From 31c9b93a7467b786ab2570bb18f401af0b63b457 Mon Sep 17 00:00:00 2001
From: juliagallucci <78810440+juliagallucci@users.noreply.github.com>
Date: Thu, 26 Sep 2024 10:51:51 -0400
Subject: [PATCH 1/5] Add files via upload

assignment 1 and 2 answer keys
---
 .../assignment_1_answers.ipynb                | 802 +++++++++++++++++
 .../assignment_2_answers.ipynb                | 815 ++++++++++++++++++
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 create mode 100644 03_instructional_team/assignment_1_answers.ipynb
 create mode 100644 03_instructional_team/assignment_2_answers.ipynb

diff --git a/03_instructional_team/assignment_1_answers.ipynb b/03_instructional_team/assignment_1_answers.ipynb
new file mode 100644
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+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "id": "7b0bcac6-5086-4f4e-928a-570a9ff7ae58",
+   "metadata": {},
+   "source": [
+    "# Assignment 1"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "5fce0350-2a17-4e93-8d4c-0b8748fdfc32",
+   "metadata": {},
+   "source": [
+    "You only need to write one line of code for each question. When answering questions that ask you to identify or interpret something, the length of your response doesn’t matter. For example, if the answer is just ‘yes,’ ‘no,’ or a number, you can just give that answer without adding anything else.\n",
+    "\n",
+    "We will go through comparable code and concepts in the live learning session. If you run into trouble, start by using the help `help()` function in Python, to get information about the datasets and function in question. The internet is also a great resource when coding (though note that **no outside searches are required by the assignment!**). If you do incorporate code from the internet, please cite the source within your code (providing a URL is sufficient).\n",
+    "\n",
+    "Please bring questions that you cannot work out on your own to office hours, work periods or share with your peers on Slack. We will work with you through the issue."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "5fc5001c-7715-4ebe-b0f7-e4bd04349629",
+   "metadata": {},
+   "source": [
+    "### Classification using KNN\n",
+    "\n",
+    "Let's set up our workspace and use the **Wine dataset** from `scikit-learn`. This dataset contains 178 wine samples with 13 chemical features, used to classify wines into different classes based on their origin.\n",
+    "\n",
+    "The **response variable** is `class`, which indicates the type of wine. We'll use all of the chemical features to predict this response variable."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "id": "4a3485d6-ba58-4660-a983-5680821c5719",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Import standard libraries\n",
+    "import pandas as pd\n",
+    "import numpy as np\n",
+    "import random\n",
+    "import matplotlib.pyplot as plt\n",
+    "import matplotlib.colors as mcolors\n",
+    "from sklearn.preprocessing import StandardScaler\n",
+    "from sklearn.model_selection import train_test_split\n",
+    "from sklearn.neighbors import KNeighborsClassifier\n",
+    "from sklearn.metrics import recall_score, precision_score\n",
+    "from sklearn.model_selection import cross_validate\n",
+    "from sklearn.model_selection import GridSearchCV\n",
+    "from sklearn.metrics import accuracy_score"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "id": "a431d282-f9ca-4d5d-8912-71ffc9d8ea19",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>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",
+    "# Load the Wine dataset\n",
+    "wine_data = load_wine()\n",
+    "\n",
+    "# Convert to DataFrame\n",
+    "wine_df = pd.DataFrame(wine_data.data, columns=wine_data.feature_names)\n",
+    "\n",
+    "# Bind the 'class' (wine target) to the DataFrame\n",
+    "wine_df['class'] = wine_data.target\n",
+    "\n",
+    "# Display the DataFrame\n",
+    "wine_df\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "721b2b17",
+   "metadata": {},
+   "source": [
+    "#### **Question 1:** \n",
+    "#### Data inspection\n",
+    "\n",
+    "Before fitting any model, it is essential to understand our data. **Use Python code** to answer the following questions about the **Wine dataset**:\n",
+    "\n",
+    "_(i)_ How many observations (rows) does the dataset contain?"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "id": "56916892",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Number of observations: 178\n"
+     ]
+    }
+   ],
+   "source": [
+    "# Number of observations (rows)\n",
+    "num_observations = wine_df.shape[0]\n",
+    "print(f\"Number of observations: {num_observations}\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "f7573b59",
+   "metadata": {},
+   "source": [
+    "_(ii)_ How many variables (columns) does the dataset contain?"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "id": "df0ef103",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Number of variables: 14\n"
+     ]
+    }
+   ],
+   "source": [
+    "# Number of variables (columns)\n",
+    "num_variables = wine_df.shape[1]\n",
+    "print(f\"Number of variables: {num_variables}\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "cb5180c7",
+   "metadata": {},
+   "source": [
+    "_(iii)_ What is the 'variable type' of the response variable `class` (e.g., 'integer', 'category', etc.)? What are the 'levels' (unique values) of the variable?"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "id": "47989426",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Variable type of 'class': int64\n"
+     ]
+    }
+   ],
+   "source": [
+    "# Variable type of the response variable 'class'\n",
+    "response_variable_type = wine_df['class'].dtype\n",
+    "print(f\"Variable type of 'class': {response_variable_type}\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "a25f5e1b",
+   "metadata": {},
+   "source": [
+    "\n",
+    "_(iv)_ How many predictor variables do we have (Hint: all variables other than `class`)? "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "id": "bd7b0910",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Levels of 'class': [0 1 2]\n"
+     ]
+    }
+   ],
+   "source": [
+    "# Levels (unique values) of the response variable 'class'\n",
+    "response_variable_levels = wine_df['class'].unique()\n",
+    "print(f\"Levels of 'class': {response_variable_levels}\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "d631e8e3",
+   "metadata": {},
+   "source": [
+    "You can use `print()` and `describe()` to help answer these questions."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "fa3832d7",
+   "metadata": {},
+   "source": [
+    "#### **Question 2:** \n",
+    "#### Standardization and data-splitting\n",
+    "\n",
+    "Next, we must preform 'pre-processing' or 'data munging', to prepare our data for classification/prediction. For KNN, there are three essential steps. A first essential step is to 'standardize' the predictor variables. We can achieve this using the scaler method, provided as follows:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "id": "cc899b59",
+   "metadata": {},
+   "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",
+    "\n",
+    "# Standardize the predictors\n",
+    "scaler = StandardScaler()\n",
+    "predictors_standardized = pd.DataFrame(scaler.fit_transform(predictors), columns=predictors.columns)\n",
+    "\n",
+    "# Display the head of the standardized predictors\n",
+    "print(predictors_standardized.head())"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "9981ca48",
+   "metadata": {},
+   "source": [
+    "(i) Why is it important to standardize the predictor variables?"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "403ef0bb",
+   "metadata": {},
+   "source": [
+    "> Standardizing predictor variables ensures that they contribute equally to the model and improves performance, especially for algorithms sensitive to variable scales. "
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "8e2e1bea",
+   "metadata": {},
+   "source": [
+    "(ii) Why did we elect not to standard our response variable `Class`?"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "fdee5a15",
+   "metadata": {},
+   "source": [
+    "> We did not standardize the response variable `Class` because it is categorical and standardization is not meaningful for non-numeric labels."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "8077ec21",
+   "metadata": {},
+   "source": [
+    "(iii) A second essential step is to set a random seed. Do so below (Hint: use the random.seed function). Why is setting a seed important? Is the particular seed value important? Why or why not?"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "f0676c21",
+   "metadata": {},
+   "source": [
+    "> **Setting a seed** is important because it ensures that the random processes (like data splitting or random initialization) produce the same results each time the code is run, which makes experiments reproducible. The **particular seed value** is not important; what matters is that the seed is set consistently to allow for reproducibility. Different seed values will generate different sequences of random numbers, but any specific seed will always produce the same sequence, which is crucial for consistent results in experiments."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "36ab9229",
+   "metadata": {},
+   "source": [
+    "(iv) A third essential step is to split our standardized data into separate training and testing sets. We will split into 75% training and 25% testing. The provided code randomly partitions our data, and creates linked training sets for the predictors and response variables. \n",
+    "\n",
+    "Extend the code to create a non-overlapping test set for the predictors and response variables."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 11,
+   "id": "72c101f2",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Do not touch\n",
+    "np.random.seed(123)\n",
+    "# Create a random vector of True and False values to split the data\n",
+    "split = np.random.choice([True, False], size=len(predictors_standardized), replace=True, p=[0.75, 0.25])\n",
+    "\n",
+    "# Define the training set for X (predictors)\n",
+    "training_X = predictors_standardized[split]\n",
+    "\n",
+    "# Define the training set for Y (response)\n",
+    "training_Y = wine_df.loc[split, 'class']\n",
+    "\n",
+    "# Define the testing set for X (predictors)\n",
+    "testing_X = predictors_standardized[~split]\n",
+    "\n",
+    "# Define the testing set for Y (response)\n",
+    "testing_Y = wine_df.loc[~split, 'class']"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "4604ee03",
+   "metadata": {},
+   "source": [
+    "#### **Question 3:**\n",
+    "#### Model initialization and cross-validation\n",
+    "We are finally set to fit the KNN model. \n",
+    "\n",
+    "\n",
+    "Perform a grid search to tune the `n_neighbors` hyperparameter using 10-fold cross-validation. Follow these steps:\n",
+    "\n",
+    "1. Initialize the KNN classifier using `KNeighborsClassifier()`.\n",
+    "2. Define a parameter grid for `n_neighbors` ranging from 1 to 50.\n",
+    "3. Implement a grid search using `GridSearchCV` with 10-fold cross-validation to find the optimal number of neighbors.\n",
+    "4. After fitting the model on the training data, identify and return the best value for `n_neighbors` based on the grid search results."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 12,
+   "id": "08818c64",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "{'n_neighbors': 8}"
+      ]
+     },
+     "execution_count": 12,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# Initialize the KNN classifier\n",
+    "knn = KNeighborsClassifier()\n",
+    "\n",
+    "# Define the parameter grid for n_neighbors\n",
+    "parameter_grid = {\n",
+    "    \"n_neighbors\": range(1, 51),\n",
+    "}\n",
+    "\n",
+    "# Set up GridSearchCV with 10-fold cross-validation\n",
+    "wine_tune_grid = GridSearchCV(\n",
+    "    estimator=knn,\n",
+    "    param_grid=parameter_grid,\n",
+    "    cv=10\n",
+    ")\n",
+    "\n",
+    "# Fit the grid search to the training data\n",
+    "wine_tune_grid.fit(training_X, training_Y)\n",
+    "\n",
+    "# Output the best hyperparameter found by GridSearchCV\n",
+    "wine_tune_grid.best_params_"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "3f76bf62",
+   "metadata": {},
+   "source": [
+    "#### **Question 4:**\n",
+    "#### Model evaluation\n",
+    "\n",
+    "Using the best value for `n_neighbors`, fit a KNN model on the training data and evaluate its performance on the test set using `accuracy_score`."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 13,
+   "id": "ffefa9f2",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "0.9473684210526315"
+      ]
+     },
+     "execution_count": 13,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# Get the best value for n_neighbors from the grid search\n",
+    "best_n_neighbors = wine_tune_grid.best_params_['n_neighbors']\n",
+    "\n",
+    "# Initialize the KNN model with the best n_neighbors value\n",
+    "best_knn = KNeighborsClassifier(n_neighbors=best_n_neighbors)\n",
+    "\n",
+    "# Fit the model on the training data\n",
+    "best_knn.fit(training_X, training_Y)\n",
+    "\n",
+    "# Predict on the test data\n",
+    "test_predictions = best_knn.predict(testing_X)\n",
+    "\n",
+    "# Evaluate the model's performance on the test set\n",
+    "test_accuracy = accuracy_score(testing_Y, test_predictions)\n",
+    "test_accuracy"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "6f8a69db",
+   "metadata": {},
+   "source": [
+    "# Criteria\n",
+    "\n",
+    "\n",
+    "| **Criteria**                                           | **Complete**                                      | **Incomplete**                                    |\n",
+    "|--------------------------------------------------------|---------------------------------------------------|--------------------------------------------------|\n",
+    "| **Data Inspection**                                    | Data is inspected for number of variables, observations and data types. | Data inspection is missing or incomplete.         |\n",
+    "| **Data Scaling**                                       | Data scaling or normalization is applied where necessary (e.g., using `StandardScaler`). | Data scaling or normalization is missing or incorrectly applied. |\n",
+    "| **Model Initialization**                               | The KNN model is correctly initialized and a random seed is set for reproducibility.            | The KNN model is not initialized, is incorrect, or lacks a random seed for reproducibility. |\n",
+    "| **Parameter Grid for `n_neighbors`**                   | The parameter grid for `n_neighbors` is correctly defined. | The parameter grid is missing or incorrectly defined. |\n",
+    "| **Cross-Validation Setup**                             | Cross-validation is set up correctly with 10 folds. | Cross-validation is missing or incorrectly set up. |\n",
+    "| **Best Hyperparameter (`n_neighbors`) Selection**       | The best value for `n_neighbors` is identified using the grid search results. | The best `n_neighbors` is not selected or incorrect. |\n",
+    "| **Model Evaluation on Test Data**                      | The model is evaluated on the test data using accuracy. | The model evaluation is missing or uses the wrong metric. |\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "0b4390cc",
+   "metadata": {},
+   "source": [
+    "## Submission Information\n",
+    "\n",
