420c-50a7-4602-a7ea-61d15131651f",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[882.9335591311493,\n",
       " 571.2995770594428,\n",
       " 376.8267075583487,\n",
       " 277.6024594276294,\n",
       " 209.65983912770113,\n",
       " 192.96312743036583,\n",
       " 181.70905052242432,\n",
       " 171.0730968885082,\n",
       " 156.69146848329484]"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Call function\n",
    "inertia = kmeans_inertia(num_clusters, X_scaled)\n",
    "inertia"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "91416909-fed2-4de3-ac9e-e6df9a469ee0",
   "metadata": {},
   "source": [
    "#### Visualize values with elbow plot\n",
    "* X-axis = various possible K values\n",
    "* y-axis = inertia"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "d344706f-bbd0-4424-a668-bfa5cc289413",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Create line plot to show the relationship between number of clusters and inertia\n",
    "plot = sns.lineplot(x=num_clusters, y=inertia, marker = 'o') # marker = sets marker style\n",
    "plot.set_xlabel('Number of clusters')\n",
    "plot.set_ylabel('Inertia')\n",
    "plt.grid(True);"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "cdb7544c-3e68-4707-8b94-4bdcedc7c442",
   "metadata": {},
   "source": [
    "Results \\\n",
    "Point where the reduction in inertia when a new cluster is added is the *elbow* \n",
    " * The 2nd in list is 571.2995770594428= 3 clusters, definitely not the best\n",
    "\n",
    "The plot does not contain an unambiguous elbow. There is a point at 4 & 5 clusters, but a more distinct *elbow* is at 6 clusters. This means that models with more than 6 clusters might reduce inertia. The original guess of 3 was likely insufficient."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b588abd2-4da0-477b-8d3d-f729242e5bd0",
   "metadata": {},
   "source": [
    "#### Get silhouette score\n",
    "**silhouette score** is the mean of the silhouette coefficients of all the observations in the model\n",
    "* silhouette coefficient can be between -1 and 1\n",
    "  * close to 1 = within its own cluster and separated from other clusters\n",
    "  * close to 0 = samples are on or very close to the decision boundary between 2 neighboring clusters = not a lot of space\n",
    "\n",
    "* compare silhouette score of each value of k, from 2 through 10\n",
    "  * call silhouette_score() function and pass to it 2 required parameters\n",
    "    * training data (X_scaled)\n",
    "    * assigned cluster labels to training data (kmeans3.labels_)\n",
    "* perform a comparison that compares the silhouette score of each value of k, from 2 through 10.\n",
    "  * write function that returns a list of each k-value's silhouette score\n",
    "    * num_clusters\n",
    "    * X_vals\n",
    "  * calculate silhouette scores for k=2-10"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "id": "d59bbbe3-9b8d-46c0-b088-4009d2f34705",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.45597569338010185"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Get silhouette score for kmeans3 model\n",
    "kmeans3_sil_score = silhouette_score(X_scaled, kmeans3.labels_)\n",
    "kmeans3_sil_score"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "id": "612bdfa8-2f84-4a7b-8bec-eef9ad8e38c1",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Write a function to iterate over each number of clusters to get silhouette scores\n",
    "def kmeans_sil(num_clusters, x_vals):      # list of number of clusters to test\n",
    "                                           # x_vals is data to cluster.\n",
    "    sil_score = []\n",
    "    for num in num_clusters:\n",
    "        kms = KMeans(n_clusters=num, random_state=42)\n",
    "        kms.fit(x_vals)             # Fits model where k = each value in the list of ints\n",
    "        sil_score.append(silhouette_score(x_vals, kms.labels_))  # Calculates a silhouette score for each k value\n",
    "                                                                 # Appends each score to a list\n",
    "\n",
    "    return sil_score"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "id": "6f028f9a-d29c-48b0-b0dd-097baf66ac0c",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Silhouette scores for k=2-10:\n",
      "# of clusters: 2, Silhouette score score: 0.4464916565029188\n",
      "# of clusters: 3, Silhouette score score: 0.45597569338010185\n",
      "# of clusters: 4, Silhouette score score: 0.5131539758666861\n",
      "# of clusters: 5, Silhouette score score: 0.5238162936931439\n",
      "# of clusters: 6, Silhouette score score: 0.5271813454904477\n",
      "# of clusters: 7, Silhouette score score: 0.47848307330207374\n",
      "# of clusters: 8, Silhouette score score: 0.42002368523568945\n",
      "# of clusters: 9, Silhouette score score: 0.4215709637881699\n",
      "# of clusters: 10, Silhouette score score: 0.3656343709208427\n"
     ]
    }
   ],
   "source": [
    "# Call function, get list of scores\n",
    "print('Silhouette scores for k=2-10:')\n",
    "sil_score = kmeans_sil(num_clusters, X_scaled)\n",
    "\n",
    "for i, score in zip(range(2, 11), sil_score):\n",
    "    print(f'# of clusters: {i}, Silhouette score score: {score}')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "id": "5fe7e3c9-7557-493f-a954-0ea46225480b",
   "metadata": {},
   "outputs": [
    {
     "data": {
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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Create a line plotting the relationship between num_clusters and sil_score\n",
    "plot = sns.lineplot(x=num_clusters, y=sil_score, marker = 'o')\n",
    "plot.set_xlabel('# of clusters')\n",
    "plot.set_ylabel('Silhouette Score')\n",
    "plt.grid(True);"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "8a10237c-ab10-412a-afc9-bae87b4e7647",
   "metadata": {},
   "source": [
    "Results \\\n",
    "The closest silhouette score to 1 (within its own cluster and separated from other clusters) is at 6 clusters (0.5271813454904477).\\\n",
    "This can be seem on the plot, although 5 clusters is close in score.\\\n",
    "This confirms the inertia elbow at k=6."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e16b4325-9a6c-431c-a4ec-f3fee06cf5ba",
   "metadata": {},
   "source": [
    "#### Validate k=6 with Davies-Bouldin Index and Gap Statistics"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "9f312f0e-f78f-4e03-a9e7-09ce8d4eb7e4",
   "metadata": {},
   "source": [
    "#### Get Davies-Bouldin Index scores\n",
    "**Davies-Bouldin Index** evaluates effectiveness of clustering into distinct groups or clusters. It measures how well-separated the clusters are and how compact they are within themselves.\n",
    "\n",
    "* Davies-Bouldin which is\n",
    "  * close to 1 = poorly clustered (ill-separated, spread out, and not compact)\n",
    "  * close to 0 = optimally clustered"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "id": "3036729f-7398-44de-ac74-2339095c69d5",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Write a function to iterates over each number of clusters to calculate the Davies-Bouldin\n",
    "def kmeans_db(num_clusters, x_vals):\n",
    "    db_score = []\n",
    "    \n",
    "    for num in num_clusters:\n",
    "        kmeans = KMeans(n_clusters=num, random_state=42)\n",
    "        kmeans.fit(x_vals)\n",
    "        db_score.append(davies_bouldin_score(x_vals, kmeans.labels_))\n",
    "    \n",
    "    return db_score"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "id": "5af5f3ad-0120-4155-8dba-070fa5053e3a",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Davies-Bouldin Scores:\n",
      "# of clusters: 2, DB score: 0.9666117048782789\n",
      "# of clusters: 3, DB score: 0.9004751097622595\n",
      "# of clusters: 4, DB score: 0.7501865069730087\n",
      "# of clusters: 5, DB score: 0.7196432841250544\n",
      "# of clusters: 6, DB score: 0.7159389140028006\n",
      "# of clusters: 7, DB score: 0.9376368528195671\n",
      "# of clusters: 8, DB score: 1.1288878348642433\n",
      "# of clusters: 9, DB score: 1.0848315459431523\n",
      "# of clusters: 10, DB score: 1.1655139004803095\n"
     ]
    }
   ],
   "source": [
    "# Call function, get list of scores\n",
    "print('Davies-Bouldin Scores:')\n",
    "db_score = kmeans_db(num_clusters, X_scaled)\n",
    "\n",
    "for i, score in zip(range(2, 11), db_score):\n",
    "    print(f'# of clusters: {i}, DB score: {score}')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "id": "e9ff7846-430a-48fa-b96c-d14a3ffc8ad9",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Plot Davies-Bouldin Scores\n",
    "plt.figure()\n",
    "plt.plot(range(2, 11), db_score, marker='o')\n",
    "plt.title('Davies-Bouldin Scores')\n",
    "plt.xlabel('# of clusters')\n",
    "plt.ylabel('Score')\n",
    "plt.grid(True)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "1c6b2082-61a0-4a44-9960-9b4ed3d57298",
   "metadata": {},
   "source": [
    "Results \\\n",
    "The plot suggests that a Davies-Bouldin score of 0.7159389140028006 at 6 clusters is best, although not great.\\\n",
    "Because this is still a high score, it suggests that even though it is best there may be some overlap or less distinct separation between the clusters.   "
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c7bf8d00-71f9-4131-8dae-3f5485570fb1",
   "metadata": {},
   "source": [
    "#### Get Gap Statistics number\n",
