diff --git a/02_activities/assignments/assignment_1.ipynb b/02_activities/assignments/assignment_1.ipynb
index 28d4df017..b5c41db91 100644
--- a/02_activities/assignments/assignment_1.ipynb
+++ b/02_activities/assignments/assignment_1.ipynb
@@ -34,7 +34,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 1,
    "id": "4a3485d6-ba58-4660-a983-5680821c5719",
    "metadata": {},
    "outputs": [],
@@ -56,10 +56,288 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 2,
    "id": "a431d282-f9ca-4d5d-8912-71ffc9d8ea19",
    "metadata": {},
-   "outputs": [],
+   "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>alcohol</th>\n",
+       "      <th>malic_acid</th>\n",
+       "      <th>ash</th>\n",
+       "      <th>alcalinity_of_ash</th>\n",
+       "      <th>magnesium</th>\n",
+       "      <th>total_phenols</th>\n",
+       "      <th>flavanoids</th>\n",
+       "      <th>nonflavanoid_phenols</th>\n",
+       "      <th>proanthocyanins</th>\n",
+       "      <th>color_intensity</th>\n",
+       "      <th>hue</th>\n",
+       "      <th>od280/od315_of_diluted_wines</th>\n",
+       "      <th>proline</th>\n",
+       "      <th>class</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>14.23</td>\n",
+       "      <td>1.71</td>\n",
+       "      <td>2.43</td>\n",
+       "      <td>15.6</td>\n",
+       "      <td>127.0</td>\n",
+       "      <td>2.80</td>\n",
+       "      <td>3.06</td>\n",
+       "      <td>0.28</td>\n",
+       "      <td>2.29</td>\n",
+       "      <td>5.64</td>\n",
+       "      <td>1.04</td>\n",
+       "      <td>3.92</td>\n",
+       "      <td>1065.0</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>13.20</td>\n",
+       "      <td>1.78</td>\n",
+       "      <td>2.14</td>\n",
+       "      <td>11.2</td>\n",
+       "      <td>100.0</td>\n",
+       "      <td>2.65</td>\n",
+       "      <td>2.76</td>\n",
+       "      <td>0.26</td>\n",
+       "      <td>1.28</td>\n",
+       "      <td>4.38</td>\n",
+       "      <td>1.05</td>\n",
+       "      <td>3.40</td>\n",
+       "      <td>1050.0</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>13.16</td>\n",
+       "      <td>2.36</td>\n",
+       "      <td>2.67</td>\n",
+       "      <td>18.6</td>\n",
+       "      <td>101.0</td>\n",
+       "      <td>2.80</td>\n",
+       "      <td>3.24</td>\n",
+       "      <td>0.30</td>\n",
+       "      <td>2.81</td>\n",
+       "      <td>5.68</td>\n",
+       "      <td>1.03</td>\n",
+       "      <td>3.17</td>\n",
+       "      <td>1185.0</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>14.37</td>\n",
+       "      <td>1.95</td>\n",
+       "      <td>2.50</td>\n",
+       "      <td>16.8</td>\n",
+       "      <td>113.0</td>\n",
+       "      <td>3.85</td>\n",
+       "      <td>3.49</td>\n",
+       "      <td>0.24</td>\n",
+       "      <td>2.18</td>\n",
+       "      <td>7.80</td>\n",
+       "      <td>0.86</td>\n",
+       "      <td>3.45</td>\n",
+       "      <td>1480.0</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>13.24</td>\n",
+       "      <td>2.59</td>\n",
+       "      <td>2.87</td>\n",
+       "      <td>21.0</td>\n",
+       "      <td>118.0</td>\n",
+       "      <td>2.80</td>\n",
+       "      <td>2.69</td>\n",
+       "      <td>0.39</td>\n",
+       "      <td>1.82</td>\n",
+       "      <td>4.32</td>\n",
+       "      <td>1.04</td>\n",
+       "      <td>2.93</td>\n",
+       "      <td>735.0</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>...</th>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>173</th>\n",
+       "      <td>13.71</td>\n",
+       "      <td>5.65</td>\n",
+       "      <td>2.45</td>\n",
+       "      <td>20.5</td>\n",
+       "      <td>95.0</td>\n",
+       "      <td>1.68</td>\n",
+       "      <td>0.61</td>\n",
+       "      <td>0.52</td>\n",
+       "      <td>1.06</td>\n",
+       "      <td>7.70</td>\n",
+       "      <td>0.64</td>\n",
+       "      <td>1.74</td>\n",
+       "      <td>740.0</td>\n",
+       "      <td>2</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>174</th>\n",
+       "      <td>13.40</td>\n",
+       "      <td>3.91</td>\n",
+       "      <td>2.48</td>\n",
+       "      <td>23.0</td>\n",
+       "      <td>102.0</td>\n",
+       "      <td>1.80</td>\n",
+       "      <td>0.75</td>\n",
+       "      <td>0.43</td>\n",
+       "      <td>1.41</td>\n",
+       "      <td>7.30</td>\n",
+       "      <td>0.70</td>\n",
+       "      <td>1.56</td>\n",
+       "      <td>750.0</td>\n",
+       "      <td>2</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>175</th>\n",
+       "      <td>13.27</td>\n",
+       "      <td>4.28</td>\n",
+       "      <td>2.26</td>\n",
+       "      <td>20.0</td>\n",
+       "      <td>120.0</td>\n",
+       "      <td>1.59</td>\n",
+       "      <td>0.69</td>\n",
+       "      <td>0.43</td>\n",
+       "      <td>1.35</td>\n",
+       "      <td>10.20</td>\n",
+       "      <td>0.59</td>\n",
+       "      <td>1.56</td>\n",
+       "      <td>835.0</td>\n",
+       "      <td>2</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>176</th>\n",
+       "      <td>13.17</td>\n",
+       "      <td>2.59</td>\n",
+       "      <td>2.37</td>\n",
+       "      <td>20.0</td>\n",
+       "      <td>120.0</td>\n",
+       "      <td>1.65</td>\n",
+       "      <td>0.68</td>\n",
+       "      <td>0.53</td>\n",
+       "      <td>1.46</td>\n",
+       "      <td>9.30</td>\n",
+       "      <td>0.60</td>\n",
+       "      <td>1.62</td>\n",
+       "      <td>840.0</td>\n",
+       "      <td>2</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>177</th>\n",
+       "      <td>14.13</td>\n",
+       "      <td>4.10</td>\n",
+       "      <td>2.74</td>\n",
+       "      <td>24.5</td>\n",
+       "      <td>96.0</td>\n",
+       "      <td>2.05</td>\n",
+       "      <td>0.76</td>\n",
+       "      <td>0.56</td>\n",
+       "      <td>1.35</td>\n",
+       "      <td>9.20</td>\n",
+       "      <td>0.61</td>\n",
+       "      <td>1.60</td>\n",
+       "      <td>560.0</td>\n",
+       "      <td>2</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "<p>178 rows × 14 columns</p>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "     alcohol  malic_acid   ash  alcalinity_of_ash  magnesium  total_phenols  \\\n",
+       "0      14.23        1.71  2.43               15.6      127.0           2.80   \n",
+       "1      13.20        1.78  2.14               11.2      100.0           2.65   \n",
+       "2      13.16        2.36  2.67               18.6      101.0           2.80   \n",
+       "3      14.37        1.95  2.50               16.8      113.0           3.85   \n",
+       "4      13.24        2.59  2.87               21.0      118.0           2.80   \n",
+       "..       ...         ...   ...                ...        ...            ...   \n",
+       "173    13.71        5.65  2.45               20.5       95.0           1.68   \n",
+       "174    13.40        3.91  2.48               23.0      102.0           1.80   \n",
+       "175    13.27        4.28  2.26               20.0      120.0           1.59   \n",
+       "176    13.17        2.59  2.37               20.0      120.0           1.65   \n",
+       "177    14.13        4.10  2.74               24.5       96.0           2.05   \n",
+       "\n",
+       "     flavanoids  nonflavanoid_phenols  proanthocyanins  color_intensity   hue  \\\n",
+       "0          3.06                  0.28             2.29             5.64  1.04   \n",
+       "1          2.76                  0.26             1.28             4.38  1.05   \n",
+       "2          3.24                  0.30             2.81             5.68  1.03   \n",
+       "3          3.49                  0.24             2.18             7.80  0.86   \n",
+       "4          2.69                  0.39             1.82             4.32  1.04   \n",
+       "..          ...                   ...              ...              ...   ...   \n",
+       "173        0.61                  0.52             1.06             7.70  0.64   \n",
+       "174        0.75                  0.43             1.41             7.30  0.70   \n",
+       "175        0.69                  0.43             1.35            10.20  0.59   \n",
+       "176        0.68                  0.53             1.46             9.30  0.60   \n",
+       "177        0.76                  0.56             1.35             9.20  0.61   \n",
+       "\n",
+       "     od280/od315_of_diluted_wines  proline  class  \n",
+       "0                            3.92   1065.0      0  \n",
+       "1                            3.40   1050.0      0  \n",
+       "2                            3.17   1185.0      0  \n",
+       "3                            3.45   1480.0      0  \n",
+       "4                            2.93    735.0      0  \n",
+       "..                            ...      ...    ...  \n",
+       "173                          1.74    740.0      2  \n",
+       "174                          1.56    750.0      2  \n",
+       "175                          1.56    835.0      2  \n",
+       "176                          1.62    840.0      2  \n",
+       "177                          1.60    560.0      2  \n",
+       "\n",
+       "[178 rows x 14 columns]"
+      ]
+     },
+     "execution_count": 2,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
    "source": [
     "from sklearn.datasets import load_wine\n",
     "\n",
@@ -94,9 +372,20 @@
    "execution_count": null,
    "id": "56916892",
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "178"
+      ]
+     },
+     "execution_count": 3,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
    "source": [
-    "# Your answer here"
+    "wine_df.shape[0]\n"
    ]
   },
   {
@@ -109,12 +398,23 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 4,
    "id": "df0ef103",
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "14"
+      ]
+     },
+     "execution_count": 4,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
    "source": [
-    "# Your answer here"
+    "wine_df.shape[1]\n"
    ]
   },
   {
@@ -127,12 +427,23 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 5,
    "id": "47989426",
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "(dtype('int64'), array([0, 1, 2]))"
+      ]
+     },
+     "execution_count": 5,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
    "source": [
-    "# Your answer here"
+    "wine_df['class'].dtype, wine_df['class'].unique()"
    ]
   },
   {
@@ -146,12 +457,23 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 6,
    "id": "bd7b0910",
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "13"
+      ]
+     },
+     "execution_count": 6,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
    "source": [
-    "# Your answer here"
+    "wine_df.shape[1] - 1"
    ]
   },
   {
@@ -175,10 +497,37 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 7,
    "id": "cc899b59",
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "    alcohol  malic_acid       ash  alcalinity_of_ash  magnesium  \\\n",
+      "0  1.518613   -0.562250  0.232053          -1.169593   1.913905   \n",
+      "1  0.246290   -0.499413 -0.827996          -2.490847   0.018145   \n",
+      "2  0.196879    0.021231  1.109334          -0.268738   0.088358   \n",
+      "3  1.691550   -0.346811  0.487926          -0.809251   0.930918   \n",
+      "4  0.295700    0.227694  1.840403           0.451946   1.281985   \n",
+      "\n",
+      "   total_phenols  flavanoids  nonflavanoid_phenols  proanthocyanins  \\\n",
+      "0       0.808997    1.034819             -0.659563         1.224884   \n",
+      "1       0.568648    0.733629             -0.820719        -0.544721   \n",
+      "2       0.808997    1.215533             -0.498407         2.135968   \n",
+      "3       2.491446    1.466525             -0.981875         1.032155   \n",
+      "4       0.808997    0.663351              0.226796         0.401404   \n",
+      "\n",
+      "   color_intensity       hue  od280/od315_of_diluted_wines   proline  \n",
+      "0         0.251717  0.362177                      1.847920  1.013009  \n",
+      "1        -0.293321  0.406051                      1.113449  0.965242  \n",
+      "2         0.269020  0.318304                      0.788587  1.395148  \n",
+      "3         1.186068 -0.427544                      1.184071  2.334574  \n",
+      "4        -0.319276  0.362177                      0.449601 -0.037874  \n"
+     ]
+    }
+   ],
    "source": [
     "# Select predictors (excluding the last column)\n",
     "predictors = wine_df.iloc[:, :-1]\n",
@@ -251,7 +600,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 8,
    "id": "72c101f2",
    "metadata": {},
    "outputs": [],
@@ -260,8 +609,12 @@
     "np.random.seed(123)\n",
     "\n",
     "# split the data into a training and testing set. hint: use train_test_split !\n",
-    "\n",
-    "# Your code here ..."
+    "X_train, X_test, y_train, y_test = train_test_split(\n",
+    "    predictors_standardized,\n",
+    "    wine_df['class'],\n",
+    "    test_size=0.25,\n",
+    "    random_state=123\n",
+    ")\n"
    ]
   },
   {
@@ -284,12 +637,28 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 9,
    "id": "08818c64",
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "15"
+      ]
+     },
+     "execution_count": 9,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
    "source": [
-    "# Your code here..."
+    "knn = KNeighborsClassifier()\n",
+    "param_grid = {'n_neighbors': list(range(1, 51))}\n",
+    "grid = GridSearchCV(knn, param_grid, cv=10)\n",
+    "grid.fit(X_train, y_train)\n",
+    "best_k = grid.best_params_['n_neighbors']\n",
+    "best_k\n"
    ]
   },
   {
@@ -305,12 +674,26 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 10,
    "id": "ffefa9f2",
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "0.9333333333333333"
+      ]
+     },
+     "execution_count": 10,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
    "source": [
-    "# Your code here..."
+    "best_knn = KNeighborsClassifier(n_neighbors=best_k)\n",
+    "best_knn.fit(X_train, y_train)\n",
+    "y_pred = best_knn.predict(X_test)\n",
+    "accuracy_score(y_test, y_pred)\n"
    ]
   },
   {
@@ -365,7 +748,7 @@
  ],
  "metadata": {
   "kernelspec": {
-   "display_name": "Python 3.10.4",
+   "display_name": "lcr-env",
    "language": "python",
    "name": "python3"
   },
@@ -379,12 +762,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.9.19"
-  },
-  "vscode": {
-   "interpreter": {
-    "hash": "497a84dc8fec8cf8d24e7e87b6d954c9a18a327edc66feb9b9ea7e9e72cc5c7e"
-   }
+   "version": "3.11.13"
   }
  },
  "nbformat": 4,
diff --git a/02_activities/exercises/exploring_a_dataset_with_regression.ipynb b/02_activities/exercises/exploring_a_dataset_with_regression.ipynb
deleted file mode 100644
index b35f62baf..000000000
--- a/02_activities/exercises/exploring_a_dataset_with_regression.ipynb
+++ /dev/null
@@ -1,1730 +0,0 @@
-{
- "cells": [
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "🚨 Don't worry if the code block below doesn't make sense, please scroll down to the instructions below  🚨"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 1,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# Setting up the notebook. \n",
-    "\n",
-    "import random\n",
-    "import numpy as np\n",
-    "import pandas as pd\n",
-    "import matplotlib.pyplot as plt\n",
-    "import seaborn as sns\n",
-    "from sklearn.datasets import load_breast_cancer\n",
-    "from sklearn.model_selection import train_test_split\n",
-    "from sklearn.linear_model import LogisticRegression, LinearRegression\n",
-    "from sklearn.metrics import classification_report, accuracy_score, confusion_matrix\n",
-    "\n",