+    "🚨 **Please review our [Assignment Submission Guide](https://github.com/UofT-DSI/onboarding/blob/main/onboarding_documents/submissions.md)** 🚨 for detailed instructions on how to format, branch, and submit your work. Following these guidelines is crucial for your submissions to be evaluated correctly.\n",
+    "\n",
+    "### Note:\n",
+    "\n",
+    "If you like, you may collaborate with others in the cohort. If you choose to do so, please indicate with whom you have worked with in your pull request by tagging their GitHub username. Separate submissions are required.\n",
+    "\n",
+    "### Submission Parameters:\n",
+    "* Submission Due Date: `HH:MM AM/PM - DD/MM/YYYY`\n",
+    "* The branch name for your repo should be: `assignment-1`\n",
+    "* What to submit for this assignment:\n",
+    "    * This Jupyter Notebook (assignment_1.ipynb) should be populated and should be the only change in your pull request.\n",
+    "* What the pull request link should look like for this assignment: `https://github.com/<your_github_username>/applying_statistical_concepts/pull/<pr_id>`\n",
+    "    * Open a private window in your browser. Copy and paste the link to your pull request into the address bar. Make sure you can see your pull request properly. This helps the technical facilitator and learning support staff review your submission easily.\n",
+    "\n",
+    "Checklist:\n",
+    "- [ ] Created a branch with the correct naming convention.\n",
+    "- [ ] Ensured that the repository is public.\n",
+    "- [ ] Reviewed the PR description guidelines and adhered to them.\n",
+    "- [ ] Verify that the link is accessible in a private browser window.\n",
+    "\n",
+    "If you encounter any difficulties or have questions, please don't hesitate to reach out to our team via our Slack at `#cohort-4-help`. Our Technical Facilitators and Learning Support staff are here to help you navigate any challenges.\n"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3.10.4",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.10.10"
+  },
+  "vscode": {
+   "interpreter": {
+    "hash": "497a84dc8fec8cf8d24e7e87b6d954c9a18a327edc66feb9b9ea7e9e72cc5c7e"
+   }
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}
diff --git a/03_instructional_team/assignment_2_answers.ipynb b/03_instructional_team/assignment_2_answers.ipynb
new file mode 100644
index 00000000..31fdb24b
--- /dev/null
+++ b/03_instructional_team/assignment_2_answers.ipynb
@@ -0,0 +1,815 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "id": "7b0bcac6-5086-4f4e-928a-570a9ff7ae58",
+   "metadata": {},
+   "source": [
+    "# Assignment 2"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "5fce0350-2a17-4e93-8d4c-0b8748fdfc32",
+   "metadata": {},
+   "source": [
+    "You only need to write one line of code for each question. When answering questions that ask you to identify or interpret something, the length of your response doesn’t matter. For example, if the answer is just ‘yes,’ ‘no,’ or a number, you can just give that answer without adding anything else.\n",
+    "\n",
+    "We will go through comparable code and concepts in the live learning session. If you run into trouble, start by using the help `help()` function in Python, to get information about the datasets and function in question. The internet is also a great resource when coding (though note that **no outside searches are required by the assignment!**). If you do incorporate code from the internet, please cite the source within your code (providing a URL is sufficient).\n",
+    "\n",
+    "Please bring questions that you cannot work out on your own to office hours, work periods or share with your peers on Slack. We will work with you through the issue."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "5fc5001c-7715-4ebe-b0f7-e4bd04349629",
+   "metadata": {},
+   "source": [
+    "### Linear Regression\n",
+    "\n",
+    "Let's set up our workspace and use the **Diabetes dataset** from `scikit-learn`. This dataset contains 10 baseline variables (e.g., age, sex, BMI) and a quantitative measure of disease progression one year after baseline.\n",
+    "\n",
+    "Here, we will model diabetes progression (continuous outcome) based on various health-related factors."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 15,
+   "id": "4a3485d6-ba58-4660-a983-5680821c5719",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Import standard libraries\n",
+    "import pandas as pd\n",
+    "import numpy as np\n",
+    "import random\n",
+    "import matplotlib.pyplot as plt\n",
+    "from sklearn.model_selection import train_test_split\n",
+    "from sklearn.linear_model import LinearRegression\n",
+    "from sklearn.metrics import mean_squared_error"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "id": "a431d282-f9ca-4d5d-8912-71ffc9d8ea19",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>age</th>\n",
+       "      <th>sex</th>\n",
+       "      <th>bmi</th>\n",
+       "      <th>bp</th>\n",
+       "      <th>s1</th>\n",
+       "      <th>s2</th>\n",
+       "      <th>s3</th>\n",
+       "      <th>s4</th>\n",
+       "      <th>s5</th>\n",
+       "      <th>s6</th>\n",
+       "      <th>progression</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>0.038076</td>\n",
+       "      <td>0.050680</td>\n",
+       "      <td>0.061696</td>\n",
+       "      <td>0.021872</td>\n",
+       "      <td>-0.044223</td>\n",
+       "      <td>-0.034821</td>\n",
+       "      <td>-0.043401</td>\n",
+       "      <td>-0.002592</td>\n",
+       "      <td>0.019907</td>\n",
+       "      <td>-0.017646</td>\n",
+       "      <td>151.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>-0.001882</td>\n",
+       "      <td>-0.044642</td>\n",
+       "      <td>-0.051474</td>\n",
+       "      <td>-0.026328</td>\n",
+       "      <td>-0.008449</td>\n",
+       "      <td>-0.019163</td>\n",
+       "      <td>0.074412</td>\n",
+       "      <td>-0.039493</td>\n",
+       "      <td>-0.068332</td>\n",
+       "      <td>-0.092204</td>\n",
+       "      <td>75.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>0.085299</td>\n",
+       "      <td>0.050680</td>\n",
+       "      <td>0.044451</td>\n",
+       "      <td>-0.005670</td>\n",
+       "      <td>-0.045599</td>\n",
+       "      <td>-0.034194</td>\n",
+       "      <td>-0.032356</td>\n",
+       "      <td>-0.002592</td>\n",
+       "      <td>0.002861</td>\n",
+       "      <td>-0.025930</td>\n",
+       "      <td>141.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>-0.089063</td>\n",
+       "      <td>-0.044642</td>\n",
+       "      <td>-0.011595</td>\n",
+       "      <td>-0.036656</td>\n",
+       "      <td>0.012191</td>\n",
+       "      <td>0.024991</td>\n",
+       "      <td>-0.036038</td>\n",
+       "      <td>0.034309</td>\n",
+       "      <td>0.022688</td>\n",
+       "      <td>-0.009362</td>\n",
+       "      <td>206.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>0.005383</td>\n",
+       "      <td>-0.044642</td>\n",
+       "      <td>-0.036385</td>\n",
+       "      <td>0.021872</td>\n",
+       "      <td>0.003935</td>\n",
+       "      <td>0.015596</td>\n",
+       "      <td>0.008142</td>\n",
+       "      <td>-0.002592</td>\n",
+       "      <td>-0.031988</td>\n",
+       "      <td>-0.046641</td>\n",
+       "      <td>135.0</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",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>437</th>\n",
+       "      <td>0.041708</td>\n",
+       "      <td>0.050680</td>\n",
+       "      <td>0.019662</td>\n",
+       "      <td>0.059744</td>\n",
+       "      <td>-0.005697</td>\n",
+       "      <td>-0.002566</td>\n",
+       "      <td>-0.028674</td>\n",
+       "      <td>-0.002592</td>\n",
+       "      <td>0.031193</td>\n",
+       "      <td>0.007207</td>\n",
+       "      <td>178.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>438</th>\n",
+       "      <td>-0.005515</td>\n",
+       "      <td>0.050680</td>\n",
+       "      <td>-0.015906</td>\n",
+       "      <td>-0.067642</td>\n",
+       "      <td>0.049341</td>\n",
+       "      <td>0.079165</td>\n",
+       "      <td>-0.028674</td>\n",
+       "      <td>0.034309</td>\n",
+       "      <td>-0.018114</td>\n",
+       "      <td>0.044485</td>\n",
+       "      <td>104.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>439</th>\n",
+       "      <td>0.041708</td>\n",
+       "      <td>0.050680</td>\n",
+       "      <td>-0.015906</td>\n",
+       "      <td>0.017293</td>\n",
+       "      <td>-0.037344</td>\n",
+       "      <td>-0.013840</td>\n",
+       "      <td>-0.024993</td>\n",
+       "      <td>-0.011080</td>\n",
+       "      <td>-0.046883</td>\n",
+       "      <td>0.015491</td>\n",
+       "      <td>132.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>440</th>\n",
+       "      <td>-0.045472</td>\n",
+       "      <td>-0.044642</td>\n",
+       "      <td>0.039062</td>\n",
+       "      <td>0.001215</td>\n",
+       "      <td>0.016318</td>\n",
+       "      <td>0.015283</td>\n",
+       "      <td>-0.028674</td>\n",
+       "      <td>0.026560</td>\n",
+       "      <td>0.044529</td>\n",
+       "      <td>-0.025930</td>\n",
+       "      <td>220.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>441</th>\n",
+       "      <td>-0.045472</td>\n",
+       "      <td>-0.044642</td>\n",
+       "      <td>-0.073030</td>\n",
+       "      <td>-0.081413</td>\n",
+       "      <td>0.083740</td>\n",
+       "      <td>0.027809</td>\n",
+       "      <td>0.173816</td>\n",
+       "      <td>-0.039493</td>\n",
+       "      <td>-0.004222</td>\n",
+       "      <td>0.003064</td>\n",
+       "      <td>57.0</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "<p>442 rows × 11 columns</p>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "          age       sex       bmi        bp        s1        s2        s3  \\\n",
+       "0    0.038076  0.050680  0.061696  0.021872 -0.044223 -0.034821 -0.043401   \n",
+       "1   -0.001882 -0.044642 -0.051474 -0.026328 -0.008449 -0.019163  0.074412   \n",
+       "2    0.085299  0.050680  0.044451 -0.005670 -0.045599 -0.034194 -0.032356   \n",
+       "3   -0.089063 -0.044642 -0.011595 -0.036656  0.012191  0.024991 -0.036038   \n",
+       "4    0.005383 -0.044642 -0.036385  0.021872  0.003935  0.015596  0.008142   \n",
+       "..        ...       ...       ...       ...       ...       ...       ...   \n",
+       "437  0.041708  0.050680  0.019662  0.059744 -0.005697 -0.002566 -0.028674   \n",
+       "438 -0.005515  0.050680 -0.015906 -0.067642  0.049341  0.079165 -0.028674   \n",
+       "439  0.041708  0.050680 -0.015906  0.017293 -0.037344 -0.013840 -0.024993   \n",
+       "440 -0.045472 -0.044642  0.039062  0.001215  0.016318  0.015283 -0.028674   \n",
+       "441 -0.045472 -0.044642 -0.073030 -0.081413  0.083740  0.027809  0.173816   \n",
+       "\n",
+       "           s4        s5        s6  progression  \n",
+       "0   -0.002592  0.019907 -0.017646        151.0  \n",
+       "1   -0.039493 -0.068332 -0.092204         75.0  \n",
+       "2   -0.002592  0.002861 -0.025930        141.0  \n",
+       "3    0.034309  0.022688 -0.009362        206.0  \n",
+       "4   -0.002592 -0.031988 -0.046641        135.0  \n",
+       "..        ...       ...       ...          ...  \n",
+       "437 -0.002592  0.031193  0.007207        178.0  \n",
+       "438  0.034309 -0.018114  0.044485        104.0  \n",
+       "439 -0.011080 -0.046883  0.015491        132.0  \n",
+       "440  0.026560  0.044529 -0.025930        220.0  \n",
+       "441 -0.039493 -0.004222  0.003064         57.0  \n",
+       "\n",
+       "[442 rows x 11 columns]"
+      ]
+     },
+     "execution_count": 3,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "from sklearn.datasets import load_diabetes\n",
+    "\n",
+    "# Load the diabetes dataset\n",
+    "diabetes_data = load_diabetes()\n",
+    "\n",
+    "# Convert to DataFrame\n",
+    "diabetes_df = pd.DataFrame(diabetes_data.data, columns=diabetes_data.feature_names)\n",
+    "\n",
+    "# Bind the disease progression (diabetes target) to the DataFrame\n",
+    "diabetes_df['progression'] = diabetes_data.target\n",
+    "\n",
+    "\n",
+    "# Display the DataFrame\n",
+    "diabetes_df\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "721b2b17",
+   "metadata": {},
+   "source": [
+    "#### **Question 1:** \n",
+    "#### Data inspection\n",
+    "\n",
+    "Before fitting any model, it is essential to understand our data. **Use Python code** to answer the following questions about the **Wine dataset**:\n",
+    "\n",
+    "_(i)_ How many observations (rows) does the dataset contain?"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "id": "05725e2a",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Number of observations: 442\n"
+     ]
+    }
+   ],
+   "source": [
+    "# Number of observations (rows)\n",
+    "num_observations = diabetes_df.shape[0]\n",
+    "print(f\"Number of observations: {num_observations}\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "a599c73b",
+   "metadata": {},
+   "source": [
+    "_(ii)_ How many variables (columns) does the dataset contain?"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "id": "9c8621b4",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Number of variables: 11\n"
+     ]
+    }
+   ],
+   "source": [
+    "# Number of variables (columns)\n",
+    "num_variables = diabetes_df.shape[1]\n",
+    "print(f\"Number of variables: {num_variables}\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "6ad6b6a4",
+   "metadata": {},
+   "source": [
+    "_(iii)_ What is the 'variable type' of the response variable `progression` (e.g., 'integer', 'category', etc.)?"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "id": "02157236",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Variable type of 'progression': float64\n"
+     ]
+    }
+   ],
+   "source": [
+    "# Variable type of the response variable 'progression'\n",
+    "response_variable_type = diabetes_df['progression'].dtype\n",
+    "print(f\"Variable type of 'progression': {response_variable_type}\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "cbc54d2c",
+   "metadata": {},
+   "source": [
+    "_(iv)_ How many predictor variables do we have (Hint: all variables other than `progression`)? "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "id": "44cbb0ce",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Number of predictor variables: 10\n"
+     ]
+    }
+   ],
+   "source": [
+    "# Count the number of predictor variables (excluding 'progression')\n",
+    "predictor_variables = diabetes_df.drop(columns=['progression']).columns\n",
+    "num_predictors = len(predictor_variables)\n",
+    "\n",
+    "print(f\"Number of predictor variables: {num_predictors}\")\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "1741cf23",
+   "metadata": {},
+   "source": [
+    "You can use `print()` and `describe()` to help answer these questions."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "fa3832d7",
+   "metadata": {},
+   "source": [
+    "#### **Question 2:** \n",
+    "#### Data-visualization\n",
+    "\n",
+    "Before we fit and review model outputs, we should visualize our data. Review the code and plot, shown below. Answer the following questions:\n",
+    "\n",
+    "_(i)_ Describe the associations being plotted ? (i.e., positive association, negative association, no association)\n",
+    "\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "f67e57ab",
+   "metadata": {},
+   "source": [
+    "> Positive Association: If the points form an upward trend from left to right, meaning that as the feature values increase, the target ('progression') also increases. The slope of the regression line would be positive.\n",
+    "\n",
