diff --git a/02_activities/assignments/assignment_1.ipynb b/02_activities/assignments/assignment_1.ipynb
index 28d4df017..3a98564ab 100644
--- a/02_activities/assignments/assignment_1.ipynb
+++ b/02_activities/assignments/assignment_1.ipynb
@@ -34,7 +34,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 1,
    "id": "4a3485d6-ba58-4660-a983-5680821c5719",
    "metadata": {},
    "outputs": [],
@@ -56,10 +56,288 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 2,
    "id": "a431d282-f9ca-4d5d-8912-71ffc9d8ea19",
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>alcohol</th>\n",
+       "      <th>malic_acid</th>\n",
+       "      <th>ash</th>\n",
+       "      <th>alcalinity_of_ash</th>\n",
+       "      <th>magnesium</th>\n",
+       "      <th>total_phenols</th>\n",
+       "      <th>flavanoids</th>\n",
+       "      <th>nonflavanoid_phenols</th>\n",
+       "      <th>proanthocyanins</th>\n",
+       "      <th>color_intensity</th>\n",
+       "      <th>hue</th>\n",
+       "      <th>od280/od315_of_diluted_wines</th>\n",
+       "      <th>proline</th>\n",
+       "      <th>class</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>14.23</td>\n",
+       "      <td>1.71</td>\n",
+       "      <td>2.43</td>\n",
+       "      <td>15.6</td>\n",
+       "      <td>127.0</td>\n",
+       "      <td>2.80</td>\n",
+       "      <td>3.06</td>\n",
+       "      <td>0.28</td>\n",
+       "      <td>2.29</td>\n",
+       "      <td>5.64</td>\n",
+       "      <td>1.04</td>\n",
+       "      <td>3.92</td>\n",
+       "      <td>1065.0</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>13.20</td>\n",
+       "      <td>1.78</td>\n",
+       "      <td>2.14</td>\n",
+       "      <td>11.2</td>\n",
+       "      <td>100.0</td>\n",
+       "      <td>2.65</td>\n",
+       "      <td>2.76</td>\n",
+       "      <td>0.26</td>\n",
+       "      <td>1.28</td>\n",
+       "      <td>4.38</td>\n",
+       "      <td>1.05</td>\n",
+       "      <td>3.40</td>\n",
+       "      <td>1050.0</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>13.16</td>\n",
+       "      <td>2.36</td>\n",
+       "      <td>2.67</td>\n",
+       "      <td>18.6</td>\n",
+       "      <td>101.0</td>\n",
+       "      <td>2.80</td>\n",
+       "      <td>3.24</td>\n",
+       "      <td>0.30</td>\n",
+       "      <td>2.81</td>\n",
+       "      <td>5.68</td>\n",
+       "      <td>1.03</td>\n",
+       "      <td>3.17</td>\n",
+       "      <td>1185.0</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>14.37</td>\n",
+       "      <td>1.95</td>\n",
+       "      <td>2.50</td>\n",
+       "      <td>16.8</td>\n",
+       "      <td>113.0</td>\n",
+       "      <td>3.85</td>\n",
+       "      <td>3.49</td>\n",
+       "      <td>0.24</td>\n",
+       "      <td>2.18</td>\n",
+       "      <td>7.80</td>\n",
+       "      <td>0.86</td>\n",
+       "      <td>3.45</td>\n",
+       "      <td>1480.0</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>13.24</td>\n",
+       "      <td>2.59</td>\n",
+       "      <td>2.87</td>\n",
+       "      <td>21.0</td>\n",
+       "      <td>118.0</td>\n",
+       "      <td>2.80</td>\n",
+       "      <td>2.69</td>\n",
+       "      <td>0.39</td>\n",
+       "      <td>1.82</td>\n",
+       "      <td>4.32</td>\n",
+       "      <td>1.04</td>\n",
+       "      <td>2.93</td>\n",
+       "      <td>735.0</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>...</th>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>173</th>\n",
+       "      <td>13.71</td>\n",
+       "      <td>5.65</td>\n",
+       "      <td>2.45</td>\n",
+       "      <td>20.5</td>\n",
+       "      <td>95.0</td>\n",
+       "      <td>1.68</td>\n",
+       "      <td>0.61</td>\n",
+       "      <td>0.52</td>\n",
+       "      <td>1.06</td>\n",
+       "      <td>7.70</td>\n",
+       "      <td>0.64</td>\n",
+       "      <td>1.74</td>\n",
+       "      <td>740.0</td>\n",
+       "      <td>2</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>174</th>\n",
+       "      <td>13.40</td>\n",
+       "      <td>3.91</td>\n",
+       "      <td>2.48</td>\n",
+       "      <td>23.0</td>\n",
+       "      <td>102.0</td>\n",
+       "      <td>1.80</td>\n",
+       "      <td>0.75</td>\n",
+       "      <td>0.43</td>\n",
+       "      <td>1.41</td>\n",
+       "      <td>7.30</td>\n",
+       "      <td>0.70</td>\n",
+       "      <td>1.56</td>\n",
+       "      <td>750.0</td>\n",
+       "      <td>2</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>175</th>\n",
+       "      <td>13.27</td>\n",
+       "      <td>4.28</td>\n",
+       "      <td>2.26</td>\n",
+       "      <td>20.0</td>\n",
+       "      <td>120.0</td>\n",
+       "      <td>1.59</td>\n",
+       "      <td>0.69</td>\n",
+       "      <td>0.43</td>\n",
+       "      <td>1.35</td>\n",
+       "      <td>10.20</td>\n",
+       "      <td>0.59</td>\n",
+       "      <td>1.56</td>\n",
+       "      <td>835.0</td>\n",
+       "      <td>2</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>176</th>\n",
+       "      <td>13.17</td>\n",
+       "      <td>2.59</td>\n",
+       "      <td>2.37</td>\n",
+       "      <td>20.0</td>\n",
+       "      <td>120.0</td>\n",
+       "      <td>1.65</td>\n",
+       "      <td>0.68</td>\n",
+       "      <td>0.53</td>\n",
+       "      <td>1.46</td>\n",
+       "      <td>9.30</td>\n",
+       "      <td>0.60</td>\n",
+       "      <td>1.62</td>\n",
+       "      <td>840.0</td>\n",
+       "      <td>2</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>177</th>\n",
+       "      <td>14.13</td>\n",
+       "      <td>4.10</td>\n",
+       "      <td>2.74</td>\n",
+       "      <td>24.5</td>\n",
+       "      <td>96.0</td>\n",
+       "      <td>2.05</td>\n",
+       "      <td>0.76</td>\n",
+       "      <td>0.56</td>\n",
+       "      <td>1.35</td>\n",
+       "      <td>9.20</td>\n",
+       "      <td>0.61</td>\n",
+       "      <td>1.60</td>\n",
+       "      <td>560.0</td>\n",
+       "      <td>2</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "<p>178 rows × 14 columns</p>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "     alcohol  malic_acid   ash  alcalinity_of_ash  magnesium  total_phenols  \\\n",
+       "0      14.23        1.71  2.43               15.6      127.0           2.80   \n",
+       "1      13.20        1.78  2.14               11.2      100.0           2.65   \n",
+       "2      13.16        2.36  2.67               18.6      101.0           2.80   \n",
+       "3      14.37        1.95  2.50               16.8      113.0           3.85   \n",
+       "4      13.24        2.59  2.87               21.0      118.0           2.80   \n",
+       "..       ...         ...   ...                ...        ...            ...   \n",
+       "173    13.71        5.65  2.45               20.5       95.0           1.68   \n",
+       "174    13.40        3.91  2.48               23.0      102.0           1.80   \n",
+       "175    13.27        4.28  2.26               20.0      120.0           1.59   \n",
+       "176    13.17        2.59  2.37               20.0      120.0           1.65   \n",
+       "177    14.13        4.10  2.74               24.5       96.0           2.05   \n",
+       "\n",
+       "     flavanoids  nonflavanoid_phenols  proanthocyanins  color_intensity   hue  \\\n",
+       "0          3.06                  0.28             2.29             5.64  1.04   \n",
+       "1          2.76                  0.26             1.28             4.38  1.05   \n",
+       "2          3.24                  0.30             2.81             5.68  1.03   \n",
+       "3          3.49                  0.24             2.18             7.80  0.86   \n",
+       "4          2.69                  0.39             1.82             4.32  1.04   \n",
+       "..          ...                   ...              ...              ...   ...   \n",
+       "173        0.61                  0.52             1.06             7.70  0.64   \n",
+       "174        0.75                  0.43             1.41             7.30  0.70   \n",
+       "175        0.69                  0.43             1.35            10.20  0.59   \n",
+       "176        0.68                  0.53             1.46             9.30  0.60   \n",
+       "177        0.76                  0.56             1.35             9.20  0.61   \n",
+       "\n",
+       "     od280/od315_of_diluted_wines  proline  class  \n",
+       "0                            3.92   1065.0      0  \n",
+       "1                            3.40   1050.0      0  \n",
+       "2                            3.17   1185.0      0  \n",
+       "3                            3.45   1480.0      0  \n",
+       "4                            2.93    735.0      0  \n",
+       "..                            ...      ...    ...  \n",
+       "173                          1.74    740.0      2  \n",
+       "174                          1.56    750.0      2  \n",
+       "175                          1.56    835.0      2  \n",
+       "176                          1.62    840.0      2  \n",
+       "177                          1.60    560.0      2  \n",
+       "\n",
+       "[178 rows x 14 columns]"
+      ]
+     },
+     "execution_count": 2,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
    "source": [
     "from sklearn.datasets import load_wine\n",
     "\n",
@@ -91,12 +369,24 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 3,
    "id": "56916892",
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "178"
+      ]
+     },
+     "execution_count": 3,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
    "source": [
-    "# Your answer here"
+    "# Your answer here\n",
+    "wine_df.shape[0]"
    ]
   },
   {
@@ -109,12 +399,23 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 4,
    "id": "df0ef103",
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "14\n"
+     ]
+    }
+   ],
    "source": [
-    "# Your answer here"
+    "# Your answer here\n",
+    "print(wine_df.shape[1])\n",
+    "\n",
+    "#print(wine_df.info())"
    ]
   },
   {
@@ -127,12 +428,32 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 5,
    "id": "47989426",
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "<class 'pandas.core.series.Series'>\n",
+      "RangeIndex: 178 entries, 0 to 177\n",
+      "Series name: class\n",
+      "Non-Null Count  Dtype\n",
+      "--------------  -----\n",
+      "178 non-null    int64\n",
+      "dtypes: int64(1)\n",
+      "memory usage: 1.5 KB\n",
+      "None\n",
+      "[0 1 2]\n"
+     ]
+    }
+   ],
    "source": [
-    "# Your answer here"
+    "# Your answer here\n",
+    "print(wine_df[\"class\"].info())\n",
+    "\n",
+    "print(wine_df[\"class\"].unique())"
    ]
   },
   {
@@ -146,12 +467,25 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 6,
    "id": "bd7b0910",
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "13"
+      ]
+     },
+     "execution_count": 6,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
    "source": [
-    "# Your answer here"
+    "# Your answer here\n",
+    "count = len(wine_df.columns.difference([\"class\"]))\n",
+    "count"
    ]
   },
   {
@@ -175,10 +509,37 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 7,
    "id": "cc899b59",
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "    alcohol  malic_acid       ash  alcalinity_of_ash  magnesium  \\\n",
+      "0  1.518613   -0.562250  0.232053          -1.169593   1.913905   \n",
+      "1  0.246290   -0.499413 -0.827996          -2.490847   0.018145   \n",
+      "2  0.196879    0.021231  1.109334          -0.268738   0.088358   \n",
+      "3  1.691550   -0.346811  0.487926          -0.809251   0.930918   \n",
+      "4  0.295700    0.227694  1.840403           0.451946   1.281985   \n",
+      "\n",
+      "   total_phenols  flavanoids  nonflavanoid_phenols  proanthocyanins  \\\n",
+      "0       0.808997    1.034819             -0.659563         1.224884   \n",
+      "1       0.568648    0.733629             -0.820719        -0.544721   \n",
+      "2       0.808997    1.215533             -0.498407         2.135968   \n",
+      "3       2.491446    1.466525             -0.981875         1.032155   \n",
+      "4       0.808997    0.663351              0.226796         0.401404   \n",
+      "\n",
+      "   color_intensity       hue  od280/od315_of_diluted_wines   proline  \n",
+      "0         0.251717  0.362177                      1.847920  1.013009  \n",
+      "1        -0.293321  0.406051                      1.113449  0.965242  \n",
+      "2         0.269020  0.318304                      0.788587  1.395148  \n",
+      "3         1.186068 -0.427544                      1.184071  2.334574  \n",
+      "4        -0.319276  0.362177                      0.449601 -0.037874  \n"
+     ]
+    }
+   ],
    "source": [
     "# Select predictors (excluding the last column)\n",
     "predictors = wine_df.iloc[:, :-1]\n",
@@ -188,7 +549,8 @@
     "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())"
+    "print(predictors_standardized.head())\n",
+    "#predictors_standardized.info()"
    ]
   },
   {
@@ -204,7 +566,8 @@
    "id": "403ef0bb",
    "metadata": {},
    "source": [
-    "> Your answer here..."