    "**Gap Statistics** determines the optimal number of clusters in a dataset. It helps identify the point where adding more clusters does not significantly improve the model's performance."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "id": "a641941c-8036-49aa-9919-3fb8ad08f41f",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Optimal # of Gap Statistics clusters:  8\n"
     ]
    }
   ],
   "source": [
    "# Gap Statistics\n",
    "GapK = OptimalK(parallel_backend='none')\n",
    "n_clusters = GapK(X_scaled, cluster_array=np.arange(2, 11))\n",
    "print('Optimal # of Gap Statistics clusters: ', n_clusters)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "5287add4-fb29-4387-a098-dc35cfd096ee",
   "metadata": {},
   "source": [
    "### Refit k-values and evaluate clusters\n",
    "* instantiate new K-means model with n_clusters=6\n",
    "* fit model\n",
    "* print unique labels to see if clustering makes sense\n",
    "* assign new column to the original unscaled dataframe with the cluster assignment from the final K-means model\n",
    "* ascertain why model partitions as it did"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "id": "4d030458-a0e7-4704-ab13-eac96f4b95ef",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<style>#sk-container-id-2 {\n",
       "  /* Definition of color scheme common for light and dark mode */\n",
       "  --sklearn-color-text: black;\n",
       "  --sklearn-color-line: gray;\n",
       "  /* Definition of color scheme for unfitted estimators */\n",
       "  --sklearn-color-unfitted-level-0: #fff5e6;\n",
       "  --sklearn-color-unfitted-level-1: #f6e4d2;\n",
       "  --sklearn-color-unfitted-level-2: #ffe0b3;\n",
       "  --sklearn-color-unfitted-level-3: chocolate;\n",
       "  /* Definition of color scheme for fitted estimators */\n",
       "  --sklearn-color-fitted-level-0: #f0f8ff;\n",
       "  --sklearn-color-fitted-level-1: #d4ebff;\n",
       "  --sklearn-color-fitted-level-2: #b3dbfd;\n",
       "  --sklearn-color-fitted-level-3: cornflowerblue;\n",
       "\n",
       "  /* Specific color for light theme */\n",
       "  --sklearn-color-text-on-default-background: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, black)));\n",
       "  --sklearn-color-background: var(--sg-background-color, var(--theme-background, var(--jp-layout-color0, white)));\n",
       "  --sklearn-color-border-box: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, black)));\n",
       "  --sklearn-color-icon: #696969;\n",
       "\n",
       "  @media (prefers-color-scheme: dark) {\n",
       "    /* Redefinition of color scheme for dark theme */\n",
       "    --sklearn-color-text-on-default-background: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, white)));\n",
       "    --sklearn-color-background: var(--sg-background-color, var(--theme-background, var(--jp-layout-color0, #111)));\n",
       "    --sklearn-color-border-box: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, white)));\n",
       "    --sklearn-color-icon: #878787;\n",
       "  }\n",
       "}\n",
       "\n",
       "#sk-container-id-2 {\n",
       "  color: var(--sklearn-color-text);\n",
       "}\n",
       "\n",
       "#sk-container-id-2 pre {\n",
       "  padding: 0;\n",
       "}\n",
       "\n",
       "#sk-container-id-2 input.sk-hidden--visually {\n",
       "  border: 0;\n",
       "  clip: rect(1px 1px 1px 1px);\n",
       "  clip: rect(1px, 1px, 1px, 1px);\n",
       "  height: 1px;\n",
       "  margin: -1px;\n",
       "  overflow: hidden;\n",
       "  padding: 0;\n",
       "  position: absolute;\n",
       "  width: 1px;\n",
       "}\n",
       "\n",
       "#sk-container-id-2 div.sk-dashed-wrapped {\n",
       "  border: 1px dashed var(--sklearn-color-line);\n",
       "  margin: 0 0.4em 0.5em 0.4em;\n",
       "  box-sizing: border-box;\n",
       "  padding-bottom: 0.4em;\n",
       "  background-color: var(--sklearn-color-background);\n",
       "}\n",
       "\n",
       "#sk-container-id-2 div.sk-container {\n",
       "  /* jupyter's `normalize.less` sets `[hidden] { display: none; }`\n",
       "     but bootstrap.min.css set `[hidden] { display: none !important; }`\n",
       "     so we also need the `!important` here to be able to override the\n",
       "     default hidden behavior on the sphinx rendered scikit-learn.org.\n",
       "     See: https://github.com/scikit-learn/scikit-learn/issues/21755 */\n",
       "  display: inline-block !important;\n",
       "  position: relative;\n",
       "}\n",
       "\n",
       "#sk-container-id-2 div.sk-text-repr-fallback {\n",
       "  display: none;\n",
       "}\n",
       "\n",
       "div.sk-parallel-item,\n",
       "div.sk-serial,\n",
       "div.sk-item {\n",
       "  /* draw centered vertical line to link estimators */\n",
       "  background-image: linear-gradient(var(--sklearn-color-text-on-default-background), var(--sklearn-color-text-on-default-background));\n",