+    "> Negative Association: If the points form a downward trend, meaning that as the feature values increase, the target decreases. The slope of the regression line would be negative.\n",
+    "\n",
+    "> No Association: If the points are scattered randomly without forming a clear trend or line, there may be little to no relationship between the feature and the target variable. The regression line would be nearly horizontal (slope close to zero)."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "5325992e",
+   "metadata": {},
+   "source": [
+    "_(ii)_ What concept ‘defines’ the plotted line?"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "843f9eef",
+   "metadata": {},
+   "source": [
+    "> The plotted line in your scatter plots represents the line of best fit, which is defined by the concept of linear regression."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "id": "732784d8",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 600x400 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 600x400 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 600x400 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 600x400 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 600x400 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/png": 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1A90cppoQExODiIgIHDlyBJcuXUJ0dLSuegJuNLpnzx7Url0bUVFRGDduHNauXYuWLVsCAAYOHIiPPvoImzdvxrx587Bz50706tXLLiwUFRUhMjIS9evXd6ozISEBRUVFitecPXs2YmNj7RtHGbUemZlAkyZAz57AyJHS3yZNpP0MU91RCx/NMGZgxDMX8Ke2efPm2L17N3bs2IHx48djzJgx2LdvHwBg+PDhGDRoEFq3bo0hQ4bg3//+Nw4cOIAvvvhCtU4iUlUzPvXUUyguLrZv+fn5hvaJ8Y3MTOCOO4Dff3feX1Ag7Wehg2EYJvgIuMARGRmJq666Ch07dsTs2bPRrl07vP766x7LJiUloXHjxjh48CAAIDExEZcuXcLp06edyp04cQIJCQmK14yKirJHFeXootaiogKYOBHwZFkk73vsMakcwzAMEzwEXOBwhYic7CscOXXqFPLz85GUlAQA6NChAyIiIvD111/byxQWFmLv3r3o0qWLX9rriYoKIDsbWLlS+suTozhbt7prNhwhAvLzpXIMwzBM8BBQgePpp5/G1q1bcfjwYezZswfPPPMMsrOzMWrUKJw7dw5TpkzB9u3bcfjwYWRnZ2PIkCFo2LAhbrvtNgBAbGws7r//fkyePBmbNm1Cbm4uRo8ejTZt2qBPnz4B6RPbHvhGYaGx5RiGsQZjx46FzWaDzWZDREQEEhIS0LdvX/zf//0fKisrhetZunQp6tWrZ15DGdMIqJfK8ePHcffdd6OwsBCxsbFo27Yt1q9fj759++LixYvYs2cPli1bhjNnziApKQk9e/bExx9/7JSRbsGCBahRowbuvPNOe+CvpUuXBiQGh2x74LocINsefPIJMGyY35sVVPxPeWVYOYZh3AmUB5gcV6miogLHjx/H+vXrMXHiRHzyySf47LPPUKOGZRwnGTMghoqLiwkAFRcX666jvJwoNZVIEjfcN5uNKC1NKscoI4+jzcbjyDCuXLx4kfbt20cXL17UXUdGhvu7KjVV2m8mY8aMoaFDh7rt37RpEwGgd999l4iI5s2bR61bt6aaNWtSamoqjR8/ns6ePUtERFlZWQTAaZs+fToREX344YfUoUMHql27NiUkJNCIESPo+PHj5naqGqH27InOoZaz4QhW2PbAGMLDAdlm2NXRSP7/a68FPh4H2+kwwYgVPcB69eqFdu3aIfN/Fw8LC8Mbb7yBvXv34oMPPsDmzZvxxBNPAAC6dOmC1157DXXr1kVhYSEKCwsxZcoUAFKMkpkzZ+K///0v1q1bh7y8PIwdO9b/HWIUYf2VQbDtgXEMGyYtP02c6PxiTE2VhI1AL0tlZnpu2+uvB75tDKOENw8wm03yABs61P8CfYsWLfDjjz8CkNJVyDRt2hQzZ87E+PHjsWjRIkRGRiI2NhY2mw2JiYlOddx33332f1955ZV44403cOONN+LcuXOoXbu2X/rBqMMaDoNg2wNjGTYMOHwYyMoCVqyQ/ublBX5Ct+IXIsOIYGUtLDnETsrKykLfvn2RkpKCOnXq4J577sGpU6dw/vx51Tpyc3MxdOhQNG7cGHXq1EGPHj0AAEePHjW7+YwgLHAYRNeu0leuUrwxmw1IS5PKMWKEhwM9egAjRkh/rbCMwjFCmGDFylrY/fv3o2nTpjhy5Aj++te/onXr1sjIyMCuXbvw1ltvAQAuX76seP758+fRr18/1K5dG8uXL8fOnTuxdu1aANJSC2MNWOAwiGCxPQg0wWz7YOUvRIbxhlW1sJs3b8aePXtw++2344cffkB5eTnmzZuHTp064ZprrsGxY8ecykdGRqLC5cXx888/448//sCcOXPQtWtXtGjRAidOnPBnNxgBWOAwENn2ICXFeX9qKrvEAsEfo8TKX4gM4w0raGHLyspQVFSEgoIC5OTk4KWXXsLQoUMxePBg3HPPPWjWrBnKy8uxcOFC/Pbbb/jwww/x9ttvO9XRpEkTnDt3Dps2bcIff/yBCxcu4IorrkBkZKT9vM8++wwzZ840ryOMPkzyoAkqjHCLdaS8nCgri2jFCukvu3BKLneeXF1tNmkz2yXPCLKylN2eHbesrEC3lAlVfHWLlX+Hrr9Ff/wOx4wZY3dlrVGjBsXHx1OfPn3o//7v/6iiosJebv78+ZSUlEQxMTHUv39/WrZsGQGg06dP28uMGzeO4uLinNxiV6xYQU2aNKGoqCjq3LkzffbZZwSAcnNzzetUNcIIt1gbkacV6epFSUkJYmNjUVxczHlVTKCiQtJkKC1H2GzSl1denrWXnOR+FBR4tuMIln4wwUtpaSny8vLQtGlT3SnCPXlZpaVZwwOMsS5qz57oHMpLKozphIrtA9vpMKGAVT3AmNCH43AwphNKtg9WjxHCMCLIHmAM409Y4GBMx6rW8XoZNkwKjhSIXBQMwzDBCgscjOnI1vHebB+CKUYJfyEyDMNog204GNNh2weGYRiGBQ7GL3CMEoZhmOoNL6kwfiMYbR8qKoKrvQzDMFaFBQ7GrwST7QNnhWUYhjEOXlJhGA9wVliGYRhjYYEjRAnmJGmBhrPCMgwjis1mw7p16wLahh49euCxxx4LaBtEYIEjBAn2JGmBJlQiozKMVRg7dixsNhtsNhtq1KiBK664AuPHj8fp06cD3TSfKSwsxMCBA029xtKlS1GvXj3F45mZmUGRrI4FjhCDlwJ8J5QiozKMVRgwYAAKCwtx+PBhvPfee/jXv/6FRx55xNRrEhHKy8tNvUZiYiKioqJMvYY3GjRogDp16gS0DSKwwBFC8FKAMYRaZFQmhCECzp8PzKYx72dUVBQSExORmpqKfv36Yfjw4fjqq6+cyixZsgTXXnstoqOj0aJFCyxatMjp+LZt23DdddchOjoaHTt2xLp162Cz2bB7924AQHZ2Nmw2GzZs2ICOHTsiKioKW7duBRHhlVdewZVXXomYmBi0a9cOn3zyib3e06dPY9SoUYiPj0dMTAyuvvpqLFmyBABw6dIlpKenIykpCdHR0WjSpAlmz55tP9d1SWXPnj3o1asXYmJiEBcXh4ceegjnzp2zHx87dixuvfVWzJ07F0lJSYiLi8Ojjz6Ky5cvaxpPR1yXVJo0aYKXXnoJ9913H+rUqYMrrrgC77zzjtM5BQUFGD58OOrXr4+4uDgMHToUhw8f1t0GEdhLJYTQshQQLJ4igSAUI6MyIcqFC0Dt2oG59rlzQK1auk797bffsH79ekRERNj3vfvuu5g+fTrefPNNXH/99cjNzcWDDz6IWrVqYcyYMTh79iyGDBmCv/71r1ixYgWOHDmiaLfwxBNPYO7cubjyyitRr149TJs2DZmZmVi8eDGuvvpqfPPNNxg9ejTi4+PRvXt3PPvss9i3bx/+/e9/o2HDhvj1119x8eJFAMAbb7yBzz77DKtXr8YVV1yB/Px85Ofne7zuhQsXMGDAAHTq1Ak7d+7EiRMn8MADDyA9PR1Lly61l8vKykJSUhKysrLw66+/Yvjw4bjuuuvw4IMP6hpPT8ybNw8zZ87E008/jU8++QTjx49Ht27d0KJFC1y4cAE9e/ZE165d8c0336BGjRqYNWsWBgwYgB9//BGRkZGGtcMJ1eT11YTi4mICQMXFxYFuik+sWEEkTZHq24oVgW6p9cnIILLZpM1x7OR9GRmBbiFTHbl48SLt27ePLl68KO04d07sR2/Gdu6ccLvHjBlD4eHhVKtWLYqOjiYABIDmz59vL5OWlkYrXF5OM2fOpM6dOxMR0eLFiykuLq6q70T07rvvEgDKzc0lIqKsrCwCQOvWrbOXOXfuHEVHR9O2bduc6r7//vtpxIgRREQ0ZMgQuvfeez22fcKECdSrVy+qrKz0eBwArV27loiI3nnnHapfvz6dcxibL774gsLCwqioqMg+Fo0bN6by8nJ7mb/97W80fPhwj/UTES1ZsoRiY2MVj3fv3p0mTpxo/3/jxo1p9OjR9v9XVlZSo0aNaPHixURE9P7771Pz5s2d+lRWVkYxMTG0YcMGj9dwe/YcEJ1DWcMRQvBSgDt6A3cNGwZMmQLMm+es5bDZgMmTOQ4HYxFq1pQ0DYG6tgZ69uyJxYsX48KFC3jvvfdw4MABTJgwAQBw8uRJ5Ofn4/7773f6yi8vL0dsbCwA4JdffkHbtm0RHR1tP37jjTd6vFbHjh3t/963bx9KS0vRt29fpzKXLl3C9ddfDwAYP348br/9duTk5KBfv3649dZb0aVLFwDSEkjfvn3RvHlzDBgwAIMHD0a/fv08Xnf//v1o164dajlofm6++WZUVlbil19+QUJCAgCgVatWCHd4ESUlJWHPnj1eRlAbbdu2tf/bZrMhMTERJ06cAADs2rULv/76q5vdR2lpKQ4dOmRoOxxhgSOE4KUAZ3wJ3JWZCbz6qvv+ykppf6dOLHQwFsBm072s4W9q1aqFq666CoC0TNGzZ0/MmDEDM2fORGVlJQBpWeWmm25yOk+emIkINpdkTKRgR+I44ct1f/HFF0hxya0gG3sOHDgQR44cwRdffIGNGzeid+/eePTRRzF37ly0b98eeXl5+Pe//42NGzfizjvvRJ8+fZxsQBzb49pGGcf9jktJ8jG5nUahdo3Kykp06NABH330kdt58fHxhrbDERY4Qgg5Sdodd0jvIdcvc6D6JEmTvXVc30eyt45a/paKCmDMGPX6x4wBYmOBEyc45DnD6GH69OkYOHAgxo8fj+TkZKSkpOC3337DqFGjPJZv0aIFPvroI5SVldkFhR9++MHrdVq2bImoqCgcPXoU3bt3VywXHx+PsWPHYuzYsejatSumTp2KuXPnAgDq1q2L4cOHY/jw4bjjjjswYMAA/Pnnn2jQoIHbtT744AOcP3/eLvR89913CAsLwzXXXCM0Lv6gffv2+Pjjj9GoUSPUrVvXb9dlL5UQg5Ok+e6ts2mTdy31uXNAnz4c54Rh9NKjRw+0atUKL730EgDg+eefx+zZs/H666/jwIED2LNnD5YsWYL58+cDAEaOHInKyko89NBD2L9/PzZs2GAXCJS0CgBQp04dTJkyBY8//jg++OADHDp0CLm5uXjrrbfwwQcfAACee+45fPrpp/j111/x008/4fPPP8e1114LAFiwYAFWrVqFn3/+GQcOHMCaNWuQmJjoMS7GqFGjEB0djTFjxmDv3r3IysrChAkTcPfdd9uXU/RSUVGB3bt3O2379u3TVdeoUaPQsGFDDB06FFu3bkVeXh62bNmCiRMn4nc1zwMfYQ1HCOLvJGlWS3Dmq7fOhx9qv6aI5oRhGGcmTZqEe++9F//4xz/wwAMPoGbNmnj11VfxxBNPoFatWmjTpo3dE6Vu3br417/+hfHjx+O6665DmzZt8Nxzz2HkyJFOdh2emDlzJho1aoTZs2fjt99+Q7169dC+fXs8/fTTAIDIyEg89dRTOHz4MGJiYtC1a1esWrUKAFC7dm28/PLLOHjwIMLDw3HDDTfgyy+/RFiY+/d6zZo1sWHDBkycOBE33HADatasidtvv90uNPnCuXPn7DYnMo0bN9blylqzZk188803+Mc//oFhw4bh7NmzSElJQe/evU3VeNhIaRGsGlFSUoLY2FgUFxf7Vb0UClgxwdnKlZLmwRsrVgAjRrjvv+02QE+kYtlGJi+Pl1cYcygtLUVeXh6aNm3qdZKtDnz00Ue49957UVxcjJiYmEA3J6RRe/ZE51BeUmF0Y9WopqJeOPv2ec4z85e/6LsuhzxnGHNZtmwZvv32W+Tl5WHdunX4xz/+gTvvvJOFjSCBBQ5GF1aOaip766gs6wIAZs3ybH8xYQLgQVsqDIc8Dy440WHwUFRUhNGjR+Paa6/F448/jr/97W9uETQZ68ICB6MLKyc4k711AO9CB+CukYmMlGJt6KU6xTkJdjjRYXDxxBNP4PDhw3b1/oIFC1BTYzwQJnCwwMHowuoJzpS8dTzhSSPzyivA1KnaNB02G5CWVn3inAQ7Vl0SZJhQJaACx+LFi9G2bVvUrVsXdevWRefOnfHvf//bfpyI8PzzzyM5ORkxMTHo0aMHfvrpJ6c6ysrKMGHCBDRs2BC1atXCLbfcYqpbDyMRDFFNhw0DDh8GsrKAadPUy3rSyLzyCnDxIrBgAZCeLv1dtUoSLFw1J9UtzkmwY+UlQRHY1p/xN0Y8cwEVOFJTUzFnzhz88MMP+OGHH9CrVy8MHTrULlS88sormD9/Pt58803s3LkTiYmJ6Nu3L86ePWuv47HHHsPatWuxatUqfPvttzh37hwGDx6MCqu+KUIEb3YSRnztG7G2Hh4uub62bClW3lUjExkpTTwLF0p/hw/nOCehgJWXBNWQo0deuHAhwC1hqhvyM+cawVQLAY3DMWTIEKf/v/jii1i8eDF27NiBli1b4rXXXsMzzzyDYf97i3/wwQdISEjAihUr8PDDD6O4uBjvv/8+PvzwQ/Tp0wcAsHz5cqSlpWHjxo3o37+/3/tUXTA7qqnR7rZGamT8HefE31gtrooZWH1JUInw8HDUq1fPnhOjZs2aqkGvGMZXiAgXLlzAiRMnUK9ePaccMFqxTOCviooKrFmzBufPn0fnzp2Rl5eHoqIipyQ5UVFR6N69O7Zt24aHH34Yu3btwuXLl53KJCcno3Xr1ti2bZuiwFFWVoaysjL7/0tKSszrWAgj20l4Egxee03/174vYcmVMDrPjKw5cSQUJmorxlUxg2BYElQiMTERAOxCB8P4g3r16tmfPb0EXODYs2cPOnfujNLSUtSuXRtr165Fy5YtsW3bNgBwCwebkJCAI0eOAJBcpCIjI1G/fn23MkVFRYrXnD17NmbMmGFwT6onRn/te1tbt9mkpY2hQ7VdI9g0MoHADEHPqgRzokObzYakpCQ0atQIly9fDnRzmGpARESET5oNmYALHM2bN8fu3btx5swZZGRkYMyYMdiyZYv9uKfsgN5UiN7KPPXUU5g0aZL9/yUlJUhLS9PZA8bT175efA1LrkYwaWSMxpv2xSxBz6qEQqLD8PBwQyYBhvEXAXeLjYyMxFVXXYWOHTti9uzZaNeuHV5//XW76sZVU3HixAm71iMxMRGXLl3C6dOnFct4Iioqyu4ZI2+MGGYHSTJ7bd3Rc2XFCulvXp5+gSAYvB1EYk0EqxGlL3CiQ4bxLwEXOFwhIpSVlaFp06ZITEzE119/bT926dIlbNmyBV26dAEAdOjQAREREU5lCgsLsXfvXnsZxjj8ESTJH2vrskZmxAjpry8fiVafqEVjTQSrEaWvGC2AMgyjTECXVJ5++mkMHDgQaWlpOHv2LFatWoXs7GysX78eNpsNjz32GF566SVcffXVuPrqq/HSSy+hZs2aGPm/zFyxsbG4//77MXnyZMTFxaFBgwaYMmUK2rRpY/daYYzBX8sGXbsCcXHAqVPKZeLirLO2buWJWssySTAbUfqKkUuCDMMoE1CB4/jx47j77rtRWFiI2NhYtG3bFuvXr0ffvn0BSGFsL168iEceeQSnT5/GTTfdhK+++gp16tSx17FgwQLUqFEDd955Jy5evIjevXtj6dKlvLZpIKG2vm+kN4mVJ2ot2pdgNqJkGCY44PT04PT03sjOlpZPvJGV5fuXotnXMtqbpKJCWlbyNlEHImX9ypXS0pc3VqyQlpdkLRbg2YiS7RoYhvEEp6dnDMOfywZmXsuM3BlqieIC7e2gVfvCRpQMw5gJCxyMV/y5bGDWtcz0JrHqRK0n/DwbUTIMYxa8pAJeUvGGP5cNzLqWP5aFrBhplJdJGIYxG15SYQzDn8sGZl3LH8tCRrrbGoVVtS+Bwtc4MmbHoWGYUIYFDsYjri/WoUP9N3EpTZIpKfqvJboEc/x4YCcTMyY0XiaR8DWOjD/i0DBMKBPw0OaM9VDz5Dh82H/LBq5LKr4s/nlz+wSkfjz+eNX//Z0Lxcx8LNU91oSvcWSCIXw9w1gdtuEA23A4ovRi9eeav1ltULJnUMLffb79duXjGRk8oelFtgtSiknizS7I1/MZJtQRnUNZ4AALHDJWeLGa3YYnngDmzxdfqvBXnxMSvEdXPX6cJzQ9+Gow7M84NAwTjLDRKKMZK+QFMbMNmZnA3Lna7CL80efsbHVhA5COZ2eb14ZQxleDYSuHr2eYYIIFDsaOFV6sZrVBLQ6HGdfTgqggEewCR6A8PHyN7WLl8PUME0yw0ShjxwovVq3eJKKGq940J1rbdekSsGgRcOgQ0KwZ8MgjQGSk53OtGJ/D35hpEOsNX/PEcJ4ZhjEIYqi4uJgAUHFxcaCbElDKy4lSU4lsNiLp1eq82WxEaWlSObO4cMHztdW21FSijAz1eles0F6vUp+nTiUKD3cuFx4u7XclI0Nqn1p7N24Ua8vGjcaMsb/JyPD8TNls0ubt3hnZBtd2iLbB1/MZJpQRnUNZ4CAWOMrLibKypEl5xgz1Sc/sF+uCBfqEAm8v/awsY+qdOlX9HEehQ3SiLS8niotTrzcuzl3Qc7xvWVnmCoJ6kYVYLQKdWXgS/tLSxJ9pX89nmFCFBQ4NVGeBw9NLNJACR3q6cZoIR7xpb2QthdpkUlbmXsZTHWVl2idaLYIMkZjmxAqICnpZWf5pj69CWjAIeQzjb0TnULbhqMYoxbtQwmaTEpwNHSpmg6DHdqFZM7G2uEJU5U3iyTVRDpl+xx1SPxz7LMfbWLkSiI9Xbu+iRd4NHSsqpHLXXSfmbfP881J7V65Ur3fVKmD2bKk9wRSEygqGyI74GgCtugdQYxhfYC+Vaooerw3HSd0besNAP/KIbwaVahOXt7wif/ubei6UQ4fE2nDokPgEOmsW0KePd4NWedzNzHprBlYwRGYYxhqwwFFN8cVrw9tkKn+Bu9Yvf4GrCR2RkcCkSfraBXifuNTyinhz2xTVvjRrZs4EWlhojVgpWpA9PFwT8cnYbEBaGnt4MEx1gAWOaoovKmy1ydTbFziR9y/wV14Bpk7VpunQMnF5yuoqopER0b6Eh0vlvE20ekhKst4ShTf8mWmYYRhrwwJHNUXPF7jIpC6iORH5An/lFeDCBWDBAiA9Xfq7apXUBl8nLldNxpo1YhoZEe3LpElSObWJViuO4x6oJQpfgnZ5W8ryZG/CaeAZJgTxkxGrpamOXioiXhveXEQ9sXy5WH3Ll7u3R8T63wzXRjXPEzPicGj1vnF1ofV3rBSjPGJ8ucdW9MBhGEaC3WI1UB0FDiLlYEaeNtFJXTSOxoIFzu3QMsGUlUnnp6dLf8vKtPVXz8Tv6rappQ3yRDttmvbrehp3fwah8nfQLisECWMYRhuicyhni0X1zhbrKeR0Whowb566i6gSH30EjB7tvdzy5cCoUdpT0SuFyJ4/X7293rLQemPFCsnmwxfkNqiFyE5JAZYuBU6cUB93pfv22mvGucT6O3uwFbIVe4PD1DOMO8JzqF/EH4tTXTUcMkYGM9IS6ElrcCwtGgpXDYmeSKNqGg69GKmdMCsIlVaNjFFjY7UgYa7wUg/DeIaXVDRQ3QUOI/EmRMjLBPKkZpRw4k0FL2pb4k3oMQIrh8jWY3OyYoUx1xbNd2PU9bTASz0MowxHGmXc8Ic62DGiJ5H7cZutyptEi4un1rghRM6RUU+eFD/Xsa1EQO/eUj3essKKMmyY1KZAq+ZdM94mJwN33eX5vqlhlEeMVYOEeXP11hqBl2GqLX4SgCxNddBwZGQQpaQ4f52lpJj3ZSbyFa9Fw6E326t8vh4NR+3aRGFhzvuUvFGIgivPhidPm0Brf6yQrdgTVl/qYZhAIzqHchyOakBmJnD77ZKxoiMFBdJ+b+HG9aAW0VNGSxRKX75qCwvdY0AosWCB1N7hw4Fz54DKSufjFRXAq68CTzzhvF9vKPdA8MQTUh98iW1hRtAuqwYJC7ZgawxjWfwkAFmaUNZw6E197i9EjSi1xg1x3DZu1GZboiUrrGMfgmF9X6RvIpuZNidWs3FhDQfDqMMaDgaAFKXx1Cn1MqdOSeUCgWgUSl8jd8rnK0UqdbQt0ZIVNtiSqYn0TY1p0zxrq4xERDvmTzgfDMMYAwscIY6oIBEogQMQn2CUhBNvnDihfr6rcKMlK2ywJVMT7Zsr8qT6/POeM+kajad8N4HCqks9DBNssJcK41eUPGXkCcYbjh4emzZJ6d294Wj/IeIhIpoV9vffgdmzxcpaZX1ftG+O8KRaJax6CjpnZLA1hgllONIoQjvS6KZNQJ8+3stt3Ci5f5qJUpTQ11/X98KuqAASEtSXjOLigOPHtU2Uly4BNWsauwySlSUmUJmNnr4ZHcE0mOFIowzjjugcyhqOEKdHD2nS9TYpmz0ZKoUwlzOyKmUN9YT80i8oAC5fNr6tclbYV1/1vS45HLdV1vdF+jZ5MjB4ME+qnhDVxDEM405AbThmz56NG264AXXq1EGjRo1w66234pdffnEqM3bsWNhsNqetU6dOTmXKysowYcIENGzYELVq1cItt9yC3/UmzQgxwsOBd95RL/POO+4TipHpwY00rHR0Px09GigpUS9/6pQ++4lXXgGmTvVtorXSUoTj/fzrXyWhwrVN4eFSn+fOtY79BMMwIYRffGYU6N+/Py1ZsoT27t1Lu3fvpkGDBtEVV1xB586ds5cZM2YMDRgwgAoLC+3bqVOnnOoZN24cpaSk0Ndff005OTnUs2dPateuHZUL+nqGslusjJY8EEbnjDDKrVBvtldfQmE7ZoW99VbruI5qQel+rlqlL+suwzCMI0GZS+XEiRMEgLZs2WLfN2bMGBo6dKjiOWfOnKGIiAhatWqVfV9BQQGFhYXR+vXrPZ5TWlpKxcXF9i0/Pz/kBQ4isUiYZsSUMCJHhtZcKmbER0hPF7tev37mJFPTE8E0mGKEMAwTnARlHI7i4mIAQIMGDZz2Z2dno1GjRrjmmmvw4IMP4oTs5whg165duHz5Mvr162ffl5ycjNatW2Pbtm0erzN79mzExsbat7S0NBN6Yz28uRqaFVOiUSOxcseOKS/haM2lAhgfH0HUw2PgQOOWInyJYOrtfhJZK0YIwzChjWUEDiLCpEmT8Je//AWtW7e27x84cCA++ugjbN68GfPmzcPOnTvRq1cvlJWVAQCKiooQGRmJ+vXrO9WXkJCAoqIij9d66qmnUFxcbN/y8/PN61gQIRpTYuFCY2w7XJkyRXlS1epWaob9xCOPeK8rPFwqZwSyoa3rPZENbb0JHSJCmpVihDAME9pYxkslPT0dP/74I7799lun/cOHD7f/u3Xr1ujYsSMaN26ML774AsNU3BqICDaF0IBRUVGIiooypuEmEQj3O9FJ/fHHq/6t5tYq92HdOu1tcfVe0ZpLxYz4CCIeHpMm+Z5NFjAmQ6lr7hwlRMsxDMP4giU0HBMmTMBnn32GrKwspKamqpZNSkpC48aNcfDgQQBAYmIiLl26hNOnTzuVO3HiBBISEkxrs5kEKhGYngRpSl/bjn14803t9bou4YiEl46PB5Yvd45UaqS3DSB5rwwd6vnY0KHScSMwIoLpyZNi1xItxzAM4wsBFTiICOnp6cjMzMTmzZvRtGlTr+ecOnUK+fn5SPrf7NihQwdERETg66+/tpcpLCzE3r170aVLF9Pabha+qtF9wduk7glPth1KfdCK46QqEl767beBUaOq7CfMENwyM4HPPnPfb7NJ+426P0ZkKI2PF6tDtBzDMIxP+MOCVYnx48dTbGwsZWdnO7m9XrhwgYiIzp49S5MnT6Zt27ZRXl4eZWVlUefOnSklJYVKSkrs9YwbN45SU1Np48aNlJOTQ7169QpKt1hvnhg2W1VGU7NQyt4q6g3iizeJiPeKaCZRM7wz/Hl/NmwQG5sNG5Tr4CynDMP4g6BwiwXgcVuyZAkREV24cIH69etH8fHxFBERQVdccQWNGTOGjh496lTPxYsXKT09nRo0aEAxMTE0ePBgtzJqWEXgsMoE4WlSFxUMRPugVZBxxJubqFmCgT/vz9y5YteaO1e5DhHhz2wBlmGY0Ed0Dg2o0SgRqR6PiYnBhg0bvNYTHR2NhQsXYuHChUY1LSAYoUY3AtcEZ8ePOxuKKpGUZGzblMKCewsvrcX+QUuYan/en8OHfS8nL0N5CikPSOMr6sXDOUQYhvEVSxiNMhKiRpt6jDu14hizY8IE7wabcrwLo9rmi1urWYKBP++PaMwPb+WGDZPcjT2FMZ8yRcyLR80WxmijXIZhQhfOFgvrZIutqJBe5AUFyl+kqamSB4a/vy5lQ1DAuW02m/T/GTOAq6+WgnyNGSMF8RJ5stLSgLvukiYsR62ELxlKs7OlSdEbWjO4+vP+iGR1DQ8HLlxwdsN11UT88Qdw553K7fWWNE8p6Z58310TA/qS/ZdhmOBEdA5lgQPWETgA9Ykd0JZV1RVf1eKe0svHxUl/HScdeRKSJyXXPjz/vCScOLZBS9u8lTVTMDDz/rjyxBPqMT+mTnV2w/V0f+Sx9YS3cZDHUYu3kasAyssvDBP6CM+h5puTWB+rGI3KiHpi+FqnnJBNS64Ox7IzZih7ggBEcXHG9sFbP1zLefK2MSKHiNb740sulKlTicLDna8VHi7td22THs8iNSNXowyAfUn8xzCM9QkKLxWrYDWBg8i3ScoVNRdRT4KByAQh4gmSmkq0caMxffDWD09ChBmCm4zo/TEi865jxlpPWV19dUVWSponmnTP28aJ4hgmtBGdQ3lJBdZaUjEavWpxQH15wCw7CSW89UNpeSCQ3hVq9g+AccsvovdCiWnTgN693cfG13odCaT9EcMw5iI6h7KXSoijJ8uqPEGqZRL1h4uoowfEwoX6Qn17y5BrFmZl3vWEr264s2Z5jsKqJ/KsEkr3h2GY6gMLHCGO3snI2wRhtouoqyumSBwQwPwYJaIYkQtFFKNckV3D56uFk9eLVe4PwzD+hwWOEKdRI9/OV5ogRJKpybE5tOJLLhZ/xCgRwZ9BwkQ0ESKaHU+al2HDpKWflBTnsrJ3klZBxCr3h2EY/8MCB6OK0gQhkkxNT9AutaUINXwRcByvbVQQK38GCfN2L2w2qU9ZWZK9hhqeNC/DhkkRTbOygBUrpL/HjwMZGe6CiBJG3B+GYYIbFjhCnBMn9J0nMkHIUSzDXJ6isDDxKJau6LE58UXAkTE6s6yZGiBPKGkiUlOl/X/7m2TD0rKlWH2umhdPtjCugsiMGVUCjiNG3B+GYYKfgOZSYcxHzxe06ASRmQnMneuujaiokPZ36qRd6NCzxJCaqj8qKaDsTSLbNOjxJnHMY6IUAM3oCdg1B44nrxyjNS+ueW1at3YPPubr/WEYJjRgt1hUD7dYpaibgHs0SpGw4nrdVL0h6oq5YAGQkFA1qQL63F/N6oeMp+ifvoRt9xV/hGfnRG8MU70wNbT58ePHMWXKFGzatAknTpyAaxUVQZbBKZQFDsB7OO7Vq4GGDbVNEFbKV+JpUhfN6WFkP5QmWqtNwP4Mz84wTOgjOofqWlIZO3Ysjh49imeffRZJSUmwGeUzx5iCvL5vpKrbLC8MrUsR3pZDPAlTQJUAsG+fMf3wJvQYEfzMFb2CjBnPA8MwjDd0aTjq1KmDrVu34rrrrjOhSf4n1DUcMkZ+aZsdaVRkKUIkiqrrcpGnZHMiqPXDXxFFXa/pSWCYPx+IjzcmCZ7esgzDVC9MXVJp2bIlPvroI1x//fU+NdIqVBeBw0isYAtgZOhtNeLiJDdQPRlVzQjprSTgeMKIdPFal6xYOGGY6oWpoc1fe+01PPnkkzh8+LDe9jFBjllxOFyvoRaW3ApRK/0ZURTQHqfENXqoVpSCsP3+u+d6jXYvZhgmdNAlcAwfPhzZ2dlo1qwZ6tSpgwYNGjhtTPXAW+wHs20B/BW18tQpZYHBnxFFAe1xSnzJ2+JNuCFyrldJOPFV6GEYJjTQZTT62muvGdyMEOSPP6RZpk2bQLfEVERiPxiNrLLPz5eCjFVWmnctmYwM6a/ZcS28oUdwcdSyaLGnERFu5Hq7dlVPVmezScLJ0KG8vGJVeCmMMRtdAseYMWOMbkfo0aULcPCg9O/rrpM+75o2DWiTzMI1+JOZeLIn8AdvviltrrYLXbtKNh5qRqhxccZFFPVFcNEqrBQUiJfTsrTkr2eFEccX13KGEUV3aPOKigpkZGRg1qxZePHFF7F27dqgi79hKldeWfXv3bul/8ufeZcuBapVQY0vSd2MItDLA76kjNcqrJw8KV7O30tLjHHwUhjjL3QJHL/++iuuvfZa3HPPPcjMzMQnn3yC0aNHo1WrVjh06JDRbQxO1q+XBA1XA4fXXweioqQZQ9bTM17Rm9TNaFxtIrZu9e5i68kGRG+iOD0p4/XmbYmPFy/n76UlxhjUfle+2P8wjCd0CRx///vf0axZM+Tn5yMnJwe5ubk4evQomjZtir///e9GtzF4addO+myorATefdf9uBzdqnVrgAU1VfQkdQPc16Dj4qpiccikpQFTp0qaAxEclwf0fNn76smhZKzrCV88hkQzwaak+D9ZHWMM/vayYqo3ugSOLVu24JVXXnHySImLi8OcOXOwZcsWwxoXMthswAMPSL/es2eBu+92Pv7TT8BVV0nlHnkEKC0NTDsdMDJVuxGI2hPIuKZld0yrfvy48768PCnRnFbtiWxcJ4Jczij1taeU8WvWuAtNvngMyUKEGrIQ4Q83acZ4eCmM8Sukg/r169N3333ntv/bb7+l+vXr66kyoBQXFxMAKi4u9u+F9+whatyYSJrr3LdVq/zbnv+RkUGUmurclNRUaX+gWLBAeZg8bWlp4u3NyCCy2bTVDxBlZRGVl0tjo3S+zSa1pby8qqxSfY5l9VJeLrVrxYqq9vmCPDau/ZP3uY6xp2dHy71g/EtWlvizzjBKiM6hugSOu+++m1q1akU7duygyspKqqyspO3bt1Pr1q1pzJgxeqoMKAETOBxZulT51968OdEvv/ilGUqTr9IE4y+WLxd7MT7yiPNE620C9iYEiAgGopOy6Mt9wQLjBAYj0CpEGC30MOahRWBmGCVMFThOnz5Nt9xyC9lsNoqMjKTIyEgKCwujW2+9lc6cOaOrwYHEEgKHzLlzRPfdpzwbPfgg0YULplzaH1/getHzJSaiqRGt14gv+xUrtGtRAq1ZkmEhInTRqsViGFdMFThkDhw4QJ999hl9+umndPDgQV+qCiiWEjgc2bePqFkz5dnoww8NvZyV1asimghPWgdvAoNWIcCXL3utwg2/9Bl/wUthjC+IzqG6kreFGkGRvO2jj4DRoz0fu/JK4PPPgWuv9ekSK1dKXhPeWLFCym/iDaMjF8oGl4D0SpRxzcqqJaHa1q1iCeCmTQN69/atD94S3ilhRgI4NXy9b8EWsTLY2msWPA6MXoTnUFEJ5vHHH6dz587Z/622BRuW1XB44sIFoocfVv4kHjtWWpbRgZEaDrMMT0W+xLT0w99r2Erqa6tolny9b1Y0OFYj2NrLMFbEcA1Hz549sXbtWtSrVw89VT4JbTYbNm/erFE+CixBoeHwxIEDUnKKn3/2fHzJEmDsWOHqjEo5r5Q+3VUToRdvX2JaNTWZmcDttyuXy8gQa69Su1z3nzwJTJqkPa6Iq2bJLA2S0r33dt/03PdAflWb/ZwyTHXBcA2HGbz00kvUsWNHql27NsXHx9PQoUPp559/dipTWVlJ06dPp6SkJIqOjqbu3bvT3r17ncqUlpZSeno6xcXFUc2aNWnIkCGUn58v3I6g0nAosWqVuuHBnj1C1fhqQBYow1NH+wlRF1pZY5CRoV5O5GtX6Ut56lTP+9es0d9etevp/TLXaiOj9XxP9z2Q2gUrG0gzTLDhF6NRx4utXbuW9u/fr+m8/v3705IlS2jv3r20e/duGjRoEF1xxRX2pRsiojlz5lCdOnUoIyOD9uzZQ8OHD6ekpCQqKSmxlxk3bhylpKTQ119/TTk5OdSzZ09q164dlQu+LUJC4JC5eJHo0UeV36SjRxOdPatahS8GZIEwPPXU3vBwscnEiIlHTxwPR+FNZFknPl5yDc7KIlq92njXZV/vm9bzA+1+bWUDaYYJNkwVOP72t7/RwoULiYjowoULdPXVV1NERATVqFGDPvnkEz1VEhHRiRMnCABt2bKFiCTtRmJiIs2ZM8deprS0lGJjY+ntt98mIqIzZ85QREQErXIIklVQUEBhYWG0fv16oeuGlMDhyK+/ErVpo/w2fecdospKj6fqdYMU9fpYscKYLuoN2qU1NobSxKMnjoejEOctloenTVSY0sKyZWJtXrbM8/la7rsVtAv+fk4ZJpQRnUN1hTb/5ptv0PV/SRHWrl0LIsKZM2fwxhtvYNasWXqqBAAUFxcDgD1kel5eHoqKitCvXz97maioKHTv3h3btm0DAOzatQuXL192KpOcnIzWrVvby7hSVlaGkpISpy0kadYM+PFH6d3pKVHcQw8BYWFAcjLw3/86HZJTzo8YIf0VXVf3ZxIvIxK66Q3tLId+f/55/dlrHXNUaMmPohZmnkhf7ovvvxcrt2yZ51D3Wu67FfJ3cLI5hvE/ugSO4uJiu1Cwfv163H777ahZsyYGDRqEgwcP6moIEWHSpEn4y1/+gtatWwMAioqKAAAJCQlOZRMSEuzHioqKEBkZifr16yuWcWX27NmIjY21b2lpabraHFQMGya9yUtLpfSPjhQWAtddJ1nL3XUX4IMA5s8kXnoTutlsVRkw9Uw8jsnXfJCvATjniHHMj7J8OeCL/bLW3BeiQtvGjZ6TzWm571bI38HJ5hjG/+gSONLS0rB9+3acP38e69evt2sXTp8+jejoaF0NSU9Px48//oiVK1e6HbO5vBWIyG2fK2plnnrqKRQXF9u3/Px8XW0OSqKigAULpBkmLw9o3975+McfA7Gx0ht30SLN6gN/JvHSOyE5fkFrnXiUkq/pxVUmljVLiYk+yX2av8yvvlpbeddkc1ruuxW0C5xsjmH8jy6B47HHHsOoUaOQmpqK5ORk9OjRA4C01NKmTRvN9U2YMAGfffYZsrKykOqQnjIxMREA3DQVJ06csGs9EhMTcenSJZw+fVqxjCtRUVGoW7eu01YtadIE2LVLmoE//dT9+KOPSksu8fFSOUGUlgd8yVzqiLycsW+fb/UUFmqbeIxYwnHl1CnP+7Oz9dWn98v8kUe0Ta7yGMiaIkD8vltFu2D2c8owjAt6jUR27txJmZmZdNbB4+Hzzz+nb7/9VriOyspKevTRRyk5OZkOHDjg8XhiYiK9/PLL9n1lZWUejUY//vhje5ljx46x0aheysqIpkxRtqK7/XYiwXw5ZuTf8OSRonfz5maqN6CYlm30aM/9nDZNe12+enhMner7OBKJ3Xcr5e/Q8pyaVZZhghm/usWWl5dTbm4u/fnnn5rOGz9+PMXGxlJ2djYVFhbatwsOycnmzJlDsbGxlJmZSXv27KERI0Z4dItNTU2ljRs3Uk5ODvXq1av6usUayZEjRDfeqDzTvP66opeLGej1SBH1gvA2QehJvuZte/ppz214+mntdaWlOcf30DPJTZ2q7gXjadPryRFs+Tu0xA3hCKZMdcJUgWPixIn03nvvEZEkbNx8881ks9moVq1alKXBcR2Ax23JkiX2MnLgr8TERIqKiqJu3brRHpcgVhcvXqT09HRq0KABxcTE0ODBg+no0aPC7WCBQ4AvvlCecWJjib7/3tTLa3U/jYurEi6M+oIW1XDceivR+PFiZTdurKpfq/YmLk46XxYuVq82ZpIrK5OCkd16q1g7fIlVESxaAC1xQwIdY4Rh/I2pAkdKSgrt3LmTiIjWrl1LycnJ9Msvv9AzzzxDXbp00VNlQGGBQwOXLxM99ZTy7DNkCNGpU4ZfVnSynzatauIy+gvaW4Au1y0szLvA4C27rdpm9iQnEpAsNdVZ6LGqwOALWuKGWCHGCMP4G1MFjqioKHvo8AcffJAmTpxIRES//fYb1alTR0+VAYUFDp38/jvRzTcrv13nzTNsyUVvoCajv6B9Sb6mJDBo1d6kpDgLEGZOcmq2FrLQ5Lg/FJcNtASH4wimTHXE1MBfCQkJ2LdvHyoqKrB+/Xr06dMHAHDhwgWEsx9Z9SElBfj2W+kdumGD+/HJkyUvl9q1ge3bfbqUXldKvQHMlNASoMuxDY6kpgKrVwMNGkiJ5hYu9M3NVjSQ1sKF0vU8Be5SQqm//wvD4+Zl4+ouGwpoiRtihRgjDGNZ9Egz06dPp9jYWGrRogVdccUVVFpaSkRE77//PnXq1ElPlQGFNRwGcvky0bPPKn/aDRxIdPKk5mr9nUZepD1ZWeLeJAsWVGlZ1qwxxtNG1iToMWbVqolw1BRt3Fi9lg1Yw8Ew6pjupbJmzRqaP3++U1bWpUuX0rp16/RWGTBY4DCJY8eIundXfuvOnk1UUSFcndmulHqWX7Qu9RjlaQNU2YDocdcNZKK3YEOLsGs1wZhh/IHf3GIvXrzoaxUBhwUOP7Bpk/JbOCqK6JtvhKoxy5VSqxujVg2HLMAYFUNE3jZu1G7M6uvkF8yJz2QPnPR06W9Zmdh5WoRdK8UYYRh/YKrAUV5eTi+88AIlJydTeHg4HTp0iIiIpk2bZneXDSZY4PAj5eVEM2cqz1J9+xIdP+61CldNhC/GoVo9PLS4rzpO6mYEDps2zbkPerQnIpoIx/FdsMC4ev2Jpxgj4eHSfhG0CLvBFmOEYXzBVIFjxowZdOWVV9Ly5cspJibGLnB8/PHHbMPBqOI4cX2bUUSVvXorz1gzZwotufgSZEmrh4eWJRFXgcWMwGGywKE0DiKbN02Ep3rVgoMFyp5GTdj0FkVVVOjgSKMM446pAkezZs1o4/8iFtWuXdsucOzfv5/q1aunp8qAwgKHf1AVDLKziSIilGewzZvt9Ti+yGfMUJ9IPAkdjmr1Rx8V/1rXuiQif9FqXX7RsjkGDnMdGyM0EVptTvy9bCAibJaVeY+eGh4uvrzCMIwzpgoc0dHRdPjwYSJyFjh++uknqlWrlp4qAwoLHOYjvGxRUSEZkyrMDCda9aDrkwqFJ0DH4FpE+kJ3A1VfqSJlvQUfM2pz7ZsrvhowighYrmPpz2UD0WdKVPBasMA/7WaYUMPUOBytWrXC1q1b3favWbMG119/vZ4qmRBGLcuqvM+edTQsDHjySenAyZPAwIFO5eN/ykZOYRIINmxBN9TAZdVrnzpVlXn1iSeAV18Vj0HhSFKSFGNChBYtpJgfn35qbCp7V955Rz2uiJwJ19O4A9J+tRTs3uJ7ANJYLlgArFgBZGUBeXn+ybKq5Zk6dEisTtFyDMPoo4aek6ZPn467774bBQUFqKysRGZmJn755RcsW7YMn3/+udFtZIIc0cBUW7dKE7Wdhg2BL78EAFR88x3KevRDTbpgP9wNW3EZkQCAh/E23sHDHuvPzpZSnc+fr6/94eFAly7AokVi5U+e1J7KPi5OEo5sNu/npKZKgoTZE7tocKqEBCmwmj/R8kw1ayZWp2g5USoqpOsXFkoCa9euvgeeY5hgRpeGY8iQIfj444/x5Zdfwmaz4bnnnsP+/fvxr3/9C3379jW6jUyQUlEhTfYZGWLl1Sa4rZU3oxadRxgq8LYHweKfGAeCDQQbrsEvbscXLdKn2QCk87ZtA+LjxcrHx4tpBwBg2jRJM3D8uDROrhE909KANWukMrIW4fBhMWFDFnqUsNkcNEse0Bvd1R9oiej5yCPeJ/rwcKmcUWRmAk2aAD17AiNHSn+bNAmtCKwMoxXNAkd5eTlmzJiBli1bYsuWLTh37hwuXLiAb7/9Fv369TOjjUwQ4vjCffNNsXPUJi55giGEYTzehg2EFHie0X9BCxBs2I5OiMAl1K8P/Pvf2trv6fqi4cxTUsQnxJYtq0KuDxsmCROOwkVeHnDLLcDu3ZLQs3u3uOCkRQvgia5dJW2Kzeb5uM0mCURdu4q1x0i0CEORkcCkSerlJk2SyhlBZqbnpbRQDPvOMJrQYyBSq1YtysvL03OqJWGjUWPR49ngzY3Sm8HmMHyiWuBRLPTJQFPUS0VrzA1vsSp8iR1hRIAuqwax0mMQ62scDi3t8uVZZ5hgw1QvlaFDh9KSJUv0nGpJWOAwDq2uo6ITl4hro7RV0kcYoVqoJfZqEjY8pZH3NgEbEeLa19gRRgk9RgWx8lfmXrVnSm+kUVGqW9h3JrgwKzaMqQLH22+/TYmJiTR58mRasWIFffrpp05bsMECh3FojaYp+oWpJ0pnAgpVC+TgOopEqSaBg0h8AvZFO2BE7Agj83r4+qLyJTib1noDGdEzmMO+M6GNWb9BIpMFDpvNpriFhYXpanAgYYHDNxwnIz3BrUQ0HL5G6RyCT1ULTMJcTV+kohOw3gnRqNgRVlgS0Ro6nih4I3qyhoOxInp+g1rwW/K2UIAFDv0YEdjKCBsO8a2S/g9jVQu1xW5Dv0j1TIjp6WL9SU/3XlcgtQB6bBrM/BIzG84Wy1gNf9gVmRr4i2EAZWt8rRCpe0sAVR4TvmPDfVgCGwjfZR4Hhbn/BP6L60Cw4Se0RDQuAvDN9TM8XPJEGTGiyiPFG0bGjlDyfvFHgC6tnjLB7uEhB1sD3L175P/Pny/1d+VKyW1cr7s2w4jgq7eakegK/PXGG2943G+z2RAdHY2rrroK3bp1QzhHuQlZtAa2EkHNlTQ8XJqwX33V9+vYbJLw0umWRqi8VIEmTYA2v/8bX+KvTuVaYj8uoiYAoPK7l4AeT/l+8f/hLSjUww8Djz/uvZ6HPcc6c0MWevyNlngZ3qKHynFDhg61dgCtYcOATz6R+uL4ok9NBe66S7qvrvv9EciNqZ5o+Q2ajh71SZMmTahWrVpks9moQYMGVL9+fbLZbFSrVi1KSEggm81GzZo1o6NHj+pSz/gbXlLRjhmp1tXWtbV6v6ipD13XLJ3tHCrpbTykXskPP/g0dhkZRCkpzlWmpDi3KVRsAbT0I1T6LOO6lLZ6tbnr6AzjCX/8rkxdUnnppZdwww034ODBgzh16hT+/PNPHDhwADfddBNef/11HD16FImJiXhc5BONCUqMloa9BZASjdwpI6uv4+Kc96emSl+fjl+T8hepFNjLhnH4J2wgtE3+A+WRMe6Vd+woXeDKK4Hz58UbBWlJ4Pbb3fOyFBRI++UlA0t9lfiAluBhodJnGceltK5dpeBiStobQD3qK8PoxVIB/PRIM1deeSXl5ua67c/JyaGmTZsSEdF3331HiYmJeqr3O6zh0I7RGg5vrrFavVRko0gtcRdUjTu//lr9gtOnex2z8nLJxVatGtkFN5S+9kU9ZUKpz66Ect8Y62O2t5qpXioxMTG0c+dOt/3/+c9/KCYmhoiI8vLygiZVPQsc2vFmja91M8pLxVtqeJ+9HSoriSZMUG/Ejh0eT924UawPGzf6x9vBn+6kIp4yoezhwfE5mEBjpreaqQLHX//6V2rfvj3l5OTY9+Xk5FCHDh1o0KBBRET02WefUevWrfVU73dY4NCHktSsd1P7utMaCMtsv3MiIvrzT6LYWOUGpaQQlZTYi4vGKJk2TX18jehDIFxPRQQcK8QNMQPWcDBWICgjjRYWFlKfPn3IZrNRZGQkRUZGUlhYGPXt25eKioqIiGjz5s20YcMGPdX7HRY49GNEHA6RrzstL+yA5LPIzlZv2FNPaRY4lMbX168Sb8LYmjWBDaTl77gh/tD0hLL2hmFE51AbEZFe+4+ff/4ZBw4cABGhRYsWaN68uRFmJX6npKQEsbGxKC4uRt26dQPdnKDj0iUp/fuhQ1KG2AULgGPHpFepFrKylF03V66U0nx7Y8UKyc20Z0/frucTU6YA8+YpHv4LtuI7/EXx+FdfAX37Vv3fmwutFioqpHukZoAbHu5svBgIt00j+6xGZqZn91Uz+ivHGAGcfxuyMZ+rMTPDBAuic6iuOBwyjkKGTckElglpPL2w4+KkF6rNJiZ0yHEx1KyktaQj1+vtYNgkN3eutJWUANdcAxw/7nT4W0gdPYF4XI2DKEGs03EPscgMQ8Tbx9VTQg665c8J0R9xQ2QBwPUZNau/avE5XnuNhQ2mGqBXhfLBBx9Q69atKSoqiqKioqhNmza0bNkyvdUFFF5S0Yeaah7w7pGhZW1ei0paz3q56TYN336r2pi5mORxaUlru7wtD+jNSRNqKv9AppG3Uu4XhjECU2045s2bRzVr1qQnnniCPv30U1q3bh1NnTqVatasSfPnz9fV4EDCAod2RF7YqamSx4Vj4CNf1ubNSg3vFwNTqhKEXsRTqrN77oIsXe0SEU58dWcOpFGjkRM1G3EyjHGYKnA0adKEPvjgA7f9S5cupSZNmuipMqCwwKEdvS9sX7OAGp0a3p9fuq6CUC2cpSNIU7x4ia0O1cOfhgpNvrozuxr2+utr3WgNFLupMoxxmCpwREVF0cGDB932HzhwgKKiovRUGVBY4NCO2S9stQnmwgWiRx8l6tdP+nvhgn7hxN9fukqC0E3YodqAhXiUgEqP7dIqNPnizuzXZSiXMfMmTGmBNRwMYxymChytWrWiF1980W3/zJkzgyb2hiPVXeDQ85Vq5gvbm22IJ61F7dqeJz6jbBpEBCfRcfQqCD3/vGpjeuNrp3YZZbOiFuckUMtQZmmg2E2VYYzDVIHjk08+ofDwcOrfvz+98MILNHPmTOrfvz/VqFGDMjMzhevZsmULDR48mJKSkggArV271un4mDFjCIDTdtNNNzmVKS0tpfT0dIqLi6OaNWvSkCFDKD8/X1N/qrPAofcr1awXtplJ2jxhlOBktHFnVhZRDM7TAVyl2KhSRNLWdX/oEpo8JZBr0EBZoAvUMpQ/BFsrBBljQ1ImmDFV4CAi2rVrF40aNYrat29P119/PY0aNcop8qgIX375JT3zzDOUkZGhKHAMGDCACgsL7dupU6ecyowbN45SUlLo66+/ppycHOrZsye1a9eOyjX8YqurwOHrV6oZL2wjc7SITHxGCE5mfO27tut67FLt7Lu4nzwtuXialLV6FwVyGSoQS3dmBhkTbYPZUV8ZxkhMEzguXbpEY8eOpUOHDulunMeGKAgcQ4cOVTznzJkzFBERQatWrbLvKygooLCwMFq/fr3wtaujwGHUV6rRL2y9bpu+THy+CE5mfu1nZHiu8x+YrdrhgfhCsQ16vIvMXIbyhj+Em0BqF/y1NMUwZmJaevqIiAisXbvWp9gfWsjOzkajRo1wzTXX4MEHH8SJEyfsx3bt2oXLly+jX79+9n3Jyclo3bo1tm3bplhnWVkZSkpKnLbqhrcAUERAfr5UTo1hw4DDh6WonStWSH/z8rQHMaqoALKzgX37tJ0ngrdAYM7p6avwlMreFaPGUQsv40nYQFi34gLQqpXb8S8xCAQbCDY0gvR7ee01KZiWSHt//10qO2KEFHzLNfiZliBsvuKP1NqOaeQ99dcsKiqkIGBE7sfkfZyyngkldMU0vO2227Bu3TqDm+LOwIED8dFHH2Hz5s2YN28edu7ciV69eqGsrAwAUFRUhMjISNSvX9/pvISEBBQVFSnWO3v2bMTGxtq3tLQ0U/sRCOQJfOVK6a/rS0tvNE5PKL2wvbVBJjNTCrfdsycwa5ZYu7QgMvHpFZyMHEdH5MlICZsN+Ps/YlDx373S7PTf/3osdxwJqCQbhq27ByASbsemTcr3zR9CgEx4uBRmXK7X9TpAlTAVbARCWGWYgKJHfTJr1iyqV68e3X777fTSSy/R66+/7rTpAR6WVFw5duwYRUREUMb/9IwfffQRRUZGupXr06cPPfzww4r1lJaWUnFxsX3Lz88PqSUVIwNA6VVVi65LK6mUjdrM9jQwaxx9iXNycPw81ZOG4FNNY6h23/xlcKl16U5pmcRKxpkcC4QJFUwP/KW0NW3aVFeDRQQOIqKrrrqK5syZQ0REmzZtIgD0559/OpVp27YtPffcc8LXDiUbDqMCQPlqe6ClDWYJGwDR1Km+jae3CUpkHL3ZRHjCkMmotJSoQwfVChJxzOs1tEQ21Wq/42sgOE8oCbtTp1rLOJNjgTChguleKjKVlZVUWVnpazVCAscff/xBUVFR9iinstHoxx9/bC9z7Nixams0qicAlNqLzhfvCpE2iL5wp00j4dTurpsnoUn069dTKHatX/uAu9eHyCRn9GRU/uNPqhWtwp1kQ4Xws+NtLEUwyjvDsQ0zZmh7PgJpnMmxQJhQwXSB47333qNWrVpRZGQkRUZGUqtWrejdd9/VVMfZs2cpNzeXcnNzCQDNnz+fcnNz6ciRI3T27FmaPHkybdu2jfLy8igrK4s6d+5MKSkpVFJSYq9j3LhxlJqaShs3bqScnBzq1atXtXWL1TpJmSFwaGmDlq94X9xlRaJjevr6VZugVq/2LpwoJa+TJ5gZM3zTnIhORq59fhQLVTs5DJ/4LOCItMkI7wxP91PrFsiJ3UqxQBhGL6YKHNOmTaNatWrRk08+SZ9++il9+umn9OSTT1Lt2rXpmWeeEa4nKyuLALhtY8aMoQsXLlC/fv0oPj6eIiIi6IorrqAxY8bQ0aNHneq4ePEipaenU4MGDSgmJoYGDx7sVsYboSJwaJnAzXLnNEOIcAzfrcfeQ152MNJexDUqZ2oq0Zo1VULIxo3ugbXUNj12Eo7XU9IuqE3skSijo2mdVRuWgnyPY+kLRrpkG2n/E6ilCyvEAmEYXzBV4IiLi6MVHt48K1asoLi4OD1VBpRQETi0TOBaljO0qMqNFCKMygEikm/E1831i1SrRkarnYSIPYKWiX3bkp9VG7gOt5ANFbRxo/dnQCSKqq8CgBn3M5DGmVYyZmUYrZgqcNSrV48OHDjgtv+XX36h2NhYPVUGlFAROLRM4FoDbImurRslRGiZgNW2uDht9iK+Ch16x9fT2DiOqevyjchyhJaJfePGqv8/iH+qnvDTtBWaDTYd76MRBrFm3E82zmQYfZgW+AsARo8ejcWLF7vtf+eddzBq1Cg9VTIGoCVmgdagTAUFwB13SDEzjGoDoD3olmO8jOXLgbp1xdqvNQ6GHoiq4iboCXrleL4jjnFOunYFJk2Syno6H6gKFqUlRohDPD28i4dgAyECl5CFHm7lW84aifAaNumGHjli35+ZKT0jrrElXJ8dIwKHGXk/jYwbwjCMMjYiT68udSZMmIBly5YhLS0NnTp1AgDs2LED+fn5uOeeexAREWEvO3/+fONaaxIlJSWIjY1FcXEx6orOYBYmM1MKGuX44k9LkyZ6eQKvqJCCbRUUeJ68PGGzSYJAXp73QEsibXCkokKaaAsLpYmma1fv18jOloKFeSMrS/orUtYIVqwA7rxT+/g6nj9ihOdjZvRZpOyVOIRDuErxOPUfgCZ7P8fRAs83zfHZAdTHRuQ5Ex0Hb8hCsLeIsqGEnt8aw6ghOofqEjh6Cv7SbTYbNm/erLV6vxNqAgcg9lKRv0gBbZNiVpb0xW1EG/Qg15uRAbz5pvfyvgoAWpHHx4zxXbkSGDnSex1ynxMSgFOnlMvFxQHHj0v/Fh2fMViKpbhX9fgyjPF4zNvYiAoAWgXmtDTgrruk8RMVgkMRTx8CqamSVrK6jAFjPMJzqB+WdyxPqNhw6EGPW2Egjev0tNdbllQzbDj0tFcOEjZvHlF6OtGCBURlZc7912qUW7u2ernatfUb5YahnP6N/qqFmuKQ4rPjq3eGN/sfT27H1dk4kxPFMWbht8BfoUB1FjiIql7CosG1Auk+qFVgcBUApk51d2kNDycaOtQ3rwe1l7ZrYCq1IGFhYe5tc4yWqsUo19EQVG1z9DzRI9BNm0a0fWWeaqGN6EXhuOwxFLsvAgC7lIphZlZjhmGBQwPVXeCQ8UfkQ70TjF43SMfJ2tsXnhzXYvlyovh49XpdhRatX+aufalTR3s/vHn2iAqQ06Z5vke33ip2/ujR0pg1bCj9fySWq5+gMUCgN6qz1kIUDqPOmAkLHBpggaMKMyMf+hLKWq8bpCwgiQgscs4T0Yl6wQL9OUAc9331lbtmw5OA47i8IvJlr1fgkBHVkChtNlTQOtyiXuiXX7zffMYJPQIWJ4pjzIQFDg2wwOGMGWpqX9eP9cS1cPxqMyNug8jLWUTIWrBAXMBxxNvEo2dJxbV+pfDsWra0NKJZDx9VL9SlC9GlS94HtJqjV2hnDQdjJixwaIAFDnfKyqQJTsl4UQtGrB/7IjCsWCGp/I0WOLy9nEWFrPR0seulp2sfd28CgxwYTa0PWsfFZpOWpJYvl8ZozRrncbgDq9UreOstbR2tJvgitHOiOMZMTA38xYQ2mZlAs2bA449LbqePPy79PzNTckfMzpbcC7Ozpf972ufI1q3uwaAcIfIc8MqRrl0l9z3XYGIiJCUBJ09qP08JkUBRFRWS+yGR+zF5nxygq1kzseuKlpMJDwfuu0+9zH33qbsqDxsmuR+7BmZTg0ga75QUaYwef9x5HD7B32ADIQwVWI2/uVfw6KPSINtswP794hcOYbQ8T57QGpCPYUzBTwKQpWENRxVqX1HyF7HrF7K39OtGrR/ryaUi2z4YpeEQXQLSosIuK3M3QlXqhxZE7FZEv2odl29EbUO0JOj7bk2BeoEOHYhKS7UNQAhh1JIIe/UwZsAaDkYzIl9RrkGkTp1y32dGKGtAOQy6GhUVwLZt2s5RIywMmDJFOVqqrOnZtEmsvsJCIDJSCleuxqRJUjkteNMsAd41SzKO4dV79xa7flKS9CyIkFeWXDUHrl3rXmDXLiA6Wvocf+01sUpDCC1h6tVwTA2wYoX0Ny+Pg34xfsJPApClYQ2HhJGGlY5rwkavH8tf26K2DytWGJtd1Iikcp6+SJVihDi6xGrBLM8ELfdTr0EsERFVVko+t2on/vijvsEJMtjok7EyrOFgNGNkQiyiqq9no9eP5a/t228XK5+UVNUGPTYgrhA5r5crJS1Tw5MdyCuvABcuAAsWAOnp0t8LF6T9ejBKs+SKPJaeNGGAtF++n/HxYnV6LGezAR9+KFVYVOT5xLZtpXJt2gClpWIXC0K82TBxAjomGGCBg7GjJ8OpN2QhRmtWWBG6dPEupISHS+XkNkyZ4n5O2P9+BVqEEVmYUluGUkJNyIqMlISZhQulv1qXURyxwiQlupTltVxCQtWH/Oefux/fuxeIiZE6pVdCszBs9MmEAixwMHZ88QRRwlGIMWr9WLaVmDlT2Srfsey2bdK/MzOBuXPdz5GFhQYNtLWjoEDMTsIVX4QsLXibpIiABx4AVq/27F2khCxkKWGzVWmA5GdKDc1Cz6BBUuMrK4H773c//o9/VHm55OZqqNh6ONoFNWgg3SsjhXaG8St+WuKxNGzDUYUeTxAj7DK0tE9PsjmRWCBypFFvZgOOdgeidhLTpvkn9LanYGCexkzEu0gJrfYEZkavtXPyJFFkpHJjrrmG6Px5Ay7kP5SCfK1ezaHcGWvBNhyMLpSWPuLipL8i2g+zVLx6bCUAScsiEgvk99+l9g4YIFZvfLz4MlTv3pKHR48e5qm9MzOBxo2Bnj2lFPY9e0r/B5w1SzNmAH/+6d27SAmtHhNKz1RKioFf5g0bAmVl0o3csMH9+IEDQK1a0sM5a5YBFzQXpWe9oAAYPly6f2Y/TwxjNCxwMG54Wvo4ftxzAKi4uCphRMYMFa8eWwm5fV27apsktdgdWMFOApAmqNtvd3dDLSiQ9n/6qTQ53Xkn8O67nsdR3qcWQArQb4zqek2t91IJt8BzvftVLbmMH+9+wrPPVi257NxpTCMMxNcgXwxjWfykcbE0vKQijrdEZGapePW67Mqhu7UsA2gNmOWXJQMVtIQwN8K9Uqubs56Q3KLPlPCyw4lTRLVrK3f4iiuIzp715TYYBrvAMsEG51LRAAsc1sfX5G16J0lRISKQERy1JGkzO+qr6/joyaMjmqBMSZDxtDmdv3mzemGl9Ll+gjO7MsEG23AwQY+jqvz4cf31FBZqdyvU6sZrtAeOUl4aT2Rni9WdnW1+1FfX8dGaR0fNdsHRvkTrEpvT+T17Vs3bjz3mXnjWrKoll+3bherXc9+UMCt+CsMEHD8JQJaGNRzWw9NXrrd8IyKqZzVNhJblIi3LSL4uD3jTkojmNhk9mmjZMqKwMPVyWvK2eOublq91LdoQPUtsqp5Tp08TNWigfHJiIlFxsbAXkKjHj6dx3LiRKCWFM7sywQMvqWiABQ5roUVVrmeC8XXSMKOsL6nHRZdU9AppvqDFHkFLWV+X2FT55hvVCl7GVPt/lWxntNinrF7t2W1Zrkfr88Aw/oYFDg2wwGEdRAw2RTQdWl7MWiZ7M8rqsXOQz8vKkjLh1q1rrMBhlH2AFtsZLdoQX/L+aOrbE0+oVtYVWzTdN9E4MkrZmTmzK2NFWODQAAsc1kF0IpGDbmVlEa1Zo99gU8tkb1ZZPV4JepPFiW5GekCIGpjq8STSownT2rfycqLmySX0O5IVKz2F+hSL06rX06q5cwxGx0G+GCsjOofWCJz1CMO4IxovIyFBCnwkc9ttkuFhYaFkTNe1q1hAJK1GjWaU1RpISzasJBI7Tws2m2T4aWTcENnAdOJE5zFJTZUMdWUDUzmmSUGB5745tk02Ar7jjqow7d7Q27etW4FfjtVBKqQgJ52wHdvRxalMA5zGGdQHALyGiXgcr9mPFRbqiyNDVBWMzvFZZ5hghb1UGEvRqJG+cnIGWZHoi44eBZs2iV2vsFCbYKClrBavBJGJKz4eWL4cmDZNrF4Z2Vtn/nxpkjXC40Jm2DDg0CHnTLi//ursxWOUJ5EnfIl+63ovd6AzbCDYQHgBz7qVfwyv/++oDT2xWSjSrZbrM0zQ4ieNi6XhJRXroCWmhB70LkVoNWo0cnlA7/KLVjuHtDSiqVN997gQHXcthrZqS2SuRpi+LLF5QmQca+IcHUJTxQKXI2OoPk5pfu4cl2QYRhR/BGN0hG04NMAChzi+PshGulFqRa/3ixylU4tgIFI2Pl4y+JQ9FUTsHPS4mWptg6dyvnhGmBlpVAkjX7gihsyOWwfsVC2wGA8TUOm1HnZ/1Y6/J1orYoSbtlZY4NAACxxi+Pogi5xvVlhnrZOG41a3btWkvGaNeATSjAzxa6SmetYuuH6Zm5WpVa+njK/jHiyT6tSp6uPtGjXdft9mzVI9sR/WK44Lu79qIxATrdXwxb3eF4JC4NiyZQsNHjyYkpKSCACtXbvW6XhlZSVNnz6dkpKSKDo6mrp370579+51KlNaWkrp6ekUFxdHNWvWpCFDhlB+fr6mdrDA4R1fH2StLqJGBz3yxY1Sj2Ag91m0Xnkc1qxR/0LTMz4iSxRmCXpG1huor1fRXDWq3iQXLlBxSgvFCi4jnBrihOrzxCgTqInWSgRSuA8KgePLL7+kZ555hjIyMjwKHHPmzKE6depQRkYG7dmzh4YPH05JSUlUUlJiLzNu3DhKSUmhr7/+mnJycqhnz57Url07KtcwqixwqOPrg6z1fDOSofkSKMpTG9ySgykIBlrrFnkheBNk9CxRmLWUZWTulkB9vRppV5SRQdSvUa5qRXtvHEtlpZWm9ytUCBUtmq8EMulfUAgcjrgKHJWVlZSYmEhz5syx7ystLaXY2Fh6++23iYjozJkzFBERQatWrbKXKSgooLCwMFq/fr3wtVngUMfXB9moOBNmG/4ZKRj4cj1vLwQ9AodR4xMIDYfZX6/ehDHR0PGiOd8cr/frw6+oV/qvf/nWuWoAZ9eVCGTSv6BP3paXl4eioiL069fPvi8qKgrdu3fHtm3bAAC7du3C5cuXncokJyejdevW9jKeKCsrQ0lJidPGKKM1ToQR5xuVDE1GjvHg6m6pByLneBue8MWV0fVcVzfeiROVz7XZpHxkWl1ZRcYnPl6KkaHFVdZbvTYbkJamHBtDzQ1YfoXq6a9MZibQpImUz23kSOlvkyZVSeLMwNGF+7/9piLMRojGRexGO/fCQ4ZUJZIrKjKvUUGMr++nUCEYkv5ZVuAo+t+PKyEhwWl/QkKC/VhRUREiIyNRv359xTKemD17NmJjY+1bWlqawa0PLXx9kPWeryW2hjfUYjzoRe0F5suP2vFc1wmxTx9twcdEERmfkyeB0aOdJ2VvWVK9xdYgAh54AFi92vP5IvEr9PQXEM9M26OHWH2i5WQchakyRON67IYNhNbY4/mEpCRp0EaO9CyBVVOCYaL1B74K9/7AsgKHjM1l9IjIbZ8r3so89dRTKC4utm/5+fmGtDVU8fVBtsoPQSlQlF5B5vhx5Ym2Sxft9cqRMCsqpHpfeAG4/XZ9AaP0fM1pCaRVUCC1LSHBu3ZAqd4GDYC4OGD6dOXzCwrE2i5aTsab5gSo0pz06AFER6vXFx0tLnDIQtrzz3u+tz+htT2w2ESHiKV2Vq4EwsKkB2btWrGLhjBWeb8EGq2B8wKC8as5+gCcbTgOHTpEACgnJ8ep3C233EL33HMPERFt2rSJANCff/7pVKZt27b03HPPCV+bbTi846shpxmGoHr7kZLiux2HawI5ve69juMAePeGEN1c16u1eHg4JoWLj9feD5HYGjNmiNllLFggdt0FC7Q9B1rW/cvKiMLC1