+    "KNN is based on calculating the distance between nieighboours, so if we do not perform standardization on predictor variables - large scale predictor variables will have more dominance on distance calculations. In short, standardization ensures that all predictor variables contribute equally in distance calculation so that the model is not bised towards large scale predictors. \n",
+    "Example : In Media Modeling, TV ad is measured in GRP (which are typically in hundreds) while YouTube ad is measured in impressions (which are in tens of thousand) "
    ]
   },
   {
@@ -220,7 +583,7 @@
    "id": "fdee5a15",
    "metadata": {},
    "source": [
-    "> Your answer here..."
+    "KNN classifies the observation into one of the category of the response variable, and since the response variable is a categorical variable, its not required to be standaerdized. Also, response variable is not used in distance calculation. "
    ]
   },
   {
@@ -236,7 +599,8 @@
    "id": "f0676c21",
    "metadata": {},
    "source": [
-    "> Your answer here..."
+    "Using a random seed is important because it helps in KNN to split the data randomly while still be able to have a reproducible model, meaning the model results are not changed everytime we run a model.\n",
+    "You can have any integer number in the random seed function beacuse any seed simply ensures reproducibility - the value itself doesnt affect the model in any way, it just affects the consistency of randomness "
    ]
   },
   {
@@ -251,17 +615,59 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 8,
    "id": "72c101f2",
    "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  class  \n",
+      "0         0.251717  0.362177                      1.847920  1.013009      0  \n",
+      "1        -0.293321  0.406051                      1.113449  0.965242      0  \n",
+      "2         0.269020  0.318304                      0.788587  1.395148      0  \n",
+      "3         1.186068 -0.427544                      1.184071  2.334574      0  \n",
+      "4        -0.319276  0.362177                      0.449601 -0.037874      0  \n"
+     ]
+    }
+   ],
    "source": [
     "# set a seed for reproducibility\n",
     "np.random.seed(123)\n",
     "\n",
+    "# doing this again cause in the previous code cell 'class' column is excluded and I want to stratify using class while splitting the data\n",
+    "standardized_wine = wine_df.copy() #make a copy of our data\n",
+    "columns_to_exclude = ['class'] #columns we dont want to scale\n",
+    "columns_to_scale = standardized_wine.columns.difference(columns_to_exclude)\n",
+    "\n",
+    "scaler = StandardScaler()\n",
+    "standardized_wine[columns_to_scale] = scaler.fit_transform(standardized_wine[columns_to_scale]) # Standardize the predictors\n",
+    "\n",
+    "print(standardized_wine.head()) # Display the head of the standardized predictors\n",
+    "\n",
+    "#standardized_wine.info()\n",
+    "\n",
+    "\n",
+    "\n",
     "# split the data into a training and testing set. hint: use train_test_split !\n",
     "\n",
-    "# Your code here ..."
+    "wine_train , wine_test = train_test_split(standardized_wine, train_size= 0.75, shuffle= True, stratify= standardized_wine[\"class\"])\n",
+    "#wine_train.head()"
    ]
   },
   {
@@ -284,12 +690,54 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 10,
    "id": "08818c64",
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "{'n_neighbors': 7}\n"
+     ]
+    },
+    {
+     "data": {
+      "text/plain": [
+       "\"\\n# Create the plot\\nplt.figure(figsize=(10, 6))\\n# Plot mean test scores with error bars\\naccuracy_grid = pd.DataFrame(wine_tune_grid.cv_results_)\\nplt.plot(accuracy_grid['param_n_neighbors'], accuracy_grid['mean_test_score'], '-o', color='blue')\\n# Add labels and legend\\nplt.xlabel('Number of Neighbors')\\nplt.ylabel('Accuracy estimate')\\nplt.title('K-Nearest Neighbors Performance')\\nplt.tight_layout()\\nplt.show()\\n\""
+      ]
+     },
+     "execution_count": 10,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
    "source": [
-    "# Your code here..."
+    "knn = KNeighborsClassifier() # Step 1 - initialize KNN classifier with deafult n_nieghbours of 5\n",
+    "\n",
+    "parameter_grid = { \"n_neighbors\" : range(1,51)} # step 2 - define parameter grid\n",
+    "\n",
+    "wine_tune_grid = GridSearchCV(estimator= knn, param_grid= parameter_grid, cv= 10) # step 3 - grid search\n",
+    "\n",
+    "wine_tune_grid.fit(wine_train[[\"alcohol\", \"malic_acid\", \"color_intensity\", \"ash\", \"alcalinity_of_ash\",\n",
+    "                               \"magnesium\", \"total_phenols\", \"flavanoids\", \"nonflavanoid_phenols\", \"proanthocyanins\",\n",
+    "                               \"hue\", \"od280/od315_of_diluted_wines\", \"proline\"]], wine_train[\"class\"]) #fit the model\n",
+    "\n",
+    "print(wine_tune_grid.best_params_)\n",
+    "\n",
+    "'''\n",
+    "# Create the plot\n",
+    "plt.figure(figsize=(10, 6))\n",
+    "# Plot mean test scores with error bars\n",
+    "accuracy_grid = pd.DataFrame(wine_tune_grid.cv_results_)\n",
+    "plt.plot(accuracy_grid['param_n_neighbors'], accuracy_grid['mean_test_score'], '-o', color='blue')\n",
+    "# Add labels and legend\n",
+    "plt.xlabel('Number of Neighbors')\n",
+    "plt.ylabel('Accuracy estimate')\n",
+    "plt.title('K-Nearest Neighbors Performance')\n",
+    "plt.tight_layout()\n",
+    "plt.show()\n",
+    "'''"
    ]
   },
   {
@@ -305,14 +753,132 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 11,
    "id": "ffefa9f2",
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "0.9333333333333333"
+      ]
+     },
+     "execution_count": 11,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
    "source": [
-    "# Your code here..."
+    "knn = KNeighborsClassifier(n_neighbors=7) # define knn model with best k value as per above code\n",
+    "\n",
+    "X = wine_train[[\"alcohol\", \"malic_acid\", \"color_intensity\", \"ash\", \"alcalinity_of_ash\",\n",
+    "                               \"magnesium\", \"total_phenols\", \"flavanoids\", \"nonflavanoid_phenols\", \"proanthocyanins\",\n",
+    "                               \"hue\", \"od280/od315_of_diluted_wines\", \"proline\"]] # defining predictors\n",
+    "y = wine_train[\"class\"] # defining response variable\n",
+    "\n",
+    "knn.fit(X,y) # fit the model\n",
+    "\n",
+    "wine_test[\"predicted_class\"] = knn.predict(wine_test[[\"alcohol\", \"malic_acid\", \"color_intensity\", \"ash\", \"alcalinity_of_ash\",\n",
+    "                               \"magnesium\", \"total_phenols\", \"flavanoids\", \"nonflavanoid_phenols\", \"proanthocyanins\",\n",
+    "                               \"hue\", \"od280/od315_of_diluted_wines\", \"proline\"]]) # evaluate model performance on test data\n",
+    "#wine_test\n",
+    "\n",
+    "\n",
+    "knn.score(wine_test[[\"alcohol\", \"malic_acid\", \"color_intensity\", \"ash\", \"alcalinity_of_ash\",\n",
+    "                               \"magnesium\", \"total_phenols\", \"flavanoids\", \"nonflavanoid_phenols\", \"proanthocyanins\",\n",
+    "                               \"hue\", \"od280/od315_of_diluted_wines\", \"proline\"]], wine_test[\"class\"]) #accuracy score"
    ]
   },
+  {
+   "cell_type": "code",
+   "execution_count": 12,
+   "id": "54896716",
+   "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>predicted_class</th>\n",
+       "      <th>0</th>\n",
+       "      <th>1</th>\n",
+       "      <th>2</th>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>class</th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>15</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>2</td>\n",
+       "      <td>15</td>\n",
+       "      <td>1</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>12</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "predicted_class   0   1   2\n",
+       "class                      \n",
+       "0                15   0   0\n",
+       "1                 2  15   1\n",
+       "2                 0   0  12"
+      ]
+     },
+     "execution_count": 12,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# confusion matrix\n",
+    "pd.crosstab(\n",
+    "    wine_test[\"class\"],\n",
+    "    wine_test[\"predicted_class\"]\n",
+    ")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "739bee1b",
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
   {
    "cell_type": "markdown",
    "id": "6f8a69db",
@@ -365,7 +931,7 @@
  ],
  "metadata": {
   "kernelspec": {
-   "display_name": "Python 3.10.4",
+   "display_name": "lcr-env",
    "language": "python",
    "name": "python3"
   },
@@ -379,12 +945,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.9.19"
-  },
-  "vscode": {
-   "interpreter": {
-    "hash": "497a84dc8fec8cf8d24e7e87b6d954c9a18a327edc66feb9b9ea7e9e72cc5c7e"
-   }
+   "version": "3.11.14"
   }
  },
  "nbformat": 4,
diff --git a/02_activities/assignments/assignment_2.ipynb b/02_activities/assignments/assignment_2.ipynb
index a05da5cd3..6bfa480ad 100644
--- a/02_activities/assignments/assignment_2.ipynb
+++ b/02_activities/assignments/assignment_2.ipynb
@@ -34,7 +34,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 63,
+   "execution_count": 1,
    "id": "4a3485d6-ba58-4660-a983-5680821c5719",
    "metadata": {},
    "outputs": [],
@@ -51,9 +51,135 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "id": "a431d282-f9ca-4d5d-8912-71ffc9d8ea19",
+   "id": "827c7174",
    "metadata": {},
    "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "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>mpg</th>\n",
+       "      <th>cylinders</th>\n",
+       "      <th>displacement</th>\n",
+       "      <th>horsepower</th>\n",
+       "      <th>weight</th>\n",
+       "      <th>acceleration</th>\n",
+       "      <th>model_year</th>\n",
+       "      <th>origin</th>\n",
+       "      <th>name</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>18.0</td>\n",
+       "      <td>8</td>\n",
+       "      <td>307.0</td>\n",
+       "      <td>130.0</td>\n",
+       "      <td>3504</td>\n",
+       "      <td>12.0</td>\n",
+       "      <td>70</td>\n",
+       "      <td>usa</td>\n",
+       "      <td>chevrolet chevelle malibu</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>15.0</td>\n",
+       "      <td>8</td>\n",
+       "      <td>350.0</td>\n",
+       "      <td>165.0</td>\n",
+       "      <td>3693</td>\n",
+       "      <td>11.5</td>\n",
+       "      <td>70</td>\n",
+       "      <td>usa</td>\n",
+       "      <td>buick skylark 320</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>18.0</td>\n",
+       "      <td>8</td>\n",
+       "      <td>318.0</td>\n",
+       "      <td>150.0</td>\n",
+       "      <td>3436</td>\n",
+       "      <td>11.0</td>\n",
+       "      <td>70</td>\n",
+       "      <td>usa</td>\n",
+       "      <td>plymouth satellite</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>16.0</td>\n",
+       "      <td>8</td>\n",
+       "      <td>304.0</td>\n",
+       "      <td>150.0</td>\n",
+       "      <td>3433</td>\n",
+       "      <td>12.0</td>\n",
+       "      <td>70</td>\n",
+       "      <td>usa</td>\n",
+       "      <td>amc rebel sst</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>17.0</td>\n",
+       "      <td>8</td>\n",
+       "      <td>302.0</td>\n",
+       "      <td>140.0</td>\n",
+       "      <td>3449</td>\n",
+       "      <td>10.5</td>\n",
+       "      <td>70</td>\n",
+       "      <td>usa</td>\n",
+       "      <td>ford torino</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "    mpg  cylinders  displacement  horsepower  weight  acceleration  \\\n",
+       "0  18.0          8         307.0       130.0    3504          12.0   \n",
+       "1  15.0          8         350.0       165.0    3693          11.5   \n",
+       "2  18.0          8         318.0       150.0    3436          11.0   \n",
+       "3  16.0          8         304.0       150.0    3433          12.0   \n",
+       "4  17.0          8         302.0       140.0    3449          10.5   \n",
+       "\n",
+       "   model_year origin                       name  \n",
+       "0          70    usa  chevrolet chevelle malibu  \n",
+       "1          70    usa          buick skylark 320  \n",
+       "2          70    usa         plymouth satellite  \n",
+       "3          70    usa              amc rebel sst  \n",
+       "4          70    usa                ford torino  "
+      ]
+     },
+     "execution_count": 6,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
    "source": [
     "import seaborn as sns\n",
     "\n",
@@ -82,12 +208,23 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 11,
    "id": "5d79f1cf",
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "(392, 9)"
+      ]
+     },
+     "execution_count": 11,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
    "source": [
-    "# Your answer here..."