-    "# Dictionary mapping feature names to their descriptions\n",
-    "feature_descriptions = {\n",
-    "    'mean_radius': \"Average distance from the center to the outer edge of the tumor.\",\n",
-    "    'mean_texture': \"How rough or smooth the surface of the tumor feels, on average.\",\n",
-    "    'mean_perimeter': \"The average length of the outline of the tumor.\",\n",
-    "    'mean_area': \"The average size of the surface of the tumor measured in square units.\",\n",
-    "    'mean_smoothness': \"How smooth or bumpy the tumor surface feels, averaged over several observations.\",\n",
-    "    'mean_compactness': \"A measure of how tightly the tumor cells are packed together, averaged over several observations.\",\n",
-    "    'mean_concavity': \"Average number of indentations or hollow areas on the tumor's surface.\",\n",
-    "    'mean_concave_points': \"Average number of sharp dips or points found along the contour of the tumor.\",\n",
-    "    'mean_symmetry': \"How evenly shaped the tumor is. A perfectly symmetrical tumor looks the same on both sides.\",\n",
-    "    'mean_fractal_dimension': \"A measure that describes the complexity of the tumor shape, showing how jagged the border is on average.\",\n",
-    "    'radius_error': \"The change in the tumor's radius across different measurements, indicating how much the size of the tumor varies.\",\n",
-    "    'texture_error': \"The change in the tumor's texture across different measurements, showing how much the roughness or smoothness varies.\",\n",
-    "    'perimeter_error': \"The change in the outline length of the tumor across different measurements, showing how much the outline varies.\",\n",
-    "    'area_error': \"The change in the surface size of the tumor across different measurements, showing how much the area varies.\",\n",
-    "    'smoothness_error': \"The change in how smooth or bumpy the tumor surface feels across different measurements.\",\n",
-    "    'compactness_error': \"The change in how tightly the tumor cells are packed together across different measurements.\",\n",
-    "    'concavity_error': \"The change in the number of indentations or hollow areas on the tumor's surface across different measurements.\",\n",
-    "    'concave_points_error': \"The change in the number of sharp dips or points found along the contour of the tumor across different measurements.\",\n",
-    "    'symmetry_error': \"The change in how evenly shaped the tumor is across different measurements.\",\n",
-    "    'fractal_dimension_error': \"The change in the complexity of the tumor shape across different measurements, showing how much the jaggedness of the border varies.\",\n",
-    "    'worst_radius': \"The largest distance from the center to the outer edge of the tumor observed among all measurements.\",\n",
-    "    'worst_texture': \"The roughest texture observed on the surface of the tumor.\",\n",
-    "    'worst_perimeter': \"The longest outline of the tumor measured among all observations.\",\n",
-    "    'worst_area': \"The largest surface area of the tumor measured among all observations.\",\n",
-    "    'worst_smoothness': \"The least smooth texture observed on the tumor's surface, indicating the roughest feel.\",\n",
-    "    'worst_compactness': \"The highest degree of how tightly the tumor cells are packed together, observed among all measurements.\",\n",
-    "    'worst_concavity': \"The deepest indentations observed on the tumor's surface.\",\n",
-    "    'worst_concave_points': \"The highest number of sharp dips or points observed along the contour of the tumor.\",\n",
-    "    'worst_symmetry': \"The most uneven shape observed in the tumor, where one side differs the most from the other.\",\n",
-    "    'worst_fractal_dimension': \"The highest complexity of the tumor shape observed, showing the most jagged border among all measurements.\"\n",
-    "}\n",
-    "\n",
-    "def scatter_plot(X, y=None, line_plot=None, title='', show_legend=True, xlabel='', ylabel=''):\n",
-    "    \"\"\"\n",
-    "    Create a scatter plot with optional labels, decision boundary, and filled areas.\n",
-    "\n",
-    "    Parameters:\n",
-    "    X (dict): The data for the first class with keys 'data', 'color', and 'label'.\n",
-    "    y (dict, optional): The data for the second class with keys 'data', 'color', and 'label'.\n",
-    "    line_plot (dict, optional): The line plot details with keys 'x', 'y', 'color', 'linestyle', 'fill_colors', and 'model'.\n",
-    "    title (str): The title of the plot.\n",
-    "    xlabel (str): The label for the x-axis.\n",
-    "    ylabel (str): The label for the y-axis.\n",
-    "    ylim (tuple, optional): The limits for the y-axis.\n",
-    "    show_legend (bool, optional): Whether to show the legend.\n",
-    "    \"\"\"\n",
-    "    plt.figure(figsize=(8, 6))\n",
-    "\n",
-    "    # Plotting the data points for X\n",
-    "    plt.scatter(X['data'][0], X['data'][1], color=X['color'], label=X[\"label\"], edgecolors='k')\n",
-    "    \n",
-    "    # Plotting the data points for y, if provided\n",
-    "    if y is not None:\n",
-    "        plt.scatter(y['data'][0], y['data'][1], color=y['color'], label=y[\"label\"], edgecolors='k')\n",
-    "\n",
-    "    # Determining the ylim\n",
-    "    all_y_values = X['data'][1]\n",
-    "    if y is not None:\n",
-    "        all_y_values = np.concatenate([all_y_values, y['data'][1]])\n",
-    "    # if line_plot is not None:\n",
-    "    #     all_y_values = np.concatenate([all_y_values, line_plot[\"y\"]])\n",
-    "\n",
-    "    y_min, y_max = all_y_values.min(), all_y_values.max()\n",
-    "    y_range = y_max - y_min\n",
-    "    y_extension = y_range * 0.1  # 10% extension\n",
-    "    ylim = y_min - y_extension, y_max + y_extension\n",
-    "    plt.ylim(ylim)\n",
-    "\n",
-    "    # Plotting the line plot\n",
-    "    if line_plot is not None:\n",
-    "        plt.plot(line_plot['x'], line_plot['y'], color=line_plot['color'], linestyle=line_plot['linestyle'], label='Decision Boundary')\n",
-    "\n",
-    "        if 'fill_colors' in line_plot and ylim is not None:\n",
-    "            point_above = np.array([[line_plot['x'][0], line_plot['y'][0] + 1]])\n",
-    "            prediction_above = line_plot['model'].predict(point_above) if 'model' in line_plot else None\n",
-    "\n",
-    "            if prediction_above == 1:\n",
-    "                plt.fill_between(line_plot['x'], line_plot['y'], ylim[1], color=line_plot['fill_colors'][1], alpha=0.2)\n",
-    "                plt.fill_between(line_plot['x'], ylim[0], line_plot['y'], color=line_plot['fill_colors'][0], alpha=0.2)\n",
-    "            else:\n",
-    "                plt.fill_between(line_plot['x'], line_plot['y'], ylim[1], color=line_plot['fill_colors'][0], alpha=0.2)\n",
-    "                plt.fill_between(line_plot['x'], ylim[0], line_plot['y'], color=line_plot['fill_colors'][1], alpha=0.2)\n",
-    "\n",
-    "    # Setting the plot title and axis labels\n",
-    "    plt.title(title)\n",
-    "    plt.xlabel(xlabel)\n",
-    "    plt.ylabel(ylabel)\n",
-    "\n",
-    "    if show_legend and (X['label'] or (y and y['label'])):\n",
-    "        plt.legend()\n",
-    "    \n",
-    "    plt.show()"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "# Notebook Title: Exploring a Dataset with Regression\n",
-    "\n",
-    "## Introduction\n",
-    "Welcome to Exercise 1! In this notebook, we will go through the importance of applying statistical concepts to our data problems and how to get meaningful answers to our questions using the power of programming. This module will help you understand many things about your data.\n",
-    "\n",
-    "In the world of statistics, we use a tool called **regression** to help us answer such questions. Regression is a method used to find how one thing (like temperature) can predict another thing (like ice cream sales)."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 2,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "image/png": 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1112HM844A82aNcNnn32GH374QZVnFAASEhIwa9Ys3HHHHRg0aBDuvPNOpKam4o8//sD69evx0ksvARCE4eDBg3HVVVdh7NixSE9Px4EDB/Dzzz9jzZo1KC8vl91HdHQ0Fi5ciCuvvBK9e/fGPffcg/79+6Nly5bYunUrKioq8OGHH+Kff/7xaqvStu3cuTM6duyIf//73yAiJCcn48MPPwzaMHxNTQ0mTpyIG2+8EX369EFKSgr27NmD999/H5988gluueUWRxhIbm4unnjiCUydOhWXX345fv31V/znP/9BTk6ORzovd/7zn/9g+fLl6NOnDyZMmICzzjoLJ0+exJYtW7B06VLMnTsXGRkZuPPOOxEfH49LLrkEaWlp2LVrF2bMmIGkpCRceOGFwWgShgktoZ3PxjCM1rhnSyASZn3PnDmTunXrRs2bN6eEhATq3LkzjRs3jn7//XfHellZWTR06FCPbQKg++67T7TMnmngueee89j3Dz/8QP369aP4+HhKTk6me+65h44ePeqx3Xnz5lGvXr2oZcuWFB8fTx07dqRbbrmFvvvuO8c6l19+OZ177rmSx7pq1Srq3bs3tWjRgsxmM91xxx20Zs0ajwwI9fX1dMcdd5DZbCaTyUQAaPPmzUREdOjQIbrjjjsoNTWVWrZsScOGDaMtW7bIZkuwZx3w51i88emnnzpm2v/222+iz3bv3k1jx46lzp07U8uWLSkhIYG6du1KJSUlZLFYvG5Xzu6lS5fS5ZdfTi1btqQWLVrQOeecQ88884xonfXr19PIkSOpXbt2FBsbS+3bt6cBAwbQ3LlzFR3TwYMH6YknnqAePXpQQkICxcbG0mmnnUY33XQTffnllz5ttKOkbX/66Se64oorKDExkdq0aUPXXXcd/fXXX4rPo9R9Q+T9+rOzbds2mjJlCl1yySXUvn17iomJocTEROrVqxe9+OKLonNUX19PDz30EKWnp1Pz5s2pR48etGjRIhozZgxlZWWJtutuOxHR3r17acKECZSTk0OxsbGUnJxMF1xwAT366KOOe+ztt9+m/v37U2pqKjVr1ow6dOhAI0eOpB9++MHrcTBMpGAiIgqJqmYYJuIYO3YsKioqcPTo0VCbwjAMwzRROFsCwzAMwzAMEzGwuGUYhmEYhmEiBg5LYBiGYRiGYSIG9twyDMMwDMMwEQOLW4ZhGIZhGCZiYHHLMAzDMAzDRAxcxAFCXfK///4biYmJktV6GIZhGIZhmNBCRDhy5Ag6dOggKnTjDotbAH///bfPsocMwzAMwzBM6Nm2bZuj6p8ULG4BJCYmAhAaq1WrViG2hmEYhmEYhnHn8OHDyMzMdOg2OVjcAo5QhFatWrG4ZRiGYRiGMTC+Qkh5QhnDMAzDMAwTMbC4ZRiGYRiGYSIGFrcMwzAMwzBMxMAxtwohIlgsFlit1lCbwjB+Exsbi+jo6FCbwTAMwzC6weJWAQ0NDdi5cyeOHz8ealMYJiBMJhMyMjKQkJAQalMYhmEYRhdY3PqgsbERmzdvRnR0NDp06IBmzZpxoQcmLCEi7N27F9u3b8cZZ5zBHlyGYRgmImFx64OGhgY0NjYiMzMTLVq0CLU5DBMQZrMZW7ZswalTp1jcMgzDMBEJTyhTiLcybwwTLvCoA8MwDBPpsGJjGIZhGIZhIgYWtwzDMAzDMEzEwOKWYTRg7NixGDFiRKjNYBiGYZgmD4vbCCVYYquyshL9+vVDUlISEhIS0LVrV/znP//BgQMHdN+3Vqxduxa5ublo164dmjdvjuzsbIwaNQr79u0LtWkMwzAMw6iExW2QsFqtqK2txfvvv4/a2tqIKAbx6KOPYtSoUbjwwgvx8ccfY+PGjZg1axbWr1+Pd999V/I7p06dCrKV3tmzZw8GDRqEtm3bYtmyZfj5558xb948pKWlcV5jhmEYhglDWNwGgaqqKmRnd0L//v0xevRo9O/fH9nZnVBVVRU0GxobG/HMM8+gU6dOiIuLw2mnnYannnrK8fmOHTswatQotGnTBikpKcjLy8OWLVtkt/fNN99g+vTpmDVrFp577jn06dMH2dnZuOKKK1BZWYkxY8YAAKZNm4bu3btj3rx5OP300xEXFwciwqFDh3DXXXehXbt2aNWqFQYMGID169c7tr9p0ybk5eUhNTUVCQkJuPDCC1FdXS2yITs7G08++SRuueUWJCQkICsrC4sXL8bevXuRl5eHhIQEdOnSBd99953scaxatQqHDx/G//73P5x//vnIycnBgAEDMHv2bJx22mkAhI7J7bffjpycHMTHx+Oss87CnDlzvLY3EeHZZ5/F6aefjvj4eHTr1g0VFRWOz//55x/ceOONMJvNiI+PxxlnnIE333zT6zYZhmEYhvENi1udqaqqQkFBAbZv7wJgNYAjAFZjx44uKCgoCJrAfeSRR/DMM8/gsccew08//YT58+cjNTUVAHD8+HH0798fCQkJ+OKLL7By5UokJCTg6quvRkNDg+T23nvvPSQkJODee++V/Lx169aO///44w+UlZWhsrIS69atAwAMHToUu3btwtKlS/H999+jR48eGDhwoCOc4ejRoxgyZAiqq6uxdu1aXHXVVRg2bBj++usv0X5KSkpwySWXYO3atRg6dChuvvlm3HLLLbjpppuwZs0adOrUCbfccguISNLO9u3bw2KxYOHChbLrNDY2IiMjA2VlZfjpp5/w+OOPY/LkySgrK5Nt7ylTpuDNN9/Eq6++ih9//BH3338/brrpJnz++ecA4DgPH3/8MX7++We8+uqraNu2rez2GIZhGIZRCDF06NAhAkCHDh3y+OzEiRP0008/0YkTJ1Rv12KxUEZGNgHDCLASQC4vK5lMwygzM4csFosWhyFizJgxlJeXR0REhw8fpri4OPrvf/8rue4bb7xBZ511FjU2NjqW1dfXU3x8PC1btkzyO4MHD6auXbv6tGPq1KkUGxtLe/bscSxbsWIFtWrVik6ePClat2PHjvTaa6/Jbuucc86hF1980fE+KyuLbrrpJsf7nTt3EgB67LHHHMtWr15NAGjnzp2y2508eTLFxMRQcnIyXX311fTss8/Srl27vB7XvffeS9dee63jvWt7Hz16lJo3b06rVq0Sfef222+nG264gYiIhg0bRrfeeqvXfehBINczwzAMw4QSb3rNFfbc6khdXR22b98CYDI8neRRIHoE27ZtRl1dna52/Pzzz6ivr8fAgQMlP//+++/xxx9/IDExEQkJCUhISEBycjJOnjyJTZs2SX6HiBQXBMjKyoLZbBbt7+jRo0hJSXHsLyEhAZs3b3bs79ixY5g0aRLOOecctG7dGgkJCfjll188PLddu3Z1/G/3RHfp0sVj2Z49e2Tte+qpp7Br1y7MnTsX55xzDubOnYvOnTtjw4YNjnXmzp2Lnj17wmw2IyEhAf/97389bLHz008/4eTJk7jiiitEx/fOO+84ju+ee+7BggUL0L17d0yaNAmrVq1S1JYMwzAMw3iHy+/qyM6dO23/nSezxnlu6+lDfHy8188bGxtxwQUX4L333vP4zFWUunLmmWdi5cqVOHXqFGJjY71uv2XLlh77S0tLQ21trce69nCGoqIiLFu2DDNnzkSnTp0QHx+PgoICjzAJ133bxbbUssbGRq82pqSk4LrrrsN1112HGTNm4Pzzz8fMmTPx9ttvo6ysDPfffz9mzZqF3r17IzExEc899xy+/vpryW3Z9/XRRx8hPT1d9FlcXBwAYPDgwdi6dSs++ugjVFdXY+DAgbjvvvswc+ZMr3YyDMMwDOMdFrc6kpaWZvtvI4CLJdbY6LaePpxxxhmIj4/HihUrcMcdd3h83qNHD3zwwQeOyV1KGD16NF544QW88sormDhxosfnBw8eFMXduu9v165diImJQXZ2tuQ6dXV1GDt2LK655hoAQgyutwluWtKsWTN07NgRx44dc9jSp08fUXyxnEcbAM455xzExcXhr7/+wuWXXy67ntlsxtixYzF27Fj07dsXRUVFLG4ZhmEYJkA4LEFH+vbti4yMbJhM0wG4ew4bYTLNQGZmDvr27aurHc2bN8fDDz+MSZMmOYbGv/rqK7zxxhsAgBtvvBFt27ZFXl4e6urqsHnzZnz++eeYOHEitm/fLrnNXr16YdKkSXjwwQcxadIkrF69Glu3bsWKFStw3XXX4e2335a1Z9CgQejduzdGjBiBZcuWYcuWLVi1ahWmTJniyGzQqZOQTWLdunVYv349Ro8e7dP76g9LlizBTTfdhCVLluC3337Dr7/+ipkzZ2Lp0qXIy8tz2PLdd99h2bJl+O233/DYY4/h22+/ld1mYmIiHnroIdx///14++23sWnTJqxduxYvv/yyo10ef/xxLF68GH/88Qd+/PFHLFmyBGeffbbmx8cwDMMwTQ323OpIdHQ05syZhYKCAphMI0D0CIRQhI0wmWYAWILZsysQHR2tuy2PPfYYYmJi8Pjjj+Pvv/9GWloa7r77bgBAixYt8MUXX+Dhhx9Gfn4+jhw5gvT0dAwcONCrJ/eZZ57BBRdcgJdffhlz585FY2MjOnbsiIKCAkcqMClMJhOWLl2KRx99FLfddhv27t2L9u3b47LLLnPEyJaUlOC2225Dnz590LZtWzz88MM4fPiwto0CwcvaokULPPjgg9i2bRvi4uJwxhln4H//+x9uvvlmAMDdd9+NdevWYdSoUTCZTLjhhhtw77334uOPP5bd7hNPPIF27dphxowZ+PPPP9G6dWv06NEDkydPBiB4hx955BFs2bIF8fHx6Nu3LxYsWKD58TEMwzBMU8NEJJP/qAlx+PBhJCUl4dChQx5i7uTJk9i8eTNycnLQvHlzv7ZfVVWFiRMftE0uE8jMzMHs2TORn58fiOkMowotrmeGYRiGCQXe9Jor7LkNAvn5+Y4h/507dyItLQ19+/YNiseWYRiGYRimKcHiNkhER0ejX79+oTaDYRiGYRgmouEJZQzDMAzDMEzEwOKWYRiGYRiGiRhY3DIMwzAMwzARA4tbhmEYhmEYJmJgccswDMMwDMNEDCxuGYZhGIZhmIiBU4ExDMMwDMPoiNVqDUqu+2Dtx+iw55YJmOzsbMyePVvzdSOZfv36obCwMNRmMAzDMDpTVVWF7OxO6N+/P0aPHo3+/fsjO7sTqqqqwnI/4QCL2whl7NixMJlMMJlMiI2NRWpqKq644grMmzcPjY2Nmu7r22+/xV133aX5uv5SW1vrOHaTyYT4+Hice+65eP3113XdL8MwDMO4UlVVhYKCAmzf3gXAagBHAKzGjh1dUFBQoJnwDNZ+wgUWtxHM1VdfjZ07d2LLli34+OOP0b9/f0ycOBG5ubmwWCya7cdsNqNFixaarxsov/76K3bu3ImffvoJ48aNwz333IMVK1YEZd96Q0SankOGYRhGW6xWKyZOfBBEuQAWAbgYQAKAi0G0CEAuCgsfgtVqDYv9hBMsbv3l2DH518mTytc9cULZun4QFxeH9u3bIz09HT169MDkyZOxePFifPzxx3jrrbcc6x06dAh33XUX2rVrh1atWmHAgAFYv369aFv/93//h549e6J58+Zo27Yt8vPzHZ+5hxpMmzYNp512GuLi4tChQwdMmDBBdt2//voLeXl5SEhIQKtWrTBy5Ejs3r1btK3u3bvj3XffRXZ2NpKSknD99dfjyJEjPo+/Xbt2aN++PXJycjBhwgRkZ2djzZo1js/r6+sxYcIEtGvXDs2bN8ell16Kb7/91vH5W2+9hdatW4u2uWjRIphMJlX2HTt2DLfccgsSEhKQlpaGWbNmedhaWlqKnj17IjExEe3bt8fo0aOxZ88ex+d2b/SyZcvQs2dPxMXF4d1330VUVBS+++470bZefPFFZGVlgYh8thHDMAyjD3V1ddi+fQuAyfCUW1EgegTbtm1GXV1dWOwnnGBx6y8JCfKva68Vr9uunfy6gweL183Oll5PIwYMGIBu3bo5hiiICEOHDsWuXbuwdOlSfP/99+jRowcGDhyIAwcOAAA++ugj5OfnY+jQoVi7di1WrFiBnj17Sm6/oqICJSUleO211/D7779j0aJF6NKli+S6RIQRI0bgwIED+Pzzz7F8+XJs2rQJo0aNEq23adMmLFq0CEuWLMGSJUvw+eef4+mnn1Z8zESETz75BNu2bUOvXr0cyydNmoTKykq8/fbbWLNmDTp16oSrrrrKcdxK8WVfUVERampqsHDhQnz66aeora3F999/L9pGQ0MDnnjiCaxfvx6LFi3C5s2bMXbsWI99TZo0CTNmzMDPP/+M4cOHY9CgQXjzzTdF67z55puOsBSGYRgmNOzcudP233kya5zntp6x9xNWEEOHDh0iAHTo0CGPz06cOEE//fQTnThxQvwBIP8aMkS8bosW8utefrl43bZtpddTyZgxYygvL0/ys1GjRtHZZ59NREQrVqygVq1a0cmTJ0XrdOzYkV577TUiIurduzfdeOONsvvKysqikpISIiKaNWsWnXnmmdTQ0OBz3U8//ZSio6Ppr7/+cnz+448/EgD65ptviIho6tSp1KJFCzp8+LBjnaKiIurVq5esPTU1NQSAWrZsSS1btqSYmBiKioqiJ5980rHO0aNHKTY2lt577z3HsoaGBurQoQM9++yzRET05ptvUlJSkmjbCxcuJNfbxpd9R44coWbNmtGCBQscn+/fv5/i4+Np4sSJssfwzTffEAA6cuSI6JgWLVokWu+DDz6gNm3aOM7funXryGQy0ebNmyW3K3s9MwzDMJpi/90GVstIgFUEgGpqasJiP0bAm15zhT23/nL0qPyrslK87p498ut+/LF43S1bpNfTECJyePW+//57HD16FCkpKUhISHC8Nm/ejE2bNgEA1q1bh4EDByra9nXXXYcTJ07g9NNPx5133omFCxfKxob+/PPPyMzMRGZmpmPZOeecg9atW+Pnn392LMvOzkZiYqLjfVpammjIXo66ujqsW7cO69atw//+9z9Mnz4dr776KgDB23rq1ClccskljvVjY2Nx0UUXifatBG/2bdq0CQ0NDejdu7fj8+TkZJx11lmibaxduxZ5eXnIyspCYmIi+vXrB0AI23DF3WM+YsQIxMTEYOHChQCAefPm2WbIZqs6BoZhGEZb+vbti4yMbJhM0wG4T+RuhMk0A5mZOejbt29Y7CecYHHrLy1byr+aN1e+bny8snU15Oeff0ZOTg4AoLGxEWlpaQ4RaH/9+uuvKCoqAgDEu9vohczMTPz66694+eWXER8fj3vvvReXXXYZTp065bGuq8j2tjw2Nlb0uclkUpTxIScnB506dcK5556LW2+9FTfffDOeeuopxz7s25Lbd1RUlEfcqtRxeLPP/ftSHDt2DFdeeSUSEhJQWlqKb7/91iFWGxoaROu2dLsWmjVrhptvvhlvvvkmGhoaMH/+fNx2220+98kwDMPoS3R0NObMmQVgCUymEXDNYiC8X4LZs2cGnIc2WPsJJ1jcNjE+++wzbNiwAdfa4oJ79OiBXbt2ISYmBp06dRK92rZtCwDo2rWrqiwD8fHxGD58OF544QXU1tZi9erV2LBhg8d655xzDv766y9s27bNseynn37CoUOHcPbZZwd4pJ5ER0fjhG0CX6dOndCsWTOsXLnS8fmpU6fw3XffOfZtNptx5MgRHHOZ0Ldu3TpV++zUqRNiY2Px1VdfOZb9888/+O233xzvf/nlF+zbtw9PP/00+vbti86dOyvyTNu54447UF1djVdeeQWnTp0STfZjGIZhQkd+fj4qKiqQnr4BQB8ArQD0QUbGRlRUVGj2ex2s/YQLXKEsgqmvr8euXbtgtVqxe/dufPLJJ5gxYwZyc3Nxyy23AAAGDRqE3r17Y8SIEXjmmWdw1lln4e+//8bSpUsxYsQI9OzZE1OnTsXAgQPRsWNHXH/99bBYLPj4448xadIkj32+9dZbsFqt6NWrF1q0aIF3330X8fHxyMrK8lh30KBB6Nq1K2688UbMnj0bFosF9957Ly6//HLZCWtq2LNnD06ePIn6+np88803ePfdd1FQUABA8IDec889KCoqQnJyMk477TQ8++yzOH78OG6//XYAcBzD5MmT8a9//QvffPONKMuEEhISEnD77bejqKgIKSkpSE1NxaOPPoqoKGe/8rTTTkOzZs3w4osv4u6778bGjRvxxBNPKN7H2WefjYsvvhgPP/wwbrvtNlWedoZhGEZf8vPzkZeXp3vlsGDtJxxgcRvBfPLJJ0hLS0NMTAzatGmDbt264YUXXsCYMWMc4spkMmHp0qV49NFHcdttt2Hv3r1o3749LrvsMqSmpgIQqmmVl5fjiSeewNNPP41WrVrhsssuk9xn69at8fTTT+OBBx6A1WpFly5d8OGHHyIlJcVjXZPJhEWLFuFf//oXLrvsMkRFReHqq6/Giy++qMnx2+NaY2JikJmZiXHjxmHatGmOz59++mk0Njbi5ptvxpEjR9CzZ08sW7YMbdq0ASDExpaWlqKoqAivv/46Bg0ahGnTpqkuQvHcc8/h6NGjGD58OBITE/Hggw/i0KFDjs/NZjPeeustTJ48GS+88AJ69OiBmTNnYvjw4Yr3cfvtt2PVqlUcksAwDGNAoqOjHXMpImE/RsdESoICI5zDhw8jKSkJhw4dQqtWrUSfnTx5Eps3b0ZOTg6au8fSMoxBeOqpp7BgwQLJ8A9X+HpmGIZhwhVves0VjrllmDDm6NGj+Pbbb/Hiiy+KimUwDMMwTFOFxS3DhDHjx4/HpZdeissvv5xDEhiGaTJYrVbU1tbi/fffR21tbZMqLcv4hmNuGSaMeeutt1RPcmMYhglnqqqqMHHig7aSswIZGdmYM2dWk8sKwEjDnluGYRiGYcKCqqoqFBQUYPv2LnDN57pjRxcUFBQ4SsszTRsWtwrheXdMJMDXMcMw4YrVasXEiQ+CKBfAIgAXA0gAcDGIFgHIRWHhQxETosChF/7D4tYH9upTx48fD7ElDBM49opnTTHvIcMw4U1dXZ0tFGEyPOVLFIgewbZtm1FXVxd84zSmqqoK2dmd0L9/f4wePdpWVr0Te6YVwjG3PoiOjkbr1q0dFaNatGghWTKWYYxOY2Mj9u7dixYtWiAmhm99hmHCi507d9r+O09mjfPc1gtP7KEXgof6fQjHtRE7dkxHQUFBk6w4phZ+wimgffv2AKCqJCrDGJGoqCicdtpp3EFjGCbsSEtLs/23EUJIgjsb3dYLPzxDL+weaiH0wmQagcLCh5CXl8cjcF7gIg5QnhTYarXi1KlTQbSMYbSlWbNmotK/DMMw4YLVakV2difs2NHFFmPr+lvWCJNpBDIyNmLz5t/DVvjV1taif//+ECbLSQn41QD6oKampklWIlOq19hzq4Lo6OiwvWEYhmEYJpyJjo7GnDmzUFBQAJNpBIgegX3I3mSaAWAJZs+uCOvndFMJvdCbkLpwvvjiCwwbNgwdOnSAyWTCokWLPNb5+eefMXz4cCQlJSExMREXX3wx/vrrL8fn9fX1+Ne//oW2bduiZcuWGD58OLZv3x7Eo2AYhmEYJhjk5+ejoqIC6ekbAPQB0ApAH2RkbIyIWFRx6IUUwQ+9CMesDSEVt8eOHUO3bt3w0ksvSX6+adMmXHrppejcuTNqa2uxfv16PPbYY2jevLljncLCQixcuBALFizAypUrcfToUeTm5oZF4zMMwzAMo478/Hxs2fIHampqMH/+fNTU1GDz5t/DXtgCQN++fZGengXgAQDvAagFYNczjTCZZiAzMwd9+/YNij3hmrXBMDG3JpMJCxcuxIgRIxzLrr/+esTGxuLdd9+V/M6hQ4dgNpvx7rvvYtSoUQCAv//+G5mZmVi6dCmuuuoqye/V19ejvr7e8f7w4cPIzMz0GcPBMAzDMAyjF1VVVbjrrnuxf/9ul6VZAMbBZFoNYEnQPNTirA2T4QwBmR5UO1xRGnNr2JkljY2N+Oijj3DmmWfiqquuQrt27dCrVy9R6ML333+PU6dO4corr3Qs69ChA8477zysWrVKdtszZsxAUlKS45WZmannoTAMwzAMw3jFLib3778IrtXXBFE5GcnJXwdNUIZ7wQzDits9e/bg6NGjePrpp3H11Vfj008/xTXXXIP8/Hx8/vnnAIBdu3ahWbNmaNOmjei7qamp2LVrl+y2H3nkERw6dMjx2rZtm67HwjAMwzAMI4c3MQn8H4BcxMe3RF5eXlDsCfeCGYbNltDY2AgAyMvLw/333w8A6N69O1atWoW5c+fi8ssvl/0uEXnN4xkXF4e4uDhtDWYYhmEYhvEDp5h8H1JiEpiM7dv7oK6uLigpwMI9a4NhxW3btm0RExODc845R7T87LPPxsqVKwEIxRUaGhrwzz//iLy3e/bsQZ8+fYJqL8MwDMMwjD+oEZNWqxV1dXXYuXMn2rVrB0DQPWlpaejbt68mqdDCvWCGYcVts2bNcOGFF+LXX38VLf/tt9+QlZUFALjgggsQGxuL5cuXY+TIkQCEE79x40Y8++yzQbeZYRiGYRhPXAWZliIsUlAqJn///XdkZ3eyeXntxACwAAAyMrIxZ86sgONy+/bti4yMbOzYMV2mYMYMZGQEL2uDaiiEHDlyhNauXUtr164lAPT888/T2rVraevWrUREVFVVRbGxsfT666/T77//Ti+++CJFR0dTXV2dYxt33303ZWRkUHV1Na1Zs4YGDBhA3bp1I4vFotiOQ4cOEQA6dOiQ5sfIMAzDME2ZyspKysjIJgCOV0ZGNlVWVobaNMNgsVgoIyObTKZhBFgJIJeXlUymYZSS0o5MJhMBwwhYTcAR299cAkwEzCCTaRiZTCZN2rayspJMJpPNplUEHCZglab7UItSvRZScVtTUyO62O2vMWPGONZ54403qFOnTtS8eXPq1q0bLVq0SLSNEydO0Pjx4yk5OZni4+MpNzeX/vrrL1V2sLhlGIZhGO2xCyR3QRZKgWRUvIlJAJSSkmprR0/xKyzPIaCBTKZhlJmZo8rJ580mz45JTsjOm1K9Zpg8t6FEad40hmEYhmGUYbVabUPoXSBkAHAf2h6BjIyN2Lz5dw5RsFFVVYWJEx8UhR1kZubgjjvGYurUqRBSg0mFLayGULGtBkAcgD6oqalRNflMKnRk8eLFmDDhAezYsdWxXnp6Fl544fmQFM0I+zy3DMMwDMOEL+GeTioUyFVfO+OMM2xreJ9wBuyEP5kMpCqRpaam4dprC7BjR1e45t39+++uKCgoMHSVMsNOKGMYhmEYJnwJ93RSoSI6OtrD46p0whmQBrWZDMSVyN6HcF7WY//+qwEMhdjrLhRxMJlGoLDwIeTl5RnS686eW4ZhGIZhNEcsyKQwdjopI2HPXiCUvm10+7QRwAwAOQAugck0A5mZyjIZyBePOAXgKIBHEY5edxa3DMMwDMNoji9BpkaENXWio6MxZ84sAEtgMo2AuDxvHoAlAO6EyXQtgCWYPXumIo+qfOhIeHvdWdwyDMMwDKM53gSZ8F65CGuKWK1W1NbW4v3330dtbS3y8vJQUVGB9PQNECaPtbL9/QRCIoPJyMjYiIqKCsWTveRDR8Lb684xtwzDMAzD6EJ+fj4qKipsGQCclUMzMnIwe7ZyEdbUkMqaYC/QsGXLH5pVKJOP5e0LIBvAUwAWI9yKOHAqMHAqMIZhGIbRE65QphzxBK/JELyqG23hHUtUeWalcC/fO2bM7fj7764SlcgqAIwEMARC7K3djhma2OEPSvUai1uwuGUYhmEiBxaS4YveuYGlPMIpKanYv383TKZhIHoEriKW6EPH53YyM3Mwe/ZMznPLMAzDMIz+SOUrzc7uZOicpIwTPXMD2z3CgnB2xj8fOHARABOSk7+CayxvRsZGVFZWYvfuHR55d40eTsIxtwzDMAwTAUjnK92IHTumo6CgICTDyIw69MoN7JnyyzNvbXz8RlRXvy8Zv6um0pkRYM8twzAMw4Q58vlKL7bFUuaisPAhWK3WEFrJ+EKv3MBKPMLbt29GdHQ0brjhBvTr1y+sQ1lY3DIMwzBMmMOlboODe3ourTsLe/fuRXR0HIAnoWVu4KZWLY7FLcMwDMOEOU1BvOgtLH2hdzxzVVUVRo0aBau1C4ClAEZAqliDP7mBlXqEd+/eHRHefRa3DMMwDBPmRHqp21BPlJObjLVjRxcUFBSI7PBHhIvDSr6GkIZLXKwhOroaZWVlfsVN+y7f+xSAGNx///0RMQGRU4GBU4ExDMMw4Y09hdSOHV0k8pUGnkIqlOid99UXatJzLV682CPVVnp6Fu666zacccYZsqnZamtr0b9/fwjC2V5MwQqgDkIp3AMAxqOmpkbx5C73lHD79u3DyJEjAeSKUn4B0wF8BOBdAB2D1q7+oFivEUOHDh0iAHTo0KFQm8IwDMMwflFZWUkmk4lMpmEErCLgMAGryGQaRiaTiSorK0NtomosFgtlZGQTMIwAKwHk8rKSyTSMMjNzyGKx6GZDTU0NASBgtdv+7a9VBICKi4vJZDLZbF1NwBECZhCQYPu+8MrIyPY4F/Pnz7d9fkRmH4cJAM2fP1+RzZWVlbZ2c+63bdtUevDBBz2WAzkEVAa9Xf1BqV7jsASGYRiGiQDspW7T08XD2RkZGw3phVOCESbKKY1nnjPnZbdsFZ9CsNvukZUPZdAyrMQZQtEWQAfH8n37dmPWrDkYNepalJSU2Ja+DOB3AK7XRvhPQGRxyzAMwzARQn5+PrZs+SPsku7LYYSJckqF54EDewD0BPAFgAYADwJQlprNV0ys0iwJztjdCwB8D+ACiCelXY1Zs2Zhy5Yttm/cAkAqTCW8JyByEQeGYRiGiSCio6PDLum+HGJhebHEGvpPlLMLzx07pkvGMwMTYDI1A1EDgKm25akAdkMopiHnce6Duro6R07ZOXNmoaCgACbTCI8yuEKWhAqf8dJOT3cDnMLaWbABWAxgON56q9S2TF27hktpZ/bcMgzDMAxjSLT0aPqbRswuPIElMJlGQOwJ7QXgOxBd6bY83fZt5R5nLcJKnNv7G3KhHMCjOHRoP8zmNFXtGuqMFaoIUgywoeEJZQzDMAxjTAKdKCc1uUpqUpcSO9y3Ex0dR0CuxGS3FYomodXU1Hjsx2KxUE1NDc2fP59qampUTepyTn7zPTmtsLBQcbvaz4F4stzqoE9WVKrXOBUYOBUYwzAMwxiZqqoqjxRbmZk5mD17plePptZpxFyH5Xfv3o37778f4vRdjjUBnAbgfAD/B89QhjxkZv7od2o2ufAAq9WK9u3TsW/fbhm7YFveBzU1NThw4IDPdlWTCk3vEAVOBaYC9twyDMMwjLFR69HUO42Y7/RdE22f54o8o8J7UFFRkV/79eWJLisrIyBGxqNsJSBXdNy+2lVpKjQpL7TWcCowhmEYhmEiBvtEuRtuuMExCcsbeqQRc43d3b17t22pVBYFK4ByCNkKNsI1hhb4EUBPLFhQoTj2177f+++/H9de671S2nXXXYeiovsBLAEwHO4lfE2mj0QlfKOjo9G3b1+kpaVh586dqKurE9llhIwVauFsCQzDMAzDRBxKRdmOHTtQW1vrMwOAVGhEdHQcrNZ/QSiZ6yqgP4cwqasSwIVwVhpLA9AXwDeibAneEO83BsBQuGdBIFoEk2kECgsfQl5eHp599llcdNFFuPfeCdi7t49jW0LIgTgUQ+q4MjKyMWfOLOTn5xsiY4VqdPchhwEclsAwDMMwkYXS4XSzOc3nZDO5CVX2EAOgp1voQQ9Fk7q8VRyzWCxUXFxs205vAl5UHR5gDzkoLS2lkpISKi0tFYUeVFZWumy/mIClBKwUTRSzh3cIE89CUyXOjlK9xuKWWNwyDMMwTKThS5QJghQ+MwD4it0Fcm1ZE5wC2WxuH1CcqlRcLdDOL8EsF6NbXl5OKSlmci8PDGQTUC4SrUYp7cwxtwzDMIzhCST/KMN4w3t+2jwA66Ckgpiv2F1gMqzWepSUlDiqwm3fvtXv/LzO8rniuFrgItsaL8kcsRAesHv3bsf9VF5eLrmtHTu64LrrrsP+/XvhXh4Y6AJgJIh6O2KSw620M6cCA6cCYxiGCQW+Yv0YRgukrjOzuT327t0FJemydu7cidGjR0MQfwkS6x4B0Arz58/HDTfcINpvQUEBgFzJimNSotBX2i1hgtgXAPYDiHX7rBeiozfAaq13LBVigrvAMyb4FIBkAP0gVC1z388IABsAbMH8+fMxcuRI1NXVYceOHdi7dy/MZjPS09ODXqFMqV5jzy3DMAwTdOS8U66zvhlGC/Lz87Flyx+oqalxeFZLSmbaPvWdAUA8oUoK6QlV/ng7fXuJH4Vwr1wOqUppVusVouVW6yAA30MQynasAF4GcBTAYAjRCO77eQTAFgDA77//7qhMdtNNN+H+++/Hv/89BQcOHDBk6V2APbcA2HPLMAwTTIyUFJ5pmtTW1qJ/f/twvHfPbd++fZGd3Qk7dnSxhSyou17lCi5I8f777yvyEgtZEywuy2MBXAV5L+xGAL/bPn8QduEqkAbgBQAFHvtJSGiFY8eOaFYEI1DYc8swDMMYEj3yjzL+E45xz4Ha3LdvX8Uxsd5id4X3S0R5Y91Rk59XqZcY6AnB+zoPwEQIYQaPQtrb+wiAzQCegiBgz4PY69sDwHUAJnnsp7GRQHQ+gEIIKc2k45INRxAmtxkezpbAMAwTPHxXdvKdJonRBl/VroyIVjarzQAgtd/MzBxN28p3hoehBCQS0OCyXNn9JGRbkK9aJlQ1K7O970EmUzOJLAqVijM+6AFnS2AYhmEMib8xjIy2hGPcs5Y2q42JlYrd3bz5d02H5X1nePgIwoiH62Qypd7ePZD37k6GEOZwJ4COANaA6Ep4ZlEowJV4DO/jevwLwsiKkSqT2eGYW3DMLcMwTDCxx9z6G8PIBE44xj3rZbOamNhgbU8qw0NKSir2798Nz3hcK4BOEMTnInjG3OYB+Ni2nq9YXti+PwSu8bstcAzzcCtGodzxjb+QihzsxoqaGp9V1rRCsV4Lih/Z4HBYAsMwTHAxSlL4porS6l3BHHL2RTjYrGWYh7262Pz586mmpoaqq6u9HH8lASZb2IJrpbShbqEF3tvOfb0BqJZamQigEehJWRnZQalMZofDEhiGYRjDEm5J4SMN51Cy71RYRsHoNmsd5uE+Ea1fv35eJsGNAHABoqOr4Xo/CTlxZwA4CKAtgCclvttoWyfdZdl5mIdbsQKDPOzqB8CEHlhs+h7Pz5llGM++KyxuGYZhmJAQjBhGRppwjHs2ss1WqxUTJz5oS5m1CN4qnvmLr6wNJtP3eP/9d1FdXY3k5LYAekMo9vBvAEkAXoUQs5sHcSztCABLcBkSMAMJ6AYA2IhtyHTs+13chBY4BhNW4XMAZvPfhu6EcswtOOaWYRiG8Q+t4zWDRTjGPRvZZjV5cwONT5WKx83MzMHs2TORn5/vw5ZJAEpgz5EbB+BVtMCtOO5Y43UkYBz6w4zXkYDj2IzTbZ8I8btm8xps374ZzZo1C+g4/IHz3DIMwzCMjlRVVTkqN40ePRr9+/dHdnYnQ2YZcCfQ3K2hwMg2BzNkwnXEo7S0FCUlJZgx4wkkJyfDarVix44dXmx5FsA89IEJBOAkIBK2ALAE4wEswV7chc2wT2CzZ2tYghtvHIlVq1YZM7+tnaBEABscnlDGMAzDqME+IQ4YZpt8c4SA1WE3IS5YuVtdJ0YFOgEpGDarJRST3aTaISUllRITk2RtaYGjtAS9JSeIXYmlbhPUxNuOjo7TZKJcICjVaxyWAA5LYBiGYZSjNiWV0UMX9LRPagg9IyMbc+bMCiheU+82Vbv9QEMm1O7PPnnNvSyuMGFsKYDWAC4FsAid8SvysBgz8RCsiMJ3SMYFOCRsKD8f/5efjxE33wwgF0SPuGxLKLGbm5uLJUs+AhD6ErycCkwF7LllGIZhlKLGSxeOFcC0Ily92/6eM3/T26ndn72KmdCu0tXGYtGOXnK7MIfjaTKZhtHFAC174QWfNmRm5lBZWZnXfZlMwygzMydo6cCU6jUWt8TilmEYhlGO0vLBhYWFYSnutMCXAAu2KFJKoIJcbciEP/vz1rm6AN/SScRKhh1cDpDZ3MGraHYPHzFabmEOS1ABhyUwDMMwSlE6M75t21Ts23cRglEBzGihD/JtZAVQB+BzANNQXV2NgQMHhsJED7SqgKb0XPi7v/fffx+jR4+Ga7WxM/AbyjAS3bHeYz8j8AQWowOA2zF58mScd955iq8RqX2JESqbzZ8/HzfccIPXbWkBhyWogD23DMMwjFLsXklh+FnaK2k2dwiax8uIoQ/S3m3PSUrJye0M48EOhpfS1TtaUlLi1/6Ki4sJAHXAYhqKDwkg6oDtZEGU48tLAUrChQTkEGAhzwpkyq6RcPXcciowhmEYhlGBkpRUN9440ra2vqmhtK6KpRWeBReqABQAENt54MBFIbXTFb3Tebmnjrv//vsBxADYpHh/1vp6tHtuFgjADuShDCORhIP4G+m4G3NxIb6CCcMwBDk4hOcBbIbgJX8KQCKAlVBzjfTt2xcpKamQq2xmMs1AZmYO+vbt60eL6EhQpLbBYc8twzAMoxZv8ZXB8gIGK65VbTovsXe7weaxNXb8rVbnTKqt5GJrgaEEmGxebS/7++EHotatPYzajHg6C/PJPnlN2L59e4dtx9PdtqxcddtXVlbatmEiIJdcJ8rZbQ+m550nlKmAxS3DMAzjD3KiT0noQqCCLlhDxoFmDwB6B2yn1rlypbZbXV1N6elZAZ0zqbZKT8+iVq1a28ShdHYDZ/iAeH+57TpQo8kkOUFsFAoIiBHtS9iOXSjbQxGiCJjhte2rq6s92lfceSon95ASIJFatWpDpaWlmp4Tb7C4VQGLW4ZhGEZr/E0NpRSlWRvmz58f8DEEkj0gObltQHYqFddqBbDUdhMSWtv+F3splRyvd+8sfApM4CUCDpMZS+gs9CeTyUTf3XuvaOV/unalFEdHwUJAlq3zUEpAjYtAFkRzq1bJito+ObmdR/vaY3udnRKLbR/zbX/rfJ4TrWFxqwIWtwzDMIwe6FlNS2/PrVZhD9XV1X7b6Utcl5eXU01NDRUWFpLZnKZYbPkSolFRYo+or3PmO/fsUBI8nxaJ4z9MJoD+47Lw87jmwv6OHiWaMIFo8WK3/Qy1bbeShJABcQcKGEomk0lCoLq/pts+92xf5/F7F8bAPFLT4QkEFrcqYHHLMAzD6IWeQ+p6hj5oGYPqj51KihWIS8LmSgpgKQ+vr+0CiQQIuYqVnDOlbSV4PJ3Lz8LP9DdSPL7QeNFFRCdOSO7LKVjt3uVSAuzZOeyvGCouLvbR9g0EJJB8uISycBLnMekfP83iVgUsbhmGYZhwRM/QBy3DHvyxU7lgbCsrVKXElvLt9lYs1JS2lSBEiS7FF1IrCa833lC4ryw3QduegEICPhKdF7m29y1e60iI6ZUTv8PIM1ZY39RgnAqMYRiGiVisVitqa2vx/vvvo7a2FlarNdQmaYLa48rPz0dFRQXS0zcA6AOgFYA+yMjYiIqKCuTn5/tti2c6L3c2uq2nnZ1WqxUrVqywvfsHQvEHd+wpu/YBmAxxEQQAiALRI9i2bTPq6uocS5Wm/AKu8viuHEraKhlAS7wAYDUKMVP06YEzzgB27hT04W23KdzXewBqAMy3/d0OoARAG9F6cm2fnPyH2/G60w2ABcBHHinvgBEAlgCYCcC1EIQ2Ke4CRhdpHWaw55ZhGCZ8MGLRAi0I5Lj0CH3QI+xBiZ1S7SDEq7qny3ItTKDcu+zdc2shYWKXfQKYMs+0fFs10r/xlGMHE9CGAFB/gN4FaGJ8gurr1hlWkUvACnJO8LJ4PS/uba80Frq4uFjifKRLnA/jeG5Z3BKLW4ZhmHAh0Nn7RsWox6V3xge5/XlO9HLN3+o6LN5ekUBzFVtlZWW2WF37pCz7up4V1Oyxq2rbKgfl9AeyPQx6HyNtgrQHAVH0zjvv+NVORUVF5JkGrAMBPRWfFzWdF7swLi0tpbZtU0kuVIFjbg0Ei1uGYZjg429hAKMXA1CL0Y9Lz4wPriib6JVNQiyovfhBGakpEOEUzz1t3x9KzklZ9kIF4uwJikV8YyPVPPWUlMImAuguzCbgSxeh7p+H03kMnrYCoKKiIsXbKisrs53THiR4rP/x2XkJdofHFRa3KmBxyzAME1z8GYIPZp17vTIcSBHM4/IXpe0RSLspn+gFm+e1J3lLh+UutjzFcyUBqWT30PrtjTx6VPh7/LhHFbEf0IwykE5iD2sOAT396rBo2RGSDv+IUdR5CVaHxx0WtypgccswDKM/duFTWFjoIkaUD8EHo2gBUfBjeoN1XHoTaLspbYcpU6ZQWVmZm/fQMx2Wu9iSFs8WAsaq71w0NhLNni2s0KIF0W+/CcsffpjogguIZs+myooKFw/rSwS8Yfub67eHU6uOkHz4Ry4BQv5gXwSzA2iHxa0KWNwyDMPoi1j4+OclC4aHM5ixr3ZxMGXKFNtxrdTtuPRGi3ZTe36lxLTZ3F42N628eFbeubBs3kxHcnI8V3r6adl20dLDqUVHyOhhMN5gcasCFrcMwzD6IRY+L/stUPUuWhDMh770kHACAeVhJTaItGs3f86vGu+hvHj2Jaq/pCHSHxAB9N099wieXC/HpZWHU4sOXjiEwcjB4lYFLG4ZhmH0wVP4BOZ50nMyS7Ae+t5Lv5pIKIkavEk6gaJlu+l5fj3Fs8UmbEsJSCPP0YRGAqzUB5d4HNQfyKEcVAT9/GjRwQvnMBgu4sAwDMOEnLq6OmzfvgXOJPuBFQaQS0jfps1XmDZtGvLy8vy2VWly/0AS1FutVkyc+CCIcgEsAnAxgATb3/8DMATA49CyGIPeaNluehWlsFqtqKurQ0HBCBB9COAiAKcB6A/gJgA7IRQlyMOdeBj70Qbf4hwAw/EVvsRP0UKhgkfwFExoRCf8ic24FkSLAOSisPChoBQSiY6Oxpw5swAs8SisILxfgtmzZyI6Olp2G1oW5zAsQRLbhoY9twzDMPrg6SWykJrUTXJYLBYqLi6m5OR2tu0HPvErGJ5bpfuYMmVK0CbpBIoe7ablUL58VoALHJ7zVCyhb9DCw/g+iKbExCSKNtgwfiCxvHqH9+hJWIQlfP7555Sbm0tpaWkEgBYuXCi77l133UUAqKSkRLT85MmTNH78eEpJSaEWLVrQsGHDaNu2barsYHHLMAyjD9LCR1nqJm/oMfErGA/9YA0JByoO1XzfyGLJV1aAAjwoG0t7Hf5NQk7dHkE5Z2oJ5ByHMldtIISFuF26dCk9+uijVFlZ6VXcLly4kLp160YdOnTwELd33303paen0/Lly2nNmjXUv39/6tatm6qTzOKWYRhGH+SFj2c1KLWeJz0mfun90A9WxodAUnL5830jiiVf18kDOMfjBGxHWzoLICEW1754he7nLBSEKldtIISFuHVFTtxu376d0tPTaePGjZSVlSUStwcPHqTY2FhasGCBY9mOHTsoKiqKPvnkE8X7ZnHLMAyjH+KKSq75PoWKSnKpm+TQWyDq+dB3Ci7pVGjC8hgqKyvz2/ZAPNqBfF/LdtMiLMH9OrkR79IK9KdpeJwAorMwn+ptjT8Nj5MJVocXVpj4aD8vFhJy6LqX63WeM6MO4/siFLlqAyEixK3VaqX+/fvT7NmziYg8xO2KFUJv6sCBA6Lvde3alR5//HHZfZ08eZIOHTrkeG3bto3FLcMwjI4UFRXZqko5hU90dJyqUqF2gjG0r+dDv7y83GZ/rsjL6SzLGprqVVp4xLWoZKZVEY358+dTCkC1btkO9qMNNcdxAg5TkoeQtVdBq3E7/lKZc5ZLgLqSt4z/RIS4nT59Ol1xxRXUaMsf5y5u33vvPWrWrJnHtq644gq66667ZPc1depU0U1jf7G4ZRimqREMz43Ycxt4fGw45+kkcrW/g9tzKIeEcI068mdSWaDtEsxUaHLiVbNY6g8+kI2lHYRPbV5adyFr95znkOCt9Tx2IWWY+znzrzPCqCfsxe13331HqamptGPHDscypeJ20KBBNG7cONl9seeWYRgmOGVmLRYLpaSkkj8Vybxt06gTmJTg9DwftAmr+ba/Fpu4zfLrnATq0Q6GR9ybeAVgu1YCPK+33OJh/AG0pi5YL9qecE2mE/CPTbza8wx7FtIQPksnoF7inBm7MxVJhH2e27q6OuzZswennXYaYmJiEBMTg61bt+LBBx9EdnY2AKB9+/ZoaGjAP//8I/runj17kJqaKrvtuLg4tGrVSvRiGIZpSlRVVaGgoADbt3eBa67MHTu6oKCgAFVVVZrs56mnnsL+/bsBPAohz60rUSB6BNu2bUZdXZ3ibWqR6zOUOPOH/gygH4AbbH8XAygA0BX+nJNA85fqnf/UmeN3KNxz/Ar5Yi+2XSv2nMiueLlWKiuBceOAL74Q3nfr5vjot+HDEQsgxdQXG3AM9jYF8iDktd0BoA2APkhJ+R4AwWR6B67tL6z7EYBnADSD+JxFQ4v8x3pjtVpRW1uL999/H7W1tUHJyRtSgiS2fQI3z+2+fftow4YNoleHDh3o4Ycfpl9++YWInBPKPvjgA8f3/v77b55QxjAM44VglZm1WCyUnNxWN29gOM72JpLzPKvP/+seUnL8+HFq2zaVhNRVKwg4TkAJAeMJmEXAUEUxt0o94mpDWnyHPUxTfq388w/R1VeLV7juOmFHhw4R7dzp2K/cdVJeXu5hv9S6ZnMHH3Yb23MbjBGaYBEWYQlHjhyhtWvX0tq1awkAPf/887R27VraunWr5PruYQlEQiqwjIwMqq6upjVr1tCAAQM4FRjDMIwXghVb6dyPfvsKt9nedjxTZ32kqp2kBIv7hD0g1u19DOXl5am0Szqllz+CqbTUPilLTrz6boPh0h8Ir2XLZPetNm+v67r19fVhGwajRz7oUBIW4lb8w+d8jRkzRnJ9KXF74sQJGj9+PCUnJ1N8fDzl5ubSX3/9pcoOFrcMwzQlglVIwLmfLJLzSAJDKTk51ZDCQE/kKqwpOSeegqWUhFjRXJn3roULfM/s9+UR91cwlZSU+BCvdSRUDvOMz26G4/QZUjy/1KwZ0TffaH5+pNrEaHl8fRGsEZpgEhbi1iiwuGUYpikRfM/tDJKqSGbPc9uqVWtDigO9kBKPiYmtFJ2T6upqN8HiHs7gPbwByKXo6OZUX1/v1UY5T2cggknw3MZ4sW0YAVEECCLyUrxC0/EAxeMzMpmGUbXrF8aPJ2po0OX8yBFuYTDhnlVECha3KmBxyzBMUyJY2QbE+yknIJXEXspsAmYY2vulNd7LwUp7LV3PSXV1tZtgcRcwygSN+yio0mH7QAST87tSHR0hx29LgH46+2zRRm+xicjlJSVE27Zpf1JUEE5hMMEaoQkmLG5VwOKWYZimRrCGWcU5btMI6E3CsHkNOXOJygvqcBITvvDl9QR6EgCv58RTsPh6Ly1oxo8f77BLTfxsIILJefw9yb308pVIlY2lXfPCC2F93kMFe26bOCxuGYZpimg1zOpLgFZWVtpm8at70AZ7lrfeQlqp2EhMbCM65pSUVCouLnbYp6XnVm38bKCCyb0UcydMp41oLy1sV67UtP2bGuGeD1oKFrcqYHHLMExTJVBBp1SA+p4pL/b4BXuWdzCEtFKvp/1lMsV42FNeXu4mWPyPufUnflYLwbRszhz6VxshRVxrgI67bOSPfv3o82XLDCO4wn3kIBwnwnmDxa0KWNwyDMOoR40AVePxC/Ys72AJaaVtACwlZxyuiYQwDqc9RUVFboLFNTuC1Ht7XKs4W4JSe6qrqyXbS5VgOnaMaPRox8Ybo6Ppy4oKmj9/Pn0wahRd2j5dl46FWnHqun5xcXFE5IcNt4lw3mBxqwIWtwzDMOpQK0DVePyCGSsYTCHtqw0EG3LINRZZvMxpT1lZmeo8t9HRzUVpwJR6kpOT23oIIcWCqaZGOuSgUyein37StWOh1hsvtb57OrVw8XhKFfgoKSmh8ePHU0lJic9sGUaFxa0KWNwyDMOowx8BqtTjF8xZ3sGedCPXBk4vbaXk/oVYWk8Pt3uxATWCRrknubekoPPqFV25UlrUAkQrVji+r1fHQq1oFq+/krzlZjZ6rKqSAh/h6IEmYnGrCha3DMMw6vBXgCrx+AVTcIYiXZK0hzBbQti6xuHO19wei8VCKSmpJOQb9uZJblAm6LZsIfruO+H/igrxgVx/PdHRo6LV9TrP/o4qONf3365Qx+j6LvARXh5od1jcqoDFLcMwjDoCESa+BEAwZ3mHKl2SvQ2mTJli2/9Kr/uX8twGSmVlpW3bUvG5Q0nsSZbZ78mTRLff7jT4oouE5Q0NRFOneq0eplfHQu059VzfPvlxHolT1qnvuAXTQ+op0r1PLjS6B1oKFrcqYHHLMAyjDt/xo7lkNnfwO7YvWLO85Y/DQsAKAnqQ2dxetxhFdXG42hfYELZfTu55Z4FEAtq5CDs3Qffll9IhB/36ER05osgGvToWakWzeP1KEvIxe/Oqy4fcBCu7hxTOAh/FNlHuXvAjOB03PWFxqwIWtwzDMOqRjx8dqonnKlizvD2Po5SADkHzwJWXl5N8dgN7tgRthb2nsLTYBNF82986kvIYr3vmGWlRCxAtWaJo33avdWlpqS3/sfeqbBaLRdVwv/+eW9cy0a7V44aR04utJKwh+B7SyspKSk5u5ybKW6sS+eEAi1sVsLhlGKapoVVsoHT8aDq5p6/yV5AFK4bR8ziCE6Mo3q84r637JCAthb3ynLvzKQ3bKAVXUWZmDlnHjhWvOGIE0eHDfh4vXI67J8l56L0N90tdH2rDWiwWC6WnZxGQICtQheXpBOQGlOZOD+RLOts7mTNCYpcesLhVAYtbhmGaElrHBtbX19s8cD1IGMp3jVEMn9i++vp6MpvTSIknUQs8RclBAl62tSNowYIFmgh7KQHoS5DF4Aua7bJgKiBcH999RzRhAtHixRocr12E5bqJXaeQ9zbcD5goJcUseR2rDWspLi5WJFDN5vYe3w3FpEQ7vks6DyUhxKRB9+s5GLC4VQGLW4Zhmgp6xAY6Y/2MGdun1Psbifl15Toy9jy57t7NHviOjqO5x8H/dckluh+v2dyBSktLPTywvoXbSsnrWE1Yi1KBWlpa6vHdUHpuladzu1iRyDc6LG5VwOKWYZimgBKBkZGRQ9XV1Yq9hUKsX1tdPFdahCSo8VJr4YEzkpD21ZGxVzqLQi5dieeljCACyFJe7rcNgRyvcuFW43Edq43VDTT7R7Cye7ijPLzEHn+rX+x6MGBxqwIWtwzDNAWUiwVl4QpO8dRbc6GmReiEeKKWby91oIIz2ELaG0o6Ml07nEbl5eWUlZ5F290M2NWtG9GBA7Lblopz9SYiPY/XfQLbPx7HqyYuONDrLVCBGqzsHu4ov6c/JAA0fvx4XWPX9YbFrQpY3DIMEwxCneBduVgo9ikExeKpgbTMp6lF6ERZWZltQpby+NlABI5am/X23MptPwoWmoGHiQCyAvTd3LlksVjoj7vvpu3nn08bH3/c63mSEvApKWZbMQixqC8vL3dc7yUlJS72VJJn6rEOHsfrn+fW/45BoAI1WNk9XHHeh9LXuTOVXF1A15NRYHGrAha3DMPoTagTvBNpM8wrv61KcqZREqezchUGSgs4BCKUncUJ1IlHi8Vim1jkmZbLm8Dxx2a9h7LdOzLnYgPtQ7JHQ6wbOVLxNqUF/AyS8o5LTRITOhsdSTrdlrB+uUsIhJJcys4cwNp0DAIVqKHowDrPy1C3e8+ewqw8LCePScHiVgUsbhmG0RMjJHgn0lYsSHuBPT1yKSmpjuNTIvAD9Wg6hWYPCfvkvXuetonTcnkTOP7aLJ8nWBB6hYWFfgukmpoaMgFUgCelDCICKF+FAJQW8N4rYAnHkU5CFgh7aqoYkvcy5noIMO+5lAXhpnXHINQjLP5QWVnp4T0Xzs30sJ08JgWLWxWwuGUYRi+MkODdFd9ioZI8dZDnMK+8oLPHUk4jAFRdXS3ary+BH2gsqtOuOxULTmnbVpI9lri4uNjr+VEz095dNEkJfvf8tqo9/A0NZLFY6Mp2aR7GfIFLqS12qb7upM+32pGAFYrPiSvSoRDtCUDQY1yNjH3kwTnBM7wnj0nB4lYFLG4ZhtGLUKYJkkM+kb7yZO9qhtXVCPxA28spNEFCUn7vXsL6+npFttXX18t685TaLOQC9hSt9uMuLCwkqSF7RYLNaiWaNk3YYU4O0alTVFlZSSsA2ox4ugVTAhKA0gLevuwgiSeHiUv2Oid8+d9xkfKmhiLGNRwIR8+zUljcqoDFLcMwehHKBO/ecH0AVldXU3p6lur4T6UTcNQI1kBjUYWcuzE2gbjAtt8eBLxEwox8p5e6srIyYGFqb0vf4R724Xhp0eq3h//XX4kyMjwNX75cOEcVFZoIQO+eW3GpYmE4vJI8Pbfad/QiWcgxnrC4VQGLW4ZpGoTiQWhEz60U/s4UV+I9UyvwA5m17iwoMYM8Z+THOP5OnTpVlW2CQJb3pnqzWfh+T6+iVVUhjMZGotdek1pJeH30kahNtLjupQV8uc1m98lk9olMPUkcw22xCeGhXtuCBSojB4tbFbC4ZZjIJ1TZCkKZ4F0t/g7z+hJP/gh8f21xilW5GfkmkZBWnkFihc9zJ2Wz2dxB0fanTJmiSGTPnz+faM4czxUuuIBoxw4/zrpyxAK+joAs8hb2IXQmytyW9ySOlWX8hcWtCljcMnrBQ2bGINTZCkKV4N0f9Lhm1Qp8uw2lpaVUUlIiKsnqC3FYgrzosk908x1SMJSEGf/uGSSkRbl7+5WWlioSrU5x6y6CG6kIz9BPyKKX7Pvavp0oKUlYYe7cgM+PGjwFvK9OwUse13tRURHHyjJ+weJWBSxuGT0wQl5TxjjZCpr65BelAj/Q+0bp8L5d3HqzzZmrtdSrMJ0/f75sp0CpZ7i6uloksrPxJ/2GTqKVj5tMZNm/XzD6xAltT5AKLBaLYk+z3PXOHX/GH1jcqoDFLaM1ofYUMk6MFPPa1B/ovgS+FveNvxP4AgkpKC4ulhXkarzWlRUVNF56R0QAfTZjhhBvawCU3lclJSVBvd6b+j0W6bC4VQGLW0ZLjOIpZASMmq2gqSInPrS6bwLpzLjbZk8T5k2YpqS08ynIFYel3Hyzh8EbAeqVlmm4DnEwYsnVClUeLYt8WNyqgMUtoyVG8hQyfD7CBa3Ok9aiy1cWBKEqlAKvrITweqx1Mm2/+GKi//s/YWdvvOHYwO/33kvz33vP0N5HPWPJ1QpVPUfLmpo32MjHy+JWBSxuGS1hT6GxCKdsBU0ZLe8brUWXXDhFcXGxKkFusVhoVVkZ/ZOZKV7xiiuEHR0/TnTggNqmU4XWwkWPWHK1QlXP0bKm5g02+vGyuFUBi1tGS9hTaDzCKVtBU0Xr+0Zr0SUlClUJ8ldflY2lpZUrZfehJXoJl0CyW0htS61QVXrtTJkyRZVdTW3uRDgcL4tbFbC4ZbSEPYXGpKlnKwglSkSbHveN3mJRiaiKBujQ2Wd7fNiYnU30+++ObentMdNbuGhlvz+dHOWFOJTb1dTmToTL8bK4VQGLW0Zr2FNoTIwcSxapqBE9Wt43euXrVTLhbCQW0CsYR8m4ijIzc2jbRRc5lMJkiTbQW3jqLVy0tN+f8BSlghhYqtiupjYCFy7Hy+JWBSxuGT1gTyHT1PFH9Ghx3+jhBZXbZlFREZlMJkrFlfQVzhEpgokAFRUVUQ5AybhKsg3Kyso0FZ5Sol5P4aK1cPbHVt+FOIaRuAywb7ua2tyJcDleFrcqYHHL6AV7CpmmSiCiJ5D7Rg8vqLdtjpFWAkQArZg+3WcbmM3tNROecgK8sLBQN+GitXD2NzzFeyEOEwGVquwygiczmM8PIxyvEnQTt2+99RYtWbLE8b6oqIiSkpKod+/etGXLFvWWGgAWtwzDMNoSioelHsPvctvsiN/pd3T0OLDjSUn09ZtvqvKYaiE8vQlwQdzpcy708Pj5G54iJe6BbAlh69uuUM+dCHbWglAfr1J0E7dnnnkmrVixgoiIVq1aRfHx8fTaa6/RsGHD6JprrvHP2hDD4pZhGEZbQjHMqYegdt3mJaijCZhNAFFzHKcDaO3Y+OabbyZye/Crm+jkv81KRH10dBwJXkxthYtenRh/w1PsnQpneeCVftulRmRr6WUNVdaCcJgropu4jY+Pp61btxIR0aRJk+jmm28mIqKNGzdS27Zt/TA19LC4ZRiG0ZZQeG71ENQVr71GK9w2dAZ+JYBoIJZTO/whu02lbWA2pwXkMVPjIdZauOjp8QtEMGpllxKRraWXNdRZC4w+V0Q3cWs2m2nNmjVERNS9e3d6++23iYjojz/+oJYtW/phauhhccswTCiJxNjsUAxzaiqoP/hAagO0EefQWfhZ0TaVtkF5eXlAHjOloj43N1cX4WJUj59Wdnm7P7X2shoh9tXIv0e6idvRo0dTjx496Pbbb6cWLVrQvn37iIho8eLFdO655/pnbYhhccswTKgwekWgQAi26FEqJuvr6+Uf3p9/LilqDyKGumKNapGutA0C8Zip8dyWl5frIlyM6vHT0y49vKzhkrUgVOgmbv/55x+67777aPjw4fTxxx87lj/++OP05JNPqrfUALC4ZRhjYyRPQiTE1gWTYIseX2KyqKjIw56BqR3o82nThA2sWCFSEzMBinasO9Qvka60DSwWCy1btoxuuukmGjFiBM2cOZPq6+tlt+taGcxbaIMzFVaurkPaRrpPXdHLLr1jvEPluTUynApMBSxuGca4GMmzaaTYOqMKCSmCbaucmLTnpAWGUSsspyW40tHovwBUWVFBZLXSultvpY6AW6djBgEJfot0JW1QVFRkm/jl3Ed0dBwVFRUpOkYpAS4cgz0VVtMWRlqjh5c1XLIWhApdxe0XX3xBN954I/Xu3Zu2b99ORETvvPMO1dXV+bO5kMPilmGMiZE8m1raYrFYqKSkxPZgfJmcyeWVeWhCkSbIiEJazi6LxULV1dU0ZcoUmjJlClVXVzuqieXiQsmwgw/Rjs5LP82xnnSno4GA3pScbKbq6mrN2sFisdCoUaNs5zJXdH0J70GjRo1yHGdZWZnktQhcQECMm9jNIWcqrKY9pK0WX9e9npkijBjDbAR0E7cVFRUUHx9Pd9xxB8XFxdGmTZuIiOjll1+mwYMH+2dtiGFxyzDGEzChnjWsly3Kc3FKC5FgC34jec6V2CUVdpCRkU3v33CDpKhtQAxdiK8dQsTZ6VAnWPy9fyorKyk9PcsmSqVSdZWTu8dY8O72lLwWgSEEtCWglIAaEnec2HOrFCXXvZ5eVqPGMIca3cSta4aEhIQEh7hdu3Ytpaam+mFq6GFxyzR1jChgjBR7ppUtcsJUPHQsv81gC34jec6V2CUIPqf3sxPWUjoWk8k0jGa6nbRXcDfFot6jMzF+/HhSO9Ts7/3jPI7eMtdXpe26cD/OoRLXi/i6AVbofn1EKmquez29rFo4HIzmtAgUXfPcbt68mYjE4nbTpk0UFxen3lIDwOKWacoYVcAYadawFrb4EqbOST8WWSESTMFvJM+5MrssBGRTS1xNZbjWYezzKCTASmegOxUDdDFe99p2aj23/t4/4uMolbi+hONRdr14XotAD83FVlPAn+veqF5WIzotAkU3cXv66afT8uXLiUgsbt9++206++yz/TA19LC4ZZoSrj356upq25CosQQMUeR5bp3bWEnCcPF8Eg8b2z1uL8kKkWAK/mC1v1rPkpxdg/CclJH0Dm6y/VtH8kP/4jRhSoeaA+kAiI9D6piUtb+wnufytm1TDSe2wgF/r3ujeUiN6rQIFN3E7TPPPEPnnHMOffXVV5SYmEh1dXW2NCRmevHFF/02OJSwuGWaCtLxnjEkzAQPrYB0x0izhuvr68lsTvMpjLzZ4hSmWW7tb4+3dZZilRMiwRT8SoX0lClT/D4H/niWXO0ywUrXolxS1BJAffG5h72AspyzSoaaAzkf4vaV8tIqLd07X/Ja9JrLl5HFSCNG/mLUURct0DVbwuTJkyk+Pt5285uoefPmNGXKFL8MNQIsbplIxN2TYK+CpC5+L7Q/5EaYNewpwPzLdVpcXGz7vly87XSyD4t7S/8VLMGvVLj5O9Tpr2eppqaGsgAy4UsCGukndBYZ9hYGU3Mcl7W3uLjYNlrhXVArGWr2RwjZ78spU6aQ05NPJI6vXUXARwrb/6WQ3BeRipFGjPwlEo5BDt3z3B47doy+/fZb+vrrr+nIkSP+bsYQsLhlIg2pB7Mww1ra8ygsl4rfC/2PYCjj2TwFWCkBHVTbYrFYKCUl1Uv7DyMgkTIyshXVug+G4PclpIVjySZgpV/lTFV7lo4fJ7r5ZseKg3AxAVa6FW/QPIyl/vjUZo9373p5eblib7HWqaCkR04SSMiIYBe4rp/HkNCZkmr/oRQVJc6Jy6EHgWOkESN/iQTvsxxcxEEFLG6ZSELaI/ayooewOH7POD/kwY5ns+dJTU5uS8JM9gaXdrGQMBO9B5nN7b1WkLLj9Np6b//i4mJFthUXF1NycjvdhY2ckPbM7qDuWlElCmXK4U4BJOxyzZYgXZ1MyzhENUJIPsODfeRkus3eOgI62Y7jURJ7c13bHzRx4kQOPdABI4wYBQJ7bhWK22uuuUbxKxxhcctECvIeMaXxe9PC7odca5TnoVX2gLBYLDaRHLgnRcq2xMTWNG3aNN2EjXR75PjdHkS+PUvROEC3SDeU8FqxwmsVMqnlZWVlusQhKhFCvjNlDCXX4gtmc3sXceLuzQUB6WErTsIFo2ZAUEIkeJ/lUKrXoqCApKQkxS+GYUJHXV0dtm/fAmAyANfbO832d6PMN+3LpwFoBaAPMjI2oqKiAvn5+XqYakiqqqpQUFCA7du7AFgN4IjtbxcABQCqXNY+DwCwc+dOr9usq6vDgQP7bO+8t39aWprM5662nSey7ciRSzBt2jQ88sgj3g/OT/Lz87Flyx+YMmWKbclSAL8DcL8ulLUH4Hqc4vZoiaMAgGxU4233L40cCRw9KjyfBwxw2FVTU4P58+ejpqYGmzf/jmeffVZyudlslrk3ACAKRI9g27bNePHFF/H++++jtrYWVqvV63FYrVYkJydj4sSJSEn5BkAfSN0/8vclbO8fBWDBlClTUFNTg+3btyIjIxsm03QAIwD8AaAGwHwAKwCcj8zMHPTt29erfYz/yF1f4fB7GB0djTlzZgFYApNpBFx/L4T3SzB79kxER0eH0EqdCZLYNjTsuWUiBXmPmPecmSbTMMrIyKHq6uomO8SpLg+tck+lOEuCvOcuJSXV50Qy7zHTMVRWVqZDywhoOdTp6lmKxQmai7uIANqHZIrHETKZhtGiFi2p8eKLiZYu1cR+7/dGDQH3EhAl8tR5mygn5dkzm9OosLDQ4/7xJwYy3IfGmdATzt5nOTjmVgUsbplIwbsAsc/G9m+2f6SjPDtAjaqhPed2Z5B0/GQuAd7jbZXaZja317xTYo93Li0tteVO9T8dmiufzZhBFomDGYILCTBReXm5psch3YZSQ/4dSJg4KB+LqzbTg78dg0gUJ0xwMVr+3UDRVdyWl5fTddddR7169aLzzz9f9ApHWNwykYLvGe49bVkTAntYRtoPJpFy7xowTVWHQHxOyiXEVCKlpLRTmCPXl23axmHK50Xu6V8HyWIhmjlTNpa2L6LJ7j3VupKSxWKx5Sq2Zx+QK23rOlnOU7T7k+khkBjISLzXGMZfdBO3c+bMoYSEBLrvvvuoWbNmNG7cOBo0aBAlJSXR5MmT/TY4lLC4ZSIJX8OZZWVlAT0sI7GkI5G6vK5qOwTic1JHQg7TaSRkYoDPbamxbfz48ZqIIPnZ/bluYldBe1itwt/XXvMw/v9wCbXCdpdtmwiYocuIQmFhoc3mXBI8tEpCUMRe1UC8sBxmwDCBoZu4PeussxxxQa7ldx977DG67777/DA19LC4ZSINvYYzI7WkI5GyvK4pKalUXV3tl3AM5Jx4eh09PfKA2CMfSIdDiXfSbO5ApaWl8kL61CmiwkKiVq2IrruOiIj+7+236e+oaDoJ0Ahc4ENYNmg+q9spTFMUdhZqyD0eNpAcokqvAfbWMow0uonb+Ph42rJlCxERmc1mWrduHRER/fbbb5ScnOyHqaGHxS0TiWj9gIzkko52ioqKbKJDHJcM5Goi3gM5J+Xl5S5eR7ncrtp0OAKaPLZ2LVFCgvgLMTH04ZtvkslkoihcrEJYapuP03kNn69IoAop9LTx3Lra4O0aiNSREYbRAt3EbU5ODn3//fdERNSzZ0+aO3cuEREtW7aM2rRp44epoYfFbXBhr0R4EsmJwYlcvdI9yb0KGRBDo0aNCvk1K4jvGDfb5CvP+dvhUO2dtFqJpk2TjaW1fP+9S8eoVIWw1L6SUmVlpUvb+RLYKzzasKyszBa3LudF99/DH8kjIwyjBbqJ29tvv52mTZtGRESvvvoqxcfH06BBg6h169Z02223+WdtiGFxGzzYKxG+aF3SUa6TE4rOj6dX2p4eaj4J1ciGiERlKK/ZsrIyW9YCV4GrvsPhrZ1Vd2TGjPFcacAAon37JLanbNt6eG5d29C7QB1GQqEEscde3AGSynwx1O/ftqYwMsIwgaKbuLVarXTq1CnH+w8++ID+9a9/0Zw5cxSVoTQiLG6DA3slwhstPbdynRyp6lLBEJLKJ2wtNcQ1axem48eP96vD4auT6S3+2IRTNBAX04fxLck6b56wwYoK5wrvvedhr7hj5D3nsp4xt64IYR4m8gzzcApU13hYT/EplUYskYSUb+p/2yJ9ZIRhtIDz3KqAxa3+sFcitGjhDdWqpKPvWfg9PTo/gJAHVi9vrvJUW/MDuma19kr7I4iUdjLdZ/e3xZ/0EO6j39DSuYMLLxQ2euoU0dGjKux0TcPlnvPXRMD0oHQgpAsxdJAsxOA8hpdt10ENAfUELCOgFQFnELCcnEU+1F0nWo+MBAMOMWOCjebidv/+/bRt2zbRso0bN9LYsWPpuuuuo/ckeuvhAotb/WGvROjQMhQk0HRGvquA5ZK4ChiRkBs2QRP75VDuua3x+5rVIyRHbYdDSaUz1/UrKyupwNye5gN00mXlhvh4onvvJbJNKPbPTinPp2voR3CKFSgVaEIaMfd4ZzMByW7Lsm3Hpu46UfMbaQRRySFmTCjQXNxef/31dP/99zve7969m9q0aUPnnnsuDR8+nGJjY+mdd97x3+IQwuJWf8LRKxEJ6BEKEkhKK/UiUjrRvtZePd9pwNxL76q7ZvUMyVHT4fCnk9l41VWOFQ517kzW11/36qX13QauYQB1ZM/1K+T9rbb9BVVXV/vcZrBEnjAJzW67/fzNkFjmXgRC+XWitKNSXl4eclHJIWZMqNBc3GZnZ4t+8J577jnq2LGjI/72ueeeo169eqky8vPPP6fc3FxKS0sjALRw4ULHZw0NDTRp0iQ677zzqEWLFpSWlkY333wz7dixQ7SNkydP0vjx4yklJYVatGhBw4YN8/Aw+4LFrf6w5zb46BkK4q+oUDf87z02U+tQFjmR6BwqryR/rtlghOQo7XCUlnrLVNBIffExvQtQ1Zw5zi8tWUI0bhzRmjV+2+dqZ3JyO5GdQqfBtW2VicFgeQ6lz5/SuGHPIhC+jslbR6WoqCjkopJDzJhQorm4bd68uSO/LRHR4MGD6aGHHnK8//XXX1XnuV26dCk9+uijjtQsruL24MGDNGjQIPrggw/ol19+odWrV1OvXr3oggsuEG3j7rvvpvT0dFq+fDmtWbOG+vfvT926dVN1Y7G41R+t4jUZ5RixQ6HOc6uP/d6EuXS52UQSQiP8u2aDdR6UdDhKSko8bGmD/VSI5+kndHYYtfqqqwKyxRvV1dU2G4pt59iiuj2C6TmUPn9Kr2NxKjEl50iuo1JWVmYIUWnE3xXGO0YIY9EKzcVtu3btHAUbiIhSUlKooqLC8f63336jli1b+mGqzRA3cSvFN998QwBo69atRCQI4NjYWFqwYIFjnR07dlBUVBR98sknivfN4jY4cPnJ4GLEUBAlVcCcHi/t7Vfi7XN9EBQXFxOAgK5ZI50HwXMbQ0AuXYLP6R3cRCcQ5zDmCKLpdZjoo6ee0s2GQDu6wfYcSp8/pSMQPRzXiRpPs5QYMYqoVHo9l5aW6moHo4xIi43WXNzm5ubSbbfdRlarlcrLy6lZs2Z04MABx+dLliyhzp07+22wEnG7fPlyMplMjoNasWIFARDZQUTUtWtXevzxx2W3c/LkSTp06JDjtW3bNha3QUKvsrCMJ0Z5GLrjffjfni1hFQEfaWq/v96+QK9ZI50Huy0JNiFrN2INzqBx6EKJtuPT25ZAOrrBbs9APLdmc3uHsA3U02yUTpLS9m/bNpV/10NMJMZGay5u165dSykpKdSsWTOKioqiKVOmiD6/6aabaNy4cf5ZS77F7YkTJ+iCCy6gG2+80bHsvffeo2bNmnmse8UVV9Bdd90lu62pU6eKHlT2F4tbMXoNZUTSEImRMXIoiJxg9MxzG0NyifbV2B+oty+Qazbk56GxkejLL4mmTCHLqVO2duhJJUik/wF0oaOtcwi4gMzmDkHJWe5vpyHYIk/6/PmKuc11tKNWnmajdJKUTb70LIDBBJdIjY3WJc/tnj17aNGiRfTVV195fLZkyRL6888/1VnpaogXcdvQ0EB5eXl0/vnniw5ITtwOGjTIq9Bmz61vIm0oo6li5FAQJRXKiouLNbE/1MIgJOdhyxaizp3FB/rNN25ZC14i4A3b3yFBv9f96TQoPZclJSWadaClz990EiYaDvV6TrW69kLeSfLZHqtInCkifAVUJBDq3zy9CLsiDnLitqGhgUaMGEFdu3alfbZSjnb8DUtwh2NuxUTiUEZTJtxDQbSwX423T6+RhcrKSkpPz9K3w9jYSDRnjtQBEo0dS/TLLw5bPCfOpRNQavh7XUnctlBaV9t2lmqzlJR2lJIiLoXsfm1q6WmWTqfmrKpWVFQU0DGqobKykszmNLdryD3zRXgKqEjAKGEsWhMR4tYubM8991zas2ePx3fsE8o++OADx7K///6bJ5QFQKQOZTR1wj0UJFD7lXoxpk6dqtuIhe6jIaWl0qL2zDOJNm/2WL2+vp7atk0loAcBK8jfylqhQFnctvYdc6nr0Ne1qbUHraioiDyLSaQT0DPoHRJnarl5JJ35IjwFVCTAntsQcuTIEVq7di2tXbuWANDzzz9Pa9eupa1bt9KpU6do+PDhlJGRQevWraOdO3c6Xq7xYHfffTdlZGRQdXU1rVmzhgYMGMCpwAIgUm+IUBDugjLY6Nlevr19QwmIsl374qT8WhZakCo57Pe2GxuJvvmGaNEi4f3kyeKb9dlnhXVkCPd7XaqzIHhsexqqY65lOIG4wtwKcpYBtoTkGMP9GopkjBTGoiVhIW6dN4b4NWbMGNq8ebPkZ+43yokTJ2j8+PGUnJxM8fHxlJubS3/99ZcqO1jcOonUoYxgwzHL6ghGe/n29qWQXFnaQAteeC85PJRSUtor3/aOHUTnn+/cSFoaUUODEGM7ezaRxCiXFJFwr7t2iKRy+BpFaGkVcx2KeGNvRKqAihSMPOfCX8JC3BoFFrdOuCceOL5ilsvLy9mj60IwY7ylY01zSCgooM91r/SeKi4u9r6h11+XDju48UYit/kIWtoV6ntdqUff6GI9OLHj9jzGwetUR6KAiiTCfc6FO7qL2927d9OGDRto/fr1olc4wuLWCffEA8O3l06fyS7hSihivKUrZOknjJSKruRks+dxWizyojYzk+jXX/1uh3C419V49MNBrOsbO15JQqYC7cNqfBFpAirSiKQQOd3E7XfffUfnnnsuRUVF2XprJsf/UVFRfhscSljciuGeuP8ofcACLwf14WNUQiFIpMVmYHZ4e3govyZctr92LdFvvwkxs926ib9QXExktWrSFka+19V69MNBrAeK/DHa8+5qH1ajxrZIEVCMcdFN3Hbp0oWuueYa+uqrr2jz5s20ZcsW0SscYXHrCffE/UOpl07wFEbWg9cfQjGULC02vSfl93Z+fHkXy8rKyF7yVr7kcDa1BWjPmWc6P7z1VmEHH3xA9NxzAXlp5bBYLFRcXEzJyW0Nda/X19dTq1bJqsWaUcS6nkJP+hhfCnonkWFCgW7iNiEhgX7//Xe/DTMiLG6lUfIDzb11Mcq9dDX88KHQeG7lvV+uw7rKhJEv72JZWZmjIpiwbc/JbKOlD1zIS+sl20GgSIny5OR2VFxcHPJQhFatkvy+LkLdMZfKZ5yenhWE2HHjxhv7gp8jjFJ0E7d5eXlUUVHht2FGhMWtf3BGAE+UlabMIT3yQYbjAyJUQ8nymRN6kvuEnMzMHMlJgErihc3m9i4irZIE7zCoOUBj5ERtSgrRhg2aHq/c8RutUEtlZaWtvexx6f6JtVDdC077E9xEp/Bey3YNl0wRvuDnCKMG3cTt3r17aciQITRt2jSqqKigxYsXi17hCItb9Rj14WgE5IXTUHKWptT24RPODwithpLVCho5D19ZWZloO07vq7hti4uVZVhwFWnnYD3F4lPKxAtkQZToC9PRnqI1FkBy7WTEQi1Ou3q7tFv4iDWLxUIpKWZyeuhd8xkLZWlTUtrp0q7hGm/MzxFGLbqJ28WLF1OrVq0ck8lcXzyhrGlg1IejkQhmgnl/HhBG8/IGOpTsr7j31Q7e2lYQMb69i60B+hQXOj64FuUEEL2Me+gZ3EhdYc/cEJx7x6hZBZx2TbX9NZN8nPJQSklJDfl164qQhSNG9ndROJYYqq6u1mX/Rok3Vgo/Rxh/0E3cZmVl0X333Ue7du3y2zij0VTErVaCxqgPR6Ph3t5lZWVeHz7FxcWqz40/Dwijenld26u6upqqq6uptLSUSkpKqLS0VLZd9PL+KEnrJoiZzyXvg3w8JRl28CiecNtGGgn5SWsIqNPt3rG37/jx4xWJcn9CZAL5jXFOLrSHcthfQ8k9ThlQkBc4yEyZMkXR7+KUKVN0s0FJJ9EoHVt+jjD+oOuEsj/++MNvw4xIUxC3WgoaoydLNxquD5Pi4mKP85CS0t42nKn+3Kh9QITDMKD0ZJkYyXbR0/ujfHJgc4LNGxuHE3QX5kqK2qOIpnNQ6iHSxK8sXe4d6TbVVlQE+hvjDPNwvTZnkGf8aqJuw/uB4BS33n8X9RS3RN7Fq5E6tvwcYfxBN3F7yy230H//+1+/DTMikS5utRY0Ta3HHYinQ+phkp6e5fDSSj/QlZ8bNQ+IcBgGdF6r4kT09phFoKeoXfS8FpWndetOZoCA6XQ7XvRcceJEqvrgAxnB3tPtOLX3Snre/wcJ6EBa5kQN9DfGeW2622Qh4FMCziAgkYCLSOuJWVrhLA7i/VrUKyzBF0br2Da15wijDbqJ2yeffJLatm1LY8aMoZkzZ9KcOXNEr3AkksWtHoImXCcv+EMgng4lZXgDPTdqHhBGf5j4DgOwZ5rIdbSLnt4fX+2ViOW0yPbmOKKoDaIpAaANAH0U34I+e+YZj+Orrq6mNm3akuDtlY8nzcjQ5v6Rb1P1ac/U7yPQ69iZXcL+MpmaUVFRUcDtogfChLJUEsIo5OKE24fkd9GIHdum9BxhtEM3cZudnS37ysnJ8dvgUBLJ4lYvQePv5AWjxHspIRBPh7I0UR0CPjeeDwgLCbGb8wlYETQhqAXKwwBeomAIdrmH72B85LEjC6IoD8KQc81nn3m9rpVmWdCik+G9fSpJ8OD6N4lP2T6UHY/ntWkX3+5ZB3INEz4jhZAKzESeccJCppRQ2R2Kjq2S3/pwmwTHhB7dxG0kEsniVk9Bo3aGu5HivXwRqKfD+8PEQs6KQiBhmNj/c+MU4T09xAoQ4/B0Gd1zqzwM4A1Huyj1/tTX1/vVqbK3bSyG0FTcJmUUEUA5WC95rqQe8MHsZPje1z8EgMaPH+93Z1OL4xFfm/5XizMC0r9zoa34FuyOrZrf+lAX3WDCCxa3Kohkcau3oFHqiTVCvJcar3Gg7Sb/MPEcahUEaWC5b4uKimzbco9VdXq6jD4M6I/nlsi396eoqMj/TlV9PVVWVlJZXLyHQa9hEDXDSdlzJfeAN47nVpt9abEP8bW5ImjtoxdGG6EKZsc2ElITMsZFV3G7bds2evnll+nhhx+m+++/X/QKRyJZ3BpB0Bgh3kut1zhQT4d8DKH0UKtncQex17G6upqmTJlCU6ZMoerqalFbqWlfIw8DKq/ulqsovVlmZg4VFRWp71QdPUo0ahTRmWcSDRxIREQNixbR31FR9Cvi6FKUkLjCnOc17OsBn5JiDso9GYz7v76+nszmNAp0gpqzzXoEdO8xngTrOWCE33omstFN3FZXV1OLFi3o3HPPpZiYGOrevTu1bt2akpKSqH///n4bHEoiWdwShT6uKdTD4f54EgK1WToW1ttkqVwC0kkYJhZ7HYVJKuJQg5QUs98ZA0I9DOgrVZEzW4JrzKJ0tgRv262vr1f3oF2xwrPxoqKItm8nslqpSmGe4urqap/7TUkRcrkGck+qHTXR4/73vJaGBrSPyspKatWqTUh/LyKVYDwHQv1bz0Q+uonbCy+8kB577DEiEnLebtq0iY4cOULDhw+nV155xT9rQ0yki1ui0AqaUE5k8teToIWnQ/wwscfYKinV6vQ6Ome0S3l6hZRI/rRvqIYBlXjQveW5VXPNesZx1pAw0a7G9n4VxQC07dprpRqNqHlzoj//9Gm/VJ5iJedaKuex0uNTOxKhx/3v2WkspUAnqAkTskBAC9IyVZkvmsqwuN7PAaNPWmXCn6AUcWjdujVt3LiRiIjWrVtHWVlZ6i01AE1B3BKF7gc8lL35QPathafD82Hi/UffPrHH6XWUSxc1jIBEysjIVpxfM9TeEjUedPu1qqRCmRzOB20pecY5Z5EJ79BPUg12441Ex46JtiVVPU0+T7F9mbLcw2rvSX/j17W8/y0WC6WnZxHQm5zV1Sy21woCepDZ3J7q6+tVbdPZES0nZwiPOOuA1qNN4TTRVQv0fA6w55bRG93EbWpqKv34449ERHTOOefQ4sWLiUgQty1btvTD1NDTVMRtqAhl3G+gngS1ng6pB4fFYqGSkhJVP/rKJ1fBMQxu1Ili9nYJdiyesw0FkRSHWnoZd9BHuJgm2sTuiwCdatFCMOTTTyW3Iyd+5PMU6/eAN0pMoyDqY0jcYcgmZ9y4+mP0vOalJl/G6FzgwngV+8IJI8zxYCIb3cRtXl4evf7660QkzNDu1KkTPfnkk9SjRw8aaJt8EW6wuNWfUMX9ajWTW2lso5wHSO2PvvK0WIIwl4tVNcpDWk+Pjtz5qa+vp+joOOqDPh47/NnWdq0Byu5wmtfhfDnxYw8N8Twme3y19sPqRvCMOXO5ylWRqyR/hp+lr3nXcJKPVG/TG0bpKEQaoZ7jwUQ2uonbTZs20fr164mI6NixY3TPPfdQly5d6JprrqEtW7b4Z22IYXEbHEIR96tEVGZk5DiGmf0ZprNYLDZPlncPkJoffTWe25qaGqqsrLTFfYq9aSkp7Q3xMNErFk+uQ1FVVkbbhw+X2hH9iXjqiDKSSpfminxJWCLnJMAYAj4kcSyv3evomcw/0Ae82nbUeghaeRW5OtUiO9jC3QgdhUgl1JNWmciF89yqgMVt8AhF3K83UQmYPCYDqYm3q6ystMUexsiKIPdUXEp+9J0iQq6UpzPmtsw2i19YtpKApSTEffYmAIZ4oOghJOS8qtkYSH9J7KQCLSgBg32eI7U2yw/NTyf3zkagD3g17ahHLKnyNult+LLePPlJX5rKJD0muOgqbv/55x/673//S//+979p//79RET0/fff0/bt2/3ZXMhhcRv5yM1y95wMpDzezimueqsSbmrCHKRLeTqzJcjHfRpraFVr4eLqQYzFCSrE87QIw6kzfiITTtEfaOHYwVA8R/7EwZaWlioSP8BU8hyaL7eNCmQHNCrgbzuWl5frEkuqJlzGn33Ip4IbSgAc1fa0gD23DBN+6CZu169fT2azmTp16kQxMTG0adMmIiKaMmUK3Xzzzf5ZG2JY3DYN3Ge8Cx5X/8SWeHhWmQjyt8SxdJ7bdlRZWRnQAzrYnhUtY/FqamqoJ0D1iBEd8PMYTEANdca71Aygtm1TbftTdo5KS0sd+1A6CRAoEV07gjBL9FvgBdqOZWVlPkIHcsls7qAqk4EdpddbIJO+hBR47pPV0slbjmN/4MlPDBN+6CZuBw4c6Og92/PcEhF9+eWXnAqMCRsC9dqIv6+vB8hischWKPN3aDVU6Y8CjsU7dYpo4kSpA6UVAJ3l2G4WAaDCwkJV3vW2bVMdtgie2xgvInGY7fNSyW1NnTpVt86Dt3ZUem27Hqs33DuF3qvI5VJGhv+CUBznvILEsczaC06e/MQw4YVu4rZVq1aOPLeu4nbLli0UFxfnh6mhh8Vt0yPQeDvx971XH9PTA6RUyJSUlDhElq8ha3ulLb28uX57jD/9lKh9e4+DXApQEq4WHYsgjgQPojgu2lsMczq5Ti5zTyUmXTENNvHlee0kJ7clPTsPcu2oPHSgh08BJx3OYyZAH0EYilABnvzEMOGDbuK2Xbt2tGbNGiISi9tly5ZRRkaGH6aGHha3TQ9tPbdEztnx7iJIeia+VvgaWgV6UnR0nOjBLbyXywAwlFyHhEOazP7UKaIPPiC65x4iq5Vo40bRScpHFAEJJO9ZHerwIoozWsiV9q0k186Is5BGT/LMt5pDwAW2vxaSunYEb3Hwc6cqn/S1wmvHy3saNHiEy2ghCEM1yYsnPzFMeKCbuL3zzjtpxIgR1NDQQAkJCfTnn3/S1q1b6fzzz6eJEyf6a29IYXHb9Ag03k76+55J5zMyvD/wvT1UfT1w7Z8XFhaSlCdNEGUgcT7SlxUKn6UBCbKAxML69URJSWLDli8nIqIvH3mE4mGfBHinomNx7aBUVlaS2ZwmIVQrJb8nnuD0EgFv2P4OIfvEMekOQiIBDaqvKy3a0neHx56uyyLZRq7b8DYaofVkOSKe5MUwjHd0E7eHDh2iSy65hFq3bk3R0dGUmZlJsbGxdNlll9HRo0f9NjiUsLg1DsH0oAQab1dWVkb24V1B8PxDQn5PIb6zuLjYq/3e4l59xcRKfa7MQ6t0yHq+34LMr3hei4Xo3/+WMojozjuJ/vhDQnD55+VzZkGYR+LctNLf85Zpw7NDkWtbZ4Ym4szf2Gj5rAOuXmr5NgqVyBQL8wZyFnCoIaCBJ3kxTBNH9zy3K1asoOeee46eeeYZWm7zqoQrLG6NQSgmOfkbbyf1PftwvtLvexvy9VYQoqioSPK7dmFVWFjoZaa/0iHrGr+EjF/lTN98U1LUHujenSrmznV0cjwFl38CzB/hJtXpkha99qH6wIfVAy0Nq9ZL7Uooc8AKKfBAQsiJq+3Ce46FZZimiy7i9tSpUxQdHU0bNmwIyDijweLWO8Hwpoo9TS+T4FV7mfSOWSVSf3xyosOef7a8vNzn/uSHfBtsD3H5ghDeYmbtni35HK3eJ7+Jh6zVCRnF5UwbGoQwg7feEr74zDMi5fTNhAmSnRwh/ML1mPybyKdlCij3a6e6upq08HhqVRq2vr6e2rZNJWF0YYXbeZXfTijDA+TL+w4lgDMYMExTRjfP7emnn07r1q3z2zAjwuJWnmB4U50PcqmJO2kE5JDZ3N6vvJxao4Xo8C4clHpWX/b6ufccrb6KQ1RKbtOXkPEliM7CfNrhuiApiejYMaI9e4j+9z+iI0cUeLRBQhW2GhKGq13LHisPLdErBZRWwllLcenPsYYqB6xWop4niDFMZKKbuJ03bx4NHjzYUZksEmBxK42aYdFAHiaeKZfcvTWCqDGb00LutdFCdHgf8lUaEzvP6+elpaU+sygA4hhdYRKU5wQppYJC6rhMsNIU/EfKUKJx4wRha8OXsAFyKSoqhjyHq80EiGfup6S09yu0RIsZ/1oIZ63DAvw51lDkgNXi/gpVDmeGYfRHN3HbvXt3SkhIoLi4ODrzzDPp/PPPF73CERa3nqjxoAT6MFGWLD+dgKEhT6yuhegIhufWdaa/9KQnEwFlJPaAgty9uWqEjPi4GqkQz0uK2kOdOxPt3KmyXYiA6bbP3Yer7ZO4QMBEAnpTRka2X2EFWhYHCEQ46xEW4M+xBjsHbKD3V6BxygzDGBvdxO20adO8vsIRFreeKH24FhcXB/wwUV7m1HtezmCghejwPuRrj7mVLjagNObW3j7SE9+yyTP0gEiotCUue6pGyFhOnaJrzO2pOy4jwEq1uEy0gzvR1eu58y5sLCRUHfOWn7c9eUtvFWwCEc5GKg2rVwdAaruB3F9ahTQwDGNcdM+WEEmwuPVEqQdFqMIU2MNEfvKTeF+ChzG0wkUr0eFtyNceoiHncX3ggQds67imIHN6WcvKykSiob6+nmpqamjKlCnkjFl1FY127+1LZI/XVSVkNm0iOv10R0NUQUiRdSWep7swm8xYoqiz459H227/NNvn1aTlTP5Qxm5GcmlYudGesrIyv++vUE6CYxgmOGgubg8cOEAvvPCC5AYPHjwo+1k4wOLWE6UPCi0eJsr3VaOpcPGXQEWHa/EFYSa7p6fUW6ox93y2rinIioqKZENEPIW5Z9GJqKhmNHXqVN8irrGR6NlnpU4WbbrySspIz5I8Ll/tIi9spDpAnvYD7cgevhCoiDFC7GYklob1FTpgT3Wn9v4KZfoyhmGCg+bi9j//+Q8VFBTIfn7dddfRk08+qdxCAxFO4jZYniQlHkqtcno6hxPlhpx9V1QKNlrmxzWb06iwsNDjfDqLRJxPQoztOySdIknw6D700EM+Q0ScwqIneZvAl5Jilj6WI0eIXnxRUtTSeecR/fUXEfl/ncrHCfcmcQeoUsZ+oS1SUtr5dW+4V32Ta8vy8nJVxxdoiEKkzPxXGjpQXl6u+v6S7iR7jkyE+reDYRj/0VzcduvWjaqrq2U/r66upu7duyu30ECEi7gNtifJl4eyuLhY4mHi3zCgvKhxrahkrLg5rfLjymWfEDoPdsHvO6er0ljcsrIyr+sKyxMJsCXMb2wky5df0hdLl1LFa6+RNTZWfKJnzxY8uRq2k9R1npKS6lK5yltWhaGUktJe9TUi3m+M1/Zx9557uw+N4AE2CmpCBwIvO+zp2Y+OjvOZh5phGOOiubhNSEigrVu3yn6+detWSkxMVG6hgQgHcRuqWcDePJRq4099Paykh+LTSRiSDu9YQ7WTXTyLASgN3fCdRUGpwMhAD/rZRcjebjsnxQA9mdSGlr76qs/j9lfYyVUEE+4Bdy9u4LGV4vvrZRVt7f0+9FbwI1yv5UDQO3TA98hE02x3hokUNBe3SUlJtHr1atnPV69eTUlJSYoNNBJGF7ehngXsKjSqq6upurraITrKysoUxccpFTlK4lGNiC/hrnayi3Pyl10EaJP/dv78+T4ERiPdi5mSYQeP4i6fYs4VPTpklZWVtkmM2gkkz/tLaVvP93of+s7b65+HOZwJxqQvXyMTRhr9YRhGHZqL2379+tHDDz8s+/mkSZOoX79+yi00EEYXt4E+ELSK2ZMTqFKTmFyFqL8iJ1xiDZUId7UeK6e4DY7nNgGHaQJmS4raP9CCsvGHKpGgZ4dMTYlbbx0z+74920NpW9d4vQ+V3rfFxcV+XHXhid4pziwWi+LUghx7yzDhh+bitqKigmJiYujFF18U/fBYLBZ64YUXKDY2NmxjmYwubgMZytMq3s+XQJWbYBNqr7PeKBXuajsogoBzjfvULubWdQLf6fiNAKLr8IGHUZMwTJXNrujpoVMqkOxppcRhLs48vvb7wPP+8t7W4gmO8veh8nR65rC9/v1BrxRnnr91nDWBYSINXfLcTp48mUwmE7Vq1Yq6d+9O559/PrVq1YqioqK8enWNjtHFrb9CQath4UAEajCGIbVGqcdYTbv4E5+ckmImZ9zgKhJij+3ZEjxFgeIUSn//TQc6diQCyAJQBhZSLPZRLbrTS8imTgAJZXh7+C0SfAu7fwgAjR8/3i+vvK/qa6NGjZKNdRXacIaPiZGu2Rhctz+UnBMcvV/LSq99o13/wUDrFGf+xEw3tTZnmEhAtyIOX3/9NU2YMIGGDBlCgwcPpokTJ9LXX3/tt6FGwOji1p+hPC09poEI1HDLPanG0622XdR6rCorK23bTxDZA8TKigKvomHePA8jGwC6VrTtbAKmuxST0MNzW0lAB0VtLIfFYqHi4mKX+Ft3z6y3bAd2z2sDmUzDKCMjh9LTsyTuL+nZ9sAFBKwgIea2hgQPrnQHJTm5HclVmxPsyDbU9R9MtAo78vyt8z3KEc6jRQzTlOEKZSowurglUi+MtPSYBiJQw8lzq9bT7U+7qPVYVVZWUrpbQYT09CwqLi6WFQWuouHzpUvJ+sADUsYRpaWRZetWSZEoHtaXT4nlK+ZWWjB65upVM5ogX+DiTBKqry1VdM0JwlRcRlraEwxHHuIHH3yQ3EsUC0K9p6T9Tq9wrtt27SnutCk40ZSR/o2R9ryHc8YVhmFY3KoiHMQtkTphpKXHNBCBqvcEEq3wx9Ptb7v4k79TtYfLfi3feaenYY8/TmS1KppsVVRUJCPOBNFXVFQka4Jnh+wfmxD0fxa7t9RaznABNdkOnPeBr/vLuW/3IhpDZdvCGV7i7n3PIaDcMNd/OCP/W+fpeTdyxhWGYXzD4lYF4SJuiZQLHS09poEKVL0mkGiJP+1lOOG+dy/RZZdRY1ISHT7jDJr/3nv0/YsvUmPHjkQDBhBt3OhYVUn4hVPw9/QQCYI466lIjHp6Wf3P+uE9tZY93EBZNgVXz619n3L3VyBhPs7wkt4ETCPgIwLqdL3+9c40YqRMJt7vXQvZK5OVlJRwJ4JhwhwWtyoIJ3GrFK2FV6ACVesJJHL4+9D119MdSuFuP9Yvx4/3MLgBoNPt7ZyeJbLDvwwPrmVMa0hNKWS7nePHj/erje0on6BVTcqyHTQovg8C7SwG6/qX25eWFdGMVnHNcJ1MhmF0g8WtCiJR3BK5C686m8doGtkrPPmTDiyQB7Te3p5AHrqBiJdgChc7C+fPp7IWLaUMpb/QnNrhI0nRqsYDqfVkwEAFolJ7BAHuGtvrmU3BPmlOaQdEi7YIhrdTHDrxMglFPV4mrSpzBZKBRc/jD4fRIYZhAkdXcXvq1Clavnw5zZ07lw4fPkxERDt27KAjR474s7mQE6nilkj40Rdi/sSTYFJSUv36wZd6QBlhiFL6obvSIeSLi4u92hWo90fLmd9et2O1UmVFBW2UUFjPIJaiMcSr/WoKIGg9GTDQNlbuua2xvS8nz1hX532gpgMSDhMjvYeRCMsDLZAQSGiG3t7eUHQyGYYJLrqJ2y1btlDnzp2pRYsWFB0dTZs2bSIiookTJ9K4ceP8szbERLq41boEqvv2Qz1EKf3Q9ZxM4suuUHt/5Nry/955hyg3l6h3b7I++ihlZGTTk+hEfyGD9iKFumMNKa2q5VnWV94DqcdwbyBt7MsewVuZTsLEtVWOdGb2zBJyk+aUEA5D304BLjXhzp6dwX8B7q/A1/s3yBUjdLQZhtEP3cRtXl4e3XTTTVRfX08JCQkOcVtbW0udOnXyz9oQE6niVu/qYMF8aHnD86HrmgZIPKPel11ae3+UPmyl2jIPT3soiBPt2pEJoHjUUJSoQpayYXPPsr7eBYoegj+QNi4vLyf5cAP4tU01dht56Lu0tJQEz7S3WOMYKi0t9Wv7/oRmRHqFQoZhgotu4jYlJYV++eUXIiKRuN28eTPFx8f7YWroiVRxq+dQqpEeWuKHrn8J3JWkxVKLUq+2a1s2w3Eqx7VSJ4sau3alhS+/TNICQ9m5zs/PJ7M5zav3MyND3D5Kxagar5k/6dCEfLztyD28wG6PXAloLTHy0HdJSYmia6CkpMSv7fvzexIO4RwMw4QPuonbNm3a0I8//khEYnFbV1dH7dq188PU0BOp4lbP6mBGemiJbbHHlBaTc1a/d7v0CK1Q49W2298Bi2kvUjwa8wUUUAx8xcHaRb18wQXP+FN376eQrzUlxaxauOoZnuKMG7fbvJqAgyRMlBJKBJeVlQW8H6UYdehb8Nz6vt/99dz6E5oRbhUKGYYxNrqJ25EjR9Kdd95JRIK4/fPPP+nIkSM0YMAAGjt2rH/WhphIFbd6ClAjPbTEE2nsnj3XiTSVsnbpEVqh2Kt98CDRQw/RznPPpV4AAYdpLboRAWRBFF2Erzxs9i4w7EP2Q91Eqz3estz2fzoJJWTdK21lEzBD9bFr1YZSolFcgjj0owRGJhgdzlBWSmQYhtFN3O7YsYPOPPNMOvvssykmJoYuvvhiSklJobPOOot2797tt8GhJFLFrZ6TYIz20BJX0pKaSFPpYZdeoRW+2uYqPO+xcJ5t/UxspWic8tqW3gSG1JC9kNNVfPzAcgJaEnAxAaUk9nIrP3at2lDK85uenkUpKalkz3hhlGvNqDjPhfpyyWpQE5oRDhPxGIYJH3RNBXb8+HGaN28e3XfffXTPPffQf//7Xzp+/LhfhhqBUIrbYOR+1WMSjJEeWr4f6tJJ+/US6FJe7TicoAUYKbUTahwyhC5qn6GqLeUERmFhoe39UhIXXBB7rgF1E8vk0KIN5Uvq2kVtsUd7il88tG0nWJPe1PxuGX0iHsMw4QMXcVBBqMRtsNJo6TUJxigPLbHAkq+kBfQW2aVXaIW74BuI5bQHbT128PfgwUQnThCRf20pJTB8i8062+cjbH8PBnTsgbahd8+vPYZ0qSZCvKlgxElvRrSJYZjwQzdxO336dHrjjTc8lr/xxhv09NNPq92cIQiFuA12Gi29PMR6PbTU2OsUWKUknbxeEEnJyeKJUkq9jsXFxepsP3yYHm2dTB8gjWJxgjKxlSyIcmz4cvSW9Gpr0Za+Y3LdJ5V1IHFMcnA9t96/b/9sJXkvp6vNcHskYcRJb0a0iWGY8EI3cZuVlUVffvmlx/KvvvqKsrOz1W7OEARb3BopjZYWaP3QUuvRdgok78nrq6urPez2XRQgkQCFpYprajzUXQF6ELCK+uBTisEXPjsvWrSltBd4Ojnzw4pz/4pjktVdg4GGp3j3/FoIyCJhgpx9spzYsw0MDbuhbRZ5DMMw/qGbuI2Li6M///zTY/mmTZsoLi5O7eYMQbDFrdEmYxkFey5TKZHqTRTW19dTdHQceZtIEx3dnOrr6z2+6/Sgy2cY8CrQTpwgGjNGMpZ2NkDZUc0oEE+skjaTEkqeHYQYr+3jXtnLv2wJuQS8RMAbtr++i2b4vhemk3Oi4HSb2HW2Z0pK+7AStkao6McwDBOu6CZuO3XqRO+++67H8nfeeYdycnLUbs4QBFvcKo1TnDJlSpPx6lRWVlJ6epZXESYnMgPtLAiC2neGAY/v/+9/kqJ2Pq6gFtjlEOWAiQoLC0XiUysPrTehZN/Htddeq6h9AhHgRUVFtg6GczvR0XFUVFTk9XtKPL8pKe08jjM52UzFxcVhdX8YpaIfwzBMuKKbuH366acpJSWF5s2bR1u2bKEtW7bQG2+8QSkpKTR9+nS/DQ4lRvXcNhWvjvOh71/Kp0AnNTm/7z3DwIK33yYqKyN6/nnhi6+/LtrRFeilSJRr4b1TI5SUts/48eP9EtqBijYlk+nCfSg/0kKRGIZhQoFu4raxsZEmTZpEzZs3p6ioKIqKiqIWLVqonnRDRPT5559Tbm4upaWlEQBauHChx76mTp1KaWlp1Lx5c7r88stp48aNonVOnjxJ48ePp5SUFGrRogUNGzaMtm3bpsqO0MXcehsmziZgZcR7dcQPfWUVltxFqr6TmoguxmviBXFxRPv2ER05QrRgAdUuW6Z4/55C8CC5VtqaMGGCT/GmVijpFQZjsViourraVhJX+zy3kTSbnkORGIZhAkf3VGBHjhyhb775hjZs2EAnT570axtLly6lRx991FGFyF3cPv3005SYmEiVlZW0YcMGGjVqFKWlpdHhw4cd69x9992Unp5Oy5cvpzVr1lD//v2pW7duqjwgociWIC464BrrmWtbXhS2Xh01XrbqatdyuSV+CYBAJzVZLBZbsYChju/Hop5exj2SYQf00ENEe/Y4vq/UM1paWuomSivJM7uDEB6hbAKdsnbSIydxeXk5mc1pLnYHLtrC3TvrDSNV9GMYhglXwi7Prbu4bWxspPbt24vSi508eZKSkpJo7ty5RER08OBBio2NpQULFjjW2bFjB0VFRdEnn3yieN+h89z2lBA3ObblOeQcGg8fr46aIffKykqb18/1+ONsx69OhAWSc9dZ4lWYFPVv3C0tavPyiCSuEaVis6TEVbxXknR2h6G25T1l7fZHKPlqn7KyMkXC0mKx0KhRo8jZMeMCC0pgzy3DMEzgaC5ur7nmGkUvf3EXt5s2bSIAtGbNGtF6w4cPp1tuuYWIiFasWEEA6MCBA6J1unbtSo8//rjsvk6ePEmHDh1yvLZt2xZUcSt+0HkrOlATVgJBTexlebk9tZNUairYBK7Yo61EpKod2rZYLHRaehYNxkV0Kf5DQDYtdFMeNyUmKQoT8OUZLS21h10cJF95W4XOjXT+Vn+Fklz7FBUVKeqQCJ2RtiSe9MeiTQlGqujHMAwTrigVtzFQSFJSktJVNWHXrl0AgNTUVNHy1NRUbN261bFOs2bN0KZNG4917N+XYsaMGSguLtbYYuXs3LnT9t95AKIB9HNb4zz7mra/GwEAaWlpepumGqvVirq6OuzYsQP3318EolwAiwBE2da4GESLYDKNQGHhQ8jLy0NVVRVuuOFmAEMBLBatK7zPA7AcQB/HfjIycjBnTgXy8/NlbcnPz0deXp7Dnr1798JsNiM5ORlWqxXR0dHiL6xZA/Tuja0NDQC2ogYtMAB/4BnMRTl+wiLk4jgSgSN9cXtdHfr16ye53+joaMyZMwsFBQUwmUaA6BEI53AjTKYZAJZg9uwKJCcn277xHoAtAN53OXY7UQAm2479QWzbtgR1bvvu27cv0tOzsGPHAwDuA5AOoC+Ea6kRJtMMZGTkoG/fvrLts3PnTqSlpWHfvn0YOXKk7by977B7x47pKCgoQEWF0OZVVVW49tprATQHYAHwqM3WvgCyAUyH+LzDqy1NDaXXiMc1yjAMw6gnSGLbJ3Dz3H755ZcEgP7++2/RenfccQddddVVRET03nvvUbNmzTy2NWjQIBo3bpzsvozluZX3dgleMbsnL4bKysq8btefmMVA4hylPIFKqn0pXRc4x/GdQO1yeCJPnSJ64AHJsIPnUUhRoiwJvr3mru1XXFzs1XPsDEfpYfvc+1C+kC/Wc9+VlZW2GGHXds8iYLqqCYhKJ6bV19fb1uvtsj9X211DLEJXhjkciPSJcwzDMHoS9jG3eoYluBOqmFvvlbGyCagjZzGBnl6HLf1JLxVISirPEIR5igSbMKytTNy5l8tVglxoRCIG0yPSO6NlALXGMq9CW2pYXar90tOzqLi4WLaz4IzvVSLuX/LYt9zxCbG6oJSUdorbTH2ssLeOiefkOBZt0kTyxDmGYRg9CXtxa59Q9swzzziW1dfXS04o++CDDxzr/P3334afUEYkP8HHLlKcL3sxAe8iS22e0UByk0p7/JR6o0FC6iu5dS0OUTdz5kxVD353u0yw0iB8SsnYRy1wmA4iRryzBQv8joUMpP3KyspsBQ+GSu5TLubWl6cVyKWMDOVxm0onpl155ZW29T60/e0gY0MDARdTYmJrqq6u9ku0sfBjGIZh5AgLcXvkyBFau3YtrV27lgDQ888/T2vXrqWtW7cSkZAKLCkpiaqqqmjDhg10ww03SKYCy8jIoOrqalqzZg0NGDAgLFKBEckN62fZPGTuxQSkh8f9SQ4faEJ5aY+fhbxNkhIqTdmH0uUmVFWSe3nV9PQs1Z7Ic1FK+5DsMOBBPEcA0QRMpDsBqlu0yOM8qMm0oEVCfvGEOqnOTXMCINq31jPu1RQTcYpaMwmT/TzDEOyTAcvLyxXt351QlaZt6oK6qR8/wzDhQ1iIW+fDVfwaM2YMETmLOLRv357i4uLosssuow0bNoi2ceLECRo/fjwlJydTfHw85ebm0l9//aXKjlCJWyLng2XKlCm241+pSrj4I3gCFUnyHj/vsZfOeFv3VFiryFnAoaXb9dDSQ+RJYrXShhEjJMMOHsYM8tVBKC4utoVM+B5W10pkSsfOZhMwg4AhBEBUtldN2WYlQkVZeEw6CZ0ReyYLk82Gnjax62p7jM9yu97aIhSlaUMlqI1CUz9+hmHCi7AQt0YhlOLWjr/D4/7kPA00obx3cVfpIXrsItHzGF3jNKNsfxPcBJPwPiEhSXqoe98+ohkzJEVtHS4hM3Z7FZ1SD/fk5HZeJ7FplZDfYrFQenoWCRO1SsnpqfeMX83IyHbrHEjtdzrZi0AoFSry4TF2IVspIXhTCDhNtJ9Wrdr4nPDorR1CUZo2VILaKDT142cYJvxgcasCI4hbIimh8Q8J8afCBCwp8aCn57a6ulpyuNK3x28oJSWl0DvvvOPhPZQ+xvE2cSs1Uco+oS7KKdYqKoi++07Y4LJlHgcwBt1k7Mql6OjmVF9fL7JF7cNdK8+t9HbkijsIYjM52SzT7tJ5g5UWsfAMj0l3E7bSoQrJyWbV2Sz0ak81hEpQG4WmfvwMw4QnLG5VYBRxS+QuNHx74fzx+Cr5TkpKO6/DlUomxHmrTOYppmJkH7T2VGhnYARtQ3Pnhz/+SGS1El1zDdG0afSlqNKYu112kQyH4Pb34a5VQn5PD7D3uGVgKEVFxREAt3avI8HDPZSAFSSO11Zmjz08Zvz48eSMi5b3So8fP16z+MxQlKZt6hXDmvrxMwwTnrC4VYGRxC2RfPUuOS+cmglRdhFTWFhIgPR3xAJRfv/yHr9Sr/a62jF//ny64oorvDxoG+kRjPN4+tYD9INbbKlTJJWSdFnjUodICvThHki5XzueNiizCQClpJjdjs8+4cs9ftd7pg3fNukvekKxT+e1cpA8KwTqI6iNRCg6FAzDMIHC4lYFRhK3/noUlSSHl1rHZPL0DguTnJTtv76+ntq2TSUhdGKFizhQ7sXMz8/3eNC2wX56CfdKPXXpO3SgFER72C2OSZUva2wX1YE+3ANNyO/pAVZmE9CDMjJyqLq6mubPn08FBQW25XIhHaU+j0XeJv2Hq4O1T9cOlTN3b+AdAq0IZtYC9twyDBOOsLhVgVHErcVicXnovuwmFH0/dLw9HMXxpTPIPeVWQkJrKi4upurqalUPPS08oE47VlEqdhJA9DwKPTZ2FwrJGZPq6dUWPJqpikSSVg/3QAWJ2AN8hyKbXIs7WCwWMpvTbO0hFcowjARvunKhooVXWi1671N6lAEe15HSoilaE+ysBaHoxDAMwwQKi1sVGEHcSj987V6kwIYLxd5ge8iDu5dvKJlMJlu4gnKPZiAeULtdWRhAv9oKLPyB08kEK52NH2kNutPbuInSMYCA9gTUk+9cuu0UiSQjPdwrKyttWROiSYidlY+5FcIr/nG0qVKRbja3D7iEsd4Vx/Tap+fEwYMkeGzlOgTKyl1rRSjToAW7E8MwDBMILG5VEGpxK19S1e5FchW46ocLnQJopVdxCOSS2dxekVjy9NyuJOnYRRl7Gxvp93s9ww6OIYrOQSmJJ6jZ20CZkCsuLlYkkoz0cHd6zGeQtwIJQoEP9eEVhYWFqm0KRXJ/rfcZSDW9YAzJhzprQSg6MQzDMP7C4lYFoS7i4L2k6jASvHXKZ7674xRASxU91M3mNMUeTYvFYpvc5J6fNpuAck97Dx4kkim2sBbZ1Motf6qQRWGkTaRNUyTk5s+fr1gkGeXhLhapnnlune9LdQmviFSk28c4k6mMcP64QhnDMOGCUr0WAyak1NXVYfv2LQDeBxDl9mkUgEcA9AEwFybTMgBLMHt2BaKjoxXvIy0tzfbft7a/58msKSy/8cZRmDNnDkymESB6xLZ8I0ymGR77X7x4Mfbv3wdgKIBHHesCTwEYCSLC7JIKRJ86BURHA/36AevWifZ6PyZgNmYDMAGwAqgDsBPAAQDjAZTZXnY2ArhYwv6NjuONjo5Gv379fLQMkJ+fj7y8PNTV1WHnzp1IS0tD3759VbWvFjjP0UYA+QDy4GyHNAAxAPoCeBnAV45z0LdvX2RkZGPHjukgWgTxNdQIk2kGMjJy0LdvX1X2WK3WkLeJFuzcudP2n+s179rW3q8jvZG2z5Xz3NbTHqX3CsMwTNgQHK1tbELpuVU6rIwAPIpO73BvxV4iJR5NX17nDhhI66NjqLFzZ6Lx44UvPfss0bnnEp1/Pll+/91n3KswWco+HF9NvmJuw2ESjJSnTElhDCBGcpKR1uEVkVSSVdoz6j2fcDCvIyN4bhmGYcIFDktQQSjFre+SqsLDraSkJKCHrTMrQQLJTaSRCjnwNlwp92C+C3M9DyQ1lejUKaKGBqLGRpFd3oSZ5wS3Mtv7HiRkDfiH7DGp4TAJxptwFM6RySZkpeJtpavUyW3Xn85QpJVkle80uGbdCF28tZEmNjIMwxgdFrcqCJW4VSs4tUg75Uz+PzTgh7qr1zkBh2kF+kvG0u4+6yyi/fu92iUnzMQCWioWVcjTazZ3MLzw8iUcy8vLZeKXc0gyftmNQK+PUE9u0gv5ano9yb0KYCjirY00sZFhGMbIsLhVQSjErXx6LnEpW/vDTauhYovFQsXFxZSc3C7gh7pdeKbgYzqERA9ROxl3kUnhkKqcMHO2U0+STmEmeDU/+OADVbYHGyXC0Wy2FxRQmXlCIyJ5iFyuA1VWVmaIyVRGmdjIMAxjZFjcqiAU4tZTSEh7JYuLiwMaKvYmGv1+qO/aRXT99dTYsyfd1LYdmUzD6DP0o5NoRtuQTmfiF009fWVlZTYPm3xeUqN7FJUKRyXx13rN4o/0kqxGzwpgdPsYhmFCDWdLMDies6TdZ8gnARiKjh07YuLEB0GUC2ARnLPhLwbRIphMI1BY+BDy8vI8ZrNXVVVh4sQHbdkYBDIysvH888/CbDaLZsIDQG1trffZ8e+8A4wZ43hrAvB0r154b/8SXIcrsR81ALpAyKwwAv5kdpDCbDYDsEDIxiCVUWIytm3rg7q6OsPO+lY6K14gNLP4xRkbQptFQA+MnhXA6PYxDMOECyxuQ4S0kIgG0M/2/2oAwN69e72mCiN6RFLYVVVVoaCgwCaK34c9Rdf27f/CyJGjIYhFgZQUM4Ao7N+/27EsIyMbc+bMQv4VVwBDhgArV3oeREEB0l95BRV1dZg48UFg+yUu38/B7NkVyM/PV9Eq0uiZLilYKa+UCse2bVOxf7+2ab2UoldaMYZhGIYJKkHyJBuaUMbc+polXVpaqnqoWD6+03WGuD28YYbEMiHkoT9ADS1aeO508mQityFTPYdU9YoFDWbKK6XnWwjBAAlp26YR8BEBdUGbXMSTmxiGYRijwjG3KghltgRfQsIfYac8t6d4WRvsp2l4nD7FIGqJQ5SMq+gETMIG2rQh+uGHoLaPHT3SJQU75ZV9Ip+v9FNCRotUkeAGYiglpV3QhCVPblIHx8oyDMMEBxa3KghlnltfQsIfYSc9MUhK8ArLrsMTHqr5dvyXgFV0LkA1K1YEvV3c0dKjGOyUV57nWDr9lJzgDkUOXxZsyoikghcMwzBGh8WtCkIpbol8Cwm1wk7acysWvEn4hz5BF8m8tM+giFKxk4w2O14rj2IwU15JC9aVZK8WV1xcLKpQFmk5ZiOZSCt4wTAMY3RY3Kog1OKWSJnAVSrspL29TkF3J16TFLWzcD9F45QuIk8rtPAoBivllRrBGsk5ZiMRfzoj7A1nGIYJDE4FFkbIpeyaM2eWI9tAfn4+8vLyFM3sj46Oxpw5s1BQUACTaQQS6V8YhR9xGhLwGJ7CP7jJse4JmHApLsUa1CIcZsdrkS5JbcorbxkVvH1WV1enONOFnhkhGO1Rc2779eun6B5nGIZhNCJIYtvQhDrmVq+hzS///W+R+88CUAZAsRhCV2MWReEfAqaTMMkp8HK84YKaOGZvMZW+4i3VeIjZcxteqDm3HL7AMAyjDRyWoIJQiVtd4iwPHybKy5MMO/hz7FhaPG+ehyBLSWnnMUPfyLPjtRjeVRLH7E2UCO3kXbCoEax6ZIRg9EPpua2uruZYaoZhGI1gcauCUIlbzb11jz8uKWrp7ruJ6usdq0mJw3CJB9Rydrq3OGbvHY8GAhJIrhywXbDU19erEqycYzZ8UNoZqa6u1vYeZxiGacKwuFVBqMRtwBObjh0j+uADog8/FN6PGSPewJdfBu1YgoEew7tyot57x0N5p0StYOUcs+GDknMbrMmLDMMwTQEWtyoIO8/tp5+KV+zenaixkWjDBqLFi4lOnQrqcQSDYKfK8i5K1AkWtYI1XLzojO9zy7HUDMMw2qFUr5mIiAKdlBbuHD58GElJSTh06BBatWoVtP1arVZkZ3fCjh1dQLQIntkKRiAjYyM2b/4d0fX1wO23AwsWeG5oyhTg8ceB2Fi/bKitrUVtbS0AoF+/fujXr59kFoZQUltbi/79+wNYDekMB6sB9EFNTU3A2RR8768WgDpbvGVVYMIbX9k0FN/jfD0wDMN4RbFeC4rUNjhGyJYgN7S5uLSU6JFHpGNpx4whOn48oH1Ll3o1G24YPNjDu95jKu0xt0OD4kVmwhuOpWYYhtEGpXrNPUEjE2Ty8/NRUVGB9PQNAPoAaIUY9MHtyV/jo7lzMXzkSODNN8Vf+uwzQUe99RYQH+/XfquqqnDttQXYv/9CCJ7GI7a/V2P//n249tprUVVVFcihaYo4N60UG93WCwx7rmBgCUymEXBtI5PpWgBH8f/t3X9clGW+//H3iIqiwu6a8jtArfUXpzTbkiKxc9ZO6oYPIjdcS09nzb7VCvuwMlPTtkJr1cXvY9vSTquVyWpA1prlqkdsOnaq1Wq1+vrjhIkGx81UaE2M4fr+AYwMzDCDwsxwz+v5ePBI7vtm5rovr+TNxXV/Lmmzm3OTJG1SQcHSDpmJa5xpLywsVGlpqRwOR7u/B9qXu//HpTQlJOxTUVERdW4BoJ2xLEGBW5bQlMPh0N+eeUYjcnPPH5w3T3riCemll+pD7IQJUkREu7xXcvIgHT06XNLrav6rUmmSpFIlJPTV4cOHguLXpYH69a674vuJiSkqKFgqSR7PdURgYSOA4OTrkhOWpgDAxfE1rxFuFeBwW1Mj3Xuv9Mc/tjz39NPSgw+2+1v6un5VUqtrWP39zbqkpETZ2dmSJsqYuarftWufbLbFkjZ5nQW70PZe6A5l7anx3o2ZKOkRnb/3fPly7+gY/MABAP7Dmts2CMia24oKYxYscL+W9rbbjPn22w57a1/Xr6qVNaztWW+2LS60VFZ7tDdQVQz8WSmCSg2+Y+cxAPAvSoG1QUDC7QcftEyVmzf75a19LU8kDyWKAv1N3VsAa37+1Vdfvej2BirMG+O/clKBvMfOxt+l6QAAhNs2CUi4rasz5u67jfnDH+q3zO1gTQPftm3bTHx8ksen/eu/YfcxUpjZsGFDi9cJ5m/q7gJaWFi48babWGvtDXSY90eliEDfY2dD/VoA8D/CbRsEshRYe3M3q+ku8PXt289ItoaAe748UX0ItDVcN6pF8Avmb+ruA9ozF9XeYAjzHd3nwXCPnQ07jwGA/1EKLASVlJQoOXmQxo4dqylTpmjs2LGKjo7XrbfeqqNHU9W0ZNWJE9eoPue+pabliaS3JfWTVCzp/6q8vEx2u935HhUVFQ1/Gu6hFcObXecfDodDubmzGx642qj6B+V6S/qhS7taar29dru94WGhR+RaoUGSusiYuS36qL2lp6crISG54eGxumZn62SzLVZiYorS09Mv6PWD4R47G3+XpgMA+I5wG4QupJZp49P0LUPsKEk2SXfqfOD7Sue/KTeGpR9KylZ9uP1KUpbcBb+2flP3V11WzwHt4kJIMIT51mvuTtLF1tUNhnvsbDr6Bw4AwEXw00xyUAvksoTmywg2bNjQ5od6vP1auX6pQYqRao1U3LDswHVtZf3ntobznn/d3frOXa6/wvbnA0qef01ca6QL/5V7MC3DuNBKEd4E0z12Juw8BgD+xZrbNghUuHUXVqSuRhpl2vJQj+/VD7a1GvTqj6cYqcZI24000vTrF2NqampatNvbN3V/P6DUeh80BvoJHtvrSVvCvD90RKmuYLvHzqSjfuAAALREuG2DQIRbT+Hv/ANdxT4HDN/r1s73MQT3Nd5mW1v7ph6IB5S8BTRpVEPVhLaHkFCYoQuFe+wo1AYGAP8g3LaBv8Ot92UEjTOotS2Cp7tfDfs+c9sYbr2F4BE+zbZ6+qYeqF9zewtoGzZsuOAQEgozdKFwjwCAzsvXvMb2u/L/9ru+b3+7Q1JGw7FqSZFat26dcnJyXK52OBxKTh6kY8dSZcxGuT5QVSfpFknvSHpS0iwf3ne7pBtdXsNmm6SEhH0qKzvo9cGlwsJCTZkypaHNvd1c4fleLpa77VATE1NUULD0ordD9fd2w4EQCvcIAOicfM1rXf3YJjTw9el0qenT6Z6f6m98mj47O1s22yQZM7fhNfbJZlssYzarb99LdOLELNX/lT8h6Q21DMFPSoqXNKbZOzSWg0qT3W5XRkZGq/fnWlHBXYhuvzJJzcNYZmamMjMzOySghYWFeb33zi4U7hEAYG2UAgsAX8tpnS9j5b20UFZWloqKihQfv1dN69YmJOxTcXGR/vd/K7Rjxw7l5d0vaXOLklJSpqQ3JT0lyV0Q9L0clL/KJLmr65ucPEivv/66MjIylJOTo4yMDGYeAQAIIYTbAPAW/qR81W+kUF+z1tdapllZWTp8+JB27NihdevWaceOHSorO6isrCznjNzvfvc7FRe3DMH9+u1peJWBHl7d99nWjq7LKnmu63vsWKqys7NVUlJywa8NAAA6L9bcyv9rbqXz4Uya6LKMoH5pwJsNV3WVVNtua0abav7r/LS0NA0c+GOP63bbsua26T12xPrXxjXG9cG2fdoKAACCm695jXCrwIRbyX34q1/z+pTqZ1Drg+6rr25oCMId3x53gdtmWyxpk4qKitocSjviASVfH8jbsWMH60cBALAIwm0bBCrcStK5c+cUH3+pvv46XtJvVf8wV2P48/8sZGuzrR31oFZbBbIaAwAACAxf8xprbgNs165d+vrr/5X0jOrLbzUNi41VCspkt9v90h5P63YluX14KxBrW319IK89qjEAAIDOhVJgAeZrWTBfqhS0l+bloBqXKxgzUVKhGpcrHDuWr+zs7AtarnAxGh/IO3Ys38P64MVKSGh7NQZqvAIA0PkxcxtgwT4L6XA4lJs7uyHYblT9Gtfekq5tCJYTlZf3gBwOh9/a1BHVGDyVFaPqAgAAnQvhNsC8lwV7Un37xjhnIR0Oh0pLS1VYWKjS0tIOD5V2u71h/e0jajlc/L9solFrdX3bOpNMWTEAAKyDB8oU2AfKpPpwdeut2ZLGS5qn82XB8lVfFsyouLhYklo87JWQkKwVK5Z12LKAQDy81ZblARe7lICyYgAAdA5sv9uJZGZmNmyPu1Pna9xKUoqkDbLZXtLdd/8fffPN392ue7311mzl5eUqMzOz3deJtvdWut7CqLtqDa0F+IvdLvb8zHShPM9M+7btMAAACDyWJQQBu92uEyf+LultSTskrWv470FJ2TLmIZ048U0r617Hq6Dg9x2yTtTXrXTT0tK8Lpfwtq41EMsDgvGBPgAAcOEIt0HgfHC6QlKGpJyG/zbOaFZJqpWnda/1SxlqJT3T7kHQl4e3br89WwMH/rjVh7G8BddXX301IA+uBfsDfQAAoG0It0HAe8D6sOG/rc8uSj/skCDY2sNbDzzwgJYuXdrqbKsvFRfuu29WQB5c83Vmuq1lxQAAQGAQboOA94oJWxr+3PrsohSrjgqC7jZ3OHTo/6mw8FWvs62lpaVeg+vf/17Z8Ll/lwd0RFkxAAAQOITbIOA9YL2nvn2jWwm/i1X/8Fnj7GLHBcGMjAzl5OQoIyNDu3bt8mm2tbS01KVdLTU97v/lAe1ZVgwAAAQW4TZItBawiouLtWrVH+Qu/EqTJG2StFTn1+j6Z52orw9jndd6cO3XLzZgywM8bTtMsAUAoHOhFFgQycrKUmZmpsdSWUVFRQ1lstKafFWKpCJJjSHswrefbStfy4RlZGRozZq1XrfLXb78aU2ePFk22yQZM1eN5c5stsWqXx5Q1KHLAy62rBgAAAg8Zm6DTPNf/TcNc01nF/Py8hqODlP9Wlv/rxP19WGsjIwMn9a1ZmdnszwAAABcFHYoU+B3KLtQ7jY8SExMUUHBUr8FwcYSX9JEt7OtTUOpr+292F3HAACA9fia1wi36rzhVgqOINiWkN0e7Q2GewYAAP5liXBbW1urRYsW6ZVXXlFlZaViY2M1ffp0zZ8/X1261K+oMMboscce06pVq3Ty5Eldc801euaZZzRs2DCf36czh9tg4a/A2dbteQEAgDX4mteC+oGyp556Ss8995xefPFFDRs2TH/961/1b//2b4qKilJubq4k6emnn9by5cu1Zs0aXX755XriiSf005/+VPv371efPn0CfAcdIxhnLv3xMFbjEoj6urqFalwCcexYvnO9LgEXAIDQFtQztxMnTlR0dLReeOEF57Fbb71VERERevnll2WMUVxcnPLy8jRnzhxJUk1NjaKjo/XUU09p5syZPr1PZ5q5DdWZS4fDoeTkQQ07oW1Uy4oLk5SQsE9lZQcDHvQBAED78zWvBXW1hOuvv17bt2/XgQMHJEmffPKJ3n33XY0fP16SVFZWpsrKSo0bN875NeHh4RozZox27drl8XVrampUVVXl8tEZNM5ctrbVrVXZ7faAbM8LAAA6l6AOt3PmzFFOTo4GDx6sbt26acSIEcrLy1NOTo4kqbKyfsvW6Ohol6+Ljo52nnNn8eLFioqKcn4kJiZ23E20E4fDodzc2V63unU4HAFsZcfxdcOI9t6VDQAAdC5BHW7Xr1+vtWvXat26ddqzZ49efPFFLV26VC+++KLLdTabzeVzY0yLY03NnTtXp0+fdn6Ul5d3SPvbU6jPXLpuGOGOf3Zla28Oh0OlpaUqLCxUaWmpZX84AQDAX4L6gbIHH3xQDz/8sG6//XZJUmpqqr788kstXrxY06ZNU0xMjCQ5Kyk0On78eIvZ3KbCw8MVHh7esY1vZ6E+c9m4YYS3Xc46ele29hSq66cBAOhIQT1ze+bMGWfJr0ZhYWGqq6vfDSslJUUxMTHaunWr8/y5c+e0c+dOpaWlqTPyNJNn1ZlLX4WFhfm0y1lneZgslNdPAwDQoUwQmzZtmomPjzebNm0yZWVlpqSkxFxyySXmoYcecl6zZMkSExUVZUpKSszevXtNTk6OiY2NNVVVVT6/z+nTp40kc/r06Y64DZ8VFxebhIRkI8n5kZCQbIqLi01tba1JSEg2NtvPjOQwkmny4TA2289MYmKKqa2tDeg9dDR3fZSYmGKKi4sD3TSfNf5dSqH9dwkAQFv4mteCuhRYdXW1FixYoNdee03Hjx9XXFyccnJy9Oijj6p79+6Szm/isHLlSpdNHIYP9/Tr+5aCoRSYaw3XR3R+G9t8NW5jK8nnrW6tLBjr/LZFaWmpxo4dq/oZ22vdXPGepDTt2LGjw2sHAwDQWVhihzJ/CXS4bUsN19dff93nrW4RnAoLCzVlyhTVL0Xo7eaKakmRWrdunbMyCAAAoc4SO5SFivOVEArluRJCmux2u7KyspSZmdmpZy5Dnev6aXczt9ZePw0AQEci3AaB1ishOCSdlCRt377dGWT5dXXnZcXKDwAABIugrpYQKjxXQiiRNEjSBEnSE088oeTkQTxJ38lZrfIDAADBhHAbBBpn8uofHqtrOFoiKVsSpaKsKCsrS0VFRYqP3yspTVKkpDQlJOwLmQcDAQDoCDxQpsA/UCadr5ZQXwlhtqTbJY2S9Lpae8CM2b3OrbNXfgAAwF+oltAGwRBupfqAe/fd9+jEiROqn8G9+FJRhCcAAGAFvuY1liUEmW+++VrSlQ2fXdxWuyUlJUpOHqSxY8dqypQpGjt2LGt2AQCApRFug4TD4VBu7uyGTRx+23D0wrfaZXtXAAAQiliWoOBYluC6a9XVqq+S4H1TB3dLDNqyKQRLFAAAQGfAsoROxrXWbZik+lJR0iQ1nXmVMuWtVNT5TSEekedNIcpkt9vb+zYAAAACinAbJFrWus2SVCTJtVRUv357vJaKan1TiPPHva3ZBQAA6GwIt0HCfa3bLEmHJG2XNFL9+sXo6NEyrzVQPW8KIZfjbO8KAACshnAbJDzvWvWBbLYC2Wwf6bnnnlH37t29vpb7oNyofnvXxES2dwUAANZDuA0i7bVrFdu7AgCAUEW1BAVHtYSm2mvjhZKSEuXmzm54uKxeYmKKCgqWsr0rAADoVNihrA2CLdy2J3YoAwAAVuBrXuvqxzYhAMLCwrxu0QsAAGAVrLkFAACAZRBuAQAAYBmEWwAAAFgG4RYAAACWQbgFAACAZVAtIURRIgwAAFgR4TYEudvcISEhWStWLGNzBwAA0KmxLCHElJSUKDs7W0ePpqrptrzHjqUqOztbJSUlAW6h/zgcDpWWlqqwsFClpaVyOByBbhIAALhI7FAma+9Q1pTD4VBy8qCGYLtRrj/b1Mlmm6SEhH0qKzto+SUKzF4DANC5+JrXmLkNIXa7vSHMPaKWf/VdZMxclZeXyW63+79xfsTsNQAA1kW4DSEVFRUNfxru4Yrhza6zHofDodzc2TJmoupnr6+V1FvStTJmo6SJyst7gCUKAAB0UoTbEBIbG9vwp30ertjX7DrrYfYaAABrI9yGkPT0dCUkJMtmy5dU1+xsnWy2xUpMTFF6enogmucXzF4DAGBthNsQEhYWphUrlknaJJttkpquN63/fJMKCpZa+mEyZq8BALA2wm2IycrKUlFRkeLj90pKkxQpKU0JCftUVFRk+UoBzF4DAGBtlAJT6JQCayqUdyhrrJYgTZQxc1W/FGGfbLbFkjaFRMgHAKCz8TWvEW4VmuE21Lmrc5uYmKKCgqUEWwAAghDhtg0It6EplGevAQDobHzNa1392CYgqISFhSkjIyPQzQAAAO2IB8oAAABgGYRbAAAAWAbhFgAAAJZBuAUAAIBlEG4BAABgGYRbAAAAWAbhFgAAAJZBuAUAAIBlEG4BAABgGYRbAAAAWAbhFgAAAJZBuAUAAIBlEG4BAABgGV0D3YBQ43A4ZLfbVVFRodjYWKWnpyssLCzQzQIAALAEwq0flZSUKDd3to4ePew8lpCQrBUrlikrKytwDQMAALAIliX4SUlJibKzs3X0aKqk9yRVS3pPx46lKjs7WyUlJQFuIQAAQOdnM8aYQDci0KqqqhQVFaXTp08rMjKy3V/f4XAoOXlQQ7DdKNefKepks01SQsI+lZUdZIkCAACAG77mNWZu/cButzcsRXhELbu8i4yZq/LyMtntdv83DgAAwEIIt35QUVHR8KfhHq4Y3uw6AAAAXAjCrR/ExsY2/Gmfhyv2NbsOAAAAF4Jw6wfp6elKSEiWzZYvqa7Z2TrZbIuVmJii9PT0QDQPAADAMgi3fhAWFqYVK5ZJ2iSbbZKaVkuo/3yTCgqW8jAZAADARSLc+klWVpaKiooUH79XUpqkSElpSkjYp6KiIurcAgAAtANKganjS4E1xQ5lAAAAbedrXmOHMj8LCwtTRkZGoJsBAABgSSxLAAAAgGUQbgEAAGAZhFsAAABYRtCH22PHjmnq1Knq27evIiIidOWVV2r37t3O88YYLVq0SHFxcerZs6cyMjL06aefBrDFAAAACJSgDrcnT57Uddddp27duumtt97SZ599pmXLlukHP/iB85qnn35ay5cv1+9//3t9+OGHiomJ0U9/+lNVV1cHruEAAAAIiKAuBfbwww/rv/7rv2S3292eN8YoLi5OeXl5mjNnjiSppqZG0dHReuqppzRz5kyf3sefpcAAAADQdr7mtaCeuX3jjTc0atQo3Xbbberfv79GjBih559/3nm+rKxMlZWVGjdunPNYeHi4xowZo127dnl83ZqaGlVVVbl8AAAAoPML6nD7xRdf6Nlnn9Vll12mLVu26J577tGsWbP00ksvSZIqKyslSdHR0S5fFx0d7TznzuLFixUVFeX8SExM7LibAAAAgN8Edbitq6vTyJEjlZ+frxEjRmjmzJmaMWOGnn32WZfrbDaby+fGmBbHmpo7d65Onz7t/CgvL++Q9gMAAMC/gjrcxsbGaujQoS7HhgwZoiNHjkiSYmJiJKnFLO3x48dbzOY2FR4ersjISJcPAAAAdH5BHW6vu+467d+/3+XYgQMHlJSUJElKSUlRTEyMtm7d6jx/7tw57dy5U2lpaX5tKwAAAAKva6Ab0Jpf//rXSktLU35+viZPnqwPPvhAq1at0qpVqyTVL0fIy8tTfn6+LrvsMl122WXKz89XRESEpkyZEuDWAwAAwN+COtxeffXVeu211zR37lz95je/UUpKigoKCvSLX/zCec1DDz2k7777Tvfee69Onjypa665Rn/5y1/Up0+fALYcAAAAgRDUdW79hTq3AAAAwc0SdW4BAACAtiDcAgAAwDIItwAAALAMwi0AAAAsg3ALAAAAyyDcAgAAwDIItwAAALAMwi0AAAAsg3ALAAAAyyDcAgAAwDIItwAAALAMwi0AAAAsg3ALAAAAyyDcAgAAwDIItwAAALAMwi0AAAAsg3ALAAAAy+ga6AYEA2OMJKmqqirALQEAAIA7jTmtMbd5QriVVF1dLUlKTEwMcEsAAADQmurqakVFRXk8bzPe4m8IqKur01dffaU+ffrIZrN1yHtUVVUpMTFR5eXlioyM7JD36KzoG8/oG8/oG8/oG8/oG8/oG8/oG/f83S/GGFVXVysuLk5dunheWcvMraQuXbooISHBL+8VGRnJ/xge0Dee0Tee0Tee0Tee0Tee0Tee0Tfu+bNfWpuxbcQDZQAAALAMwi0AAAAsg3DrJ+Hh4Vq4cKHCw8MD3ZSgQ994Rt94Rt94Rt94Rt94Rt94Rt+4F6z9wgNlAAAAsAxmbgEAAGAZhFsAAABYBuEWAAAAlkG4BQAAgGUQbjvAsWPHNHXqVPXt21cRERG68sortXv3bud5Y4wWLVqkuLg49ezZUxkZGfr0008D2GL/8NYv06dPl81mc/m49tprA9hi/0hOTm5x3zabTffdd5+k0B0vkve+CdUxI0m1tbWaP3++UlJS1LNnTw0YMEC/+c1vVFdX57wmVMeOL30TymOnurpaeXl5SkpKUs+ePZWWlqYPP/zQeT5Ux43kvW9CZdy88847+tnPfqa4uDjZbDZt3LjR5bwvY6Smpka/+tWvdMkll6hXr1665ZZbdPToUf/cgEG7+uabb0xSUpKZPn26ef/9901ZWZnZtm2bOXTokPOaJUuWmD59+pji4mKzd+9e8/Of/9zExsaaqqqqALa8Y/nSL9OmTTP/+q//aioqKpwfJ06cCGCr/eP48eMu97x161YjyezYscMYE5rjpZG3vgnVMWOMMU888YTp27ev2bRpkykrKzOvvvqq6d27tykoKHBeE6pjx5e+CeWxM3nyZDN06FCzc+dOc/DgQbNw4UITGRlpjh49aowJ3XFjjPe+CZVxs3nzZjNv3jxTXFxsJJnXXnvN5bwvY+See+4x8fHxZuvWrWbPnj1m7Nix5oorrjC1tbUd3n7CbTubM2eOuf766z2er6urMzExMWbJkiXOY2fPnjVRUVHmueee80cTA8JbvxhT/49GZmamfxoUxHJzc83AgQNNXV1dyI4XT5r2jTGhPWYmTJhg7rrrLpdjWVlZZurUqcaY0P23xhjvfWNM6I6dM2fOmLCwMLNp0yaX41dccYWZN29eSI8bb31jTGiOm+bh1pcxcurUKdOtWzfzpz/9yXnNsWPHTJcuXczbb7/d4W1mWUI7e+ONNzRq1Cjddttt6t+/v0aMGKHnn3/eeb6srEyVlZUaN26c81h4eLjGjBmjXbt2BaLJfuGtXxqVlpaqf//+uvzyyzVjxgwdP348AK0NnHPnzmnt2rW66667ZLPZQna8uNO8bxqF6pi5/vrrtX37dh04cECS9Mknn+jdd9/V+PHjJYXuvzWS975pFIpjp7a2Vg6HQz169HA53rNnT7377rshPW689U2jUBw3TfkyRnbv3q3vv//e5Zq4uDgNHz7cL+OIcNvOvvjiCz377LO67LLLtGXLFt1zzz2aNWuWXnrpJUlSZWWlJCk6Otrl66Kjo53nrMhbv0jSzTffrFdeeUX/+Z//qWXLlunDDz/UjTfeqJqamgC23L82btyoU6dOafr06ZJCd7y407xvpNAeM3PmzFFOTo4GDx6sbt26acSIEcrLy1NOTo6k0B473vpGCt2x06dPH40ePVqPP/64vvrqKzkcDq1du1bvv/++KioqQnrceOsbKXTHTVO+jJHKykp1795dP/zhDz1e05G6dvg7hJi6ujqNGjVK+fn5kqQRI0bo008/1bPPPqs777zTeV3TmSepfnF282NW4ku//PznP3deP3z4cI0aNUpJSUl68803lZWVFZB2+9sLL7ygm2++WXFxcS7HQ228uOOub0J5zKxfv15r167VunXrNGzYMH388cfKy8tTXFycpk2b5rwuFMeOL30TymPn5Zdf1l133aX4+HiFhYVp5MiRmjJlivbs2eO8JhTHjeS9b0J53DR3IWPEX+OImdt2Fhsbq6FDh7ocGzJkiI4cOSJJiomJkaQWP7kcP368xU9BVuKtXzx9TVJSkg4ePNjRzQsKX375pbZt26Zf/vKXzmOhOl6ac9c37oTSmHnwwQf18MMP6/bbb1dqaqruuOMO/frXv9bixYslhfbY8dY37oTS2Bk4cKB27typb7/9VuXl5frggw/0/fffKyUlJaTHjdR637gTSuOmkS9jJCYmRufOndPJkyc9XtORCLft7LrrrtP+/ftdjh04cEBJSUmS5PzHY+vWrc7z586d086dO5WWlubXtvqTt35x58SJEyovL1dsbGxHNy8orF69Wv3799eECROcx0J1vDTnrm/cCaUxc+bMGXXp4vpPeFhYmLPcVSiPHW99404ojZ1GvXr1UmxsrE6ePKktW7YoMzMzpMdNU+76xp1QHDe+jJGrrrpK3bp1c7mmoqJC+/bt88846vBH1kLMBx98YLp27WqefPJJc/DgQfPKK6+YiIgIs3btWuc1S5YsMVFRUaakpMTs3bvX5OTkWL7Mird+qa6uNrNnzza7du0yZWVlZseOHWb06NEmPj7e0v3SyOFwmEsvvdTMmTOnxblQHC9NeeqbUB8z06ZNM/Hx8c5yVyUlJeaSSy4xDz30kPOaUB073vom1MfO22+/bd566y3zxRdfmL/85S/miiuuMD/5yU/MuXPnjDGhO26Mab1vQmncVFdXm48++sh89NFHRpJZvny5+eijj8yXX35pjPFtjNxzzz0mISHBbNu2zezZs8fceOONlALrzP785z+b4cOHm/DwcDN48GCzatUql/N1dXVm4cKFJiYmxoSHh5sbbrjB7N27N0Ct9Z/W+uXMmTNm3Lhxpl+/fqZbt27m0ksvNdOmTTNHjhwJYIv9Z8uWLUaS2b9/f4tzoTpeGnnqm1AfM1VVVSY3N9dceumlpkePHmbAgAFm3rx5pqamxnlNqI4db30T6mNn/fr1ZsCAAaZ79+4mJibG3HfffebUqVPO86E6boxpvW9Cadzs2LHDSGrxMW3aNGOMb2Pku+++M/fff7/50Y9+ZHr27GkmTpzot76yGWNMx88PAwAAAB2PNbcAAACwDMItAAAALINwCwAAAMsg3AIAAMAyCLcAAACwDMItAAAALINwCwAAAMsg3AIAAMAyCLcAAL944YUXNG7cuHZ5rQceeECzZs1ql9cCYC2EWwAhxWaztfoxffr0QDex3WVkZCgvLy+gbaipqdGjjz6qBQsWOI85HA7de++9io2N1c0336zKykrnuUWLFrn9+9m2bZsk6aGHHtLq1atVVlbm93sBENwItwBCSkVFhfOjoKBAkZGRLsdWrFgR6Cb67Pvvv+8071dcXKzevXsrPT3deaywsFBHjhzRli1bdNVVV7kEX0kaNmyYy99NRUWFbrjhBklS//79NW7cOD333HMX3CYA1kS4BRBSYmJinB9RUVGy2Wwux9555x1dddVV6tGjhwYMGKDHHntMtbW1zq+32WxauXKlJk6cqIiICA0ZMkTvvfeeDh06pIyMDPXq1UujR4/W//zP/zi/ZtGiRbryyiu1cuVKJSYmKiIiQrfddptOnTrl0rbVq1dryJAh6tGjhwYPHqw//OEPznOHDx+WzWbThg0blJGRoR49emjt2rU6ceKEcnJylJCQoIiICKWmpqqwsND5ddOnT9fOnTu1YsUK5+zn4cOHtWbNGv3gBz9wef+NGzfKZrO1aPcf//hHDRgwQOHh4TLG6PTp07r77rvVv39/RUZG6sYbb9Qnn3zSar//6U9/0i233OJy7NSpU0pKStLw4cOVmpqq06dPu5zv2rWry99NTEyMunfv7jx/yy23uNwrAEiEWwBw2rJli6ZOnapZs2bps88+08qVK7VmzRo9+eSTLtc9/vjjuvPOO/Xxxx9r8ODBmjJlimbOnKm5c+fqr3/9qyTp/vvvd/maQ4cOacOGDfrzn/+st99+Wx9//LHuu+8+5/nnn39e8+bN05NPPqnPP/9c+fn5WrBggV588UWX15kzZ45mzZqlzz//XDfddJPOnj2rq666Sps2bdK+fft0991364477tD7778vSVqxYoVGjx6tGTNmOGc/ExMTfe6TxnYXFxfr448/liRNmDBBlZWV2rx5s3bv3q2RI0fqn//5n/XNN994fB273a5Ro0a5HLvjjjv03//93woPD9fs2bNbzNx685Of/ETl5eX68ssv2/R1ACzOAECIWr16tYmKinJ+np6ebvLz812uefnll01sbKzzc0lm/vz5zs/fe+89I8m88MILzmOFhYWmR48ezs8XLlxowsLCTHl5ufPYW2+9Zbp06WIqKiqMMcYkJiaadevWubz3448/bkaPHm2MMaasrMxIMgUFBV7va/z48Wb27NnOz8eMGWNyc3NbvXdjjHnttddM028LCxcuNN26dTPHjx93Htu+fbuJjIw0Z8+edfnagQMHmpUrV7ptz8mTJ40k884777g9X1FRYWpra12OLVy40HTp0sX06tXL+XH11Ve7XHP69GkjyZSWlrp9XQChqWsggzUABJPdu3frww8/dJmpdTgcOnv2rM6cOaOIiAhJ0j/90z85z0dHR0uSUlNTXY6dPXtWVVVVioyMlCRdeumlSkhIcF4zevRo1dXVaf/+/QoLC1N5ebn+/d//XTNmzHBeU1tbq6ioKJc2Np/9dDgcWrJkidavX69jx46ppqZGNTU16tWr18V2hyQpKSlJ/fr1c36+e/duffvtt+rbt6/Ldd99953LUozm5ySpR48ebs/HxMS4Pf7jH/9Yb7zxhvPz8PBwl/M9e/aUJJ05c8bLXQAIJYRbAGhQV1enxx57TFlZWS3ONQ1m3bp1c/65cY2qu2N1dXUe36vxGpvN5rzu+eef1zXXXONyXVhYmMvnzUPrsmXL9Lvf/U4FBQVKTU1Vr169lJeXp3Pnznm+UUldunSRMcblmLsHxpq/X11dnWJjY1VaWtri2uZreBv17dtXNptNJ0+ebLVNzXXv3l2DBg3yeL5xGUTT8A0AhFsAaDBy5Ejt37+/1UB1oY4cOaKvvvpKcXFxkqT33ntPXbp00eWXX67o6GjFx8friy++0C9+8Ys2va7dbldmZqamTp0qqT58Hjx4UEOGDHFe0717dzkcDpev69evn6qrq/WPf/zDGWAb19S2ZuTIkaqsrFTXrl2VnJzsUxu7d++uoUOH6rPPPmu3OreStG/fPnXr1k3Dhg1rt9cE0PnxQBkANHj00Uf10ksvadGiRfr000/1+eefa/369Zo/f/5Fv3aPHj00bdo0ffLJJ7Lb7Zo1a5YmT57s/JX8okWLtHjxYq1YsUIHDhzQ3r17tXr1ai1fvrzV1x00aJC2bt2qXbt26fPPP9fMmTNd6sVKUnJyst5//30dPnxYX3/9terq6nTNNdcoIiJCjzzyiA4dOqR169ZpzZo1Xu/jX/7lXzR69GhNmjRJW7Zs0eHDh7Vr1y7Nnz/f+TCdOzfddJPeffdd7x3VBna7Xenp6c7lCQAgEW4BwOmmm27Spk2btHXrVl199dW69tprtXz5ciUlJV30aw8aNEhZWVkaP368xo0bp+HDh7uU+vrlL3+p//iP/9CaNWuUmpqqMWPGaM2aNUpJSWn1dRcsWKCRI0fqpptuUkZGhmJiYjRp0iSXax544AGFhYVp6NCh6tevn44cOaIf/ehHWrt2rTZv3uwsH7Zo0SKv92Gz2bR582bdcMMNuuuuu3T55Zfr9ttv1+HDh53rj92ZMWOGNm/e3KLc18UoLCx0WaMMAJJkM80XXQEA2tWiRYu0ceNGn37tb2WTJ0/WiBEjNHfu3It+rTfffFMPPvig/va3v6lrV1bYATiPmVsAgF/89re/Ve/evdvltf7xj39o9erVBFsALfCvAgDAL5KSkvSrX/2qXV5r8uTJ7fI6AKyHZQkAAACwDJYlAAAAwDIItwAAALAMwi0AAAAsg3ALAAAAyyDcAgAAwDIItwAAALAMwi0AAAAsg3ALAAAAy/j/NZncycNKzxkAAAAASUVORK5CYII=",
-      "text/plain": [
-       "<Figure size 800x600 with 1 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "# Generate some made-up data\n",
-    "np.random.seed(0)\n",
-    "temperatures = np.random.uniform(60, 100, 500)  # Randomly choose temperatures between 60 and 100 degrees\n",
-    "ice_cream_sales = temperatures * 1.5 + np.random.normal(0, 10, 500)  # Randomly (kinda) choose sales made\n",
-    "\n",
-    "# Reshape the temperatures for sklearn\n",
-    "temperatures_reshaped = temperatures.reshape(-1, 1)\n",
-    "\n",
-    "# Create a linear regression model\n",
-    "ice_cream_model = LinearRegression()\n",
-    "ice_cream_model.fit(temperatures_reshaped, ice_cream_sales)\n",
-    "predictions = ice_cream_model.predict(temperatures_reshaped)\n",
-    "\n",
-    "# Plot the graph\n",
-    "scatter_plot(\n",
-    "    X={'data': [temperatures, ice_cream_sales], 'color': 'blue', 'label': 'Ice Cream Sales'}, \n",
-    "    line_plot={'x': temperatures, 'y': predictions, 'color': 'red', 'linestyle': '--'},\n",
-    "    title='Temperature vs Ice Cream Sales',\n",
-    "    show_legend=True,\n",
-    "    xlabel='Temperature (°F)',\n",
-    "    ylabel='Ice Cream Sales'\n",
-    ")"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "We can also use regression to classify things, like determining if a student will pass or fail an exam based on their study hours and sleep hours. If you look at the graph below, you can see that the blue line separates the data points. If you fall on the left side of the line, there is a good chance you will fail, and if you land on the right side of the line, there is a good chance you will pass!"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 3,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "image/png": 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",
-      "text/plain": [
-       "<Figure size 800x600 with 1 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "# Generate sample data: Hours studied, hours of sleep, and pass/fail outcomes\n",
-    "np.random.seed(42)\n",
-    "hours_studied = np.random.uniform(1, 10, 500)\n",
-    "hours_sleep = np.random.uniform(4, 10, 500)\n",
-    "pass_fail = (hours_studied + hours_sleep > 12).astype(int)\n",
-    "\n",
-    "# Fit the regression model\n",
-    "study_sleep_data = np.column_stack((hours_studied, hours_sleep))\n",
-    "study_sleep_model = LogisticRegression()\n",
-    "study_sleep_model.fit(study_sleep_data, pass_fail)\n",
-    "\n",
-    "# Line Plot Coordinates\n",
-    "study_sleep_x_values = np.linspace(0, 12, 300)\n",
-    "study_sleep_y_values = -(study_sleep_model.intercept_ + study_sleep_model.coef_[0][0] * study_sleep_x_values) / study_sleep_model.coef_[0][1]\n",
-    "\n",
-    "# Defining fail data and pass data\n",
-    "fail_data = [hours_studied[pass_fail == 0], hours_sleep[pass_fail == 0], \"Fail\"]\n",
-    "pass_data = [hours_studied[pass_fail == 1], hours_sleep[pass_fail == 1], \"Pass\"]\n",
-    "\n",
-    "# Plot the graph\n",
-    "scatter_plot(\n",
-    "    X={'data': [fail_data[0], fail_data[1]], 'color': 'red', 'label': 'Fail'}, \n",
-    "    y={'data': [pass_data[0], pass_data[1]], 'color': 'green', 'label': 'Pass'}, \n",
-    "    line_plot={'x': study_sleep_x_values, 'y': study_sleep_y_values, 'color': 'blue', 'linestyle': '--'},\n",
-    "    title='Decision Boundary', \n",
-    "    show_legend=False\n",
-    ")"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Being able to find patterns in your data and learning from it is powerful. Yes, ice cream sales and exam results may be low stakes, but regression can be used in more critical environments.\n",
-    "\n",
-    "Imagine you have a big source of cancer data at your disposal. You want to see if there is a pattern among them to better aid future patients. This is a big deal, as it allows you to better take care of patients and essentially buy more time by intervening sooner.\n",
-    "\n",
-    "Wouldn't it be nice to answer some key questions? For example, how big does a tumor have to be, to be considered cancerous? How smooth or rough must the texture be for it to be cancerous? \n",
-    "\n",
-    "In this exercise, we will go through breast cancer data to answer our questions."