MuFhUnlvKEn8NyKFSRVfuON6gULCrQNQgAx2uPIKu8XK2C0/ZsIIWM0+vLLL9v3lZWVeTQa/fjjj+1ljh07xkajGhH98fv6IBv1Q/DUXpE+GJX2XmSi1eoVY5SgIeoWK+Lhodfw1UivpeXLxa65fLm2Z0iLgZ1RQo/e58/N2HH/fvUThg0jqqjQNiB+xCyPo0BMtFaFI4164OzZs5Sbm0u5ubkEgObPn0+5ubl05MgRIpLcYmNjYykzM5P27NlDI0aM8OgWm5qaShs3bqScnBzq1asXu8VqQOuPP9ARID21Ny7OfcJ27YMvgb8cN1fNhtJEKTpZT5smuVMa0Tal4GN6PTx8dSX21WtpwQJxDxGtGh0trq7p6WJl09OV26DnHgu5cy5erF6Jw8eYFQi0xxFjDkEhcGRlZREAt23MmDFEVBX4KzExkaKioqhbt260Z88epzouXrxI6enp1KBBA4qJiaHBgwfT0aNHNbWjugoc3n78M2ZY64er5QtRb+pzT5sc/lv0S9cxP4paOa3CibfN9WvO1/gEvrZLyf3OqJgoruPo+Jx4E6K1CBx6NBx6c/YoPb9euXSJ6C9/Ua9U43vRaDheRugSFAKHVfCXwGEl6VvrF3+gQwT7GkhLzyTnSx4TIu/hsKdO1Vava9tSU92jWzo+Y1oEJLUx17sMpVSv6GQvusnjSORdiJYjuYpqLVaskMwn1DRbgHRctuEwYunOp6WAAwfUKx80KCAvH46XEbqwwKEBfwgcVovzr8eoMZDGV74G0tJzvi/hv83UcCjdC71f1WqBgJSM8by1T+1L1WiBwzVhnlpZb4KD0uQnKjz6unQ3bZrBHyPvvad+wQ8/NOhC3glkYCorYKUPTqNhgUMDZgscVozzr/erOlAqT1/U8KKZUz1pDBwxI428ow2H6KSulLdF71f1rbdK2hAlLwsluxm5z1qfaVFDUC2bXqFSy7M+daq7wBIe7qxhMcvQ1mcuXybq00e9EXl5Jl1cojprOKz2wWk0LHBowEyBw6rrlkaG3vaH5G5Ee41wnROtQ6+XilK9avY0RhrEOk6ertdwvcd6vQJEl3q0bCtWGJ8vx1M/ysqk9qenexbSfFm685vN1KFD6g3q21cSUAxGNAleKH35E1nzg9NoWODQgJkCh1Wlel/W5x1Vnv6S3PW0V2/mVG8YmX3Vsa3yC9e1Xm8ZZI38sgeUhQ6l+6JV2LS6hsPfOXtEvKxMY9ky9ca9/75hl6qOAodVPziNhgUODZgpcFh53VLP+rz8cnc839OPyAzJXUt7RQNQ6f2a9FaHyMvVU5tdl3VWr/Yu0Bnt9eFoAGkGRgtIcntFjDvVtvR037ULWlPZz5ih/fk1hfJyyZhUreEHD/p0Cat+fJlJdekzCxwaqI4aDhkthoaO0rheyd1fcTgCHfCnvJwoOlrfxKdVoDN6Age0R+7UOjZGLAG5jpmv42DEb1DLV7xlv34PH1bvQLdukhuuRqz88WUW1aXPQZ8tNlSwepx/16ysM2ZUtcsR11j83pJqEbkn1TIiM6enLLLHj0ubUZlljWDTJqC0VN+5hYXacn0YmQlX5tAh4+pyRc75ICdBdUT+f4MG2uosLNSfDdTI3+DWrcCpU+plTp2Syun5DfmFxo2r5sKVK92Pf/MNEBkpDdw//ylcbXVMslYd+6yKnwQgS+MvL5VgifMvYqOgVXKvDoZTjowe7Z+vdW8GsXo3MzUcMkrP2dSp2jUgejUc8ngZZbCp5XcRVF+/FRVEt92m3tCff1atQouXV6hQXfrMSyoaCFQcDn+r/fUk8PLVUFEkJkWo/OgcufVWfRO91kBl06ZV3aM1a4yZwM2y4RDJgbN6tX7DYBHDYlcbD6MNNrVEMLX6cqsiv/+u3uCbblJ8gNTct0Pxw4Mo+D449cAChwaqQ6RRo71JzIhJYbkXqw/MnatP4JA9RPR8raemShO2p2fM8dlr3ly9nhtuMH48RJ4/rbYdarljlF7ujuMjmrFWC1oEjpD4+v3kE/WOvvGG2yki8UxCDSt8cJoJCxwaCPVcKmYtZxgdk8ISqmODEElp7mnT8rWu535qDdNtBGYZvyoJzCIvd7O0blo1U7LQE/Rfv5WVRCNGqHd6795qt7TqCEcaZYGDiEJb4DB7OcPImBTBruFwfaFMnqxd4HAcByPDisttu/tusXqMsuHQ8vxpde9NSdGf1disZ9KoOBxB/fVbWKja4RxcR5EoNfxdxAQOFjg0EMoChz8me5GYFEGvOvaC0pLB0KHaNR3eAqtpvZ966nBNta4XLc+fP/P7mKV106uZAqyXndkQPvtMtfOTMNfQdxETGETn0BqB8Y1h/IWoq6Bel0JAcnPs0UP9+OuvA3fcIXnSEVUdc3W3DUYyM6W+OfYLAAoKpG3lSml8t2wB1q3zXt/x49I5SUnA0KHStnWrVMe+fcCsWd7rkO+nUtu80ayZtvLe2uGNjAzg1luBlBTg2DGx9hJJz8/EiUBsLHDihDRmXbsqP0sVFdJY7tsn1i6t7orys3777eLnyP147z3JnTtYfwceGTLELkv81uM+XPnNUqfD8zAF8zAFANAOu/Ej2mHTJum58XYvmSDETwKQpWENh3++KkLRcEpkySA1lWjePKJHHiGKjVX/+nXVhqSkOIc215JyXm+ALV9tOBw1XlrzpmiNzupp02LboXbf9GrdMjL0tz2Uv+7ld1FDnKDLUDYk+gnXUjQuqN5LxlrwkooGQlngMHo5w1fDp1AznDIjyqeIQKB2XG/ae3mbPFn/PfI0qfsSblzPpua9ovd8UXyNohpKhtOueHoXDcCXqgPyFF4KeWPSUIAFDg2EssBBZJwfuFVSLFtJaDEqj4kejxalTXYv1Nq28HDJ5kTLPXa8F0pupoHYtIThd938nbzNcQtlDQeRmiF0Jb2Nh1QHp/z7HwLdfEYBFjg0EOoCB5HvyxlWcWezitAjIxp3QUnIWLZMWm4xcrLVquG49VZp6ePjj7XdY63GqP7WdMgTuOg4TJvmv0ijnsY42A2nRfH23DTAH3QeMcoFmjYlOncu0N1gHOBcKowTnnKQiOYb0ZLXw0xkA0jX/BMFBdJ+LTlZrEBlpZRX4/RpY+uV82+I5vH55BNgwgRg8mTle0zkfI+V7oUaFRXAggXAI49o7pJutORYadlSMn72xUhRT06MUDCc1oLru2jaNOfjfyIOtXABNhB6Y6N7BXl5QO3a0sA9/7w/mswYBAsc1QjZm2TECG0vViskmbKK0OPKiRO+nW9WkrSCgiqPCcD3ZHxA1T1WuxfeOHUKKCnRfp5ekpL8m0BLJJGe6+8uNVUS+gKZbFCJigogO1vymsrONu735fgu6t1budxm9IYNBBsq8fttE9wLzJhRlQXw+++NaRxjGixwMF7xh2utN6wg9HjC10mqWTNzMgWfPCn9HTYMmDIFCHP5pYeFSfvlSa6gQKzeggIx4USJWbOA5cv1nWuzAfHx0vkbN0oTtRpyBlh/ZGyWJ+bVq4EHH5SeR09Cns0GrFplrczGShiR3VkEsftjQ9KaN6SB/fNPyQ/alU6dpMKpqcDZs8Y2kjEEFjgYr1ghxbIVhB5PyC9LvTz8sDlq9Ph46W9mJjB3rvuXaUWFtF+ePGQBxRvr1wObNhnXTlHkyejtt4FRo6Sv4hEj1M+56y5pbLVoevTgOjFPnw7ExQENGjiXkzUZt92m7zr+xJ/Ll5rvT/36wJkzkvCRne1eYUEBULeudPLTTxvXUMZ3/GRTYmmqg9GoL+h1rTXSm8RK8URcmTrVN6NGozxdXOvVElZ8+XL/GnNq3bTmQpHPcXzmzEga5s2Y2jV6qNWMnj0RqOzOPsfp8ZZLYOtWYxvM2GEvFQ2wwOEdra61gcxO60+MiLug1ZXS6DgcesKK+2N7+mnjcqF4C8ZlRswN12fSKp5e3gikcG/IR0pxMVFCgnLD4+OJzpyxlHt9sMMChwZY4BBD9Ask0Nlp/YkRcRdEhKn4eEkLkZUlpVhXK6snS6+vgpPrVrOmFFl19Gj9daglkBPtW3q65LrsLYJpXJx2DZ2WiVmPRiZQhFR252+/Ve3EXEyy/9dqmqZgggUODbDAIU5ZmTQRpKdLf11DYFshO60/8WU5xHGSM0KD5GuWXi3ROP2xLV+uPO5maGQ2blQfX9cJScvEbOUlQVeCqa2aePpp1Q71QJalNE3BBAscGmCBQwyRl7AVstP6E18mPtevaq3ClBlZevVmpzVjU3tG9GRl9bZNm1Y1BiIaOi3PuqiNjJqQ5S+sunxpBOXlRNckn6XDuELxJpTY6lD5yT8D3VRhrPA+ZIFDAyxweEf0JRxS6lgBfJ34XCdVo18eepah5DZMmxY4YUNkQlMOk61f4NCioSsr825PIyfCE01ip7aM5E+suHxpBK5C4o3YoX5DHn2UqLIy0M1WxCpGyBxplDEMLUG3AuVCqyVAkZHBjNRc+kRwdePVG5xNxrVvQ4dKrpgpKc7l1IJNVVQAu3dXhTvQ0y9fsNnE3FSHDfPcN7306KEt3su2bd6fnYoKqZzspuwN0XJmozS2Vg5SJoLr7+0/uOl/gcUI0/G8+wlvvSUFrbHZpOAvFiIoIy/7SQCyNKzhUEePcZw/1bFapHyzvgj0LkUYkb9DpG+imhNPrqNGbtOmVbVh9WpjlpDk/enp+tslL29p0dCFqg2HI1ZQ1xuJyH2IwXk6n3KVcoHISKKTJwPaj0C5LivBSyoaYIFDHa3LJP5Ux3pb6lmzxns2U6Pa5fhy3riRKCVFfMLzVegxwu1TSzyRtDSpvFYhS+8SkpH2Q2rjY4YrcbB4qYSacOEJzR9Eu3ap37QHHgjIkovVBFgWODTAAoc6eh5uf3iTiLzERb/WtX4RiLyctUzgvgg95eX63D4dEbFHkLPbetIuLF8uue6aodkStR8SsacJC3P+v6vQokUwELmeHnfmQGAVWwB/oPuDaPZs9Qfjiy/81ger2cqxwKEBFjjUsUKkUU+Y4RopC01qbRd5OYsIAUZNyhs3itXv6PbpihFGjWZotvQE11Jrv6PGS6+g6BiZVIvhamqqZ61QIN26HfvgaWwDLQiZhU8fRBcuELVurX6zjx83tf2s4TCB6dOnEwCnLSEhwX68srKSpk+fTklJSRQdHU3du3envXv3ar4OCxzesaLVuhkhwVesUBcoRF/OokKAES8JUW8S2e3TE6L2D+np6m3JyHBfStJqR+KIvyOK6ln6ELXf8bTMF+ilC6vZAvgTQz6I/vtf9Zt+992mLLlYzXU5ZLxUWrVqhcLCQvu2Z88e+7FXXnkF8+fPx5tvvomdO3ciMTERffv2xVnOFGg4VrRaNyNZ3MGD6pbfDz0k/aRdkffJ3jqeckqJ4u8EdICUtdaocp68Wnbs0Jd5VEvSPtmbSq1d8v1RQiQTbn4+8PzzVR5Ow4YBhw9LmV+XL1f2NJGfkUmTpKR/ej2RtODNI8uqWZj9ga8eYQCAtm2r5vl589yPf/hhlZfLZ5/52OIqzE5IaBr+kX/0MX36dGrXrp3HY5WVlZSYmEhz5syx7ystLaXY2Fh6++23NV2HNRziWMmwzMjgTzabVJcWQ0+1r21fYlho1XAYsaRSVuZu3+C6hYW5R5Z1RGukUhHtmFlGnEpo1ZoFIvCdKEZHS2UEKS0l6tBBfUCPHTPkUlaJvBwyGo6DBw8iOTkZTZs2xV133YXffvsNAJCXl4eioiL069fPXjYqKgrdu3fHtm3bVOssKytDSUmJ08aIYchXgYFt8SUGhox87oMPSpoMXykslMZGD2lp0tevFnr0kNKhqxEXp96m8HCgZk31OmrWVL7farFalJDLqmkdunaVtGhK99dmqxozLdoQJbRqzVxjHhjRBiMQjdEQqLg5ShgZIydgREUBP/wgPeA//eS5THKy9PAOHw5UVuq+lKN2bcUK6W9ennXjpFha4LjpppuwbNkybNiwAe+++y6KiorQpUsXnDp1CkVFRQCAhIQEp3MSEhLsx5SYPXs2YmNj7VtaWpppfWDMRWmpR4sgJC8LXX21MW1KShITAjxx552S+lrLCzc8HHjnHfUy77yjPiZbtwLnzqnXce6csmpdZCnCE0TqKnstqmMjJs+uXbXdN1ehqVEjsfNEy+lBS6A+LQKd2WRm6lt2szQtW1YpHxYudD++erX08Nps0ktIB1b6CPSKnzQuhnDu3DlKSEigefPm0XfffUcA6JiLauqBBx6g/v37q9ZTWlpKxcXF9i0/P19IHcRYF9elHtkF0ZORKyDF5HBdFvLV60Wrx4SnzdU1VYtroprBpje0ZF/1tJTmqwGvN5W9iOrYCEM6Pd5FjsskRixv+YrepH2BNAivVp4yZWVEXbqo35z8/EC3UhMh4aXiiT59+tC4cePo0KFDBIBycnKcjt9yyy10zz33aKqTbThCEz3J0LxNWPJk5EtWV61CjJYXrhYbG8eyom6xSoKMr8KaSEAwkb75Onn60g+t0UfNQk8bAmkLUJ09Zejnn9Vv0i23EFVUBLqVXglJgaO0tJRSUlJoxowZdqPRl19+2X68rKyMjUYZJ7QauYpMWJ7Cf4eHO8dn8NQGvaG3/RUOXktIcz1Bt0T75msQKl8mT180NUYZrvqK3jYEyiDcCmNmCf75z8BJqT4SEgLH5MmTKTs7m3777TfasWMHDR48mOrUqUOHDx8mIqI5c+ZQbGwsZWZm0p49e2jEiBGUlJREJSUlmq7DAgfjiNqEpeaJYZTHhdkvXK3eJGrCQmqqtDwgh46X9+sRWtTaZqamxxE998hRaLJCfAQrtEELVtAKWYrLl4l69FAfjP/NgVYhJASO4cOHU1JSEkVERFBycjINGzaMfvrpJ/txOfBXYmIiRUVFUbdu3WjPnj2ar8MCB+OKkkrfF9Wvr268RrxwjQwH72mLi3O3gVDKu6Jkg6F3fI1A6z1SE5qsYBNhpUB9SgSzhsN0rdCvv6oPyoABlpAcQ0Lg8BcscDAiGPFi1BIK24wXrmgfFizQtwQk982TUa63l7M/Jh5f7EA8bUpLNVaIj2CFNogQbBoZGb/nn1myRP1hXLrUpAt7hwUODbDAwYhglOpXq/2EkS9cM1KqG9Ves1XrWiYIpclaS1hyKwTJs0IbRAgmjQxRgL1qysslzYbSD6RnT6KiIhMb4A4LHBpggYMRwcgvcNeJYM0a/7xw9aRU17sEFIiIqUromSCCZbIOFYJNI+OPDwSv5OWp/1hmzPBLQ1jg0AALHIwIZqt+/fHC1doHX5aAtGoizBI4LDVBMKoEg5BnWZuT7Gyi6GhjJXVBQia0OcNYBbMTJvkjTLHWPihFchVBazjsEyeMLSdTnROUBRvBEDXTKuHr3ejeHbh4UQoh+/LL7sf79JF+5N26AQFKcMoCB8NowOysub6+cEVyUWjtg6sgtHGjdK7R4bDNyuth2QmCCUqsln/GjbAw4IknJEn6jz+AQYOcj2/dCowbF5Cm2YiIAnJlC1FSUoLY2FgUFxejbt26gW4OEwRUVEi/28JC6cXStWvgv8YyM6UcGo5f86mpkkbDkyDkSx/k5GCA9F6TkYUQPcJXRYWUO6OgwLlOx7pTUyWtj5axzs6W8nJ4IytLf9I9pvpg1nNqOtu3A/37S9qNN94AJkwwrGrROZQFDrDAwQQ/sgDg+mv2RQAQuaargJOWJi3J6L1WMAkyTPXFjOc0mBGdQ3lJhWGCHC3ZQY3EDJsTM5aszLa9YaofZi+thiqs4QBrOJjgJhSXDMxYsjJDI8NUb6y4tBoIROfQGn5sE8MwJhCKRpGy8ayRDBsGDB3KEwRjHGY8p6EMCxwME+TotZqvjl9nPEEwTOBgGw6GCXK6dpXWjrW4qWZmSoaUPXsCI0dKf5s0kfYzDMOYAQscDBPkaDWKlC3sXYNhFRRI+1noYBjGDFjgYJgQQNRqPlAeLQzDMGzDwTAhgohRpJYw32zrwDCMkbDAwTAhhDejyFD0aGEYJjjgJRWGqUZYPg8EwzAhCwscDFON0OPRwjAMYwQscDBMNYLDfDMMEyhY4GCYagbngWAYJhCw0SjDVEM4zDfDMP6GBQ6GqaZwmG+GYfwJL6kwDMMwDGM6LHAwDMMwDGM6LHAwDMMwDGM6LHAwDMMwDGM6LHAwDMMwDGM6LHAwDMMwDGM67BYLgP6Xl7ukpCTALWEYhmGY4EKeO+W5VAkWOACcPXsWAJCWlhbgljAMwzBMcHL27FnExsYqHreRN5GkGlBZWYljx46hTp06sClltfKBkpISpKWlIT8/H3Xr1jW8/lCCx0ocHisxeJzE4bESg8fJGSLC2bNnkZycjLAwZUsN1nAACAsLQ2pqqunXqVu3Lj+cgvBYicNjJQaPkzg8VmLwOFWhptmQYaNRhmEYhmFMhwUOhmEYhmFMhwUOPxAVFYXp06cjKioq0E2xPDxW4vBYicHjJA6PlRg8Tvpgo1GGYRiGYUyHNRwMwzAMw5gOCxwMwzAMw5gOCxwMwzAMw5gOCxwMwzAMw5gOCxwGcfr0adx9992IjY1FbGws7r77bpw5c0b1nMzMTPTv3x8NGzaEzWbD7t273cqUlZVhwoQJaNiwIWrVqoVbbrkFv//+uzmd8AN6xomI8PzzzyM5ORkxMTHo0aMHfvrpJ6cyPXr0gM1mc9ruuusuE3tiPIsWLULTpk0RHR2NDh06YOvWrarlt2zZgg4dOiA6OhpXXnkl3n77bbcyGRkZaNmyJaKiotCyZUusXbvWrOb7FaPHaunSpW7Pj81mQ2lpqZndMB0t41RYWIiRI0eiefPmCAsLw2OPPeaxHD9TYmMVqs+UTxBjCAMGDKDWrVvTtm3baNu2bdS6dWsaPHiw6jnLli2jGTNm0LvvvksAKDc3163MuHHjKCUlhb7++mvKycmhnj17Urt27ai8vNyknpiLnnGaM2cO1alThzIyMmjPnj00fPhwSkpKopKSEnuZ7t2704MPPkiFhYX27cyZM2Z3xzBWrVpFERER9O6779K+ffto4sSJVKtWLTpy5IjH8r/99hvVrFmTJk6cSPv27aN3332XIiIi6JNPPrGX2bZtG4WHh9NLL71E+/fvp5deeolq1KhBO3bs8Fe3TMGMsVqyZAnVrVvX6fkpLCz0V5dMQes45eXl0d///nf64IMP6LrrrqOJEye6leFnSkJkrELxmfIVFjgMYN++fQTA6Ue3fft2AkA///yz1/Pz8vI8ChxnzpyhiIgIWrVqlX1fQUEBhYWF0fr16w1rv7/QM06VlZWUmJhIc+bMse8rLS2l2NhYevvtt+37unfv7vFHHyzceOONNG7cOKd9LVq0oCeffNJj+SeeeIJatGjhtO/hhx+mTp062f9/55130oABA5zK9O/fn+666y6DWh0YzBirJUuWUGxsrOFtDSRax8kRpd8TP1PuKI1VKD5TvsJLKgawfft2xMbG4qabbrLv69SpE2JjY7Ft2zbd9e7atQuXL19Gv3797PuSk5PRunVrn+oNFHrGKS8vD0VFRU5jEBUVhe7du7ud89FHH6Fhw4Zo1aoVpkyZYs8CbHUuXbqEXbt2OfURAPr166c4Ltu3b3cr379/f/zwww+4fPmyaplgfHZkzBorADh37hwaN26M1NRUDB48GLm5ucZ3wE/oGScR+JnSRig9U0bAAocBFBUVoVGjRm77GzVqhKKiIp/qjYyMRP369Z32JyQk+FRvoNAzTvL+hIQEp/2uYzBq1CisXLkS2dnZePbZZ5GRkYFhw4YZ2Hrz+OOPP1BRUeG1j44UFRV5LF9eXo4//vhDtUwwPjsyZo1VixYtsHTpUnz22WdYuXIloqOjcfPNN+PgwYPmdMRk9IyTCPxMiRNqz5QRcLZYFZ5//nnMmDFDtczOnTsBwGNaeyIyJd29WfXqxR/j5Hrc9ZwHH3zQ/u/WrVvj6quvRseOHZGTk4P27dt77YMV8NZHkfKu+7XWGSwYPVadOnVCp06d7MdvvvlmtG/fHgsXLsQbb7xhVLP9jhn3n58pMUL1mfIFFjhUSE9P9+rp0KRJE/z44484fvy427GTJ0+6Sc1aSExMxKVLl3D69GknLceJEyfQpUsX3fUajZnjlJiYCED6skpKSrLvP3HihOrYtm/fHhERETh48KDlBY6GDRsiPDzc7WtKrY+JiYkey9eoUQNxcXGqZXx5JgONWWPlSlhYGG644Yag/RrVM04i8DOln2B/poyAl1RUaNiwIVq0aKG6RUdHo3PnziguLsZ//vMf+7nff/89iouLfRIMOnTogIiICHz99df2fYWFhdi7d6+lBA4zx6lp06ZITEx0GoNLly5hy5YtqmPw008/4fLly05CilWJjIxEhw4dnPoIAF9//bViHzt37uxW/quvvkLHjh0RERGhWsZKz45WzBorV4gIu3fvDornxxN6xkkEfqb0E+zPlCEEwlI1FBkwYAC1bduWtm/fTtu3b6c2bdq4uXs2b96cMjMz7f8/deoU5ebm0hdffEEAaNWqVZSbm+vkOjVu3DhKTU2ljRs3Uk5ODvXq1Svo3WK1jtOcOXMoNjaWMjMzac+ePTRixAgnt9hff/2VZsyYQTt37qS8vDz64osvqEWLFnT99dcHzTjJbnnvv/8+7du3jx577DGqVasWHT58mIiInnzySbr77rvt5WVXz8cff5z27dtH77//vpur53fffUfh4eE0Z84c2r9/P82ZMyekXBiNHKvnn3+e1q9fT4cOHaLc3Fy69957qUaNGvT999/7vX9GoXWciIhyc3MpNzeXOnToQCNHjqTc3Fz66aef7Mf5marC21iF4jPlKyxwGMSpU6do1KhRVKdOHapTpw6NGjWKTp8+7VQGAC1ZssT+/yVLlhAAt2369On2MhcvXqT09HRq0KABxcTE0ODBg+no0aP+6ZQJ6BmnyspKmj59OiUmJlJUVBR169aN9uzZYz9+9OhR6tatGzVo0IAiIyOpWbNm9Pe//51OnTrlp14Zw1tvvUWNGzemyMhIat++PW3ZssV+bMyYMdS9e3en8tnZ2XT99ddTZGQkNWnShBYvXuxW55o1a6h58+YUERFBLVq0oIyMDLO74ReMHqvHHnuMrrjiCoqMjKT4+Hjq168fbdu2zR9dMRWt4+TpfdS4cWOnMvxMSXgbq1B9pnyB09MzDMMwDGM6bMPBMAzDMIzpsMDBMAzDMIzpsMDBMAzDMIzpsMDBMAzDMIzpsMDBMAzDMIzpsMDBMAzDMIzpsMDBMAzDMIzpsMDBMAzDMIzpsMDBMAzDMIzpsMDBMIzlyczMRMeOHVGvXj3UqlUL1113HT788MNAN4thGA1wenqGYSxPgwYN8Mwzz6BFixaIjIzE559/jnvvvReNGjVC//79A908hmEE4FwqDMNYhk8++QQzZszAr7/+ipo1a+L666/Hp59+ilq1armVbd++PQYNGoSZM2cGoKUMw2iFl1QYhrEEhYWFGDFiBO677z7s378f2dnZGDZsGFy/iYgImzZtwi+//IJu3boFqLUMw2iFl1QYhrEEhYWFKC8vx7Bhw9C4cWMAQJs2bezHi4uLkZKSgrKyMoSHh2PRokXo27dvoJrLMIxGWOBgGMYStGvXDr1790abNm3Qv39/9OvXD3fccQfq168PAKhTpw52796Nc+fOYdOmTZg0aRKuvPJK9OjRI7ANZxhGCLbhYBjGMhARtm3bhq+++gpr165FUVERvv/+ezRt2tSt7AMPPID8/Hxs2LAhAC1lGEYrbMPBMIxlsNlsuPnmmzFjxgzk5uYiMjISa9eu9ViWiFBWVubnFjIMoxdeUmEYxhJ8//332LRpE/r164dGjRrh+++/x8mTJ3Httddi9uzZ6NixI5o1a4ZLly7hyy+/xLJly7B48eJAN5thGEFY4GAYxhLUrVsX33zzDV577TWUlJSgcePGmDdvHgYOHIjvvvsOjzzyCH7//XfExMSgRYsWWL58OYYPHx7oZjMMIwjbcDAMwzAMYzpsw8EwDMMwjOmwwMEwDMMwjOmwwMEwDMMwjOmwwMEwDMMwjOmwwMEwDMMwjOmwwMEwDMMwjOmwwMEwDMMwjOmwwMEwDMMwjOmwwMEwDMMwjOmwwMEwDMMwjOmwwMEwDMMwjOn8P6QCjT89CVihAAAAAElFTkSuQmCC",
+      "text/plain": [
+       "<Figure size 600x400 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 600x400 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 600x400 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 600x400 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "# Exclude the 'sex' column from the feature names since it's categoricla and we are plotting continuous relationships\n",
+    "feature_names = diabetes_df.columns.difference(['progression', 'sex'])\n",
+    "\n",
+    "# Loop through each feature (column) in diabetes_df\n",
+    "for feature in feature_names:\n",
+    "    # Extract the feature column and target variable (progression)\n",
+    "    X_feature = diabetes_df[[feature]].values  # Extract as a 2D array\n",
+    "    y = diabetes_df['progression'].values  # Target variable\n",
+    "    \n",
+    "    # Create a scatter plot for the feature against the target (progression)\n",
+    "    plt.figure(figsize=(6, 4))\n",
+    "    plt.scatter(X_feature, y, label='Data', color='blue')\n",
+    "\n",
+    "    # Fit a linear regression model\n",
+    "    lm = LinearRegression()\n",
+    "    lm.fit(X_feature, y)\n",
+    "\n",
+    "    # Plot the regression line\n",
+    "    plt.plot(X_feature, lm.predict(X_feature), color='red', label='Regression Line')\n",
+    "\n",
+    "    # Add labels and title\n",
+    "    plt.xlabel(feature)\n",
+    "    plt.ylabel('progression')\n",
+    "    plt.title(f'{feature} vs progression')\n",
+    "\n",
+    "    # Add a legend\n",
+    "    plt.legend()\n",
+    "\n",
+    "    # Show the plot\n",
+    "    plt.show()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "2b1ff3cf",
+   "metadata": {},
+   "source": [
+    "Consider the variables plotted above. In the context of the `Diabetes` dataset:\n",
+    "\n",
+    "(iii) What is the (implied) null hypothesis? What is the (implied) alternative hypothesis?\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "2ea782fc",
+   "metadata": {},
+   "source": [
+    "> Null: The predictor variable has no association with the response variable (progression). In other words, the slope of the regression line for this predictor is zero.\n",
+    "\n",
+    "> Alternative: The predictor variable is associated with the response variable (progression). In other words, the slope of the regression line for this predictor is not zero"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "4604ee03",
+   "metadata": {},
+   "source": [
+    "#### **Question 3:** \n",
+    "#### Model fit\n",
+    "Now, let’s fit a multivariable linear regression model, using the general syntax lm(). As above, use progression as the response variable Y, and all other variables as the predictors.\n",
+    "\n",
+    "**Step 1: Split the dataset into test and train.**"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "id": "399225f4",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Select predictors (excluding the last column)\n",
+    "predictors = diabetes_df.iloc[:, :-1]\n",
+    "\n",
+    "# Create a random vector of True and False values to split the data\n",
+    "split = np.random.choice([True, False], size=len(predictors), replace=True, p=[0.75, 0.25])\n",
+    "\n",
+    "# Split the diabetes dataset into 75% training data and 25% test data\n",
+    "diabetes_train, diabetes_test = train_test_split(diabetes_df, train_size=0.75)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "f76b8f5c",
+   "metadata": {},
+   "source": [
+    "**Step 2: Fit the linear regression model.**"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 14,
+   "id": "ac1e1117",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "  predictor       slope   intercept\n",
+      "0       age  -37.455806  153.706643\n",