+    "mpg_data.shape # first value is number or rows and second is number of columns"
    ]
   },
   {
@@ -100,12 +237,27 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 12,
    "id": "ac306190",
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "<class 'pandas.core.series.Series'>\n",
+      "Index: 392 entries, 0 to 397\n",
+      "Series name: mpg\n",
+      "Non-Null Count  Dtype  \n",
+      "--------------  -----  \n",
+      "392 non-null    float64\n",
+      "dtypes: float64(1)\n",
+      "memory usage: 6.1 KB\n"
+     ]
+    }
+   ],
    "source": [
-    "# Your answer here..."
+    "mpg_data[\"mpg\"].info()"
    ]
   },
   {
@@ -113,7 +265,7 @@
    "id": "6d759089",
    "metadata": {},
    "source": [
-    "Your explanation... \n"
+    "MPG is miles per gallon and it shows that how many miles the car travels per one gallon of fuel\n"
    ]
   },
   {
@@ -126,12 +278,28 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 35,
    "id": "9f034a5d",
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "116    230.0\n",
+       "8      225.0\n",
+       "13     225.0\n",
+       "95     225.0\n",
+       "6      220.0\n",
+       "Name: horsepower, dtype: float64"
+      ]
+     },
+     "execution_count": 35,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
    "source": [
-    "# Your answer here... "
+    "mpg_data[\"horsepower\"].nlargest(5)"
    ]
   },
   {
@@ -144,12 +312,24 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 14,
    "id": "1b91233e",
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "8"
+      ]
+     },
+     "execution_count": 14,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
    "source": [
-    "# Your answer here..."
+    "count = len(mpg_data.columns.difference([\"mpg\"]))\n",
+    "count"
    ]
   },
   {
@@ -173,10 +353,71 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 15,
    "id": "732784d8",
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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8HjQ++OAD2e4FChSQ9+jqq68W/fr187m/gVxDR4wYEbSeq1evFi1atPBeA2V/8MEHxeLFiwPWUwW0M8pbsWJF+ezg/GXKlBHt2rUT33//va3fn4prKDh58qTo3bu3SE1Nle2L5xztoXeDDXSfjX73TGyRhD/RFmgYhmEYhold2GaCYRiGYZiQYGGCYRiGYZiQYGGCYRiGYZiQYGGCYRiGYZiQYGGCYRiGYZiQYGGCYRiGYZiQiPugVQjpu2/fPhl0xm4oZIZhGIZJRIQQMlQ6AujpsxMnnDABQcI/ERPDMAzDMOogmioi2yasMKGFwUVDIGQtwzAMwzBqnDhxQg7Ig4WUj3thQpvagCDBwgTDMAzDWCeYmQAbYDIMwzAMExIsTDAMwzAMExIsTDAMwzAMExJxbzOh6vpy6dIlSktLi3ZRmAQgc+bMlCVLFnZVZhgmbkh4YeLChQu0f/9+OnPmTLTvBZNA5MqVi4oVK0bZsmWLdlEYhmFCJqGFCQS02rFjhxwpIiAHXuwc2IoJtxYMAuw///wjn71y5coFDATDMAwTCyS0MIGXOgQK+NBipMgwkSBnzpyUNWtW2rVrl3wGc+TIwQ3vIjDb+cMPRPv3ExUrRnTbbZiainapGMbdJLQwocEjQ4afOQbMmkXUsyfR339fbg8E/fvf/4hatOA2YhgzWL/KMAzznyBx//2+ggTYu9ezHtsZhjGGhQmGYRIeTG1AIyFExqbQ1vXq5ZkCYRgmIyxMMAyT8MBGwl8j4S9Q7NnjsaVgGCYjLEzEKB06dJCeJ1hgzFekSBG64447aNy4cdKoVJUJEyZQvnz5wlpWhnE7MLZ0cj+GSTRYmHAAqD6XLiWaOtXzGSlVaJMmTWSMjJ07d9LXX39N9erVo549e1LTpk1lEC6GYdSA14aT+zFMosHCRIjAKKt0aaJ69YjatPF84nskjLWyZ89ORYsWpeLFi1P16tVpwIABNHfuXClYQOMARo4cSZUrV6bcuXNLF9iuXbvSqVOn5LalS5fSo48+SsePH/dqOQYPHiy3ffrpp3TjjTfKtLO4Rps2bejQoUPhrxTDRAG4f8JrwywxItaXKOHZj2GYjLAwEWfW3/Xr16eqVavSrP8uDrfXt99+m/744w/65JNP6LvvvqNnnnlGbrvlllvorbfekqnZoeHA8vTTT8ttFy9epKFDh9LatWtpzpw5UvuBqRWGiUcQRwLun8BfoNC+v/UWx5tgGDM4zkSYrL/xAoL1d7NmkX8BVahQgdatWyf/74VC/Efp0qXp5Zdfps6dO9Po0aNlxM+8efNKjQS0D3oee+wx7/9lypSRAkmNGjWkViM5OTmCtWGYyIA4EjNnGseZgCDBcSYYxhwWJiJg/X377RTxkM1aWPBFixbRsGHDaNOmTXTixAlpS3Hu3DmZiyRQ1M+VK1fKKQ9oJv7991+vUefu3bupYsWKEasLw0QSCAwYAHAETIaxBk9zxKH1959//klXXXWVnJqAMWaVKlXo888/lwLCe++9J/dBGGczTp8+TY0bN5bTH5MnT6bff/+dZs+eHfQ4hokHoEnEAKB1a88nh9JmmOCwZiLOrL9hE7F+/Xrq3bu3FB6gUXjzzTe9IcOnT5/usz+mOvxTr0OLceTIEXrttdek0SZYsWJFBGvBMAzDxBKu0Uyg44JqXj/Hf/vtt3u9DLQF8/1uwA3W3+fPn6cDBw7Q3r17adWqVfTqq69Ss2bNpDaiXbt2VLZsWWlI+c4779Bff/0lPTTGjBnjcw7YUcAOYvHixXT48GE5/VGyZEkpZGjHzZs3TxpjMgzDMIxrhQmo0T/44AOpjvenU6dOXk8DLMOHDyc34Abr7wULFlCxYsWkQICYE0uWLJGGknAPRVp1eHXANfT111+nSpUqySkL2E/ogUcHBLRWrVpRoUKFZPviE66lM2bMkPYREPTeeOON8FWEYRiGiWmSBKz1oghGxYiRAO8CeBpcf/310l1R00zov9sBRofwWEAsBdgA6IEh4o4dO6R9gd000EZZBqGRYOtvJhBnz56jTZt20PbtV1HBgjk4zTXDMK4kUB/qKs1Et27d6O6776aGDRsabsdoumDBgnJk3b9/f6mGD6b6R+X1S7itv3fuJFqyhGjKFM/njh3sRsaY8++/RFu2EB08SISwHpEMdMYwDBN3BpjTpk2Tc/2Y5jACURdLlSpFqampMm7Cs88+S5s3b/YGZDICavwhQ4ZQNKy/GUZFkNi+PeN6LdAZ4hxwPAMmkYE9OLvmxh5REyb27Nkj80h8++23plMMTzzxhPd/hISGfUCDBg1o+/btdPXVVxseA+1Fnz59vN+hmdA8EhgmmmixR8y2RTPQGcO4AaNpYxi6wz6NhWx3E7VpDrgtItcD7CWyZMkil2XLlkkDQvzv764IatasKT+3bdsWMF8F5nX0C8O4AaRECRSmg9NcM4mMG9MTuJW0KCWXdKVmAhoGxEPQg6RTCAWN6Qx4I/izZs0a+QkNBcPEGqrxvjjNNZNouDk9gduY5VLtTdSECWSjhFGlHmS2LFCggFyPqYwpU6bQXXfdJdfBZgKBmOrUqWPoQsowbidbNrX9WFZmEg03pydwo/ZGCPfZXEXdm8MMBE1CXolGjRpJbUXfvn2pZcuW9MUXX0S7aAxjC+RHCyRQcJprJlFxc3qCWNHeAGhvojXl4apw2ksx+fMfMJqEDQXDxAuasGDkzcFprplExq3pCdzEDy7X3rhWM8Ew8Uj+/ERwRMqa1Xc95jzZLZRJVNyQnsDtRpL7Xa69YWGCcQ3Icor8K5qhbbRAePJQoq6qCBTXXENUpAgRopRzoDMm0XFDegKnbRtKl/YEpGvTxpnAdG7X3rAwEYN06NDBm/gsa9asMhz4M888I8ODxzKY2kL+FX/DXKcZPHiwDNNuBoKo6WOchAO8IBFepWlTTnPNJC760XtKCrIaExUvHttau1lhcnF1u/bGVTYTjDpI7DV+/HiZFRQxO9q3by+FCyT1CheI/YFraOnMnQbuwEWLFqVog0RnDMNEx8Vx5Ej8Bj3qeoyy0TnGikYiLYwurpr2BgIJzqO/hhu0N6yZ0IO7c/p0dBaL+dYQnAsdL0bzzZs3l7lNEE1UIz09XYYWh9YiZ86cMoPoTIj3OpBavFy5cjICab169eiTTz6RwsKxY8fkdmQOzZcvn9wP2UNxzd27d8v8J08//TQVL15cuvMimJjeeHbXrl10zz33UP78+eX26667jr766iu57d9//6W2bdvKDhvlwvUhFJlNc8AI96abbpLXRnyR5557ji5duuTdjmRwPXr0kJqZlJQU2SbQPDg5zYEyffTRR3TfffdRrly5ZJnRJno2bNhAd955JyUnJ1ORIkXokUcekSndGYaxNnpv1Yro6FGi1q1jT2v3gwUjSTtAO4PXuBu1NyxM6EESMfjvRWMJksAsEOjIfv75Z+lOqwFBYuLEiTRmzBj6448/ZIyOhx9+2Oshg2yp999/vxRE1q5dS08++SQ9//zzGc6NxGrQdqAzxXkKFy5M3bt3p+XLl8vcKoj/8cADD0hNydatW73J2yBwfP/99zIwGY5HJwtefPFF2rhxI3399df0559/0vvvvy8TuRmxd+9eGWekRo0asozY9+OPP5bZZfVACILQ8uuvv8oU6i+99JKPYOUEyPfy4IMPyvqiTBCIjuKNRySFr/r161O1atVoxYoVMjX8wYMH5f4Mw8SWi6PbjSRbuDW5pIhzjh8/jsdTfvpz9uxZsXHjRvkpOXUKz3J0Flxbkfbt24vMmTOL3Llzi+zZs8v6ZcqUScycOVNuP3funMiVK5f4+eeffY7r2LGjaN26tfz/2WefFZUqVfLZ/vzzz8tz/fvvv/L7+PHj5fc1a9Z499m1a5e89t69e32ObdCggejfv7