       "  background-size: 2px 100%;\n",
       "  background-repeat: no-repeat;\n",
       "  background-position: center center;\n",
       "}\n",
       "\n",
       "/* Parallel-specific style estimator block */\n",
       "\n",
       "#sk-container-id-2 div.sk-parallel-item::after {\n",
       "  content: \"\";\n",
       "  width: 100%;\n",
       "  border-bottom: 2px solid var(--sklearn-color-text-on-default-background);\n",
       "  flex-grow: 1;\n",
       "}\n",
       "\n",
       "#sk-container-id-2 div.sk-parallel {\n",
       "  display: flex;\n",
       "  align-items: stretch;\n",
       "  justify-content: center;\n",
       "  background-color: var(--sklearn-color-background);\n",
       "  position: relative;\n",
       "}\n",
       "\n",
       "#sk-container-id-2 div.sk-parallel-item {\n",
       "  display: flex;\n",
       "  flex-direction: column;\n",
       "}\n",
       "\n",
       "#sk-container-id-2 div.sk-parallel-item:first-child::after {\n",
       "  align-self: flex-end;\n",
       "  width: 50%;\n",
       "}\n",
       "\n",
       "#sk-container-id-2 div.sk-parallel-item:last-child::after {\n",
       "  align-self: flex-start;\n",
       "  width: 50%;\n",
       "}\n",
       "\n",
       "#sk-container-id-2 div.sk-parallel-item:only-child::after {\n",
       "  width: 0;\n",
       "}\n",
       "\n",
       "/* Serial-specific style estimator block */\n",
       "\n",
       "#sk-container-id-2 div.sk-serial {\n",
       "  display: flex;\n",
       "  flex-direction: column;\n",
       "  align-items: center;\n",
       "  background-color: var(--sklearn-color-background);\n",
       "  padding-right: 1em;\n",
       "  padding-left: 1em;\n",
       "}\n",
       "\n",
       "\n",
       "/* Toggleable style: style used for estimator/Pipeline/ColumnTransformer box that is\n",
       "clickable and can be expanded/collapsed.\n",
       "- Pipeline and ColumnTransformer use this feature and define the default style\n",
       "- Estimators will overwrite some part of the style using the `sk-estimator` class\n",
       "*/\n",
       "\n",
       "/* Pipeline and ColumnTransformer style (default) */\n",
       "\n",
       "#sk-container-id-2 div.sk-toggleable {\n",
       "  /* Default theme specific background. It is overwritten whether we have a\n",
       "  specific estimator or a Pipeline/ColumnTransformer */\n",
       "  background-color: var(--sklearn-color-background);\n",
       "}\n",
       "\n",
       "/* Toggleable label */\n",
       "#sk-container-id-2 label.sk-toggleable__label {\n",
       "  cursor: pointer;\n",
       "  display: block;\n",
       "  width: 100%;\n",
       "  margin-bottom: 0;\n",
       "  padding: 0.5em;\n",
       "  box-sizing: border-box;\n",
       "  text-align: center;\n",
       "}\n",
       "\n",
       "#sk-container-id-2 label.sk-toggleable__label-arrow:before {\n",
       "  /* Arrow on the left of the label */\n",
       "  content: \"▸\";\n",
       "  float: left;\n",
       "  margin-right: 0.25em;\n",
       "  color: var(--sklearn-color-icon);\n",
       "}\n",
       "\n",
       "#sk-container-id-2 label.sk-toggleable__label-arrow:hover:before {\n",
       "  color: var(--sklearn-color-text);\n",
       "}\n",
       "\n",
       "/* Toggleable content - dropdown */\n",
       "\n",
       "#sk-container-id-2 div.sk-toggleable__content {\n",
       "  max-height: 0;\n",
       "  max-width: 0;\n",
       "  overflow: hidden;\n",
       "  text-align: left;\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
       "}\n",
       "\n",
       "#sk-container-id-2 div.sk-toggleable__content.fitted {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-0);\n",
       "}\n",
       "\n",
       "#sk-container-id-2 div.sk-toggleable__content pre {\n",
       "  margin: 0.2em;\n",
       "  border-radius: 0.25em;\n",
       "  color: var(--sklearn-color-text);\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
       "}\n",
       "\n",
       "#sk-container-id-2 div.sk-toggleable__content.fitted pre {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-0);\n",
       "}\n",
       "\n",
       "#sk-container-id-2 input.sk-toggleable__control:checked~div.sk-toggleable__content {\n",
       "  /* Expand drop-down */\n",
       "  max-height: 200px;\n",
       "  max-width: 100%;\n",
       "  overflow: auto;\n",
       "}\n",
       "\n",
       "#sk-container-id-2 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {\n",
       "  content: \"▾\";\n",
       "}\n",
       "\n",
       "/* Pipeline/ColumnTransformer-specific style */\n",
       "\n",
       "#sk-container-id-2 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
       "  color: var(--sklearn-color-text);\n",
       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