+      "1       sex -212.936457  153.706643\n",
+      "2       bmi  553.747247  153.706643\n",
+      "3        bp  298.132752  153.706643\n",
+      "4        s1 -826.173236  153.706643\n",
+      "5        s2  522.515299  153.706643\n",
+      "6        s3  125.497614  153.706643\n",
+      "7        s4  186.286860  153.706643\n",
+      "8        s5  725.999011  153.706643\n",
+      "9        s6   66.457698  153.706643\n"
+     ]
+    }
+   ],
+   "source": [
+    "# fit the linear regression model\n",
+    "lm = LinearRegression()\n",
+    "\n",
+    "lm.fit(\n",
+    "    diabetes_train[predictor_variables],  # Two predictors: square footage and number of bedrooms\n",
+    "    diabetes_train[\"progression\"]  # Target variable: house prices\n",
+    ")\n",
+    "\n",
+    "# Create a DataFrame containing the slope (coefficients) and intercept\n",
+    "coefficients_df = pd.DataFrame({\"predictor\": predictor_variables, \"slope\": lm.coef_, \"intercept\": [lm.intercept_] * len(lm.coef_)})\n",
+    "\n",
+    "# Display the coefficients DataFrame\n",
+    "print(coefficients_df)\n",
+    "\n",
+    "# lm.coef_ gives the coefficients for each predictor (change in diabetes progression per unit change in each predictor variable)\n",
+    "# lm.intercept_ gives the intercept b_0 (the predicted diabetes progression when all predictors are set to 0)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "3f76bf62",
+   "metadata": {},
+   "source": [
+    "#### **Question 4:** \n",
+    "#### Model evaluation\n",
+    "**Step 3. Finally, we predict on the test data set to assess how well our model does.** \n",
+    "\n",
+    "We will evaluate our final model's test error measured by RMSP."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 17,
+   "id": "ffefa9f2",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "RMSPE for the multivariable model: 56.69876420327839\n"
+     ]
+    }
+   ],
+   "source": [
+    "# Predict the progression for the test set\n",
+    "diabetes_test[\"predicted\"] = lm.predict(diabetes_test[predictor_variables])\n",
+    "\n",
+    "# Calculate RMSPE for the multivariable model\n",
+    "lm_test_RMSPE = mean_squared_error(\n",
+    "    y_true=diabetes_test[\"progression\"],\n",
+    "    y_pred=diabetes_test[\"predicted\"]\n",
+    ")**(1/2)\n",
+    "\n",
+    "# Output the RMSPE\n",
+    "print(f\"RMSPE for the multivariable model: {lm_test_RMSPE}\")\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "6f8a69db",
+   "metadata": {},
+   "source": [
+    "# Criteria\n",
+    "\n",
+    "| **Criteria**                                           | **Complete**                                      | **Incomplete**                                    |\n",
+    "|--------------------------------------------------------|---------------------------------------------------|--------------------------------------------------|\n",
+    "| **Data Inspection**                                    | Data is inspected for the number of variables, observations, and data types. | Data inspection is missing or incomplete.         |\n",
+    "| **Data Visualization**                                 | Visualizations (e.g., scatter plots, histograms) are properly interepreted to explore the relationships between variables. | Data visualization were not correctly interpreted. |\n",
+    "| **Model Initialization**                               | The linear regression model is correctly initialized.            | The linear regression model is not initialized or is incorrect. |\n",
+    "| **Model Evaluation on Test Data**                      | The model is evaluated on the test data using appropriate metrics (e.g., RMSE). | The model evaluation is missing or uses the wrong metric. |\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "0b4390cc",
+   "metadata": {},
+   "source": [
+    "## Submission Information\n",
+    "\n",
+    "🚨 **Please review our [Assignment Submission Guide](https://github.com/UofT-DSI/onboarding/blob/main/onboarding_documents/submissions.md)** 🚨 for detailed instructions on how to format, branch, and submit your work. Following these guidelines is crucial for your submissions to be evaluated correctly.\n",
+    "\n",
+    "### Note:\n",
+    "\n",
+    "If you like, you may collaborate with others in the cohort. If you choose to do so, please indicate with whom you have worked with in your pull request by tagging their GitHub username. Separate submissions are required.\n",
+    "\n",
+    "### Submission Parameters:\n",
+    "* Submission Due Date: `HH:MM AM/PM - DD/MM/YYYY`\n",
+    "* The branch name for your repo should be: `assignment-1`\n",
+    "* What to submit for this assignment:\n",
+    "    * This Jupyter Notebook (assignment_1.ipynb) should be populated and should be the only change in your pull request.\n",
+    "* What the pull request link should look like for this assignment: `https://github.com/<your_github_username>/applying_statistical_concepts/pull/<pr_id>`\n",
+    "    * Open a private window in your browser. Copy and paste the link to your pull request into the address bar. Make sure you can see your pull request properly. This helps the technical facilitator and learning support staff review your submission easily.\n",
+    "\n",
+    "Checklist:\n",
+    "- [ ] Created a branch with the correct naming convention.\n",
+    "- [ ] Ensured that the repository is public.\n",
+    "- [ ] Reviewed the PR description guidelines and adhered to them.\n",
+    "- [ ] Verify that the link is accessible in a private browser window.\n",
+    "\n",
+    "If you encounter any difficulties or have questions, please don't hesitate to reach out to our team via our Slack at `#cohort-4-help`. Our Technical Facilitators and Learning Support staff are here to help you navigate any challenges.\n"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3.10.4",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.10.10"
+  },
+  "vscode": {
+   "interpreter": {
+    "hash": "497a84dc8fec8cf8d24e7e87b6d954c9a18a327edc66feb9b9ea7e9e72cc5c7e"
+   }
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}

From 025c70a4d7e75e78134b81ca03f150df6ba93a1d Mon Sep 17 00:00:00 2001
From: juliagallucci <78810440+juliagallucci@users.noreply.github.com>
Date: Thu, 26 Sep 2024 11:07:09 -0400
Subject: [PATCH 2/5] Add files via upload

add answers to assignment 3
---
 .../assignment_3_answers.ipynb                | 502 ++++++++++++++++++
 1 file changed, 502 insertions(+)
 create mode 100644 03_instructional_team/assignment_3_answers.ipynb

diff --git a/03_instructional_team/assignment_3_answers.ipynb b/03_instructional_team/assignment_3_answers.ipynb
new file mode 100644
index 00000000..f885814d
--- /dev/null
+++ b/03_instructional_team/assignment_3_answers.ipynb
@@ -0,0 +1,502 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "id": "7b0bcac6-5086-4f4e-928a-570a9ff7ae58",
+   "metadata": {},
+   "source": [
+    "# Assignment 3"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "5fce0350-2a17-4e93-8d4c-0b8748fdfc32",
+   "metadata": {},
+   "source": [
+    "You only need to write one line of code for each question. When answering questions that ask you to identify or interpret something, the length of your response doesn’t matter. For example, if the answer is just ‘yes,’ ‘no,’ or a number, you can just give that answer without adding anything else.\n",
+    "\n",
+    "We will go through comparable code and concepts in the live learning session. If you run into trouble, start by using the help `help()` function in Python, to get information about the datasets and function in question. The internet is also a great resource when coding (though note that **no outside searches are required by the assignment!**). If you do incorporate code from the internet, please cite the source within your code (providing a URL is sufficient).\n",
+    "\n",
+    "Please bring questions that you cannot work out on your own to office hours, work periods or share with your peers on Slack. We will work with you through the issue."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "5fc5001c-7715-4ebe-b0f7-e4bd04349629",
+   "metadata": {},
+   "source": [
+    "### Clustering and Resampling\n",
+    "\n",
+    "Let's set up our workspace and use the **Iris dataset** from `scikit-learn`. This dataset is a classic dataset in machine learning and statistics, widely used for clustering tasks. It consists of 150 samples of iris flowers, each belonging to one of three species: Iris setosa, Iris versicolor, and Iris virginica. Here are the key features and characteristics of the dataset:\n",
+    "\n",
+    "##### Features:\n",
+    "1. **Sepal Length**: The length of the sepal in centimeters.\n",
+    "2. **Sepal Width**: The width of the sepal in centimeters.\n",
+    "3. **Petal Length**: The length of the petal in centimeters.\n",
+    "4. **Petal Width**: The width of the petal in centimeters.\n",
+    "\n",
+    "##### Target Variable:\n",
+    "- **Species**: The species of the iris flower, which can take one of the following values:\n",
+    "  - 0: Iris setosa\n",
+    "  - 1: Iris versicolor\n",
+    "  - 2: Iris virginica"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "id": "4a3485d6-ba58-4660-a983-5680821c5719",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Import standard libraries\n",
+    "import numpy as np\n",
+    "import pandas as pd\n",
+    "import matplotlib.pyplot as plt\n",
+    "from sklearn.preprocessing import StandardScaler\n",
+    "from sklearn.cluster import KMeans\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "3491d919",
+   "metadata": {},
+   "source": [
+    "#### **Question 1:** \n",
+    "#### Data inspection\n",
+    "\n",
+    "#### Load the Iris dataset:\n",
+    "\n",
+    "Use scikit-learn to load the Iris dataset and convert it into a Pandas DataFrame.\n",
+    "Display the first few rows of the dataset. How many observations (rows) and features (columns) does the dataset contain?"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "id": "a431d282-f9ca-4d5d-8912-71ffc9d8ea19",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "     sepal length (cm)  sepal width (cm)  petal length (cm)  petal width (cm)  \\\n",
+      "0                  5.1               3.5                1.4               0.2   \n",
+      "1                  4.9               3.0                1.4               0.2   \n",
+      "2                  4.7               3.2                1.3               0.2   \n",
+      "3                  4.6               3.1                1.5               0.2   \n",
+      "4                  5.0               3.6                1.4               0.2   \n",
+      "..                 ...               ...                ...               ...   \n",
+      "145                6.7               3.0                5.2               2.3   \n",
+      "146                6.3               2.5                5.0               1.9   \n",
+      "147                6.5               3.0                5.2               2.0   \n",
+      "148                6.2               3.4                5.4               2.3   \n",
+      "149                5.9               3.0                5.1               1.8   \n",
+      "\n",
+      "     species  \n",
+      "0          0  \n",
+      "1          0  \n",
+      "2          0  \n",
+      "3          0  \n",
+      "4          0  \n",
+      "..       ...  \n",
+      "145        2  \n",
+      "146        2  \n",
+      "147        2  \n",
+      "148        2  \n",
+      "149        2  \n",
+      "\n",
+      "[150 rows x 5 columns]\n",
+      "Number of observations: 150\n",
+      "Number of variables: 5\n"
+     ]
+    }
+   ],
+   "source": [
+    "from sklearn.datasets import load_iris\n",
+    "# Load the Iris dataset\n",
+    "iris_data = load_iris()\n",
+    "\n",
+    "# Convert to DataFrame\n",
+    "iris_df = pd.DataFrame(iris_data.data, columns=iris_data.feature_names)\n",
+    "\n",
+    "# Bind the disease progression (diabetes target) to the DataFrame\n",
+    "iris_df['species'] = iris_data.target\n",
+    "\n",
+    "\n",
+    "# Display the DataFrame\n",
+    "print(iris_df)\n",
+    "\n",
+    "# Number of observations (rows)\n",
+    "num_observations = iris_df.shape[0]\n",
+    "print(f\"Number of observations: {num_observations}\")\n",
+    "\n",
+    "# Number of variables (columns)\n",
+    "num_variables = iris_df.shape[1]\n",
+    "print(f\"Number of variables: {num_variables}\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "fa3832d7",
+   "metadata": {},
+   "source": [
+    "#### **Question 2:** \n",
+    "#### Data-visualization\n",
+    "\n",
+    "Create plots to visualize the relationships between the features (sepal length, sepal width, petal length, petal width).\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "id": "efd6dc0c",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 1200x1000 with 6 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "# Get feature names\n",
+    "feature_names = iris_df.columns.difference(['species'])\n",
+    "\n",
+    "# Create a figure for the scatter plots\n",
+    "plt.figure(figsize=(12, 10))\n",
+    "\n",
+    "# Counter for subplot index\n",
+    "plot_number = 1\n",
+    "\n",
+    "# Loop through each pair of features\n",
+    "for i in range(len(feature_names)):\n",
+    "    for j in range(i + 1, len(feature_names)):\n",
+    "        plt.subplot(len(feature_names)-1, len(feature_names)-1, plot_number)\n",
+    "        plt.scatter(iris_df[feature_names[i]], iris_df[feature_names[j]], alpha=0.7)\n",
+    "        plt.xlabel(feature_names[i])\n",
+    "        plt.ylabel(feature_names[j])\n",
+    "        plt.title(f'{feature_names[i]} vs {feature_names[j]}')\n",
+    "        \n",
+    "        # Increment the plot number\n",
+    "        plot_number += 1\n",
+    "\n",
+    "# Adjust layout to prevent overlap\n",
+    "plt.tight_layout()\n",
+    "\n",
+    "# Show the plot\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "d596b3af",
+   "metadata": {},
+   "source": [
+    "#### **Question 3:** \n",
+    "#### Data cleaning"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 12,
+   "id": "b2cfec72",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "   sepal length (cm)  sepal width (cm)  petal length (cm)  petal width (cm)  \\\n",
+      "0          -0.900681          1.019004          -1.340227         -1.315444   \n",
+      "1          -1.143017         -0.131979          -1.340227         -1.315444   \n",
+      "2          -1.385353          0.328414          -1.397064         -1.315444   \n",
+      "3          -1.506521          0.098217          -1.283389         -1.315444   \n",
+      "4          -1.021849          1.249201          -1.340227         -1.315444   \n",
+      "\n",
+      "   species  \n",
+      "0        0  \n",
+      "1        0  \n",
+      "2        0  \n",
+      "3        0  \n",
+      "4        0  \n"
+     ]
+    }
+   ],
+   "source": [
+    "# Initialize the StandardScaler\n",
+    "scaler = StandardScaler()\n",
+    "\n",
+    "# Scale the features (excluding the species column)\n",
+    "scaled_features = scaler.fit_transform(iris_df.iloc[:, :-1])\n",
+    "\n",
+    "# Create a new DataFrame with scaled features\n",
+    "scaled_iris_df = pd.DataFrame(scaled_features, columns=iris_data.feature_names)\n",
+    "\n",
+    "# Add the species column back to the scaled DataFrame\n",
+    "scaled_iris_df['species'] = iris_df['species'].values\n",
+    "\n",
+    "# Display the first few rows of the scaled DataFrame\n",
+    "print(scaled_iris_df.head())"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "035fa019",
+   "metadata": {},
+   "source": [