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## Step 1: Loading the Dataset\n",
-    "In this notebook, we will explore the Breast Cancer dataset, use a Regression method to classify data, and evaluate the model's performance. \n",
-    "\n",
-    "First, we load the Breast Cancer dataset using the `load_breast_cancer()` function from the scikit-learn library. This dataset is a well-known collection of breast cancer data that includes various measurements and features related to tumor characteristics."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 4,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# Load the Breast Cancer dataset\n",
-    "breast_cancer = load_breast_cancer()\n",
-    "X = breast_cancer.data\n",
-    "y = breast_cancer.target\n",
-    "\n",
-    "# Convert the dataset into a DataFrame for visualization\n",
-    "df = pd.DataFrame(data=np.c_[X, y], columns=np.append(breast_cancer.feature_names, 'target'))"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "The data is loaded into variables `X` for the measurements and `y` for the target labels (cancerous or not cancerous). For easier visualization and manipulation, we convert the dataset into a Pandas DataFrame, combining the measurements and target labels into one structured table."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 5,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
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-       "<table border=\"1\" class=\"dataframe\">\n",
-       "  <thead>\n",
-       "    <tr style=\"text-align: right;\">\n",
-       "      <th></th>\n",
-       "      <th>mean radius</th>\n",
-       "      <th>mean texture</th>\n",
-       "      <th>mean perimeter</th>\n",
-       "      <th>mean area</th>\n",
-       "      <th>mean smoothness</th>\n",
-       "      <th>mean compactness</th>\n",
-       "      <th>mean concavity</th>\n",
-       "      <th>mean concave points</th>\n",
-       "      <th>mean symmetry</th>\n",
-       "      <th>mean fractal dimension</th>\n",
-       "      <th>...</th>\n",
-       "      <th>worst texture</th>\n",
-       "      <th>worst perimeter</th>\n",
-       "      <th>worst area</th>\n",
-       "      <th>worst smoothness</th>\n",
-       "      <th>worst compactness</th>\n",
-       "      <th>worst concavity</th>\n",
-       "      <th>worst concave points</th>\n",
-       "      <th>worst symmetry</th>\n",
-       "      <th>worst fractal dimension</th>\n",
-       "      <th>target</th>\n",
-       "    </tr>\n",
-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <th>0</th>\n",
-       "      <td>17.99</td>\n",
-       "      <td>10.38</td>\n",
-       "      <td>122.80</td>\n",
-       "      <td>1001.0</td>\n",
-       "      <td>0.11840</td>\n",
-       "      <td>0.27760</td>\n",
-       "      <td>0.30010</td>\n",
-       "      <td>0.14710</td>\n",
-       "      <td>0.2419</td>\n",
-       "      <td>0.07871</td>\n",
-       "      <td>...</td>\n",
-       "      <td>17.33</td>\n",
-       "      <td>184.60</td>\n",
-       "      <td>2019.0</td>\n",
-       "      <td>0.16220</td>\n",
-       "      <td>0.66560</td>\n",
-       "      <td>0.7119</td>\n",
-       "      <td>0.2654</td>\n",
-       "      <td>0.4601</td>\n",
-       "      <td>0.11890</td>\n",
-       "      <td>0.0</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>1</th>\n",
-       "      <td>20.57</td>\n",
-       "      <td>17.77</td>\n",
-       "      <td>132.90</td>\n",
-       "      <td>1326.0</td>\n",
-       "      <td>0.08474</td>\n",
-       "      <td>0.07864</td>\n",
-       "      <td>0.08690</td>\n",
-       "      <td>0.07017</td>\n",
-       "      <td>0.1812</td>\n",
-       "      <td>0.05667</td>\n",
-       "      <td>...</td>\n",
-       "      <td>23.41</td>\n",
-       "      <td>158.80</td>\n",
-       "      <td>1956.0</td>\n",
-       "      <td>0.12380</td>\n",
-       "      <td>0.18660</td>\n",
-       "      <td>0.2416</td>\n",
-       "      <td>0.1860</td>\n",
-       "      <td>0.2750</td>\n",
-       "      <td>0.08902</td>\n",
-       "      <td>0.0</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>2</th>\n",
-       "      <td>19.69</td>\n",
-       "      <td>21.25</td>\n",
-       "      <td>130.00</td>\n",
-       "      <td>1203.0</td>\n",
-       "      <td>0.10960</td>\n",
-       "      <td>0.15990</td>\n",
-       "      <td>0.19740</td>\n",
-       "      <td>0.12790</td>\n",
-       "      <td>0.2069</td>\n",
-       "      <td>0.05999</td>\n",
-       "      <td>...</td>\n",
-       "      <td>25.53</td>\n",
-       "      <td>152.50</td>\n",
-       "      <td>1709.0</td>\n",
-       "      <td>0.14440</td>\n",
-       "      <td>0.42450</td>\n",
-       "      <td>0.4504</td>\n",
-       "      <td>0.2430</td>\n",
-       "      <td>0.3613</td>\n",
-       "      <td>0.08758</td>\n",
-       "      <td>0.0</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>3</th>\n",
-       "      <td>11.42</td>\n",
-       "      <td>20.38</td>\n",
-       "      <td>77.58</td>\n",
-       "      <td>386.1</td>\n",
-       "      <td>0.14250</td>\n",
-       "      <td>0.28390</td>\n",
-       "      <td>0.24140</td>\n",
-       "      <td>0.10520</td>\n",
-       "      <td>0.2597</td>\n",
-       "      <td>0.09744</td>\n",
-       "      <td>...</td>\n",
-       "      <td>26.50</td>\n",
-       "      <td>98.87</td>\n",
-       "      <td>567.7</td>\n",
-       "      <td>0.20980</td>\n",
-       "      <td>0.86630</td>\n",
-       "      <td>0.6869</td>\n",
-       "      <td>0.2575</td>\n",
-       "      <td>0.6638</td>\n",
-       "      <td>0.17300</td>\n",
-       "      <td>0.0</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>4</th>\n",
-       "      <td>20.29</td>\n",
-       "      <td>14.34</td>\n",
-       "      <td>135.10</td>\n",
-       "      <td>1297.0</td>\n",
-       "      <td>0.10030</td>\n",
-       "      <td>0.13280</td>\n",
-       "      <td>0.19800</td>\n",
-       "      <td>0.10430</td>\n",
-       "      <td>0.1809</td>\n",
-       "      <td>0.05883</td>\n",
-       "      <td>...</td>\n",
-       "      <td>16.67</td>\n",
-       "      <td>152.20</td>\n",
-       "      <td>1575.0</td>\n",
-       "      <td>0.13740</td>\n",
-       "      <td>0.20500</td>\n",
-       "      <td>0.4000</td>\n",
-       "      <td>0.1625</td>\n",
-       "      <td>0.2364</td>\n",
-       "      <td>0.07678</td>\n",
-       "      <td>0.0</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>...</th>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>564</th>\n",
-       "      <td>21.56</td>\n",
-       "      <td>22.39</td>\n",
-       "      <td>142.00</td>\n",
-       "      <td>1479.0</td>\n",
-       "      <td>0.11100</td>\n",
-       "      <td>0.11590</td>\n",
-       "      <td>0.24390</td>\n",
-       "      <td>0.13890</td>\n",
-       "      <td>0.1726</td>\n",
-       "      <td>0.05623</td>\n",
-       "      <td>...</td>\n",
-       "      <td>26.40</td>\n",
-       "      <td>166.10</td>\n",
-       "      <td>2027.0</td>\n",
-       "      <td>0.14100</td>\n",
-       "      <td>0.21130</td>\n",
-       "      <td>0.4107</td>\n",
-       "      <td>0.2216</td>\n",
-       "      <td>0.2060</td>\n",
-       "      <td>0.07115</td>\n",
-       "      <td>0.0</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>565</th>\n",
-       "      <td>20.13</td>\n",
-       "      <td>28.25</td>\n",
-       "      <td>131.20</td>\n",
-       "      <td>1261.0</td>\n",
-       "      <td>0.09780</td>\n",
-       "      <td>0.10340</td>\n",
-       "      <td>0.14400</td>\n",
-       "      <td>0.09791</td>\n",
-       "      <td>0.1752</td>\n",
-       "      <td>0.05533</td>\n",
-       "      <td>...</td>\n",
-       "      <td>38.25</td>\n",
-       "      <td>155.00</td>\n",
-       "      <td>1731.0</td>\n",
-       "      <td>0.11660</td>\n",
-       "      <td>0.19220</td>\n",
-       "      <td>0.3215</td>\n",
-       "      <td>0.1628</td>\n",
-       "      <td>0.2572</td>\n",
-       "      <td>0.06637</td>\n",
-       "      <td>0.0</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>566</th>\n",
-       "      <td>16.60</td>\n",
-       "      <td>28.08</td>\n",
-       "      <td>108.30</td>\n",
-       "      <td>858.1</td>\n",
-       "      <td>0.08455</td>\n",
-       "      <td>0.10230</td>\n",
-       "      <td>0.09251</td>\n",
-       "      <td>0.05302</td>\n",
-       "      <td>0.1590</td>\n",
-       "      <td>0.05648</td>\n",
-       "      <td>...</td>\n",
-       "      <td>34.12</td>\n",
-       "      <td>126.70</td>\n",
-       "      <td>1124.0</td>\n",
-       "      <td>0.11390</td>\n",
-       "      <td>0.30940</td>\n",
-       "      <td>0.3403</td>\n",
-       "      <td>0.1418</td>\n",
-       "      <td>0.2218</td>\n",
-       "      <td>0.07820</td>\n",
-       "      <td>0.0</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>567</th>\n",
-       "      <td>20.60</td>\n",
-       "      <td>29.33</td>\n",
-       "      <td>140.10</td>\n",
-       "      <td>1265.0</td>\n",
-       "      <td>0.11780</td>\n",
-       "      <td>0.27700</td>\n",
-       "      <td>0.35140</td>\n",
-       "      <td>0.15200</td>\n",
-       "      <td>0.2397</td>\n",
-       "      <td>0.07016</td>\n",
-       "      <td>...</td>\n",
-       "      <td>39.42</td>\n",
-       "      <td>184.60</td>\n",
-       "      <td>1821.0</td>\n",
-       "      <td>0.16500</td>\n",
-       "      <td>0.86810</td>\n",
-       "      <td>0.9387</td>\n",
-       "      <td>0.2650</td>\n",
-       "      <td>0.4087</td>\n",
-       "      <td>0.12400</td>\n",
-       "      <td>0.0</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>568</th>\n",
-       "      <td>7.76</td>\n",
-       "      <td>24.54</td>\n",
-       "      <td>47.92</td>\n",
-       "      <td>181.0</td>\n",
-       "      <td>0.05263</td>\n",
-       "      <td>0.04362</td>\n",
-       "      <td>0.00000</td>\n",
-       "      <td>0.00000</td>\n",
-       "      <td>0.1587</td>\n",
-       "      <td>0.05884</td>\n",
-       "      <td>...</td>\n",
-       "      <td>30.37</td>\n",
-       "      <td>59.16</td>\n",
-       "      <td>268.6</td>\n",
-       "      <td>0.08996</td>\n",
-       "      <td>0.06444</td>\n",
-       "      <td>0.0000</td>\n",
-       "      <td>0.0000</td>\n",
-       "      <td>0.2871</td>\n",
-       "      <td>0.07039</td>\n",
-       "      <td>1.0</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "<p>569 rows × 31 columns</p>\n",
-       "</div>"
-      ],
-      "text/plain": [
-       "     mean radius  mean texture  mean perimeter  mean area  mean smoothness  \\\n",
-       "0          17.99         10.38          122.80     1001.0          0.11840   \n",
-       "1          20.57         17.77          132.90     1326.0          0.08474   \n",
-       "2          19.69         21.25          130.00     1203.0          0.10960   \n",
-       "3          11.42         20.38           77.58      386.1          0.14250   \n",
-       "4          20.29         14.34          135.10     1297.0          0.10030   \n",
-       "..           ...           ...             ...        ...              ...   \n",
-       "564        21.56         22.39          142.00     1479.0          0.11100   \n",
-       "565        20.13         28.25          131.20     1261.0          0.09780   \n",
-       "566        16.60         28.08          108.30      858.1          0.08455   \n",
-       "567        20.60         29.33          140.10     1265.0          0.11780   \n",
-       "568         7.76         24.54           47.92      181.0          0.05263   \n",
-       "\n",
-       "     mean compactness  mean concavity  mean concave points  mean symmetry  \\\n",
-       "0             0.27760         0.30010              0.14710         0.2419   \n",
-       "1             0.07864         0.08690              0.07017         0.1812   \n",
-       "2             0.15990         0.19740              0.12790         0.2069   \n",
-       "3             0.28390         0.24140              0.10520         0.2597   \n",
-       "4             0.13280         0.19800              0.10430         0.1809   \n",
-       "..                ...             ...                  ...            ...   \n",
-       "564           0.11590         0.24390              0.13890         0.1726   \n",
-       "565           0.10340         0.14400              0.09791         0.1752   \n",
-       "566           0.10230         0.09251              0.05302         0.1590   \n",
-       "567           0.27700         0.35140              0.15200         0.2397   \n",
-       "568           0.04362         0.00000              0.00000         0.1587   \n",
-       "\n",
-       "     mean fractal dimension  ...  worst texture  worst perimeter  worst area  \\\n",
-       "0                   0.07871  ...          17.33           184.60      2019.0   \n",
-       "1                   0.05667  ...          23.41           158.80      1956.0   \n",
-       "2                   0.05999  ...          25.53           152.50      1709.0   \n",
-       "3                   0.09744  ...          26.50            98.87       567.7   \n",
-       "4                   0.05883  ...          16.67           152.20      1575.0   \n",
-       "..                      ...  ...            ...              ...         ...   \n",
-       "564                 0.05623  ...          26.40           166.10      2027.0   \n",
-       "565                 0.05533  ...          38.25           155.00      1731.0   \n",
-       "566                 0.05648  ...          34.12           126.70      1124.0   \n",
-       "567                 0.07016  ...          39.42           184.60      1821.0   \n",
-       "568                 0.05884  ...          30.37            59.16       268.6   \n",
-       "\n",
-       "     worst smoothness  worst compactness  worst concavity  \\\n",
-       "0             0.16220            0.66560           0.7119   \n",
-       "1             0.12380            0.18660           0.2416   \n",
-       "2             0.14440            0.42450           0.4504   \n",
-       "3             0.20980            0.86630           0.6869   \n",
-       "4             0.13740            0.20500           0.4000   \n",
-       "..                ...                ...              ...   \n",
-       "564           0.14100            0.21130           0.4107   \n",
-       "565           0.11660            0.19220           0.3215   \n",
-       "566           0.11390            0.30940           0.3403   \n",
-       "567           0.16500            0.86810           0.9387   \n",
-       "568           0.08996            0.06444           0.0000   \n",
-       "\n",
-       "     worst concave points  worst symmetry  worst fractal dimension  target  \n",
-       "0                  0.2654          0.4601                  0.11890     0.0  \n",
-       "1                  0.1860          0.2750                  0.08902     0.0  \n",
-       "2                  0.2430          0.3613                  0.08758     0.0  \n",
-       "3                  0.2575          0.6638                  0.17300     0.0  \n",
-       "4                  0.1625          0.2364                  0.07678     0.0  \n",
-       "..                    ...             ...                      ...     ...  \n",
-       "564                0.2216          0.2060                  0.07115     0.0  \n",
-       "565                0.1628          0.2572                  0.06637     0.0  \n",
-       "566                0.1418          0.2218                  0.07820     0.0  \n",
-       "567                0.2650          0.4087                  0.12400     0.0  \n",
-       "568                0.0000          0.2871                  0.07039     1.0  \n",
-       "\n",
-       "[569 rows x 31 columns]"
-      ]
-     },
-     "execution_count": 5,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "df"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## Step 2: Exploring the Dataset\n",
-    "Next, let's take a quick look at the dataset to understand its structure and contents."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 9,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
-       "<div>\n",
-       "<style scoped>\n",
-       "    .dataframe tbody tr th:only-of-type {\n",
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-       "\n",
-       "    .dataframe thead th {\n",
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-       "</style>\n",
-       "<table border=\"1\" class=\"dataframe\">\n",
-       "  <thead>\n",
-       "    <tr style=\"text-align: right;\">\n",
-       "      <th></th>\n",
-       "      <th>mean radius</th>\n",
-       "      <th>mean texture</th>\n",
-       "      <th>mean perimeter</th>\n",
-       "      <th>mean area</th>\n",
-       "      <th>mean smoothness</th>\n",
-       "      <th>mean compactness</th>\n",
-       "      <th>mean concavity</th>\n",
-       "      <th>mean concave points</th>\n",
-       "      <th>mean symmetry</th>\n",
-       "      <th>mean fractal dimension</th>\n",
-       "      <th>...</th>\n",
-       "      <th>worst texture</th>\n",
-       "      <th>worst perimeter</th>\n",
-       "      <th>worst area</th>\n",
-       "      <th>worst smoothness</th>\n",
-       "      <th>worst compactness</th>\n",
-       "      <th>worst concavity</th>\n",
-       "      <th>worst concave points</th>\n",
-       "      <th>worst symmetry</th>\n",
-       "      <th>worst fractal dimension</th>\n",
-       "      <th>target</th>\n",
-       "    </tr>\n",
-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <th>0</th>\n",
-       "      <td>17.99</td>\n",
-       "      <td>10.38</td>\n",
-       "      <td>122.80</td>\n",
-       "      <td>1001.0</td>\n",
-       "      <td>0.11840</td>\n",
-       "      <td>0.27760</td>\n",
-       "      <td>0.30010</td>\n",
-       "      <td>0.14710</td>\n",
-       "      <td>0.2419</td>\n",
-       "      <td>0.07871</td>\n",
-       "      <td>...</td>\n",
-       "      <td>17.33</td>\n",
-       "      <td>184.60</td>\n",
-       "      <td>2019.0</td>\n",
-       "      <td>0.16220</td>\n",
-       "      <td>0.66560</td>\n",
-       "      <td>0.7119</td>\n",
-       "      <td>0.2654</td>\n",
-       "      <td>0.4601</td>\n",
-       "      <td>0.11890</td>\n",
-       "      <td>0.0</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>1</th>\n",
-       "      <td>20.57</td>\n",
-       "      <td>17.77</td>\n",
-       "      <td>132.90</td>\n",
-       "      <td>1326.0</td>\n",
-       "      <td>0.08474</td>\n",
-       "      <td>0.07864</td>\n",
-       "      <td>0.08690</td>\n",
-       "      <td>0.07017</td>\n",
-       "      <td>0.1812</td>\n",
-       "      <td>0.05667</td>\n",
-       "      <td>...</td>\n",
-       "      <td>23.41</td>\n",
-       "      <td>158.80</td>\n",
-       "      <td>1956.0</td>\n",
-       "      <td>0.12380</td>\n",
-       "      <td>0.18660</td>\n",
-       "      <td>0.2416</td>\n",
-       "      <td>0.1860</td>\n",
-       "      <td>0.2750</td>\n",
-       "      <td>0.08902</td>\n",
-       "      <td>0.0</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>2</th>\n",
-       "      <td>19.69</td>\n",
-       "      <td>21.25</td>\n",
-       "      <td>130.00</td>\n",
-       "      <td>1203.0</td>\n",
-       "      <td>0.10960</td>\n",
-       "      <td>0.15990</td>\n",
-       "      <td>0.19740</td>\n",
-       "      <td>0.12790</td>\n",
-       "      <td>0.2069</td>\n",
-       "      <td>0.05999</td>\n",
-       "      <td>...</td>\n",
-       "      <td>25.53</td>\n",
-       "      <td>152.50</td>\n",
-       "      <td>1709.0</td>\n",
-       "      <td>0.14440</td>\n",
-       "      <td>0.42450</td>\n",
-       "      <td>0.4504</td>\n",
-       "      <td>0.2430</td>\n",
-       "      <td>0.3613</td>\n",
-       "      <td>0.08758</td>\n",
-       "      <td>0.0</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>3</th>\n",
-       "      <td>11.42</td>\n",
-       "      <td>20.38</td>\n",
-       "      <td>77.58</td>\n",
-       "      <td>386.1</td>\n",
-       "      <td>0.14250</td>\n",
-       "      <td>0.28390</td>\n",
-       "      <td>0.24140</td>\n",
-       "      <td>0.10520</td>\n",
-       "      <td>0.2597</td>\n",
-       "      <td>0.09744</td>\n",
-       "      <td>...</td>\n",
-       "      <td>26.50</td>\n",
-       "      <td>98.87</td>\n",
-       "      <td>567.7</td>\n",
-       "      <td>0.20980</td>\n",
-       "      <td>0.86630</td>\n",
-       "      <td>0.6869</td>\n",
-       "      <td>0.2575</td>\n",
-       "      <td>0.6638</td>\n",
-       "      <td>0.17300</td>\n",
-       "      <td>0.0</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>4</th>\n",
-       "      <td>20.29</td>\n",
-       "      <td>14.34</td>\n",
-       "      <td>135.10</td>\n",
-       "      <td>1297.0</td>\n",
-       "      <td>0.10030</td>\n",
-       "      <td>0.13280</td>\n",
-       "      <td>0.19800</td>\n",
-       "      <td>0.10430</td>\n",
-       "      <td>0.1809</td>\n",
-       "      <td>0.05883</td>\n",
-       "      <td>...</td>\n",
-       "      <td>16.67</td>\n",
-       "      <td>152.20</td>\n",
-       "      <td>1575.0</td>\n",
-       "      <td>0.13740</td>\n",
-       "      <td>0.20500</td>\n",
-       "      <td>0.4000</td>\n",
-       "      <td>0.1625</td>\n",
-       "      <td>0.2364</td>\n",
-       "      <td>0.07678</td>\n",
-       "      <td>0.0</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>...</th>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>564</th>\n",
-       "      <td>21.56</td>\n",
-       "      <td>22.39</td>\n",
-       "      <td>142.00</td>\n",
-       "      <td>1479.0</td>\n",
-       "      <td>0.11100</td>\n",
-       "      <td>0.11590</td>\n",
-       "      <td>0.24390</td>\n",
-       "      <td>0.13890</td>\n",
-       "      <td>0.1726</td>\n",
-       "      <td>0.05623</td>\n",
-       "      <td>...</td>\n",
-       "      <td>26.40</td>\n",
-       "      <td>166.10</td>\n",
-       "      <td>2027.0</td>\n",
-       "      <td>0.14100</td>\n",
-       "      <td>0.21130</td>\n",
-       "      <td>0.4107</td>\n",
-       "      <td>0.2216</td>\n",
-       "      <td>0.2060</td>\n",
-       "      <td>0.07115</td>\n",
-       "      <td>0.0</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>565</th>\n",
-       "      <td>20.13</td>\n",
-       "      <td>28.25</td>\n",
-       "      <td>131.20</td>\n",
-       "      <td>1261.0</td>\n",
-       "      <td>0.09780</td>\n",
-       "      <td>0.10340</td>\n",
-       "      <td>0.14400</td>\n",
-       "      <td>0.09791</td>\n",
-       "      <td>0.1752</td>\n",
-       "      <td>0.05533</td>\n",
-       "      <td>...</td>\n",
-       "      <td>38.25</td>\n",
-       "      <td>155.00</td>\n",
-       "      <td>1731.0</td>\n",
-       "      <td>0.11660</td>\n",
-       "      <td>0.19220</td>\n",
-       "      <td>0.3215</td>\n",
-       "      <td>0.1628</td>\n",
-       "      <td>0.2572</td>\n",
-       "      <td>0.06637</td>\n",
-       "      <td>0.0</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>566</th>\n",
-       "      <td>16.60</td>\n",
-       "      <td>28.08</td>\n",
-       "      <td>108.30</td>\n",
-       "      <td>858.1</td>\n",
-       "      <td>0.08455</td>\n",
-       "      <td>0.10230</td>\n",
-       "      <td>0.09251</td>\n",
-       "      <td>0.05302</td>\n",
-       "      <td>0.1590</td>\n",
-       "      <td>0.05648</td>\n",
-       "      <td>...</td>\n",
-       "      <td>34.12</td>\n",
-       "      <td>126.70</td>\n",
-       "      <td>1124.0</td>\n",
-       "      <td>0.11390</td>\n",
-       "      <td>0.30940</td>\n",
-       "      <td>0.3403</td>\n",
-       "      <td>0.1418</td>\n",
-       "      <td>0.2218</td>\n",
-       "      <td>0.07820</td>\n",
-       "      <td>0.0</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>567</th>\n",
-       "      <td>20.60</td>\n",
-       "      <td>29.33</td>\n",
-       "      <td>140.10</td>\n",
-       "      <td>1265.0</td>\n",
-       "      <td>0.11780</td>\n",
-       "      <td>0.27700</td>\n",
-       "      <td>0.35140</td>\n",
-       "      <td>0.15200</td>\n",
-       "      <td>0.2397</td>\n",
-       "      <td>0.07016</td>\n",
-       "      <td>...</td>\n",
-       "      <td>39.42</td>\n",
-       "      <td>184.60</td>\n",
-       "      <td>1821.0</td>\n",
-       "      <td>0.16500</td>\n",
-       "      <td>0.86810</td>\n",
-       "      <td>0.9387</td>\n",
-       "      <td>0.2650</td>\n",
-       "      <td>0.4087</td>\n",
-       "      <td>0.12400</td>\n",
-       "      <td>0.0</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>568</th>\n",
-       "      <td>7.76</td>\n",
-       "      <td>24.54</td>\n",
-       "      <td>47.92</td>\n",
-       "      <td>181.0</td>\n",
-       "      <td>0.05263</td>\n",
-       "      <td>0.04362</td>\n",
-       "      <td>0.00000</td>\n",
-       "      <td>0.00000</td>\n",
-       "      <td>0.1587</td>\n",
-       "      <td>0.05884</td>\n",
-       "      <td>...</td>\n",
-       "      <td>30.37</td>\n",
-       "      <td>59.16</td>\n",
-       "      <td>268.6</td>\n",
-       "      <td>0.08996</td>\n",
-       "      <td>0.06444</td>\n",
-       "      <td>0.0000</td>\n",
-       "      <td>0.0000</td>\n",
-       "      <td>0.2871</td>\n",
-       "      <td>0.07039</td>\n",
-       "      <td>1.0</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "<p>569 rows × 31 columns</p>\n",
-       "</div>"
-      ],
-      "text/plain": [
-       "     mean radius  mean texture  mean perimeter  mean area  mean smoothness  \\\n",
-       "0          17.99         10.38          122.80     1001.0          0.11840   \n",
-       "1          20.57         17.77          132.90     1326.0          0.08474   \n",
-       "2          19.69         21.25          130.00     1203.0          0.10960   \n",
-       "3          11.42         20.38           77.58      386.1          0.14250   \n",
-       "4          20.29         14.34          135.10     1297.0          0.10030   \n",
-       "..           ...           ...             ...        ...              ...   \n",
-       "564        21.56         22.39          142.00     1479.0          0.11100   \n",
-       "565        20.13         28.25          131.20     1261.0          0.09780   \n",
-       "566        16.60         28.08          108.30      858.1          0.08455   \n",
-       "567        20.60         29.33          140.10     1265.0          0.11780   \n",
-       "568         7.76         24.54           47.92      181.0          0.05263   \n",
-       "\n",
-       "     mean compactness  mean concavity  mean concave points  mean symmetry  \\\n",
-       "0             0.27760         0.30010              0.14710         0.2419   \n",
-       "1             0.07864         0.08690              0.07017         0.1812   \n",
-       "2             0.15990         0.19740              0.12790         0.2069   \n",
-       "3             0.28390         0.24140              0.10520         0.2597   \n",
-       "4             0.13280         0.19800              0.10430         0.1809   \n",
-       "..                ...             ...                  ...            ...   \n",
-       "564           0.11590         0.24390              0.13890         0.1726   \n",
-       "565           0.10340         0.14400              0.09791         0.1752   \n",
-       "566           0.10230         0.09251              0.05302         0.1590   \n",
-       "567           0.27700         0.35140              0.15200         0.2397   \n",
-       "568           0.04362         0.00000              0.00000         0.1587   \n",
-       "\n",
-       "     mean fractal dimension  ...  worst texture  worst perimeter  worst area  \\\n",
-       "0                   0.07871  ...          17.33           184.60      2019.0   \n",
-       "1                   0.05667  ...          23.41           158.80      1956.0   \n",
-       "2                   0.05999  ...          25.53           152.50      1709.0   \n",
-       "3                   0.09744  ...          26.50            98.87       567.7   \n",
-       "4                   0.05883  ...          16.67           152.20      1575.0   \n",