       "}\n",
       "\n",
       "#sk-container-id-2 div.sk-label.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
       "  background-color: var(--sklearn-color-fitted-level-2);\n",
       "}\n",
       "\n",
       "/* Estimator-specific style */\n",
       "\n",
       "/* Colorize estimator box */\n",
       "#sk-container-id-2 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
       "}\n",
       "\n",
       "#sk-container-id-2 div.sk-estimator.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-2);\n",
       "}\n",
       "\n",
       "#sk-container-id-2 div.sk-label label.sk-toggleable__label,\n",
       "#sk-container-id-2 div.sk-label label {\n",
       "  /* The background is the default theme color */\n",
       "  color: var(--sklearn-color-text-on-default-background);\n",
       "}\n",
       "\n",
       "/* On hover, darken the color of the background */\n",
       "#sk-container-id-2 div.sk-label:hover label.sk-toggleable__label {\n",
       "  color: var(--sklearn-color-text);\n",
       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
       "}\n",
       "\n",
       "/* Label box, darken color on hover, fitted */\n",
       "#sk-container-id-2 div.sk-label.fitted:hover label.sk-toggleable__label.fitted {\n",
       "  color: var(--sklearn-color-text);\n",
       "  background-color: var(--sklearn-color-fitted-level-2);\n",
       "}\n",
       "\n",
       "/* Estimator label */\n",
       "\n",
       "#sk-container-id-2 div.sk-label label {\n",
       "  font-family: monospace;\n",
       "  font-weight: bold;\n",
       "  display: inline-block;\n",
       "  line-height: 1.2em;\n",
       "}\n",
       "\n",
       "#sk-container-id-2 div.sk-label-container {\n",
       "  text-align: center;\n",
       "}\n",
       "\n",
       "/* Estimator-specific */\n",
       "#sk-container-id-2 div.sk-estimator {\n",
       "  font-family: monospace;\n",
       "  border: 1px dotted var(--sklearn-color-border-box);\n",
       "  border-radius: 0.25em;\n",
       "  box-sizing: border-box;\n",
       "  margin-bottom: 0.5em;\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
       "}\n",
       "\n",
       "#sk-container-id-2 div.sk-estimator.fitted {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-0);\n",
       "}\n",
       "\n",
       "/* on hover */\n",
       "#sk-container-id-2 div.sk-estimator:hover {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
       "}\n",
       "\n",
       "#sk-container-id-2 div.sk-estimator.fitted:hover {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-2);\n",
       "}\n",
       "\n",
       "/* Specification for estimator info (e.g. \"i\" and \"?\") */\n",
       "\n",
       "/* Common style for \"i\" and \"?\" */\n",
       "\n",
       ".sk-estimator-doc-link,\n",
       "a:link.sk-estimator-doc-link,\n",
       "a:visited.sk-estimator-doc-link {\n",
       "  float: right;\n",
       "  font-size: smaller;\n",
       "  line-height: 1em;\n",
       "  font-family: monospace;\n",
       "  background-color: var(--sklearn-color-background);\n",
       "  border-radius: 1em;\n",
       "  height: 1em;\n",
       "  width: 1em;\n",
       "  text-decoration: none !important;\n",
       "  margin-left: 1ex;\n",
       "  /* unfitted */\n",
       "  border: var(--sklearn-color-unfitted-level-1) 1pt solid;\n",
       "  color: var(--sklearn-color-unfitted-level-1);\n",
       "}\n",
       "\n",
       ".sk-estimator-doc-link.fitted,\n",
       "a:link.sk-estimator-doc-link.fitted,\n",
       "a:visited.sk-estimator-doc-link.fitted {\n",
       "  /* fitted */\n",
       "  border: var(--sklearn-color-fitted-level-1) 1pt solid;\n",
       "  color: var(--sklearn-color-fitted-level-1);\n",
       "}\n",
       "\n",
       "/* On hover */\n",
       "div.sk-estimator:hover .sk-estimator-doc-link:hover,\n",
       ".sk-estimator-doc-link:hover,\n",
       "div.sk-label-container:hover .sk-estimator-doc-link:hover,\n",
       ".sk-estimator-doc-link:hover {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-3);\n",
       "  color: var(--sklearn-color-background);\n",
       "  text-decoration: none;\n",
       "}\n",
       "\n",
       "div.sk-estimator.fitted:hover .sk-estimator-doc-link.fitted:hover,\n",
       ".sk-estimator-doc-link.fitted:hover,\n",
       "div.sk-label-container:hover .sk-estimator-doc-link.fitted:hover,\n",
       ".sk-estimator-doc-link.fitted:hover {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-3);\n",
       "  color: var(--sklearn-color-background);\n",
       "  text-decoration: none;\n",
       "}\n",
       "\n",
       "/* Span, style for the box shown on hovering the info icon */\n",
       ".sk-estimator-doc-link span {\n",
       "  display: none;\n",
       "  z-index: 9999;\n",
       "  position: relative;\n",
       "  font-weight: normal;\n",
       "  right: .2ex;\n",
       "  padding: .5ex;\n",
       "  margin: .5ex;\n",
       "  width: min-content;\n",
       "  min-width: 20ex;\n",
       "  max-width: 50ex;\n",
       "  color: var(--sklearn-color-text);\n",
       "  box-shadow: 2pt 2pt 4pt #999;\n",
       "  /* unfitted */\n",
       "  background: var(--sklearn-color-unfitted-level-0);\n",
       "  border: .5pt solid var(--sklearn-color-unfitted-level-3);\n",
       "}\n",
       "\n",
       ".sk-estimator-doc-link.fitted span {\n",
       "  /* fitted */\n",
       "  background: var(--sklearn-color-fitted-level-0);\n",
       "  border: var(--sklearn-color-fitted-level-3);\n",
       "}\n",
       "\n",
       ".sk-estimator-doc-link:hover span {\n",
       "  display: block;\n",
       "}\n",
       "\n",
       "/* \"?\"-specific style due to the `<a>` HTML tag */\n",
       "\n",
       "#sk-container-id-2 a.estimator_doc_link {\n",
       "  float: right;\n",
       "  font-size: 1rem;\n",
       "  line-height: 1em;\n",
       "  font-family: monospace;\n",
       "  background-color: var(--sklearn-color-background);\n",
       "  border-radius: 1rem;\n",
       "  height: 1rem;\n",
       "  width: 1rem;\n",
       "  text-decoration: none;\n",
       "  /* unfitted */\n",
       "  color: var(--sklearn-color-unfitted-level-1);\n",
       "  border: var(--sklearn-color-unfitted-level-1) 1pt solid;\n",
       "}\n",
       "\n",
       "#sk-container-id-2 a.estimator_doc_link.fitted {\n",
       "  /* fitted */\n",
       "  border: var(--sklearn-color-fitted-level-1) 1pt solid;\n",
       "  color: var(--sklearn-color-fitted-level-1);\n",
       "}\n",
       "\n",
       "/* On hover */\n",
       "#sk-container-id-2 a.estimator_doc_link:hover {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-3);\n",
       "  color: var(--sklearn-color-background);\n",
       "  text-decoration: none;\n",
       "}\n",
       "\n",
       "#sk-container-id-2 a.estimator_doc_link.fitted:hover {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-3);\n",
       "}\n",
       "</style><div id=\"sk-container-id-2\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>KMeans(n_clusters=6, random_state=42)</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item\"><div class=\"sk-estimator fitted sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-2\" type=\"checkbox\" checked><label for=\"sk-estimator-id-2\" class=\"sk-toggleable__label fitted sk-toggleable__label-arrow fitted\">&nbsp;&nbsp;KMeans<a class=\"sk-estimator-doc-link fitted\" rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.4/modules/generated/sklearn.cluster.KMeans.html\">?<span>Documentation for KMeans</span></a><span class=\"sk-estimator-doc-link fitted\">i<span>Fitted</span></span></label><div class=\"sk-toggleable__content fitted\"><pre>KMeans(n_clusters=6, random_state=42)</pre></div> </div></div></div></div>"
      ],
      "text/plain": [
       "KMeans(n_clusters=6, random_state=42)"
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Fit a 6-cluster model\n",
    "kmeans6 = KMeans(n_clusters=6, random_state=42)\n",
    "kmeans6.fit(X_scaled)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "id": "9321b3fc-ce39-42e5-92c2-152548cd017c",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.scatter(X_scaled[:, 0], X_scaled[:, 1], c=kmeans6.labels_, cmap='viridis')\n",
    "plt.title('K-means Clustering K=6')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "53a5d4bd-3912-4fb8-bdf0-4b799560bb39",
   "metadata": {},
   "source": [
    "Results   \n",
    "As indicated by the high Davies-Bouldin Index score the K-means6 plot shows overlap and less distinct separation between the clusters. However, the optimal Gap Statistics number, elbow plot, and silhouette scores confirm this is the best configuration."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e2f4c7ad-a0d1-4cfb-b2a8-a496b2d0c729",
   "metadata": {},
   "source": [
    "### Determine how K-means clustering algorithm divided Penguin data set"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "id": "eadc9b30-4313-4a85-841b-64b1cb1bb0eb",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Unique labels: [0 1 2 3 4 5]\n"
     ]
    }
   ],
   "source": [
    "# Print unique labels\n",
    "print('Unique labels:', np.unique(kmeans6.labels_))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "id": "5cdc01e2-c41c-47b0-bc0b-bf32369b40e1",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>species</th>\n",
       "      <th>bill_length_mm</th>\n",
       "      <th>bill_depth_mm</th>\n",
       "      <th>flipper_length_mm</th>\n",
       "      <th>body_mass_g</th>\n",
       "      <th>sex_MALE</th>\n",
       "      <th>cluster</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Adelie</td>\n",