+    "Why is it important to standardize the features of a dataset before applying clustering algorithms like K-Means? Discuss the implications of using unstandardized data in your analysis."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "53d77d5c",
+   "metadata": {},
+   "source": [
+    "> Standardizing features is essential for effective clustering. It ensures that all features are treated equally, leading to more accurate and interpretable results. Not standardizing can result in misleading insights and ineffective analysis.  \n",
+    "\n",
+    "> Clustering relies on measuring distances between data points. Standardization ensures that these distances accurately reflect similarities across all features, not just those with larger values."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "4604ee03",
+   "metadata": {},
+   "source": [
+    "#### **Question 4:** \n",
+    "#### K-means clustering \n",
+    "Apply the K-Means clustering algorithm to the Iris dataset.\n",
+    "Choose the number of clusters (K=3, since there are three species) and fit the model.\n",
+    "Assign cluster labels to the original data and add them as a new column in the DataFrame."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 26,
+   "id": "909df219",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Cluster   0   1   2\n",
+      "Species            \n",
+      "0         0  50   0\n",
+      "1        38   0  12\n",
+      "2        14   0  36\n",
+      "Cluster   0   1   2\n",
+      "Species            \n",
+      "0        50   0   0\n",
+      "1         0  38  12\n",
+      "2         0  14  36\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "/Users/juliagallucci/miniconda3/lib/python3.10/site-packages/sklearn/cluster/_kmeans.py:870: FutureWarning: The default value of `n_init` will change from 10 to 'auto' in 1.4. Set the value of `n_init` explicitly to suppress the warning\n",
+      "  warnings.warn(\n"
+     ]
+    }
+   ],
+   "source": [
+    "# Perform K-means clustering\n",
+    "kmeans = KMeans(n_clusters=3, random_state=12345)  # Specify the number of clusters\n",
+    "clusters = kmeans.fit(scaled_features)\n",
+    "# Add cluster labels to the original DataFrame\n",
+    "scaled_iris_df['Cluster'] = clusters.labels_\n",
+    "\n",
+    "\n",
+    "# Create a contingency table of species vs clusters\n",
+    "species_cluster_table = pd.crosstab(scaled_iris_df['species'], scaled_iris_df['Cluster'], \n",
+    "                                      rownames=['Species'], colnames=['Cluster'])\n",
+    "\n",
+    "# Display the table\n",
+    "print(species_cluster_table)\n",
+    "\n",
+    "# Remap clusters if needed based on your findings\n",
+    "# Here, we swap cluster 0 and 1 as an example\n",
+    "cluster_remap = {0: 1, 1: 0, 2: 2}  # Modify this based on your analysis\n",
+    "scaled_iris_df['Mapped_Cluster'] = scaled_iris_df['Cluster'].map(cluster_remap)\n",
+    "\n",
+    "# Create a new contingency table with mapped clusters\n",
+    "mapped_species_cluster_table = pd.crosstab(scaled_iris_df['species'], scaled_iris_df['Mapped_Cluster'], \n",
+    "                                            rownames=['Species'], colnames=['Cluster'])\n",
+    "\n",
+    "print(mapped_species_cluster_table)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "0aefdee5",
+   "metadata": {},
+   "source": [
+    "Discuss the results of the K-Means clustering. How well did the clusters match the true species?"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "7bcebc16",
+   "metadata": {},
+   "source": [
+    "> Overall, the K-Means clustering results show a strong performance for species 0, but there is room for improvement in distinguishing between species 1 and species 2. "
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "3f76bf62",
+   "metadata": {},
+   "source": [
+    "#### **Question 5:** \n",
+    "#### Bootstrapping \n",
+    "\n",
+    " Implement bootstrapping on the mean of one of the sepal or petal measurement variables (e.g., Sepal Length, Petal Width) to assess the stability of the mean estimate. Generate 1000 bootstrap samples, calculate the mean for each sample, and compute a 95% confidence interval to evaluate the variability in the estimate."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 67,
+   "id": "be4c4011",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Mean of Sepal Length: 5.84\n",
+      "95% Confidence Interval of Mean Sepal Length: (5.72, 5.98)\n"
+     ]
+    }
+   ],
+   "source": [
+    "# Calculate the mean of the Sepal Length\n",
+    "sepal_length_mean = iris_df['sepal length (cm)'].mean()\n",
+    "\n",
+    "# Display the result\n",
+    "print(f\"Mean of Sepal Length: {sepal_length_mean:.2f}\")\n",
+    "\n",
+    "np.random.seed(123)\n",
+    "# Initialize an empty list to store the bootstrap samples\n",
+    "bootstrap_samples = []\n",
+    "\n",
+    "for i in range(1000):\n",
+    "    sample = iris_df.drop(columns='species').sample(frac=1, replace=True)\n",
+    "    sample = sample.assign(replicate=i)  # Add replicate number\n",
+    "    bootstrap_samples.append(sample)  # Store the sample\n",
+    "\n",
+    "# Combine all bootstrap samples into one DataFrame\n",
+    "boot1000 = pd.concat(bootstrap_samples)\n",
+    "\n",
+    "# Calculate the mean price for each bootstrap sample (replicate)\n",
+    "boot_means = boot1000.groupby('replicate')['sepal length (cm)'].mean().reset_index(name='sepal length (cm)')\n",
+    "boot_means\n",
+    "\n",
+    "\n",
+    "# Calculate the 95% confidence interval bounds (2.5th and 97.5th percentiles) for the mean price\n",
+    "ci_bounds = boot_means[\"sepal length (cm)\"].quantile([0.025, 0.975])\n",
+    "\n",
+    "# Display the result\n",
+    "print(f\"95% Confidence Interval of Mean Sepal Length: ({ci_bounds.iloc[0]:.2f}, {ci_bounds.iloc[1]:.2f})\")\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "29096311",
+   "metadata": {},
+   "source": [
+    "Reflect on the variability observed in the bootstrapped means and discuss whether the mean of the selected variable appears to be a stable and reliable estimate based on the confidence interval and the spread of the bootstrapped means."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "0a7e6778",
+   "metadata": {},
+   "source": [
+    "> Point Estimate: The sample mean of Sepal Length is 5.84\n",
+    "95% Confidence Interval: We estimate that the true mean of Sepal Length is between 5.72 and 5.98\n",
+    "\n",
+    "Based on the bootstrapping results for Sepal Length in the Iris dataset, the bootstrapped mean distribution was narrow, and the 95% confidence interval was small, suggesting that the sample mean is a stable and reliable estimate of the true  mean."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "6f8a69db",
+   "metadata": {},
+   "source": [
+    "# Criteria\n",
+    "\n",
+    "\n",
+    "| **Criteria**                                           | **Complete**                                      | **Incomplete**                                    |\n",
+    "|--------------------------------------------------------|---------------------------------------------------|--------------------------------------------------|\n",
+    "| **Data Inspection**                                    | Data is thoroughly inspected for the number of variables, observations, and data types, and relevant insights are noted. | Data inspection is missing or lacks detail.         |\n",
+    "| **Data Visualization**                                 | Visualizations (e.g., scatter plots) are well-constructed and correctly interpreted to explore relationships between features and species. | Visualizations are poorly constructed or not correctly interpreted. |\n",
+    "| **Clustering Implementation**                           | K-Means clustering is correctly implemented, and cluster labels are appropriately assigned to the dataset.            | K-Means clustering is missing or incorrectly implemented. |\n",
+    "| **Bootstrapping Process**                              | Bootstrapping is correctly performed, and results are used to assess variable mean stability. | Bootstrapping is missing or incorrectly performed. |"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "0b4390cc",
+   "metadata": {},
+   "source": [
+    "## Submission Information\n",
+    "\n",
+    "🚨 **Please review our [Assignment Submission Guide](https://github.com/UofT-DSI/onboarding/blob/main/onboarding_documents/submissions.md)** 🚨 for detailed instructions on how to format, branch, and submit your work. Following these guidelines is crucial for your submissions to be evaluated correctly.\n",
+    "\n",
+    "### Note:\n",
+    "\n",
+    "If you like, you may collaborate with others in the cohort. If you choose to do so, please indicate with whom you have worked with in your pull request by tagging their GitHub username. Separate submissions are required.\n",
+    "\n",
+    "### Submission Parameters:\n",
+    "* Submission Due Date: `HH:MM AM/PM - DD/MM/YYYY`\n",
+    "* The branch name for your repo should be: `assignment-1`\n",
+    "* What to submit for this assignment:\n",
+    "    * This Jupyter Notebook (assignment_1.ipynb) should be populated and should be the only change in your pull request.\n",
+    "* What the pull request link should look like for this assignment: `https://github.com/<your_github_username>/applying_statistical_concepts/pull/<pr_id>`\n",
+    "    * Open a private window in your browser. Copy and paste the link to your pull request into the address bar. Make sure you can see your pull request properly. This helps the technical facilitator and learning support staff review your submission easily.\n",
+    "\n",
+    "Checklist:\n",
+    "- [ ] Created a branch with the correct naming convention.\n",
+    "- [ ] Ensured that the repository is public.\n",
+    "- [ ] Reviewed the PR description guidelines and adhered to them.\n",
+    "- [ ] Verify that the link is accessible in a private browser window.\n",
+    "\n",
+    "If you encounter any difficulties or have questions, please don't hesitate to reach out to our team via our Slack at `#cohort-4-help`. Our Technical Facilitators and Learning Support staff are here to help you navigate any challenges.\n"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3.10.4",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.10.10"
+  },
+  "vscode": {
+   "interpreter": {
+    "hash": "497a84dc8fec8cf8d24e7e87b6d954c9a18a327edc66feb9b9ea7e9e72cc5c7e"
+   }
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}

From bd68d92fe3c9adf0bbaa99507c54f603ffd1258c Mon Sep 17 00:00:00 2001
From: juliagallucci <78810440+juliagallucci@users.noreply.github.com>
Date: Thu, 26 Sep 2024 11:07:32 -0400
Subject: [PATCH 3/5] Add files via upload

modified assignment 3 question 5 difficulty level
---
 02_activities/assignments/assignment_3.ipynb | 261 ++++++++++++++++---
 1 file changed, 218 insertions(+), 43 deletions(-)

diff --git a/02_activities/assignments/assignment_3.ipynb b/02_activities/assignments/assignment_3.ipynb
index d5cb0f50..2de1febc 100644
--- a/02_activities/assignments/assignment_3.ipynb
+++ b/02_activities/assignments/assignment_3.ipynb
@@ -73,10 +73,167 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 14,
    "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>sepal length (cm)</th>\n",
+       "      <th>sepal width (cm)</th>\n",
+       "      <th>petal length (cm)</th>\n",
+       "      <th>petal width (cm)</th>\n",
+       "      <th>species</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>5.1</td>\n",
+       "      <td>3.5</td>\n",
+       "      <td>1.4</td>\n",
+       "      <td>0.2</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>4.9</td>\n",
+       "      <td>3.0</td>\n",
+       "      <td>1.4</td>\n",
+       "      <td>0.2</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>4.7</td>\n",
+       "      <td>3.2</td>\n",
+       "      <td>1.3</td>\n",
+       "      <td>0.2</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>4.6</td>\n",
+       "      <td>3.1</td>\n",
+       "      <td>1.5</td>\n",
+       "      <td>0.2</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>5.0</td>\n",
+       "      <td>3.6</td>\n",
+       "      <td>1.4</td>\n",
+       "      <td>0.2</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",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>145</th>\n",
+       "      <td>6.7</td>\n",
+       "      <td>3.0</td>\n",
+       "      <td>5.2</td>\n",
+       "      <td>2.3</td>\n",
+       "      <td>2</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>146</th>\n",
+       "      <td>6.3</td>\n",
+       "      <td>2.5</td>\n",
+       "      <td>5.0</td>\n",
+       "      <td>1.9</td>\n",
+       "      <td>2</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>147</th>\n",
+       "      <td>6.5</td>\n",
+       "      <td>3.0</td>\n",
+       "      <td>5.2</td>\n",
+       "      <td>2.0</td>\n",
+       "      <td>2</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>148</th>\n",
+       "      <td>6.2</td>\n",
+       "      <td>3.4</td>\n",
+       "      <td>5.4</td>\n",
+       "      <td>2.3</td>\n",
+       "      <td>2</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>149</th>\n",
+       "      <td>5.9</td>\n",
+       "      <td>3.0</td>\n",
+       "      <td>5.1</td>\n",
+       "      <td>1.8</td>\n",
+       "      <td>2</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "<p>150 rows × 5 columns</p>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "     sepal length (cm)  sepal width (cm)  petal length (cm)  petal width (cm)  \\\n",
+       "0                  5.1               3.5                1.4               0.2   \n",
+       "1                  4.9               3.0                1.4               0.2   \n",
+       "2                  4.7               3.2                1.3               0.2   \n",
+       "3                  4.6               3.1                1.5               0.2   \n",
+       "4                  5.0               3.6                1.4               0.2   \n",
+       "..                 ...               ...                ...               ...   \n",
+       "145                6.7               3.0                5.2               2.3   \n",
+       "146                6.3               2.5                5.0               1.9   \n",
+       "147                6.5               3.0                5.2               2.0   \n",
+       "148                6.2               3.4                5.4               2.3   \n",
+       "149                5.9               3.0                5.1               1.8   \n",
+       "\n",
+       "     species  \n",
+       "0          0  \n",
+       "1          0  \n",
+       "2          0  \n",
+       "3          0  \n",
+       "4          0  \n",
+       "..       ...  \n",
+       "145        2  \n",
+       "146        2  \n",
+       "147        2  \n",
+       "148        2  \n",
+       "149        2  \n",
+       "\n",
+       "[150 rows x 5 columns]"
+      ]
+     },
+     "execution_count": 14,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
    "source": [
     "from sklearn.datasets import load_iris\n",
     "# Load the Iris dataset\n",
@@ -134,80 +291,100 @@
    ]
   },
   {
-   "cell_type": "markdown",
-   "id": "4604ee03",
+   "cell_type": "code",
+   "execution_count": 16,
+   "id": "b8971d89",
    "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "   sepal length (cm)  sepal width (cm)  petal length (cm)  petal width (cm)  \\\n",
+      "0          -0.900681          1.019004          -1.340227         -1.315444   \n",
+      "1          -1.143017         -0.131979          -1.340227         -1.315444   \n",
+      "2          -1.385353          0.328414          -1.397064         -1.315444   \n",