/8v3LlymLw4MGGZb/nnnvEo48+arhtx44d8nqrV6+W3wcMGCDKly8v0tPTvfu89957Ijk5WaSlpcnvdevWFbfeeqvPeWrUqCHrZ8agQYNE1apVTbeXKlVKjBo1yvsdZXrhhRe830+dOiXXff311/L70KFDRaNGjXzOsWfPHrnP5s2bDa+R4dljmARhyRK1VyL2izWWxGHdAvWhethmQk+uXJ4ECtG6tgUwLYGR+unTp2nUqFEynwmCemm5S6BRuOOOO3yOuXDhghw9A2RfxYhfD6YT/IG2Qx9xFJoG2E5cA3cEHdBEIFIpwLRDly5daOHChXL6BeXSzoH1+I5ssQhIBs3ILbfcYlhHaC5q1aolpxk0ateuTadOnaK///6bSpYsKdf5R0TFdAjyvjiJ/hrQgiDni3YNaE2WLFni1b7oQSRX/7ZimETG7S6OoXDbf0aSmK4x0rzgVYbtseTiqgoLE/53OnduigXQoZUtW1b+P27cOGkTgSmAjh07ys4WzJ8/X9o16IHtgRVg16DvzHFuGErC6NM/f4rWmT7++OPUuHFjeX0IFJhyefPNN+mpp56SdgWwqYANBaYikKMF0yJvwEfSJvBo0YPywmbESQJdA20CGxEj41fOI8Mw/r+J+A1QldnlRpLhhG0m4gB4VwwYMIBeeOEFOnv2rI+xJAQO/aKlYy9fvryc3/d3iQwGNBvQTGBU7n9uvScGrtO5c2eaNWuWDIX+4YcferfB+BLeJ5MmTZKGjmPHjjW81rXXXittMzwzDR5++uknmdflSoj3LgGZb2FPAsNN/zaB0McwTOy4OIZKCxcbSYYTFibiBBhBQlPw3nvvyc4W3hYwuoRxIlTtmFZ455135HcAg8tNmzbJDK1btmyh6dOnS+8NoNdE+AOVPYwP27VrJwUFGHL+9ttvUvsATQTo1asXffPNN3IbrospAAgGYODAgTR37lw5FYMO+Msvv/Ru86dr1660Z88eqdFAWXHcoEGDqE+fPiG7p0LogteIfkE72QGaFRhjtm7dWgpkOA/qjyy4ELwYhonfAFUxZSQZRliYiBNgMwEvC3gzwI5i6NCh0nMCnTw6a3hboLOHqyjAJ1xFIRDAHgD2F5o3R7CpELhyQpiAxgEaDtg9oBPVbBjQgaKD1a4LAWT06NFeG4z+/fvLayIDLAQgeIUYgSkaTIdAWME0DjQdmMaBBiZUIEBBy6JfIGDZITU1VWpMUG/YgVSuXFkKVHCrDVdMDoaJZRJh9J45s8e1NRZdXO2QBCtMimNOnDhBefPmpePHj0ujOT2IGInRMzpWxFpIdF555RXpSgptABNe+NljGI/7J2IuxGKAqkThRIA+VA8bYCYw0BbAowNeGBhZjxgxQmo3GIZhIjl6Z2IfFiYSGASZQgAozPdjigLTFpiCYGIbHu0xDBNpWJhIYBCfAgsT//kOYPAWD/PQDMO4E7YOY5g4IVzZChmGYYLBwoQnpHjQhmIYNz9z8ZzvgGEY95PQwoQW1RChpxkmkmjPnH9kTbdmK2QYhglEQttMIMYBYgFoORaQXjpQwCaGcUIjAUECzxyePf+Q5HaJ53wHiQgb0TKxRkILE0ALAe10YiiGCQQECX348VCJ53wHiQYb0TKxSEIHrdKD6IUXL16MaNmYxARTG05pJPQj2dKlg2crREhfDgrkfiNa/3uoKUzjJTokEztw0CqL4OXu9AueYSJFImcrjBeCGdHiPsKItlkzvo+M+0hoA0yGiScSId9BPMNGtEwsk/A2EwwTT0BgwMiV8x3EHmxEy8QyLEwwTJzB+Q5iEzaiZWIZ10xzvPbaa9ItE6mb9ZkVkcoaiaiSk5OpZcuWdPDgwaiWk2EYJhwgYyampMy807G+RAnPfgzjNlwhTPz+++/0wQcfUJUqVXzW9+7dm7744guaMWMGLVu2jPbt20cteOKXYZg4NqIF/gIFG9EybifqwsSpU6eobdu29OGHH1L+/Pm96+HK+fHHH9PIkSOpfv36dMMNN9D48ePp559/pl9++SWqZWYYhgkHbETLxCpRFyYwjXH33XdTw4YNfdavXLlSxn3Qr69QoYJMlb18+XLT850/f176xeoXhmGYaLh6Ll1KNHWq51M1LwoEip07iZYsIZoyxfOJ+CCslGXcTFQNMKdNm0arVq2S0xz+HDhwgLJlyyYjBeopUqSI3GbGsGHDaMiQIWEpL8MwTCSiWLIRLRNrRE0zsWfPHurZsydNnjyZcuTI4dh5+/fvL6dItAXXYRiGiRScCp5JRKKmmcA0BvJhVK9e3Sek9ffff0/vvvsuffPNN3ThwgU6duyYj3YC3hyBchpkz55dLgzDxF8yK7cnwOIolkyiEjXNRIMGDWj9+vW0Zs0a73LjjTdKY0ztf+QwWLx4sfeYzZs30+7du6lWrVrRKjbDMA6N3pFLpF49ojZtPJ/4jvVOHhNpOIolk6hETTORJ08eqlSpks+63Llzy5gS2vqOHTtSnz59KCUlRSbpeuqpp6QgcfPNN0ep1AzDhCuZFZKUYb1R6G87x0QDjmLJJCpR9+YIxKhRo6hp06YyWFWdOnXk9MYsNw1DGIZxdBoAIG6d3vPBzjHRgqNYMokKpyBnGCZiwEUS0xPBgDvk7bfbPyZacCp4JlFTkLtaM8EwTHxhZxoglqYOOIolk6iwMMEwjKunAWJt6oCjWDKJCE9zMAzj6mmAWJ06MHJjBW52bWUYu9McnIKcYeKMQLEYohWnQX/dTp2IBg3yCAF64cAsmZU2dQCvDf9jAL4//ji5Dv8olqFGxWQYN8OaCYaJIwJ1WCAanZlRmQoU8HweOXJ5HdJrQ5AwK4vRefS4uWM2c23VBCi3uLYyjF3NBAsTDBMnBOqwjKYHtG3h7MyCdaKDBxOVK2ctAuYrr3g0G4Hq0qyZe6YTtGkaMyHIrdM0DANYmLDYEAwTywTrsAIRrs4sHJ2oyjlTUoiQ7gc2Fm7QWsSSayvD+MOuoQyTQAQL4xwIaA2QDw/niGSZ7FxX5ZyYOtELEvpImdGIeRdLrq0MYxd2DWWYOMCJjsjpziwcnajdMkYzUmasubYyjB1YmGCYOMCJjsjpzkz1fBs3eqYCVDr5UMoYLg1MMGCvgWkWzabDH6yH8anmOsowsQgLEwwTB6Aj0jwkrBKuzixYJ6rx8svqGUBVzxlODQyEHgg/U6eqCUFORcW0el2GiSQsTDBMgoMRu0pnZpVAnagRKnYNVs/ptHbDbhr0UKNixkL6dSaxYWGCYeIAqO71MRvcglknGopdQ6COGdqZcE0naG6u/gagqsadKPfOnR6vjSlTPJ/wZFERJEK5LsNEAo4zwTBxAFTfGLHaIRJxDrQImIsXe6Y1nHCTNIrmOXeup4MFRtE17cbTiFasCI5RwUQbdg1lmATC7YaJWmjpihWds2vQztm6tecT38OVZCscbq5uvi7DWIVzczBMHKAZJpolw3JLnINIuElCYHA6Ama0YkWEct1o5WFhEhMWJhgmDgiWDMstcQ6CCT3adEGoniX+SbZiNVaE3etyUjEm0rABJsPEGGYugtEyTLSCU26SkSZasSLsXJcNNplowMIEwyjgFh9/IxdB5KK4805PJ9y0aUaPAXwfO1atA49EPSH0PP00USa/tw++Y70T+TOcrocdIciJMli9Lq6BzKpGWp9oRgFlEgAR5xw/fhw/IfnJMHb4/HMhrrwSr+LLC75jfSTB9ZKSfMvhv2TOLES/fur1KFHicj0iVc9A9cD6UK8XznoEa8NwlUH1ukuWBH4+tAX7MYyTfSgLEwxjo+PDOic6PlUuXcrYmQRazAQKnAcdyZQpnk98j2Q9g9UD10InqZXLKpGoh1kbhrsMwa4LsE3l+cB+DOOkMMFxJhgmBnz8VdNYa6A8Z84QZcvmrnqGMx23G+5XtMvA6c4Zp+E4EwwTRz7+Vl0O0amNHu2+eobTxdIN9yvaZeCkYky0YANMhnFZbAEjChe2fsz27e6rZzhdLN1wv6Jdhlj1lmFiHxYmGMZlsQWc4uqro19Pf4+GW24Jn4ulG+6XG8oQriigDBMItplgmCDz38ECLEXCZsJq7g0VmwktQiLq17s30eHDztbTLHASwl+/8Ub48mc4eb+sRpF00zPDETCZSNpMRNWbY/To0aJy5coiT548crn55pvFV1995d1et25daUWqX5588klL12DXUCYUNMt8f+v8SHtzqLr8BfPm0NdLxTvEbj2DeTSgfCqujtG8X3bdO93yzDBMwriGzps3T8yfP19s2bJFbN68WQwYMEBkzZpVbNiwwStMdOrUSezfv9+7WI0XwcIEEyqqPv7hRHOptBtnQu9WOGSIulBip56q7p/nzwd3dYzW/QrVvdMNzwzDJLRraEpKCo0YMYI6duxIt99+O11//fX0FiyGwq2iYRiXq4y1MMnA6FfboQPRBx9knNowmm4IRKFCRKNGeebc7dTTDe6Jodwvp9w73fDMMEyoqPahrkn0lZaWRjNmzKDTp09TrVq1vOsnT55MkyZNoqJFi9I999xDL774IuXKlcv0POfPn5eLviEYJlScThwVimGdv2AAg0XI20Z2BpoAYmXI8M8/HkHCbn2j7dEQ6v2y4t4Z6BpueGYYJlJEXZhYv369FB7OnTtHycnJNHv2bKpYsaLc1qZNGypVqhSlpqbSunXr6Nlnn6XNmzfTLLwhTRg2bBgNGTIkgjVgmMhhJb12oDwNTnX0RqNvN3g0hIIbhCGGSQhhYuvWrbRkyRI6dOgQpaen+2wbOHCgpXOVL1+e1qxZI1UoM2fOpPbt29OyZcukQPHEE09496tcuTIVK1aMGjRoQNu3b6erTfze+vfvT3369PHRTJTA0I1hHCZaamzVEW+wEXaoHb2Zt8bIkYHTjGt1gPeIG4l1YYhhooFlm4kPP/yQunTpQgULFpRTD0k6h3H8v2rVqpAK1LBhQykofIDJXz8wBQLtxYIFC6hx48ZK52ObCSYcmHWkCBjkFj/+yZOJHn7Y+nGoBzKNBhKMzKZP8DrAulatiD77LPB1sK8b4x64yb2TYZRZuZJo2jSiJ58kKluWXG8z8fLLL9Mrr7wipxzCATQdepsHPdBgAGgoGCZamHWk6Hyw3i0dJGwf7HD2LNHcueZ1UElzHUyQ0EA6bEzbhCvuQyhRJDVjVyM4iiTjCnbv9owY9PHZ//iD6KuvIl8Wq24iiAexfft24QTPPfecWLZsmdixY4dYt26d/J6UlCQWLlwotm3bJl566SWxYsUKuX3u3LmiTJkyok6dOpauwa6hjJOEO+ulk0yaZC02har7o9WYF06lw450Kni42GbKpOZ6yzARAy6a7dqZ/6AWL46Ka6jlcNoPPPAALVy40BFBBjYX7dq1k3YTsIX4/fff6ZtvvqE77riDsmXLRosWLaJGjRpRhQoVqG/fvtSyZUv64osvHLk2w8RiIiejENX4boR/OGVVNO0CtAZG53ba8FDlfJo2yL/tNW1QAJtsW+B8I0ZAU+q7Hu2B9U5fj2ECcukS0aBBnjm2vHmJJk703Q4Px8WLPT/e+vUpGlie5ihbtqx0z/zll1+kUWTWrFl9tvfo0UP5XB9//LHpNhhNwhCTYdxEtC39rdhqaBkk7RhhBnJ/dHqWMdj5Llwg6tzZfFoF71er0yWBgMCgs/02BNuduh7DGIKHe8IEosceI1PGjydq39482Y2bDTCvuuoq85MlJdFff/1FboINMBkniWZApkBGj8DIVsNOnAk9U6Z4cmlYMVBURcWQEeWHPZmK54dTbY4BXsOGwfdbtIioQYPQr8cwPnz3HdE993iS6xgBDcXzzxP5DeTDRdgMMHfgl88wCQpG+wUKEB05Yr4PttvJehmIYEaPZqNzs0BXiHKpYqBppDXQGyhq3htWUUmHbVUQckobBIFRdT8WJhhH+