-       "..                      ...  ...            ...              ...         ...   \n",
-       "564                 0.05623  ...          26.40           166.10      2027.0   \n",
-       "565                 0.05533  ...          38.25           155.00      1731.0   \n",
-       "566                 0.05648  ...          34.12           126.70      1124.0   \n",
-       "567                 0.07016  ...          39.42           184.60      1821.0   \n",
-       "568                 0.05884  ...          30.37            59.16       268.6   \n",
-       "\n",
-       "     worst smoothness  worst compactness  worst concavity  \\\n",
-       "0             0.16220            0.66560           0.7119   \n",
-       "1             0.12380            0.18660           0.2416   \n",
-       "2             0.14440            0.42450           0.4504   \n",
-       "3             0.20980            0.86630           0.6869   \n",
-       "4             0.13740            0.20500           0.4000   \n",
-       "..                ...                ...              ...   \n",
-       "564           0.14100            0.21130           0.4107   \n",
-       "565           0.11660            0.19220           0.3215   \n",
-       "566           0.11390            0.30940           0.3403   \n",
-       "567           0.16500            0.86810           0.9387   \n",
-       "568           0.08996            0.06444           0.0000   \n",
-       "\n",
-       "     worst concave points  worst symmetry  worst fractal dimension  target  \n",
-       "0                  0.2654          0.4601                  0.11890     0.0  \n",
-       "1                  0.1860          0.2750                  0.08902     0.0  \n",
-       "2                  0.2430          0.3613                  0.08758     0.0  \n",
-       "3                  0.2575          0.6638                  0.17300     0.0  \n",
-       "4                  0.1625          0.2364                  0.07678     0.0  \n",
-       "..                    ...             ...                      ...     ...  \n",
-       "564                0.2216          0.2060                  0.07115     0.0  \n",
-       "565                0.1628          0.2572                  0.06637     0.0  \n",
-       "566                0.1418          0.2218                  0.07820     0.0  \n",
-       "567                0.2650          0.4087                  0.12400     0.0  \n",
-       "568                0.0000          0.2871                  0.07039     1.0  \n",
-       "\n",
-       "[569 rows x 31 columns]"
-      ]
-     },
-     "execution_count": 9,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "# Let's look at the data\n",
-    "df"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Each row in the table you created represents an individual sample (or patient) from the breast cancer dataset. The columns correspond to various features measured or calculated for that sample, except for the last column labeled “target,” which indicates the classification of the sample (0 indicates a benign tumor, which is not cancerous, and 1 indicates a malignant tumor, which is cancerous.)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 10,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "       mean radius  mean texture  mean perimeter    mean area  \\\n",
-      "count   569.000000    569.000000      569.000000   569.000000   \n",
-      "mean     14.127292     19.289649       91.969033   654.889104   \n",
-      "std       3.524049      4.301036       24.298981   351.914129   \n",
-      "min       6.981000      9.710000       43.790000   143.500000   \n",
-      "25%      11.700000     16.170000       75.170000   420.300000   \n",
-      "50%      13.370000     18.840000       86.240000   551.100000   \n",
-      "75%      15.780000     21.800000      104.100000   782.700000   \n",
-      "max      28.110000     39.280000      188.500000  2501.000000   \n",
-      "\n",
-      "       mean smoothness  mean compactness  mean concavity  mean concave points  \\\n",
-      "count       569.000000        569.000000      569.000000           569.000000   \n",
-      "mean          0.096360          0.104341        0.088799             0.048919   \n",
-      "std           0.014064          0.052813        0.079720             0.038803   \n",
-      "min           0.052630          0.019380        0.000000             0.000000   \n",
-      "25%           0.086370          0.064920        0.029560             0.020310   \n",
-      "50%           0.095870          0.092630        0.061540             0.033500   \n",
-      "75%           0.105300          0.130400        0.130700             0.074000   \n",
-      "max           0.163400          0.345400        0.426800             0.201200   \n",
-      "\n",
-      "       mean symmetry  mean fractal dimension  ...  worst texture  \\\n",
-      "count     569.000000              569.000000  ...     569.000000   \n",
-      "mean        0.181162                0.062798  ...      25.677223   \n",
-      "std         0.027414                0.007060  ...       6.146258   \n",
-      "min         0.106000                0.049960  ...      12.020000   \n",
-      "25%         0.161900                0.057700  ...      21.080000   \n",
-      "50%         0.179200                0.061540  ...      25.410000   \n",
-      "75%         0.195700                0.066120  ...      29.720000   \n",
-      "max         0.304000                0.097440  ...      49.540000   \n",
-      "\n",
-      "       worst perimeter   worst area  worst smoothness  worst compactness  \\\n",
-      "count       569.000000   569.000000        569.000000         569.000000   \n",
-      "mean        107.261213   880.583128          0.132369           0.254265   \n",
-      "std          33.602542   569.356993          0.022832           0.157336   \n",
-      "min          50.410000   185.200000          0.071170           0.027290   \n",
-      "25%          84.110000   515.300000          0.116600           0.147200   \n",
-      "50%          97.660000   686.500000          0.131300           0.211900   \n",
-      "75%         125.400000  1084.000000          0.146000           0.339100   \n",
-      "max         251.200000  4254.000000          0.222600           1.058000   \n",
-      "\n",
-      "       worst concavity  worst concave points  worst symmetry  \\\n",
-      "count       569.000000            569.000000      569.000000   \n",
-      "mean          0.272188              0.114606        0.290076   \n",
-      "std           0.208624              0.065732        0.061867   \n",
-      "min           0.000000              0.000000        0.156500   \n",
-      "25%           0.114500              0.064930        0.250400   \n",
-      "50%           0.226700              0.099930        0.282200   \n",
-      "75%           0.382900              0.161400        0.317900   \n",
-      "max           1.252000              0.291000        0.663800   \n",
-      "\n",
-      "       worst fractal dimension      target  \n",
-      "count               569.000000  569.000000  \n",
-      "mean                  0.083946    0.627417  \n",
-      "std                   0.018061    0.483918  \n",
-      "min                   0.055040    0.000000  \n",
-      "25%                   0.071460    0.000000  \n",
-      "50%                   0.080040    1.000000  \n",
-      "75%                   0.092080    1.000000  \n",
-      "max                   0.207500    1.000000  \n",
-      "\n",
-      "[8 rows x 31 columns]\n"
-     ]
-    }
-   ],
-   "source": [
-    "# Display the summary statistics of the dataset\n",
-    "print(df.describe())"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 11,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "image/png": 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",
-      "text/plain": [
-       "<Figure size 640x480 with 1 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "# Visualize the distribution of target classes\n",
-    "sns.countplot(x='target', data=df)\n",
-    "plt.title('Distribution of Target Classes')\n",
-    "plt.xlabel('Target')\n",
-    "plt.ylabel('Count')\n",
-    "plt.xticks(ticks=[1, 0], labels=breast_cancer.target_names)\n",
-    "plt.show()"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "The Breast Cancer dataset includes a large number of measurements related to tumor characteristics, making it challenging to visualize all of them at once. \n",
-    "\n",
-    "To simplify our task and create a clear visualization, we decided to use the first two columns in the dataset: _\"average radius\"_ and _\"average texture\"_. This choice is made for convenience, allowing us to quickly build and visualize our model. Focusing on just these two measurements makes the visualization clearer and more interpretable while still providing meaningful insights."
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "So with that in mind lets draw a scatter plot of our data to see things better. "
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 12,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "image/png": 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",
-      "text/plain": [
-       "<Figure size 800x600 with 1 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "scatter_plot(\n",
-    "    X={'data': [X[:, 0], X[:, 1]], 'color': 'blue', 'label': \"mean_radius\"}, \n",
-    "    title=\"mean_texture vs mean_radius\", \n",
-    ")"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "By using the benign and malignant labels to color each data point, we can see how average radius and average texture (or mean radius and mean texture) affect (or don't affect) the classification."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 13,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "image/png": 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",
-      "text/plain": [
-       "<Figure size 800x600 with 1 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "# Plot the color-coded data points (with colors indicating benign and malignant tumors)\n",
-    "\n",
-    "feature1_index = np.argwhere(breast_cancer.feature_names == \"mean radius\").flatten()[0]\n",
-    "feature2_index = np.argwhere(breast_cancer.feature_names == \"mean texture\").flatten()[0]\n",
-    "\n",
-    "# Defining benign data and malignant data\n",
-    "benign_data = [X[y == 0][:, feature1_index], X[y == 0][:, feature2_index], \"Benign\"]\n",
-    "malignant_data = [X[y == 1][:, feature1_index], X[y == 1][:, feature2_index], \"Malignant\"]\n",
-    "\n",
-    "# Plot the graph\n",
-    "scatter_plot(\n",
-    "    X={'data': [benign_data[0], benign_data[1]], 'color': 'green', 'label': 'Benign'}, \n",
-    "    y={'data': [malignant_data[0], malignant_data[1]], 'color': 'red', 'label': 'Malignant'}, \n",
-    "    title=\"mean texture vs mean radius\", \n",
-    "    show_legend=True\n",
-    ")"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Now that we can see our data visually, we notice that there may be a way to classify the benign and malignant cases based on mean radius and mean texture. As humans, we can visually observe potential patterns in the data points. To formalize this observation, we will use regression as a tool to help us classify these cases. The next step is to split the dataset into training and testing sets."
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## Step 3: Splitting the Dataset\n",
-    "Now, we will split the dataset into **training** and **testing** sets for model training and evaluation. This is important because it allows us to train the model on one part of the data and test it on another part to see how well it performs on new data. This helps us understand the model's ability to make accurate predictions on data it hasn't seen before. We will learn more about this process and its significance later on in the module.\n",
-    "\n",
-    "In our example today, we will use 80% of the data for training and 20% for testing."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 14,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Dataset split was successful\n"
-     ]
-    }
-   ],
-   "source": [
-    "# y = y_train + y_test\n",
-    "try:\n",
-    "    # Split the dataset into training and testing sets\n",
-    "    X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)\n",
-    "\n",
-    "    # random_state=42 ensures the split is reproducible, meaning the data is split the same way each time the code is run\n",
-    "    \n",
-    "    print(\"Dataset split was successful\")\n",
-    "except Exception as e:\n",
-    "    print(f\"An error occurred: {e}\")"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## Step 4: Building and Training a Classification Model\n",
-    "Next, we will build and train a regression model for breast cancer classification. Specifically, we will use a **logistic regression** model that classifies the data.\n",
-    "\n"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 15,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Model training was successful\n"
-     ]
-    }
-   ],
-   "source": [
-    "try:\n",
-    "    # Initialize the model\n",
-    "    model = LogisticRegression(max_iter=10000)  # Try up to 10,000 times to find the best fit\n",
-    "\n",
-    "    # Select specific variables (mean radius and mean texture) for training\n",
-    "    X_train_selected = X_train[:, [0, 1]]  # Columns 0 and 1 correspond to mean radius and mean texture\n",
-    "\n",
-    "    # Train the model on the selected variables of the training data\n",
-    "    model.fit(X_train_selected, y_train)\n",
-    "    \n",
-    "    print(\"Model training was successful\")\n",
-    "except Exception as e:\n",
-    "    print(f\"An error occurred: {e}\")"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Now, if everything went well, we should have successfully initialized and trained our regression model using the selected variables (mean radius and mean texture). The model has learned from the training data and is now ready to make predictions. In the next step, we will evaluate the performance of our model to see how well it can classify new data points."
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## Step 5: Evaluating the Model\n",
-    "Now, let's evaluate the performance of the trained model on the testing data.\n",
-    "\n",
-    "We will use the `model.predict` method to make predictions on the testing data. This method takes the testing data as input and outputs the predicted classifications for each data point."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 16,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# Select specific variables (mean radius and mean texture) for training\n",
-    "X_test_selected = X_test[:, [0, 1]]  # Columns 0 and 1 correspond to mean radius and mean texture"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 17,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Prediction made successfully, results are in y_pred\n"
-     ]
-    }
-   ],
-   "source": [
-    "try:\n",
-    "    # Make a prediction\n",
-    "    y_pred = model.predict(X_test_selected)\n",
-    "    \n",
-    "    print(\"Prediction made successfully, results are in y_pred\")\n",
-    "except Exception as e:\n",
-    "    print(f\"An error occurred: {e}\")"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "We will evaluate it using something called a **confusion matrix**. A confusion matrix allows us to see how well our model is performing by comparing the predicted classifications to the actual classifications. "
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 18,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "image/png": 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",
-      "text/plain": [
-       "<Figure size 640x480 with 1 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "# Calculate the confusion matrix\n",
-    "cm = confusion_matrix(y_test, y_pred)\n",
-    "\n",
-    "# Plot the heatmap\n",
-    "sns.heatmap(cm, annot=True, fmt='d', \n",
-    "            cmap='Greens', \n",
-    "            cbar=False, \n",
-    "            xticklabels=['benign', 'malignant'], \n",
-    "            yticklabels=['benign', 'malignant'])\n",
-    "plt.title('Confusion Matrix')\n",
-    "plt.xlabel('Predicted')\n",
-    "plt.ylabel('Actual')\n",
-    "plt.show()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 19,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "array(['malignant', 'benign'], dtype='<U9')"
-      ]
-     },
-     "execution_count": 19,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "breast_cancer.target_names"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 22,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "0.6228070175438597"
-      ]
-     },
-     "execution_count": 22,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "y_test.mean()"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "We will also calculate its **accuracy** and generate a **classification report**.\n",
-    "\n",
-    "First, we calculate the accuracy of the model using the `accuracy_score` function. This function compares the true labels (`y_test`) with the predicted labels (`y_pred`) and returns the percentage of correct predictions. The accuracy score provides a simple metric to understand how often the model makes correct predictions."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 23,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Accuracy: 0.9122807017543859\n"
-     ]
-    }
-   ],
-   "source": [
-    "# Calculate model Accuracy\n",
-    "accuracy = accuracy_score(y_test, y_pred)\n",
-    "print(\"Accuracy:\", accuracy)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Next, we generate a classification report using the `print_classification_report` function. This report provides a more detailed evaluation of the model, including precision, recall, and F1-score for each class. These metrics help us understand the performance of the model beyond just accuracy, giving insight into how well the model identifies each class. You don’t need to know the details of these metrics right now, but it’s good to be aware of them. They may come in handy down the road, so consider reading more about them when you have time."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 24,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "              precision    recall  f1-score   support\n",
-      "\n",
-      "   malignant       0.90      0.86      0.88        43\n",
-      "      benign       0.92      0.94      0.93        71\n",
-      "\n",
-      "    accuracy                           0.91       114\n",
-      "   macro avg       0.91      0.90      0.91       114\n",
-      "weighted avg       0.91      0.91      0.91       114\n",
-      "\n"
-     ]
-    }
-   ],
-   "source": [
-    "# Generate a classification report\n",
-    "the_report = classification_report(y_test, y_pred, target_names=breast_cancer.target_names)\n",
-    "print(the_report)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## Step 6: Visualizing the Decision Boundary\n",
-    "\n",
-    "Now that we see the accuracy is satisfactory, we will take the next step to visualize our model on the graph. By plotting the decision boundary of the regression model, we can better understand how the model classifies the data points and observe the separation between benign and malignant cases. This visualization will help us **see** the effectiveness of our model in distinguishing between the two classes."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 25,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "image/png": 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",
-      "text/plain": [
-       "<Figure size 800x600 with 1 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "# Line Plot Coordinates\n",
-    "x_values = np.linspace(X[:, feature1_index].min(), X[:, feature1_index].max(), 100)\n",
-    "y_values = -(model.intercept_[0] + model.coef_[0][0] * x_values) / model.coef_[0][1]  # Decision boundary equation\n",
-    "\n",
-    "# Plot the graph\n",
-    "scatter_plot(\n",
-    "    X={'data': [benign_data[0], benign_data[1]], 'color': 'green', 'label': 'Benign'}, \n",
-    "    y={'data': [malignant_data[0], malignant_data[1]], 'color': 'red', 'label': 'Malignant'}, \n",
-    "    line_plot={'x': x_values, 'y': y_values, 'color': 'blue', 'linestyle': '--'},\n",
-    "    title=\"mean texture vs mean radius\", \n",
-    "    show_legend=True\n",
-    ")"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "We can see why the accuracy wasn't 100% by looking at the graph. The data points are not perfectly separable by a linear line, indicating that there is some overlap between the benign and malignant cases. This overlap means that a simple linear model will never be able to achieve 100% accuracy with this dataset. The complexity and nature of the data make it challenging for a linear boundary to perfectly classify all instances."
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "What if we repeat all of the steps above **using different columns**? The following Python cell will randomly select two columns and run regression on them to classify the data. Each time you run the cell, it will select different columns for the classification, and you will observe that the accuracy changes accordingly. This is because different variables have varying degrees of correlation with the target variable, impacting the model's performance.\n",
-    "\n",
-    "run the cell below many times..."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 26,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Accuracy on test set: 0.7280701754385965\n"
-     ]
-    },
-    {
-     "data": {
-      "image/png": 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",
-      "text/plain": [
-       "<Figure size 800x600 with 1 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "The graph plots \"texture error\" on the x-axis and \"worst texture\" on the y-axis.\n",
-      "\t\"texture error\": The change in the tumor's texture across different measurements, showing how much the roughness or smoothness varies. (x-axis)\n",
-      "\t\"worst texture\": The roughest texture observed on the surface of the tumor. (y-axis).\n",
-      "\n",
-      "The green points 🟢 represent \"benign tumors\", and the red points 🔴 represent \"malignant tumors\".\n",
-      "The blue dashed line represents the decision boundary determined by the regression model.\n",
-      "\n",
-      "Each run of this script might result in different features being selected, hence different visualizations and boundaries.\n",
-      "\n"
-     ]
-    }
-   ],
-   "source": [
-    "#  running all of it again, but this time with different columns\n",
-    "\n",
-    "# Randomly select two features to visualize\n",
-    "new_feature_indices = random.sample(range(X.shape[1]), 2)\n",
-    "new_feature1_index = new_feature_indices[0]\n",
-    "new_feature2_index = new_feature_indices[1]\n",
-    "\n",
-    "# Extract the feature base names correctly\n",
-    "new_feature1_base = breast_cancer.feature_names[new_feature1_index].replace(' ', '_').lower()\n",
-    "new_feature2_base = breast_cancer.feature_names[new_feature2_index].replace(' ', '_').lower()\n",
-    "\n",
-    "# Split the data into training and testing sets\n",
-    "new_X_train, new_X_test, new_y_train, new_y_test = train_test_split(X, y, test_size=0.2, random_state=42)\n",
-    "\n",
-    "# Initialize and train the regression model\n",
-    "new_model = LogisticRegression(max_iter=10000)\n",
-    "new_model.fit(new_X_train[:, new_feature_indices], new_y_train)  # Fit model only on selected features\n",
-    "\n",
-    "# Line Plot Coordinates\n",
-    "new_x_values = np.linspace(new_X_test[:, new_feature1_index].min(), new_X_test[:, new_feature1_index].max(), 100)\n",
-    "new_y_values = -(new_model.intercept_ + new_model.coef_[0][0] * new_x_values) / new_model.coef_[0][1]\n",
-    "\n",
-    "# Predict on the test set using the same features\n",
-    "new_y_pred = new_model.predict(new_X_test[:, new_feature_indices])\n",
-    "\n",
-    "# Calculate the accuracy\n",
-    "accuracy = accuracy_score(new_y_test, new_y_pred)\n",
-    "print(\"Accuracy on test set:\", accuracy)\n",
-    "\n",
-    "# Plot the graph\n",
-    "scatter_plot(\n",
-    "    X={'data': [new_X_test[new_y_test == 0][:, new_feature1_index], new_X_test[new_y_test == 0][:, new_feature2_index]], 'color': 'green', 'label': breast_cancer.target_names[0]}, \n",
-    "    y={'data': [new_X_test[new_y_test == 1][:, new_feature1_index], new_X_test[new_y_test == 1][:, new_feature2_index]], 'color': 'red', 'label': breast_cancer.target_names[1]},\n",
-    "    line_plot={'x': new_x_values, 'y': new_y_values, 'color': 'blue', 'linestyle': '--'},\n",
-    "    title=f'{breast_cancer.feature_names[new_feature2_index]} vs {breast_cancer.feature_names[new_feature1_index]}', \n",
-    "    show_legend=True,\n",
-    "    xlabel=breast_cancer.feature_names[new_feature1_index],\n",
-    "    ylabel=breast_cancer.feature_names[new_feature2_index]\n",
-    ")\n",
-    "# Print statements to describe the plotted graph and its components\n",
-    "print(f\"The graph plots \\\"{breast_cancer.feature_names[new_feature1_index]}\\\" on the x-axis and \\\"{breast_cancer.feature_names[new_feature2_index]}\\\" on the y-axis.\\n\"\n",
-    "    f\"\\t\\\"{breast_cancer.feature_names[new_feature1_index]}\\\": {feature_descriptions[new_feature1_base]} (x-axis)\\n\"\n",
-    "    f\"\\t\\\"{breast_cancer.feature_names[new_feature2_index]}\\\": {feature_descriptions[new_feature2_base]} (y-axis).\\n\\n\"\n",
-    "    \"The green points 🟢 represent \\\"benign tumors\\\", and the red points 🔴 represent \\\"malignant tumors\\\".\\n\"\n",
-    "    \"The blue dashed line represents the decision boundary determined by the regression model.\\n\\n\" # explain what a decision boundary is better\n",
-    "    \"Each run of this script might result in different features being selected, hence different visualizations and boundaries.\\n\")\n",
-    "\n"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Circling back to the real-world application of our work, this model has the potential to save lives. By using past data to train the model, we can now quickly and accurately classify new patients as having benign or malignant tumors based on their data. This means that if a new patient's data falls on one side of the decision boundary, with the accuracy of the model in mind, we can quickly determine their diagnosis and take appropriate action. This ability to rapidly and accurately diagnose breast cancer can lead to earlier interventions, better treatment plans, and ultimately, improved patient outcomes."
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## Conclusion\n",
-    "\n",
-    "Regression is a method for modeling the relationship between a dependent variable and one or more independent variables. By leveraging past data, regression helps us find patterns and make predictions about future outcomes.\n",
-    "\n",
-    "With the power of programming, regression becomes an even more potent tool in the hands of data scientists. It allows us to automate the analysis of large datasets, quickly build models, and visualize complex relationships. This enables us to gain insights and make data-driven decisions more efficiently.\n",
-    "\n",
-    "However, there is always room for improvement. While our current model uses a simple regression approach, we can explore more advanced techniques. For instance, we can use polynomial regression to fit curves rather than straight lines, or employ regularization methods to enhance model performance, you don't need to know what these methods are now, but they open doors for future exploration. Additionally, there are many ways to refine the models we've created, such as incorporating more variables, and using cross-validation to ensure robustness.\n",
-    "\n",
-    "As we continue to learn and apply these advanced techniques, we can build more accurate and reliable models, ultimately leading to better predictions and more informed decisions in various fields, including healthcare, finance, and beyond.\n"
-   ]
-  }
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