       "      <td>39.1</td>\n",
       "      <td>18.7</td>\n",
       "      <td>181.0</td>\n",
       "      <td>3750.0</td>\n",
       "      <td>True</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Adelie</td>\n",
       "      <td>39.5</td>\n",
       "      <td>17.4</td>\n",
       "      <td>186.0</td>\n",
       "      <td>3800.0</td>\n",
       "      <td>False</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Adelie</td>\n",
       "      <td>40.3</td>\n",
       "      <td>18.0</td>\n",
       "      <td>195.0</td>\n",
       "      <td>3250.0</td>\n",
       "      <td>False</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Adelie</td>\n",
       "      <td>36.7</td>\n",
       "      <td>19.3</td>\n",
       "      <td>193.0</td>\n",
       "      <td>3450.0</td>\n",
       "      <td>False</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Adelie</td>\n",
       "      <td>39.3</td>\n",
       "      <td>20.6</td>\n",
       "      <td>190.0</td>\n",
       "      <td>3650.0</td>\n",
       "      <td>True</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  species  bill_length_mm  bill_depth_mm  flipper_length_mm  body_mass_g  \\\n",
       "0  Adelie            39.1           18.7              181.0       3750.0   \n",
       "1  Adelie            39.5           17.4              186.0       3800.0   \n",
       "2  Adelie            40.3           18.0              195.0       3250.0   \n",
       "3  Adelie            36.7           19.3              193.0       3450.0   \n",
       "4  Adelie            39.3           20.6              190.0       3650.0   \n",
       "\n",
       "   sex_MALE  cluster  \n",
       "0      True        4  \n",
       "1     False        2  \n",
       "2     False        2  \n",
       "3     False        2  \n",
       "4      True        4  "
      ]
     },
     "execution_count": 36,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Set cluster assignment with new feature that indicates cluster assignment in original dataframe \n",
    "penguins['cluster'] = kmeans6.labels_\n",
    "penguins.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "id": "a2c9ba1d-ada1-4bc1-9dc7-78d3b14a5e07",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "cluster  body_mass_g\n",
       "0        3250.0         1\n",
       "         3300.0         1\n",
       "         3400.0         1\n",
       "         3450.0         1\n",
       "         3550.0         1\n",
       "                       ..\n",
       "5        3800.0         1\n",
       "         3850.0         1\n",
       "         3900.0         1\n",
       "         3950.0         1\n",
       "         4150.0         1\n",
       "Length: 164, dtype: int64"
      ]
     },
     "execution_count": 37,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Check if cluster is partitioned by body_mass_g\n",
    "penguins.groupby(by=['cluster', 'body_mass_g']).size()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "id": "4355fefc-a897-47ba-94ef-cffee6d48a7e",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "cluster  bill_depth_mm\n",
       "0        17.5             1\n",
       "         18.0             1\n",
       "         18.1             1\n",
       "         18.2             3\n",
       "         18.5             1\n",
       "                         ..\n",
       "5        18.4             1\n",
       "         18.6             1\n",
       "         18.7             2\n",
       "         18.9             1\n",
       "         19.4             1\n",
       "Length: 151, dtype: int64"
      ]
     },
     "execution_count": 38,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Check if cluster is partitioned by bill_depth_mm\n",
    "penguins.groupby(by=['cluster', 'bill_depth_mm']).size()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "id": "261def65-086e-4f6b-9588-94341ee42238",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "cluster  species  \n",
       "0        Adelie        3\n",
       "         Chinstrap    33\n",
       "1        Gentoo       60\n",
       "2        Adelie       74\n",
       "         Chinstrap     1\n",
       "3        Gentoo       61\n",
       "4        Adelie       71\n",
       "5        Chinstrap    32\n",
       "dtype: int64"
      ]
     },
     "execution_count": 39,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Check if cluster is partitioned by species\n",
    "penguins.groupby(by=['cluster', 'species']).size()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "id": "0fe16c89-a96f-4a11-a3cf-0a5525565f41",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 600x500 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Visualize clustering by species\n",
    "penguins.groupby(by=['cluster', 'species']).size().plot.bar(title='Clusters by species', figsize=(6, 5), \n",