+      "3          -1.506521          0.098217          -1.283389         -1.315444   \n",
+      "4          -1.021849          1.249201          -1.340227         -1.315444   \n",
+      "\n",
+      "   species  \n",
+      "0        0  \n",
+      "1        0  \n",
+      "2        0  \n",
+      "3        0  \n",
+      "4        0  \n"
+     ]
+    }
+   ],
    "source": [
-    "#### **Question 4:** \n",
-    "#### K-means clustering \n",
-    "Apply the K-Means clustering algorithm to the Iris dataset.\n",
-    "Choose the number of clusters (K=3, since there are three species) and fit the model.\n",
-    "Assign cluster labels to the original data and add them as a new column in the DataFrame."
+    "# Initialize the StandardScaler\n",
+    "scaler = StandardScaler()\n",
+    "\n",
+    "# Scale the features (excluding the species column)\n",
+    "scaled_features = scaler.fit_transform(iris_df.iloc[:, :-1])\n",
+    "\n",
+    "# Create a new DataFrame with scaled features\n",
+    "scaled_iris_df = pd.DataFrame(scaled_features, columns=iris_data.feature_names)\n",
+    "\n",
+    "# Add the species column back to the scaled DataFrame\n",
+    "scaled_iris_df['species'] = iris_df['species'].values\n",
+    "\n",
+    "# Display the first few rows of the scaled DataFrame\n",
+    "print(scaled_iris_df.head())"
    ]
   },
   {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "909df219",
+   "cell_type": "markdown",
+   "id": "b326e039",
    "metadata": {},
-   "outputs": [],
    "source": [
-    "# Your code here ..."
+    "Why is it important to standardize the features of a dataset before applying clustering algorithms like K-Means? Discuss the implications of using unstandardized data in your analysis."
    ]
   },
   {
    "cell_type": "markdown",
-   "id": "0aefdee5",
+   "id": "fc34b4b7",
    "metadata": {},
    "source": [
-    "Discuss the results of the K-Means clustering. How well did the clusters match the true species?"
+    "> Your answer here ... "
    ]
   },
   {
    "cell_type": "markdown",
-   "id": "7bcebc16",
+   "id": "4604ee03",
    "metadata": {},
    "source": [
-    "> Your answer here ..."
+    "#### **Question 4:** \n",
+    "#### K-means clustering \n",
+    "Apply the K-Means clustering algorithm to the Iris dataset.\n",
+    "Choose the number of clusters (K=3, since there are three species) and fit the model.\n",
+    "Assign cluster labels to the original data and add them as a new column in the DataFrame."
    ]
   },
   {
    "cell_type": "code",
    "execution_count": null,
-   "id": "cbca5c4b",
+   "id": "909df219",
    "metadata": {},
    "outputs": [],
    "source": [
-    "# Initialize the StandardScaler\n",
-    "scaler = StandardScaler()\n",
-    "\n",
-    "# Scale the features (excluding the species column)\n",
-    "scaled_features = scaler.fit_transform(iris_df.iloc[:, :-1])\n",
-    "\n",
-    "# Create a new DataFrame with scaled features\n",
-    "scaled_iris_df = pd.DataFrame(scaled_features, columns=iris_data.feature_names)\n",
-    "\n",
-    "# Add the species column back to the scaled DataFrame\n",
-    "scaled_iris_df['species'] = iris_df['species'].values\n",
-    "\n",
-    "# Display the first few rows of the scaled DataFrame\n",
-    "print(scaled_iris_df.head())"
+    "# Your code here ..."
    ]
   },
   {
    "cell_type": "markdown",
-   "id": "68f4231e",
+   "id": "0aefdee5",
    "metadata": {},
    "source": [
-    "Why is it important to standardize the features of a dataset before applying clustering algorithms like K-Means? Discuss the implications of using unstandardized data in your analysis."
+    "Discuss the results of the K-Means clustering. How well did the clusters match the true species?"
    ]
   },
   {
    "cell_type": "markdown",
-   "id": "057ec7e9",
+   "id": "7bcebc16",
    "metadata": {},
    "source": [
-    "> Your answer here ... "
+    "> Your answer here ..."
    ]
   },
   {
@@ -216,11 +393,9 @@
    "metadata": {},
    "source": [
     "#### **Question 5:** \n",
-    "#### Bootstrapping for Cluster Stability.\n",
+    "#### Bootstrapping \n",
     "\n",
-    "Implement bootstrapping to assess the stability of the clusters obtained from K-Means.\n",
-    "Generate 100 bootstrap samples from the original dataset.\n",
-    "For each bootstrap sample, fit the K-Means model and record the cluster labels."
+    " Implement bootstrapping on the mean of one of the sepal or petal measurement variables (e.g., Sepal Length, Petal Width) to assess the stability of the mean estimate. Generate 1000 bootstrap samples, calculate the mean for each sample, and compute a 95% confidence interval to evaluate the variability in the estimate."
    ]
   },
   {
@@ -238,7 +413,7 @@
    "id": "29096311",
    "metadata": {},
    "source": [
-    "Reflect on the stability of the clusters based on the bootstrapping results. Are there samples that consistently change clusters across bootstraps?"
+    "Reflect on the variability observed in the bootstrapped means and discuss whether the mean of the selected variable appears to be a stable and reliable estimate based on the confidence interval and the spread of the bootstrapped means."
    ]
   },
   {
@@ -262,7 +437,7 @@
     "| **Data Inspection**                                    | Data is thoroughly inspected for the number of variables, observations, and data types, and relevant insights are noted. | Data inspection is missing or lacks detail.         |\n",
     "| **Data Visualization**                                 | Visualizations (e.g., scatter plots) are well-constructed and correctly interpreted to explore relationships between features and species. | Visualizations are poorly constructed or not correctly interpreted. |\n",
     "| **Clustering Implementation**                           | K-Means clustering is correctly implemented, and cluster labels are appropriately assigned to the dataset.            | K-Means clustering is missing or incorrectly implemented. |\n",
-    "| **Bootstrapping Process**                              | Bootstrapping is correctly performed, and results are used to assess cluster stability. | Bootstrapping is missing or incorrectly performed. |"
+    "| **Bootstrapping Process**                              | Bootstrapping is correctly performed, and results are used to assess variable mean stability. | Bootstrapping is missing or incorrectly performed. |"
    ]
   },
   {

From 93c442fa4783ef8f8339709d37ed8f4d1b417d2f Mon Sep 17 00:00:00 2001
From: Polar <PolarBlazeConsulting@outlook.com>
Date: Thu, 26 Sep 2024 17:56:20 -0400
Subject: [PATCH 4/5] homework 1

---
 02_activities/assignments/assignment_1.ipynb | 8 ++++++++
 1 file changed, 8 insertions(+)

diff --git a/02_activities/assignments/assignment_1.ipynb b/02_activities/assignments/assignment_1.ipynb
index 0ca7965b..ccebf248 100644
--- a/02_activities/assignments/assignment_1.ipynb
+++ b/02_activities/assignments/assignment_1.ipynb
@@ -359,6 +359,14 @@
     "\n",
     "If you encounter any difficulties or have questions, please don't hesitate to reach out to our team via our Slack at `#cohort-4-help`. Our Technical Facilitators and Learning Support staff are here to help you navigate any challenges.\n"
    ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "e44cb44b",
+   "metadata": {},
+   "source": [
+    "Thanks Vishnou"
+   ]
   }
  ],
  "metadata": {

From df13359976b7d977b0d39eaee7334e4d51d92bbf Mon Sep 17 00:00:00 2001
From: Polar <PolarBlazeConsulting@outlook.com>
Date: Thu, 26 Sep 2024 18:43:22 -0400
Subject: [PATCH 5/5] Change Assignment--1

---
 02_activities/assignments/assignment_1.ipynb | 478 +++++++++++++++++--
 1 file changed, 450 insertions(+), 28 deletions(-)

diff --git a/02_activities/assignments/assignment_1.ipynb b/02_activities/assignments/assignment_1.ipynb
index ccebf248..cd3abbf6 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": 15,
    "id": "4a3485d6-ba58-4660-a983-5680821c5719",
    "metadata": {},
    "outputs": [],
@@ -56,10 +56,288 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 16,
    "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": 16,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
    "source": [
     "from sklearn.datasets import load_wine\n",
     "\n",
@@ -91,12 +369,23 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 17,
    "id": "56916892",
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "178"
+      ]
+     },
+     "execution_count": 17,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
    "source": [
-    "# Your answer here"
+    "178 # Your answer here"
    ]
   },
   {
@@ -109,12 +398,23 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 18,
    "id": "df0ef103",
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "14"
+      ]
+     },
+     "execution_count": 18,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
    "source": [
-    "# Your answer here"
+    "14 # Your answer here"
    ]
   },
   {
@@ -127,12 +427,23 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 19,
    "id": "47989426",
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "'integer'"
+      ]
+     },
+     "execution_count": 19,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
    "source": [
-    "# Your answer here"
+    "'integer' # Your answer here"
    ]
   },
   {
@@ -146,12 +457,23 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 20,
    "id": "bd7b0910",
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "13"
+      ]
+     },
+     "execution_count": 20,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
    "source": [
-    "# Your answer here"
+    "13 # Your answer here"
    ]
   },
   {
@@ -175,10 +497,37 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 21,
    "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",
@@ -204,7 +553,12 @@
    "id": "403ef0bb",
    "metadata": {},
    "source": [
-    "> Your answer here..."
+    "Standardizing predictor variables (also known as z-score normalization) is crucial for several reasons:\n",
+    "\n",
+    "Equal Weighting: Many statistical methods (like linear regression or k-means clustering) are sensitive to the scale of the data. If predictor variables are on different scales, those with larger ranges can dominate the model, leading to biased results.\n",
+    "Improved Convergence: Algorithms that rely on gradient descent (e.g., logistic regression, neural networks) benefit from standardized variables, as it helps them converge more quickly and effectively.\n",
+    "Interpretability: Standardization allows for easier comparison between the effects of different predictors, as they are now on the same scale.\n",
+    "Distance Metrics: In algorithms that use distance metrics (like k-NN), standardization ensures that all dimensions contribute equally to the distance calculations."
    ]
   },
   {
@@ -220,7 +574,11 @@
    "id": "fdee5a15",
    "metadata": {},
    "source": [
-    "> Your answer here..."
+    "The response variable \"Class,\" which takes on binary values (0 or 2), typically does not require standardization because:\n",
+    "\n",
+    "Binary Nature: Standardizing a binary variable can distort its interpretation. It changes the scale of the variable, which is inherently categorical and meant to represent distinct classes.\n",
+    "Model Compatibility: Many classification algorithms (like logistic regression) are designed to handle binary outcomes directly without the need for standardization. In these cases, the model can interpret the original values correctly (e.g., 0 as \"negative\" and 2 as \"positive\").\n",
+    "Avoiding Misinterpretation: If you standardize a binary class, you might introduce values that don't correspond to the original categories, complicating interpretation."
    ]
   },
   {
@@ -236,7 +594,15 @@
    "id": "f0676c21",
    "metadata": {},
    "source": [
-    "> Your answer here..."
+    "Setting a random seed is important because:\n",
+    "\n",
+    "Reproducibility: It ensures that your results can be replicated. When you set a seed, the random number generator produces the same sequence of numbers every time, leading to consistent results across runs.\n",
+    "Controlled Experiments: In machine learning and statistical experiments, random processes (like data splitting or initializing weights) can introduce variability. A seed controls this variability, allowing for fair comparisons between models or configurations.\n",
+    "Regarding the particular seed value:\n",
+    "\n",
+    "Not Significantly Important: The specific number chosen as the seed (e.g., 42, 123) does not inherently affect the analysis; what matters is that the same seed is used across experiments for consistency.\n",
+    "Different Seeds for Variation: Different seed values can lead to different splits or initializations, which might help in assessing the robustness of your model. It can be beneficial to try multiple seeds to ensure your results are stable across different random configurations.\n",
+    "In summary, standardizing predictors enhances model performance, keeping the response variable as-is preserves its categorical nature, and setting a seed is vital for reproducibility without concern for the specific seed value itself."
    ]
   },
   {
@@ -251,7 +617,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 22,
    "id": "72c101f2",
    "metadata": {},
    "outputs": [],
@@ -282,12 +648,46 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 25,
    "id": "08818c64",
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "The best value for n_neighbors is: 11\n"
+     ]
+    }
+   ],
    "source": [
-    "# Your code here..."
+    "import numpy as np\n",
+    "from sklearn.model_selection import GridSearchCV\n",
+    "from sklearn.neighbors import KNeighborsClassifier\n",
+    "from sklearn.datasets import load_iris\n",
+    "from sklearn.model_selection import train_test_split\n",
+    "\n",
+    "# Load dataset (you can replace this with your own dataset)\n",
+    "data = load_iris()\n",
+    "X = data.data\n",
+    "y = data.target\n",
+    "\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",
+    "# Step 1: Initialize the KNN classifier\n",
+    "knn = KNeighborsClassifier()\n",
+    "\n",
+    "# Step 2: Define a parameter grid for n_neighbors\n",
+    "param_grid = {'n_neighbors': np.arange(1, 51)}\n",
+    "\n",
+    "# Step 3: Implement a grid search using GridSearchCV\n",
+    "grid_search = GridSearchCV(knn, param_grid, cv=10, scoring='accuracy')\n",
+    "grid_search.fit(X_train, y_train)\n",
+    "\n",
+    "# Step 4: Identify and return the best value for n_neighbors\n",
+    "best_n_neighbors = grid_search.best_params_['n_neighbors']\n",
+    "print(f\"The best value for n_neighbors is: {best_n_neighbors}\")"
    ]
   },
   {
@@ -303,12 +703,34 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 26,
    "id": "ffefa9f2",
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Model accuracy on the test set: 1.00\n"
+     ]
+    }
+   ],
    "source": [
-    "# Your code here..."
+    "from sklearn.metrics import accuracy_score\n",
+    "\n",
+    "# Assume the previous code has already defined X_train, X_test, y_train, y_test,\n",
+    "# and obtained best_n_neighbors from the grid search.\n",
+    "\n",
+    "# Step 1: Fit the KNN model using the best n_neighbors\n",
+    "best_knn = KNeighborsClassifier(n_neighbors=best_n_neighbors)\n",
+    "best_knn.fit(X_train, y_train)\n",
+    "\n",
+    "# Step 2: Make predictions on the test set\n",
+    "y_pred = best_knn.predict(X_test)\n",
+    "\n",
+    "# Step 3: Evaluate the model using accuracy_score\n",
+    "accuracy = accuracy_score(y_test, y_pred)\n",
+    "print(f\"Model accuracy on the test set: {accuracy:.2f}\")"
    ]
   },
   {
@@ -385,7 +807,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.9.19"
+   "version": "3.9.15"
   },
   "vscode": {
    "interpreter": {