    "                                                            ylabel='Count', xlabel='Cluster: Species');\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f18e88c2-fd8f-48b2-97ed-31b1fcf2f923",
   "metadata": {},
   "source": [
    "Results\\\n",
    "Clusters mostly work by species"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "id": "d54b9e63-0570-497a-88ad-69b87e23cb78",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "cluster  species    sex_MALE\n",
       "0        Adelie     True         3\n",
       "         Chinstrap  True        33\n",
       "1        Gentoo     True        60\n",
       "2        Adelie     False       74\n",
       "         Chinstrap  False        1\n",
       "3        Gentoo     False       61\n",
       "4        Adelie     True        71\n",
       "5        Chinstrap  False       32\n",
       "dtype: int64"
      ]
     },
     "execution_count": 41,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Check clustering by species by sex_MALE\n",
    "# sex_MALE because pd.get_dummies(penguins, drop_first = True happened to be male\n",
    "penguins.groupby(by=['cluster','species', 'sex_MALE']).size()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "id": "aea877f5-1e56-470a-8596-56584673f20c",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 600x500 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Visualize clustering by species by sex_MALE \n",
    "penguins.groupby(by=['cluster','species','sex_MALE']).size().unstack(level='species', fill_value=0).plot.bar(title='Clusters by species by sex',\n",
    "                                                                                                                      figsize=(6, 5),\n",
    "                                                                                                                      ylabel='Count',\n",
    "                                                                                                                      xlabel='(Cluster: Male?)')\n",
    "plt.legend(bbox_to_anchor=(1.3, 1.0));"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "id": "e4cb6cd3-aa27-4cae-9e11-388a3e355b87",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "species    sex   \n",
       "Adelie     Female    74\n",
       "           Male      74\n",
       "Chinstrap  Female    33\n",
       "           Male      33\n",
       "Gentoo     Female    61\n",
       "           Male      60\n",
       "Name: count, dtype: int64"
      ]
     },
     "execution_count": 43,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Compare clusters to original cleaned df\n",
    "df0.groupby('species')['sex'].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "id": "c23d63ca-5d81-448c-a55b-80c0dda3be8c",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(335, 7)\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "(335, 7)"
      ]
     },
     "execution_count": 44,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "print(penguins.shape)\n",
    "df0.shape"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a9f70de2-409b-44db-a3ee-25160ade1b70",
   "metadata": {},
   "source": [
    "#### Results     \n",
    "*Male*\n",
    "* 0 = Chinstrap\n",
    "   * there are 33 male Chinstraps in the data set\n",
    "   * **the model also included 3 male Adelies**\n",
    "* 1 = Gentoo 60\n",
    "   * there are 60 male Gentoos in the data set\n",
    "* 4 = Adelie 71\n",
    "   * there are 74 male Chinstraps in the data set\n",
    "   * **missing 3 male Adelies**\n",
    "\n",
    "*Female*\n",
    "* 2 = Adelie 74\n",
    "   * there are 74 female Adelies in the data set\n",
    "   * **the model also included 1 Chinstrap**\n",
    "* 3 = Gentoo 61\n",
    "   * there are 61 female Gentoos in the data set\n",
    "* 5 = Chinstrap 32\n",
    "   * there are 33 female Chinstraps in the data set\n",
    "   * **missing 1 female Chinstraps**\n",
    "     \n",
    "The K=mean6 model mostly partitioned the data correctly.\n",
    "* all sex assignments were correct\n",
    "* it correctly assigned sex and species to 331 out of 335 observations\n",
    "\n",
    "  \n",
    "4 errors:\n",
    "* the model missed 3 male Adelies\n",
    "* the model placed the 3 male Adelies it missed in with the male Chinstraps\n",
    "* the model missed 1 female Chinstrap\n",
    "* the model placed the 1 female Chinstrap it missed in with the female Adelies\n",
    "\n",
    "These errors are in keeping with the data. From the extensive EDA, ANOVA, and post hoc testing, it can be confirmed that Chinstraps and Adelies have similar body mass.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "c9a72ec2-5700-4a41-99ef-6efa3acedce5",
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
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