PNPz8O+caPx9nbtiN55h8jFUZxDSkH+X24P50rDMIzjthoQKODqiZE7NA34xLlCSQVuluZalWDpsO0E3GInLyamOHiQ6O67PT82BGr0FyTq1CHatcvzI/jkE1cLEraFiYkTJ0p7iZw5c8qlSpUq9OmnnzpfOoZxGeisA2klALY7ZYCpGVt+/nloo3Mt0BWmLPCJ/B3QLgB/gUJFawAgCGBKVxUEptWEGSg4A7nPWgm4FUzw0doQcTdQJ3wGMlxVnSrhUNmMZc6cIerWzfPQFi2a0YWzZEmiFSs8AgRsBvE9RrA8zTFy5EhpgNm9e3eqXbu2XPfjjz9S586d6fDhw9S7d+9wlJNhEs4A02qCLv/RebCYDGZTINAamOX68OfQIbVyYQrGSnBcq+1nJvgEakMzw1UICSpTWZEWJjhxWIySnu4JC9uvn/k+COxy770U01j1OS1durT45JNPMqyfMGGC3OY2OM5EbKBPke1kOmqnUY2xoBo7wWoKbNX4FlZiMoTS9uFqD9XzFipkHmdCpQ3N4mlgXaDjIp1KPNIxNhgHmDkz8EP09ttCpKe7vqlV+1DLwkT27NnF1q1bM6zfsmWL3OY2WJhwP7H0otSCVpl1Uk4ErQoWGMvomvpO0awTDRaMyk3tEey8miBx/nzg41Xaz6x8aKfixaP/XEbyfjIhsny5EIULmz9sffoIcfZsTDVz2ISJ6667TrzyyisZ1g8dOlRUqlRJuA0WJtxNLL4otTL7l9upMluNMInOULtmNCJ0hqs9AmkWsH76dHONitU2NNOcRFtjFksRVxOWv/4S4qabzG/SffcJ8c8/IlYJmzAxc+ZMkTlzZtG4cWMZ7hoL/s+SJYuYNWuWcBssTLiXWH5RGmlT9J16KKDjUukAu3e334mGOg0TqfZA6GqEsNafF9+bNQuszVJtQ23B/tEWHIyI1v1kgvDvv0I89JD5DalWTYjNm+OiGVX7UMsGmAhp/euvv9KoUaNozpw5ct21115Lv/32G1WrVi0cZh1MnGLF3dFtlvMw2kNMh3AknVJ1cWzZMmO72DEQdcKwLxztAePJN97wPAd6UF7Yq/mjT7Rm1U1061ZPIC63ZYGNdsRVxi8UKwJGvfaacbPky0c0b57zQWZiBMvCBLjhhhto0qRJzpeGSShUX4CaW2Q4MkSGguZuGQpGHbkWBjtYCmyjd5ZqJ6rtpxKe26iMwEhwcErosxNnQh+8a9u2wG3o75kxeHD0ssAGEuas3s9oEpfeJngoxo71xHM3A77GbdpEslTuRFXNobq4DZ7mcC9W57XdapTp5NQAjAp79RJiyBB7dghWDCJV7FWMyliggGcJ570JNTMpjlf1iPGvSySn2oIZH0fC4DcS9Yg5vv464/yafoHdYLQbPRZtJpAWPFOmTAEXbR+3wcKEe1Gx2LfSkcYSKh2dUaetYoegYhCpYq8SqJMN972xavPgv+B4rS3M6om2hNCmKpw4TSBhDp8oG+qhlTFcBr/hrIcbyqfMunVClC1r/hA8/rgQJ0+KROO4ojChlOjLSvbOunXrkpvgRF/uRsu/AII/iWrJodyOlihLNRgVIkeWK2dNdWw0fYEokVowKtWEZVZw8t6EWj59Ei5N/Y5pC+QjQV4ShAFHW06frqahNkt4Zletb/UZwFQM0AfS0t/PaBGsHq7/ve7b58m6iQfGCGR8Qyjr1FRKVE44mejLbQICEz+YRWE0w81GmapYDRX90UfWX8b+BpGFC1+OWImOGh2r0zh5b4LZjVghkC2HXZsEM1uTUaOIChYMLmBYeQbA0aP2BctwEpNG1KdOESFSM35YRpQt6zHUqlIl0iWLaZSEiXXr1imfEHk6GMYK+o4Pv+F3341v63UrZQ/lZax1ouj4OnTwfemjwwsXTtybUDOTqob5tmPsapbNFO37wAO+68w8Qqy2Ea5lV7AMJzHjbQIVCrwwXnjBeDsa9MsviZo0iXTJEkuYuP766ykpKSlohlDsk2aWPYdhFEePKsKEG6zX7WKn7HZfxmYd3+HDFDacujdWtVbBymA2LWEmtBglPLPqZWLmEWKnjdw4yne9twnmp9q2Nd8+ZgzRE0+Yp89llFGymdiFNKiKlCpVitwE20zEFtocbLCRoptGZ07X0Qhk2rTagajOy5t1oikpHvW6ahnDdW/0QgCmazDFjaluK89HMBfYYDYmodhyGJXJzjMQyH4jWrjy94qHBUmzjh0z3v7cc575IqTOZZzrQ0Wcw94csUe4w1W7uY5Ouv5ZSZZl5DGiWkYjTxH/SJLBoktaiT5p9fkI5jkDbwmVMobqZeLvEWKlffXLCy+4J0Kna36vW7YIcf315o3WqpUQR49GoCDxR9jCaWv88ccf4uuvvxZz5871WdwGCxOxSTjDVbuFQG6Lqi/jQB2gasc3aZL5OVTjTOgFEKtxKezEKFB9PlQTfqnGRAgl/oXmrqoHYcHtns9NcRyi8ntFvgvkvTBroJo1hdi+PYwFSAyOh0uY2L59u6hSpYo3rgQ+9XEo3AYLE7GLG3MlhKuOCFRVsKC1l3GwTtipvA6qmgYradM1QQm5N+zGKFB5Pqx0/iqjaKuxUQK1M+puV5DQt5EWjyLav5GI/F6RcbN3b/NGQcZOZO5k3C9MNG3aVDRr1kz8888/Ijk5WWzcuFH88MMP4qabbhLff/+9cBssTDCxgh1Vf6BOOJLRE62mTdeuHyjIoBPlszItoXo9q9MTRudF6vRAdY8HbYVjpKcL8fbbgSs+Y0a0Sxm3hE2YKFCggFi7dq38/4orrhCbNm2S/y9evFhcjzkrl8HCBBNvgoWVbKuRms8ONfy1lRG9ShuFUi6VaJfBpqiCtfOoUWplueMO6wJavNgVCUybB6rsiBFCpKVFu5Rxz3FFYSITWQSun3ny5JH/FyxYkPbBrPo/L47NmzdbPR3DMH7AswAW8vAaQHRGfOI71lsNFKS5VyLiox5Y2DuZwCqccQSMzh2sjfzjSFjx/FOpC9pt506Plw28K/CJaJq4lko7b9+uVharHou49wDJzmLSS3/lSqKSJT0VR/AZf7p2JTp92lPRp58mymS5C2PckjW0UqVKtHbtWrrqqquoZs2aNHz4cMqWLRuNHTuWypQpE55SMkyCYBYXQh+v4Px5a51iONOlRyKOgFH0yWBtpHXe+jgSdq9nJbImrqvSzldfrXYNRLtcuJAs4cZ4FAFBYR9+mOj7742333UX0bhxREWKRLpkjNNxJvR88803dPr0aWrRogVt27aNmjZtSlu2bKECBQrQZ599RvXr1yc3wXEmmFhJqaya52D8eE/KgGAg3QDKG4ny24mbgPpgSU+3Hp/Bai4ICCA9egQOIx7JmAgXLhDlyhVYe4AynDxJdM01sR+PIgMnThA99RTRxInG2ytWpLRpM+iHIxXjK6V5DBLROBNHjhwR6TCSscjo0aNF5cqVRZ48eeRy8803i6+++sq7/ezZs6Jr164iJSVF5M6dW7Ro0UIcOHDA0jXYZoKJlZTKqvP7ixYFn6+HK2bx4pEtv924CYEW//KG4qECmwqzLKHRsDUI5s2B7aG0azgynYbksXHxohADB5oXOFcuz8MdjynNY5iwx5lwgnnz5on58+eLLVu2iM2bN4sBAwaIrFmzig0bNsjtnTt3FiVKlJDGnStWrJDCxi233GLpGixMMLGSUlnV8wD72XErtOtuadXLRCXOhF1hwkobxUIME9xHf68OfNcECX2ZVdvQSS8d/zJY7uAxyBw3LnCBx4/37BdvKc3jBMeFiX379snOXqN27dqiWrVq3uXGG28Uf//9d2ilFkLkz59ffPTRR+LYsWNSsJihc/n5888/ZaWWW/AjZmGCsYIVTwk3aybslN9OwCmrAgkCZPnH07BS1nDGzogWcBOFd0f37p5PfPcH5fPXNNkVGO1guYNfvNijaTArKDQUFy646vfHREiYeOGFF0SXLl283xFjokePHmLw4MFyqVmzpujbt6+wy6VLl8TUqVNFtmzZZHRNaCNQgX///ddnv5IlS4qRI0eanufcuXOy0tqyZ88epYZg4gu7nYVTnZXdMqvEhYAwYUeQCFR+OwGn7HRYobpqRjJ2hpuwEhrdaUFCuYNfv1GIihXNd2zXDj2Ta39/TISECcSQ0AelgjCBaJgaCxYsEBXxIFlk3bp10h4ic+bMIm/evHLaA0yePFkKFv7UqFFDPPPMM6bnGzRokKy4/8LCROIQynyrE2r0UMseLC5EKPkhjMpvN+CUnU7bTtkRHdRqG8UbVkKjO02gDr4QHRRf0l3mO9x2mxC7djlez3D9/pgIxJnYuXOndAfVuOOOOyh37tze7+XLl6cdMIO2CI5bs2YN/frrr9SlSxdq3749bdy4kezSv39/aXWqLXvgdsQkDJrboL+1v+Y26B+HwG0plVXiQjhxbf05gsWtMELvfmj3uqogc6f+vkUqdoabUG03/zZxAv+4GznpDL1D3UlQEh2iInQ3feW7A9KtrljheUjg7om4EYpE+/fHhIBQBNqDVatWmW7HNuwTKg0aNBBPPPGE7WkOf9hmInFwYr7VLWp0lQiYdrwmjMofiqbD6gjRTtnN2txNdg/hJprPJdo2idJEXxoR8EatGzon5Gu55ffHhFEzAQ3Czz//bLr9hx9+oGvgEB0i6enpdP78ebrhhhsoa9astHjxYu82RNjcvXs31apVK+TrMPGHlciQZmhBjoyiD2rfMVIOh787Yg4sXUo0daqnjPCrf/BBzzZEV8Q27KMvYyBUyx/KKM/oWMRQwDUQRgCf+K7SvlbvmxY0CrEU8BnPMQhU7nlYnsvPP6fb6yVROmWmN6hfhs1P0duUidKpZAlBFfsbRKy0iJ3fn/53o/1GmCggFBk+fLiM96Dl5dCzZs0ambMD+1jhueeeE8uWLRM7duyQthP4jgykCxcu9LqGQhPx3XffSdfQWrVqycUKrJlwL06PLJ2cb420+6AdT4pAboVWyu+ktsCKq6NVOw2eJ1dv35D45RchihQxvRFvUm+Rnc6G3XtE5fnleBQxaIB54cIFUadOHZElSxZx5513il69eskF/2PdbbfdJvexwmOPPSZKlSolDS0LFSokpzg0QUIftAruorly5RL33Xef2L9/v6VrsDDhTsLxEnDaEjxSavRwpe62k4VUpRxmHYhqECaVIFKh3Ld4JazxF/76S4iaNc0b/777xBfj/4mogB3s+eV4FDEctOr8+fNi2LBhomrVqiJnzpxyqVKlilwHl0w3wsKE+wjXSyAW51vteFJoo1GrWgOntCNGHYhKSm1s18dQUK27G+9bpAlL/AXYoz30kPlJkQV682ZX2qlwPIrIERMRMCMBCxPuItwvgVhzG4xG6u5A2I2AqZpSG/vZqbvb7lukcUzrBmnuuefMT5AvnxC6EABuheNRuK8PtZw1lGEiZSRpJ+Oh5jbYs6fvdeA2CMMtt7kNRjp1t51MmCDYvVBNqa3fT7V8SKfttvsWaVTbynA//KjGjiXq3Nn8wMmTPbncE6E9mLDAwgQTdy+BSKTcjsXU3eFENaW2fj/V8uFeJjq24i988w3R3Xebuze8/DLRc8/5/DAinSnXLhyPIg5SkMcanILcXcB1q1694PstWWJPMxFr2EndDfCCR+puo2MCpdK201nAtXP0aI9WAcJA165E2bL57nP2rCeldjDOnCHKmfPyeRFk6fBheynB3d7xOVk+leekUCGivQvWU9ZWLYi2bTPe6fHHiUaNIkpOzrAJgcGMNHpw1XSbZihYe0QynXy8cyKSKcjdDNtMuItYNJIMN7AHULUd0NqtVSvrNgZ2PGhUXRGtzmGruoaalc3tLoHhKJ+ZPVAx2isWUkPzRmzYUIi9e5XObfS8udHWKBbto2KVsBhgwvWzTJkyYuPGjSJWYGHCffBLwL4woZrO28gf32pnYcXV00qMDyuusGZCkZs7vnCWTxNSctNJMZYeN224k0WvRgCguPeMcFM6+XglbN4cqampLEwwIcMvAXX3SGSCRAInxGRQjQOhf/nb6SysunqGI326Ubnc3vGFtXw46JVXTE9+kTKLJvSV5WvEumeEW9xV4xXHw2lrdOvWjV5//XW6dOlSiDMxTCKDOdidOz22EVOmeD4xv+m2udlwo5Jk659/iJD77t13Pa/1YPiHn7YTZhw2EsHCEmM79gOwB8ActVmIbKxH/iegmlTMqFxOhEwPJ2Ep3/r1HkPJLFmInn8+w+YnaQwh4HVWukQL6E7L14h1z4hECqseV94cv//+u8yXsXDhQqpcubJP5lAwK1haRoYJ4oaYSKi+oF991f657XQWVl09tZwKyMwKwUEv9OhzKhw6pHZes3K5veNzrHy7d3ukbCwQJvx4jZ6lgfQSXaRsIZeFPSOYqAgT+fLlo5YtW3LrM4zL3TehzYD3TOHC1sui6up5/LjnGtBMqMT4wL5W0ZfL7R1fSOU7csTTgIj5oFcrwHXmrruI2raVn0t/y0X96zlXFk2rFMwzAvsx7iUt2t5NIs5hA0zGzYSSTlx1wfmTkwPvA6NOqzYT/tfQjN6cSp8eyGbCrd5Alst3+rQQ06YJcc89QmTN6rvj7bcLMXasEEePhnYNBdgoOrb5PIzeTWENp33x4kXx7bffijFjxogTJ07IdXv37hUnT54UboOFCcbtWEmyFa5FEyb0gkAw91O7ngoq9Q10PpWOzymjPDvnCVa+WdMvCrFggRCPPJJByjtR9nqR9tpwIXbvDukadjoRNoqOTT4Ps3dT2ISJnTt3igoVKsgsnpkzZxbbt2+X63v06CGefPJJ4TZYmGBiwUrcTkpupxd4i/iXAX1dpkzqAoXqiDhYfYOl1Q7U8Tk1SgvlPBnjc6SLWpl+ET9Uf0qIwoV9Trorc2nxMg0Q19Iflq4Rjs6fPSNii0sR8G4KmzDRrFkz8fDDD8sMosnJyV5hYsmSJaJs2bLCbbAwwdghGkGRtBf5Cy9EV6jwfxlZPcZKivdAKciDjaqMOj6nRmmhnEd/bDnaLAbTQLGFyvqeqEABsa1JV3EL/SQFDbtl5c4/sVkSAbfesAkTKSkpYtOmTfJ/vTCxY8cOmZLcbbAwwURbbWj1hR8JO4pwLqinCrDLKFjQuVGVU6O0UM6DddWL7RO9aKT4nW7wOfAU5RKTqI1oX/BLcf7UBVfHy2BigykWgsW5Ls5Eeno6pRk4oP/999+UJ08ep+xCGSYq4NGGNwJ+gv5o65DFMlgMBg14SiOHAPKRICkjPvE9kAe15mYJ/OM2mMVxcBMqXgSoPzwEzPJyaO1tJV6CUzEebJ0Hbi3jx9PxmnfQb/uvpFHUh26klXSJMtN8uova0iQqQgfpYZpMnxy+m0Z/mNXV8TKY2KCYi7ybLAsTjRo1orfg5/UfSUlJdOrUKRo0aBDdBfclholhnAw6hA4TcRf8zwcXPKwPJFBobpZIhKUHHfCdd5JtwimcaIGpgrkQau2CYFwqOB1bIth+quf58vPzRLNnEz3wAFGRIkSPPUYpKxdRZkqnn6kWdaN3qRjtp6Y0n6ZQWzpNyZbjeLg1UBTjDm5TDBYXCbdey8LEm2++ST/99BNVrFiRzp07R23atKHSpUvT3r17ZWRMhollnOqQnNBwmEUJbdSILIEXCpZ+/YyFkyFDyDEwzgjk2x6oXSI1+gq2X6DtiDRZl5bSWOpEz79b9LLUd/480bXX0l8dX6YytJ1q0880mrrRYSpkeB5op1RQjRHCJCaZFbSYwX6TUU1BjlDan332Ga1du1ZqJapXr05t27alnFpuYRfBKcgZK8FdnEqRbuc8qkFnkLob6b5Vp1owMtGCRhldA9hJg65HNVW1arvYSSNtJy21WnsIqkprqS1NptY0la6kvd5z7s+USoV7tKbM7doSXX89paUnKZXho4+IGjcOXqdFi4gaNKCooJJ63lVBkxKYWQbp4/W/e9elIF++fLkYMGCAePrpp8VXX30lYgE2wGSseGk4FRDIqmGUVe+RYBk9ESPCidgIKgs8MlQNBVXbRVvsuHOqxl8I1OZYStNfYgC9LDZQRZ+d/qW84kPqKG6n70QmuiSNSP3PG6wMkTCcCwXV1POxkhI+EbgUJld2x705ZsyYITJlyiRy584t8uXLJ/8fMWKEcDssTDBWvTScCAhkxWXLqvcIXhLBAkoFitHgVKwLOG9Z7SxU28WuMKEaf8GszQvRIdGN3hU7Um/x2XCWsosZ1FI0p1kiG50zvFfBBBV9GdycqdNK6vlYSAnPuEyYqF69ugxKdek/cefVV18V+fPnF26HhQnGjrtfqAGBVDUccI+0Wq7ixYN3Qvr04HZGNxMmqHV2Z85YP38o4bSt1iNQSG/tOrnolGhNk8WXdJe4QFm8G9IoSXxLDUQHGieuoGOWyxpKWPFouYZaTT3v9pTwjAuFCWgktm7d6v2OoFVZsmQRBw8eFG6GhQnG7kgwVLWhiobDCQ2G2TJqlP17362b2jW6dLEfblq1Hk6PznG+LHRB3EnzZdwHxH/QXxDxIXrTm6IY7VUuo52yujEfBp4ZK8+WmzUsjDM4HmfizJkzPsYX2bJloxw5ckgDTIaJFy8NGM75p0hv3drzadWYLJB7J9Zju5VyWfWCUHU/NGLrVrX9xo2zFkND3y4pKRF2j0Tj/fwzpb7ajfZRKn1Fd1NbmkK56Qxto6vpJXqRKtCfVINWyDgR+ynV8iWslFXl+QgHMJSEIezUqZ5PvSGv1dTzbk8Jz7g0BflHH31EycnJPl4dEyZMoIIFC3rX9ejRw9kSMowDqLoN9u5NBKckp17kOE+zZuZW7qrlQkyGQPEvjFBNI25EuXJECxcG3w8ekUYxNIJ1htiWNy9Rw4YRCLiDXOzwrcWyYwdd89/qg1SYPqNWNJna0m90k3T8DBWrZQ32fETC6l/viaP6zGj7uSloEhNdlF1DEUsCAaoCniwpif766y9yE+wayqi4DWpoj3g4R4Z23BmHDSN6+GH186IzOnMmsCtfIBDQMV8+e8equnTaceVUBifF0HvyZKI1ay6vT06m9Ob3UfsFbWja4YZ0yWA8hetmyqTuehtyWSOEFizMv631z3zTpsHdjvXPVljvIRNTfaiyZmInoucwTIwHd8HLNBB4IeIFiIBSGDHafQGq+tzry4Xr6l/I+qAzqlMCGn36+AoSVmIAoNPp3Jlso48SGigWB+jUiWjQIPPt/gF39PWAQnT9ek9HJeMgtP6Xsn3xuUeAWLbscmNmyULUpAldatWWxuy7lzbvyUVZ7yFKm+DRRRi1OdrvjTcu1ycQkQ4OZIdgQdT0zzzqPmKE2rOl+vy6tV0YBxFRBB4hN954o0wYVqhQIZmRVEsiplG3bl1p/KFfrKQ6ZwNMRg+M2gIll3LCaMyOz30wv35VLwijWABWymPVONJujAQ7KciNjslOZ0VLmiFmUXNxjrL5brz1ViHef1+Iw4cN2xep1ZFi3cxjx+h6BQp4FrNj3IpVQ0kn4kzEQrswUcwa6iSNGzcW48ePFxs2bBBr1qwRd911lyhZsqQ4deqUjzDRqVMnsX//fu8SrFJ6WJhg/Jk0KfTO0Aw7PveBOnCV+Bfa0qFDRndQK+UJ5uZndTETxlQEFrPYH1IIoEuiAX0rPqZHxTG6wufAdVRJzK8zTIidOx0J8GXk0ROLab/tBMnCswSvje7dPZ/BXI1jsV2YOBEm/Dl06JAs9LJly3yEiZ49e9o+JwsTjD/hcmez43MfzvgXVs9tNaBUoPMWKuQR2oLFeAh2Hm8sjuLpojqtEG9Sb7GXivnsuItKiGH0rKhMazPEQbAaNyFeiUcXThZeIkNMChOIY4FCr1+/3keYKFiwoChQoIC47rrrxHPPPSdOnz5teo5z587JSmvLnj17lBqCSRzCFTDIzgs7nPEvrJ7baqhrra2C7aOfUrEqsFxNW8XPTYaIP6m8z4YjlF+MoSfEbbRMJFGaaRwEq3ET4hW3BsmyC4fvdp8wYck1NJykp6dTr169qHbt2lSpUiXvemQlLVWqFKWmptK6devo2Wefpc2bN9MsE2f2YcOG0RAn0yAycUe4jMbs+NzbOUaLf+F0eVTd9woVIhozxvO/v5uhEXp3UX9XUiMK00FqRZ/JxFo16TeiBZ71ZykHzaN7pSvnAmpCFylb0DgIVuMmJLIBcqwYSpp5pai6JTNhwqqUsnLlSrFu3Trv9zlz5kjDyf79+8uomHbp3LmzKFWqlNQkBGLx4sVSStq2bZvhdtZMMKo4bTRmRROgaRdeeCF86mermgkVI09MX+h/5lo9MKURyLBVG/kuWmS8PZlOiIdpoviaGouLdHle4hJlEjvLNxKP0CdyH1WNBmsmjLFqWOk2OHx3HE1zwPti5syZ8v/t27eLHDlyiNatW4uyZcvatm3o1q2buPLKK8Vff/0VdF8YZ6JiCxYsUDo320wwkZp3VVUlz5hh3W7ArFyh5H/QpiD0xwQz8mzSxDgfh6rgAmFCK1NWOi+a0jwxlVqJ05TTZ8df6CbRk94S1VP3e/OXqHqZWLWZgFeH1RwjsUg8JOSKR9uPhBUmrrjiCq9W4LXXXhONGjWS///4449SILBCenq6FCRSU1PFli1blI7BdVCxtWvXKu3PwgQTSfBCDvSSwwjQquul2UteZd44mOcEOlIVF0z/pVkzm94Ck9LE0qHfi/fpSXGYUnw2bqZyYiANFmVpi3ImV7M21hPMm8Oo3eKNeBnRuz11ezwSNmEiT5483o6/YcOG4q233pL/79q1S2oprNClSxeRN29esXTpUh/XzzP/DRMgtLz00ktixYoVYseOHWLu3LmiTJkyok6dOsrXYGGCiaQVeDBhwj9GgV1hwsooU0WA8e+A0Sa1a6sLFMFGjJVonfS4OFukpM+GfVRUjKRe4gb6XRClB5xuUk2RbtReRup9o7aLV4EiXkb0qvUYMiTaJY0fwiZM1KtXT7Rr105MnDhRZM2a1ZtJFAIBbB6s4B+MSlsQewLs3r1bCg4pKSkie/bsciqlX79+HGeCcaUVuNNxGsxGjFZGmapBqPzdIyHPq5RPmx4wmlIpQbvEM/SaWEuVfQ46kZRHjKMOMlYEYkYEs8vQg/UqthlGAiLKmjdv4PrEwug8kUf0qsHb4lkwjBthAtMLlSpVktMdgwcP9q7v3r27tJ1wG6yZYDSCdawYzYTSkTgVpyHYiNGqfYLqNfTukaopyLGfvn1T6Ih4ksaIZXSbz47nKavYe1NzsWHQdJGDzliqr502Njo+XkbndoinugfT/gUTKhmXuIZWqVKF1iMgvh8jRoygzLHgV8QkJIFyE2ggR8SHH17OoGiVcKZZtuNOivTSVjKN6t0jVVOQy/2Q9emLL6jF5MnUPMsCynTpotyWTkm0jOrSgvxt6Na37qd72uWnZVOJzimc16yOoaS81qeXD4TqfrEEcrEg4VawhFzYz+3gtwnv/0A5Xazkh2GcwVaciWPHjtHMmTNp+/bt1K9fP0pJSaGNGzdSkSJFqHjx4g4VjWGcAy8VlY4V+9j1VQ9nmmX9ucN1HX366WApyDPTJWpAi+mlXZOJiswmOnVKrs+EF3nVqvTXzW1pQ6WHKG+lEvSqjZTr2M8oOVkoKa+Rxl0F1f1iiXhLyIXnM9oCPuMLfvuWQOCocuXK0euvv05vvPGGFCwAgkj179/f6ukYJiJYfakgg6KVFNT60Z/2cvYH6wsU8Hya7WN0TIkSviNGlevgGCsjMnQiXbte/m6cNVJQDfqN3qKe9DddSd9QE6q5+VOPIIE81AMGEG3YQOkr19Ceh/rRmQIlMpxBtezo0HHKevUQuM7zie+HD6sdr28v3EdoaVS1LQjKZRftWsh+jk+rz1A4gXAMIdl/vIf2jLVAT6EIleEgzcX3PWJYnD4RDRo0kEaQANk+EWsC/PTTT5YNMCMB20wwdu0Z7Mwfm7kw6r0sVL0SgnlzBDtG1VgNi1HQInhrYFs52iwG00Cxhcr6HHQ8WwG4ZMFfG37elt1VzdrIzPtEv121vay2dSh2A7ES4jkeclq4KTx4rNx3V8eZ0AsTO3fulB4XboOFCQZY6VhDtWxXiaypf6HD8DMcwoS2n0qdM1xj3z4hRo4UW/Ld6LPjKcolJlNr8VLNL4W4cMG2u6pZGwUL6IXz5MmjJhjZSadutwOKh4BQsYaK4B6pMlAc3/ewCROFChUSq1atyiBMLFy40HLQqkjAwgSjYbVzCcWyXXX0F4lMo+igA8VY8O5/5JgQ48YhgIwnmtV/O6Rnziw2lLxTDKs8SfTudNIwWqTdevi3kRMeMagrymhFIxHKyz9eAkLFIk6HxLdCotz34+ESJjp27CiaN28uLly4IIUJhMBGwKpq1aqFlCo8XLAwwejBS6Z48eAdS6ReApHINBpo/2x0TjSj2WI63S/Ssmb33VirlhDvvivEwYNhqYcRdjKXGi2qrq1OqKXjye0yFonWtE2i3Pfj4XINffPNN+n++++nwoUL09mzZ6lu3bp04MABqlWrFr3yyivhMexgGIeAkVmzZkR4VI1cyyJt2R6JTKP++ydROtWh72VWzvtpJuUnjxE1waOzQgWitm09Vo9lygS9huZx8fnn1usRToM5VWNLDbz27RKKuyoTOqpZdJ2G77svloWJvHnz0rfffks//vij9Ow4deoUVa9enRo2bGj1VAwTtZfPwIFEyHTvn0Iblu0QJCJl2W7HKt3qMZ5PQVVprRQgWtNUupIuB1PYS6k0lVrT7WPb0o2PX6/saoJU0CopyM3qYScegh03VxX27Qu/S3CkPAuYyMD33ZckqCcojjlx4oQUgI4fP05XXHFFtIvDuAyjWAaR9LXH9eHyGCyY0I4dl8tl6ZjdOyh90hTa+tIUKn9po3efY5SXZtL9NJna0g9Uh1JLZPa5hooggc5X9e1hVI9g5wb682syDj7T082Pz5SJqGhRj4BgBStlDPUeMrFPotz3E4p9qJJm4u2331a+cI8ePZT3ZZhEVZGGEkxIf4wRKeIwjakynY5WnEyFtvwsg8mUJ0SezE5fUlMpQHxNd9J5ymF6Db2QVbiwZ92hQx6B65ZbgkcTtTp15C/UTZ9O1Lu3sdbol1/M4mB4gKBhVZAARlETVYTNUANCRVugZewRb4HAQkbFAKN06dJKy1VXXSXcBhtgMvFqlV6jxuV9c9Ep0Zomiy/pLnGBsng3pFGS+CF7fbGi68di3sR/la4RLDZDoERbZt4VRnEsAl0P36dPNzesM8oCCgeU5OTQjTc1l2Cr8QPs3MN4j1GQCETTo8RNfShPczCMS7AyQm3enGj+3It0B30r7SCa0xzKTWe821fQDTSF2tA0eogOJKXKdbAHgPFpoGtYnb5QBSM1I3sEs+tpI7tANgwXLhCNHu3JKQIbieuuI2rUKPSyLllCdPSovXJZuYeh1J1xF2lxrF1SneZgYYJhYgkh6PzS5fRh/SnUij6jQnTYu2k7lZFTGBAiNlMFy/O32hywFYNKVQLZfphdz+qcM0IZwwkl1DJu2+YRTpwqlxFO151hYsJmok+fPjR06FDKnTu3/D8QI0eOtF5ahomzUYbjI5U//ySaPJloyhTKvmMHdf9v9SEqJLUPECB+pZrS8VPVHsBuMjQ7GF0/2PVUymzXWyLQHPfPPztbLiOcrjvDRBslYWL16tV08eJF7/9mJKlmL2IYl2Lk7ogRopW05E6cQwIzcQy3IUSsWeNdfSZzMs1Mu08KEIuoIaVZ8PAO5BsfiTgIocTLcCrN9ptvYoBk7hKMJneyXKEcy7EpmFhB6S20BJOIBv8zTDxhNoeNzkk1BkHI50AWXuwEAWLZsssnypKFqEkTGVDq+SX30ltjc9mqY6DReyTiIIQSL8Mp63q0PxYzzVEk4gdwjAIm3mCbCYZxaA7b9jnOnSOaP5/EpMkk5s+nTBcvXN52662eiJQPPODJX05EZ88S5bIhS+DaW7YQffDBZaNFpB3Plu2yQSPOG470ySHHy7AwRWSkGUJactVgZJGIHxDKNfwNT/X3kGFcb4D52GOPKV143Lhx5CY4aBWjwtKlRPXqBd8PijmzOWxL57gtzXMANBCIRX3ihHf7eqokDSmXFX2I+r1X2rADhDfH3LlkiRw5PB2RPuATOiqo/IcPVy+/XVBNI2+Oli2tHRMJm5VggbOc8LSwc41nnoFdmq/Ap7+HDONqA0wwYcIEKlWqFFWrVg2xKZwqJ8O4AifmsIOfQ1B1WkVFh08majPN54DdVELaQGBZT1XkuqSD5lMjc+aYCxRZsxL9Z+LkAxQg/qBT0gJAVatGrmPDBqLz560LBKEGI0N7o93DGW7d6jUgSBgF69LfQxYomGihrJno1q0bTZ06VQoUjz76KD388MOUkpJCboc1E0y0NRNlaLuMBQFRoQJtvrwhf35Kv/8BenBOW5r1z60kZKxKayp1THn06+dJbFWuHNFrr3lydUF9bgWc++uvnYnT4JRrqD+2jFhjIH6AyjVUpqBwzJkzPOXBRKkPtRIJ69y5c2LKlCmiYcOGIleuXOKBBx4QCxYsEOnp6cKtcARMRgVEV0QUO6Qft5uWXH+OwnRAdKe3xXKq6XOis0k5RNoDDwoxd64Q5887nsZY9XxGy5tvBm+DAgUu/2/nGnZSqeuvj2XIEPN005FIRx2NlNejRqm1EfZjmGj0oRmHQgHInj07tW7dWmYN3bhxI1133XXUtWtXKl26tMweyjCxiuYJAPw9nFXj7Gc+c5Jm3fcpfSWa0F4qTu9QD7qZfqU0ykTfUCPqQBNo4cSDlGn6Z0T33iuHkE67CIbiSgitQbA2GDvWY8dgVylpxzVUQ+sykToewamgBYJmA7YHAJ/4jvVG250gEtcwAsaWTu7HME6TyfaBmTLJuBKYJUkLh/k3w0QYbQ471RN92kvx4gEM7qB//uILooceIipShGq8046a0DeUhdLoV7qJetJbVJz2UqcS39C9n7enex++IqwugqG4K8IzQGsD1Nl/ikFrA4TkhjFnpFxDA4HpHBhwovnx6T9lornkOtHZawaT/tfAd6euEWpKdaup1xkmKq6h58+fp1mzZkmPjR9//JGaNm0q7SeaNGkihQs3wjYTjBWUAk7BHeKnnzyeGDNmeBI5aMBwoW1bSmvVhn44UC7oXLvTbojBzqc63x5oHt+O14cTNhN2iUT4a839NFzhr9lmgokbbw5MZ0ybNo1KlCgh3URhjFmwYEGnysswUSdYwKmFIzdQw4OekNa0e/flHYoW9QyNEQ/ihhtk75V2gWjNgsuxAJC226iTCZZO3Goa40CBmwIB10J9rIJA3hB2p1LMUqkHcg11gkChqVUNLFVCjdsNf61SBtwb3KNAqdf97yHDRBRVI4ykpCRRqlQp0bx5c3HfffeZLlZ49dVXxY033iiSk5NFoUKFRLNmzcSmTZt89jl79qzo2rWrSElJEblz5xYtWrQQBw4cUL4GG2AyKmjGk/4GbSVol3iGXhNrqbLvhjx5hOjQQYiFCzNY4Bmlxw6WhtvOMVbTIqPISNNt9Rr+Bocvvmjd8LJvX/Pzt2pl32jUTmpxO+m/J01Suwb2C/U+BUpB3qyZ8XWxnmHCgWofqqyZaNeuneO5N5YtWyZdTmvUqEGXLl2iAQMGUKNGjaRxJ5KKgd69e9P8+fNpxowZUtXSvXt3atGiBf0ENTPDOIR+5JmfjtIDNEO6c9ahH7z7XKCsdPzWu6lQjzZETZsS5cyZ4Tx2YgFAI/LGGxm1CJhNwfqbb7buDqnZNviPeFEOK9ETjaZ97IB8GNDOGAWt+uwzigh6Gw2rYc//+UftGqr72SkD9p83z/hcWI/tnLKciRrCRRw6dEhKQMuWLZPfjx07JrJmzSpmzJjh3efPP/+U+yxfvlzpnKyZYFT4bPxp8SBNE3PoXnGesvoM+5ZQXfE4jRX56GiG0a2e8+czahf8F2zHfsE0IlZcUsMFRsd2XUCNFv96BKu7U4t/G9ppc6c1E1bL4ObnhIlvwuIaGm5g4AG0YFgrV66U2UobNmzo3adChQpUsmRJWr58uamRKAxG9AvDGHLpEtHChUTt21OLrkXoM3qImtE8ykYXaQ1VpX40nErQbqpHS+kj6kTHKL/P6BajfBgjIsskPt99N3heC00zYCcVdSRBOaGR8B81h4J/PVRTnmOUPmSI53+7ylG9vYadNodZjAqq+1ktg1ufE4bRUM9dHGbS09OpV69eVLt2bapUqZJcd+DAAcqWLRvly5fPZ98iRYrIbUYMGzaMhmhvHoYxeuv+/rvHEwP69YMHvT+EPZlL0aS0NjSJ2tJGus7QIwBTBWbq/+Rk67EA3JqKWrWjj0ScCajuW7cmwmvB6pQLnMz69vVV/7uhza2WwQ1lZpiYECZgO7FhwwbpchoK/fv3pz4wa/4PaCbggcIkOEiXOfk/T4xt2y6vRybOBx+Unhi/H7iFnn/gv6GvSfpqjG7N5rpV47bpYwFEOhW1qvdCuDqlUFKQ+9uBIIT44MGebWYaFKz3tzux0+aHDqkdo7qfahk2bvRovQoXdva8DOM4wgV069ZNXHnlleKvv/7yWb948WI5V/Pvv//6rC9ZsqQYOXKk0rnZZiKB2bdPCDwnN97oO8GcK5cQrVsL8eWXQly4ENS6HnPRMNuBJwPmxAsVsj9/b2YzEUoY73B4DoQSllu1HqHWHesRWjslxZ7NhJXrqrbHokUZQ20bhd8OVgb/BfsilHkknhM3Eo0Q5oy1PjSqwgRyekCQSE1NFVu2bMmwXTPAnDlzpncdXEfZAJMxBQ/8+PFCNGzo6weJXvzOOz3SwMmTll5c06c7Zyho5IapGTr6dxTaOjM3QSvgHIHK5X+NcBhHGtXDbt2NBCMreUHstkewvCXFi/uuxzotn4leMMD5zepudn6j/51+TtyIVfdZJgGFiS5duoi8efOKpUuXiv3793uXM2fOePfp3Lmz1ER89913YsWKFaJWrVpyUYU1EwnAiROemA9FigiRPbvvWwfPyjvvCHHwoCs8Gqx0jhhpOvHCREfo36H5L9juP9qrUUO9XiirWQyEQIKUnbrbvSd6TxyrwoT+ukaduZVy6Dt/K0KRmcDi1HPiRszudbwLUG4iJoSJ/2amMyzjMbL0C1qVP39+makUgbEgcKjCwkSccvGiEIMHG791K1QQYuhQIbZvD+kSTo/OVdT24VDlQvWuUj7spwF5XuWYCRM8ZcX+Vt1i7dQ9lHuiaSZCcbM0GyUHE9YCXUOr+wsvqN+nRFD5szusO4gJYSISsDARRyDVPXqvQG/aVas8+zmA03YD/p1apFDtpLCfRrduasdgPyspsh95JLQO0M498RcOQk377i/4qAprZtfQzte9u9oxgWKdxBOh3icmgeNMMIwhS5Z4/C7h59ehQ8btL77oyYSEd0u1avaDEUTIoyEW3PfgKWFlP9XU159+GlrabqttZ5Q+PlQ3Sy1vCdxV8anqwWHE3LmXU5ojTokKieKxwe6wsQULE4w72bSJqHJlT29Qvz7R6dO+2x9+mOjYMY8A8dJLRFmzOl6EcL20I90ZqCaeQvhuuCHCfRTJT1XQ9rOa+tpuanCrbadPnW71HJFw24WQoxo3Az8FeLlrsU7inUi7TTMRTEEei3AKcvdhGusAQ7yOHYm+/NL4wFtvJZo0iahUqYiUUyXts55gWTqdSIVtB5S/SBGiI0fU9kcZkT+kTZvg+yJtOVKUWG0ru+2hkmK9UCGiUaOIihc3jqPhlrTvdjQs/oJRPOP0fWLC3IeKOIdtJqKL//wy4jXoDdhy0BkxLneAyWLs/Pvvlq7hlEGa1fl5zMvDYyGQ+6DT1ucwfITdQqNGnk+dI5QPwbwX/MuJJZg3h3+mStTdrt2Aaj2ccqV12h3XipunnSUWPDbC8TuMhNs0Exg2wLTYEIzzmLm9JVGa6E1vBn57zp4ddR90vBRVXvQwnNO/PCOVJtrqddDZ+6cgDyRQoAMrU8Z4+9VXG1/Dijupttxwg/X2csKV1ml3XDuxL6w+W24lnL/DcLpNM8FhYcJiQzDh9w+/jwIPjwfle0tcupAW0jWcHLXYsSbv29derAWrBIvp4N8ROx0vw6gudjUTVurh9EjY6dG0dj4tUmqobR4LngqRiAXBETDd34eyzQQTtrlOGJbVoN9oLjWjYmScmG0U9aL+NIzOUw6v44aKwaD+GkYEmk9VzU9hdc52+nSiVq0ClxsOKRMmXDakszPXe/asxz5B1Z4hWFvZBWXHNbJls2czoYpWj3Cj+lyoouVwAfrnR7N/wCeMXlXa162E8jtkYgO2mbAoVTHOsXzqDvET1TIdbs2i5qIA/ROSD71dH3Sr6ljVSIlWbBJUrhsIqzEgwhUvAwviS1iJM2FnadJExKya3kxFj5wi8aCZ4FgQ8c9xjjPBRBS4acL8PymJbm59Fd1Cy302r6GqVJ42URIJakGz6QgVDMnNy44PujZS9B9F2XVT1I/OnnjC+nF2r2s1BkQ441po8SVU40zYYcEC+/dGhXA9FwCeFzt3ejRuSFiLT4zSVV1v3R6ThGNBMBocZ4Kxz8WLRM8/79Fl5s9PNHWqz+bjdAXVpaVSgKhGa2gLlXfMh96qDzo6/J49jacrtHW9evmq6bVjApUZxyxerO52qXLdYFiNARFOP3wtvoRqnIm2bT2um1ax2kaq2HkurOIf5Arf4yWGQrzUg3EAEefwNIdzSCOo79LFr4+PDaibTZs4yVJ6Zb2hltUcDaopme2oY1WPefjh0FX5VtTZqnkzNPdKq+mukWcC7R/M80OfawOfKrk5Fi6MTBu5XU0fydTz4SRe6sGYw9McjKP8OHAhpWXJRrfXz0Q3fWSg0x861KOpEIIyPdKW/vc/z2qVyNZalEKghRbGjEmgsMsY3Zldw6kQyqrHnDqltp/qdYMBY8RmzQLvg+2a0SLaYORI49G3npQUoiFDiN5/n+iZZwIbB4I+fS4bB+IT34Ptf/gw2SYcKv9oqem159fsnmC9/vl1K/FSDyZ0eJqDMWf9eqJrrpG9861DG1M2uuizeRw9RlfQCZr1uSB64QWiLFl85oohICAKoR5MZcDrwX8OGVidtza7hlMhlFWPQWDOULGqBp4zx1ygwHps10DbmXX0mHKAGh/3AQFIK1XyeKSoeH7cfLPvd0TN7NcvY8eB71iP7aGou8OhKmc1PcM4A7uGMhmHYEimtXChYcsspvrUjibSPiqu5Pql4m4XqnuZlWtYCc2resy2bUSpqfbsJjQBy67rHNxE0VHD2BI2EiNG+LpRasaFZiPHGTMuuy9acSENdE/gJjp6tMcoE7YUXbte1mDYCT0dTvfCaIVsjheXynipB2MOu4ZanO9JaE6dEuKJJ0wnjLdRGVGF1oRtTtmpeetg9hZ2QvMGOgafcPHD9VRd/YyWcEXy0+azza5r164k1PtuJ/Q02jdc8+7RCNkcLy6V8VIPxhy2mWCCDymGDfMMHZDee+zYjNGV5s+nqVMElaXttI6qhm1O2Yl5a4zAg9lbWJkWCXYM7AsKFCAaNMhzPXziOxYrYIohXImboK0JpGXAa37PHs9+du+hnWPM2jRQ+6F97aYtt1ueQM9FqMSLS2W81IMJncuT3ExiMG2ax0fNDOinO3f2WjEWW6p22kjMhZvtZ6bK1+wt9B0CPmFTYCXSof8xmFIYPDjj9Y4e9XzCiBG2qC+/HLxOwQwpI/mit3MPCxf2pC23GjXS7D6AV17xCA/+GN1Pp7DzXIRCvNhqxEs9GAcQcQ5PcwghfvxRiJQUcx3kM89c9vGLgutXKNewqsoPFdXroTmj7TJnVQVtxYUU+8CF1OmokZG+n9EiXlwq46UejDk8zZGAsxYYISJuFD7TNm8juuEGj4YB7gbasFnjwQc9FoP4zb/+umkCAKsumMrlSzO+hhlm17CqyreLVn5oJFSu9/PP1tstUBuZAWNHnOeppzyf+K6BkTVU9Wbuuf6BwgLda//jUE88Pk5HjbRyP+20l1UCta8RqmVy6ncVbeKlHowDiDgnETQTWvz/FDosZlIL82Ed8kNv2xbSNeykAVbNe4Csk/6Bj/A9UKbNXr3URt+qOT9Uy696PdV2s5MbQqW97BqdBqovglklJ4dHe6Ca9h33PVwpr+0+j3buYbyk146XejAZ4RTkFhsiVpk97Zx4g8zzXp+9opAQP/3kyLXspAFWTU9sJ42xleRadq3J7abu1l9P1cvESt2Dpfv2FyisvuhRxlA8VOy2eShJyZz0vrDSvqGm4Y6X9NrxUg/GF05BbtVHNpbAewqGkt27m+7yIH1GM5MejKqPt6oPOuI0lCnjUZEH2s8oBoRKTAQEZsJ+VlM520ndbTX1OdbBi8AsMqT+fADHQ82PUCBW0ldbTa/tRNpyBCULZOtrN+4DHI3Mpg+ciGugkk5d374ca4GJZzjOhEWpKib44ouAw6WnabhIojTHRuWhojrKfPRR6yNdqyNYOypwq9ewOnUAA8YrrlA7N7QEVqdatPTg4bx3TmsmVGJ7hPPaVtKpa+3LsRaYeIYNMOOF1as9QzUMue65J8Pm0dSFctFpmZnzDepHwiBCerR8vFWvO3689fNZrZMdw0Cr1zCLS2CW4hoGjCdOqJ0brpJWtQShpAUP5ZmxmgHWStwHxOUId/lV203bj2MtMAzn5nAn6DUQdQlv5erViXbt8t3epIl8gy1dIqgbjaazlMuVPt5OX9dO3gwNO+mkVa+BtCRajhF/QSJQiutwo5oW3Ml755QFP9px586MOVxU43KE8uyptpu2H8daYBjOzRERlOarT54k6tGDaMIE45NUqOAZrl13nSN5BQLlT3AKJ+bdzfJX2MnxoIG0I3/8EbzuobSvds8XL1YLXuU0TtlMBKo7ooAiD4j+/uI+QZAIV1TPSOTSsGszYbdMdu5NpIJrWcGt5WISwGZi2bJlomnTpqJYsWLSrmH27Nk+29u3by/X65fGjRvHlM1EQHexixeFGDzYfFI2Rw4hvv026PkDzeuaeQJYdcG0W3fYBZjZF1iZf7eSN0PFtkG17nba144rqdNLMG8OFRsSFbfSaFjw27knkfLmsJrfw+q9sXsvw41by8UkiGvoV199JZ5//nkxa9YsU2GiSZMmYv/+/d7l6NGjMSNMGLuLpYv2NCHwm+qjj4RITw/Li9XqS9LZul9eIGRYcT208nJ1ogO22752XUmdWsziTNh1o3Rj/IBICBNOxZkI1FZW702o9zJcuLVcTAIJE3rMhIlmzZqFdN5oCRP+YYHr0hJxggJE+nnhBSEuXAjpGsGCByHEs//L0agzMoms7Vi5sGB7sJDT/i9ls1GvNjqeNEmIQoXsd+b+dbfavir1tluuQOGK8+UTomtXj3dBKOWPhfgBkQ63jfZEu3bvnrF9Q2krp5+taIWtdmu5GOeIG2Eib968olChQuKaa64RnTt3FocPHw54nnPnzslKa8uePXuiIkzgRVKe/hTrqJLpL20iPSy+/+JYSNew4iZn1eUtEuVyOvCU3akPfd21DgHynZVyOeFOGWixqkKPR5fFeKmT1Xq4td5uLRfjHHHhGtqkSROaOHEiLV68mF5//XVatmwZ3XnnnZQWwDJq2LBh0lhEW0rAIiyS/POPdOG8vV4SbaJrqTJt8Nn8A91KpWindOVsR5/S3yfz2r6UVZc0qy5vkSgXDPWcdPczcytElnVV40wtlbmq4aRWLqvuiEi3rVoutJHVFNnx6LIYL3WyWg+31tut5WIij6tTkD/00EPe/ytXrkxVqlShq6++mpYuXUoNGjQwPKZ///7Up08fH0vUsAsUZ88SPfss0TvvGG7+m4rTfTSbVlCNqKbuturyFqlywd0PHgBOndconfSqVUR9+wY/9uuv1a5hVC4rrqR4fGHtjmRQDRsGPwb1eeMNa9by8eiyGC91sloPt9bbreViooBw8TSHEQULFhRjxoyJms2Ed050UprY2nVkQN3e4wVm2U6rbWXeVfUakbaZUC1XqGmMVea1VepuZ9oh1HqEs+5OpYe2ajMRThuLeEl5HenfiFvqwcQecWEz4Q/sH5KSksTcuXOjIkxo1tov0wDzHuatt4RIS/Pu74RbYTBXMSsuaZH25lAtl10LfSsW98Hqbmdxoh7hrLtdl0U3uy6GWie3YOc34sZ6u7VcTAIJEydPnhSrV6+WCwo7cuRI+f+uXbvktqefflosX75c7NixQyxatEhUr15dlCtXThpZRlqY0Ls/HaKCPr+aUdRLzJ561vAYqx2RHRcrKy5pkRImIlEuu8cYdcBNm9oTJvyvAeejQPsbOSfZjRWiWi677p1udl10o8uqHey4k7qx3m4tF5MgWUNh+1APlm5+tG/fnt5//31q3rw5rV69mo4dO0apqanUqFEjGjp0KBUpUiSiWUP9IzlWp5XSBuJ/1JMOUyFbWS39jwk186BK9Dmrkf2cIFzlCqUuRtE/f/7ZY3RpFf01YDqDMgUD+yNyJLBz3+3U3emsoU4/v4kccZEjYDJuJiYiYEYCJzQTdtyf3Oj6FSnX0EiUy+m6BJv7VblGt25q+2M/DTv3PRL30Y3PL8MwkScuXEPdgh33Jze6fkXKNTQS5XK6LhjR/u9/vsmqVNGusXWr2v76/ezc90jcRzc+vwzDuBcWJsLk/uRG169IuYZGolzhqItZjArVa5Qrp7a/fj879z0S99GNzy/DMO4lqjYTkcBJmwkrWQGtHuPGbIiRItI2E6pz2Hv2EHXoQJSeHn6bCSv3PRL30Y3PL8Mw7u1DWTMRogpc+46gS/qXpNVj7FzDKuhYdPG8DMF2JwUJdHwo91NPeT7x3YlyhVIXdHwIFjV1qufTv1NGG99+O9EjjwQPdHX//USff+45D66F4FKBwHZNkPC/72b43/dw1t3Nzy/DMC5GxDnhiDNhxf3Jja5fkUpBbvU6dsqVK5exoR/WOxUHoUgR42tkymR8nho1jPfHejPMXEoD5bmzeoydult9HiP1bDEMExliIs5EJAhbBEwL0f3cFEHQbjZEq9iNZ2GlXGadvLZge6hxEMwEA6MlmCeI2TXstFUkY0CoPo+B0q9z8CKGiU1iIs5ErNhMMNaIxJz+0aOeRFnBOHKEKCXFXhyEU6eI8uSxV75wx4xwWwyIaMSZYBgm/LDNBBM1EBAqUOcIsB372aVuXWv7waDSrKMDEKlhcIn9NGAz4SRG17DTVlbrYqfuVonENRiGcS9sgMk4TiTiIOzbZ22/cMZzsEqoMSPcGAOC40wwTGLDwgTjOJGIg5Caam2/cMZzsEqoMSPcGAOC40wwTGLDNhOM4zidN8PIrsKuzYSVOAhut5lwUwwIjjPBMPEJ20wwUcNuHIRnnvF0rL17E737rucT37HeHwgIwfK9YTv208dBMDM3xnr/OAjJyUQ1apAywcJwG13DTlu5MQYEx5lgmMSGpzmYsHDzzda2Q2AYMSLjCB3fsd5IoDhwwFygwHps1/PLL4HLZLT9t9/MBYqsWX2/Y3Tfrx9ZZvhwz3H+nTm+Yz22q4b+RhmwHttD2d8OkbgGwzDuhKc5GMex6iYYqisppjzgtQFjS9hILFt2WSOhEeo1MOUB7w5t+uXTTz2RLPUpsG+5xbPNrnuk6hSP29NXx0tqcIZhSHmag4UJxnEQprleveD7LVniCVsNFTumNIIxahRRr172yhSJa1itN8MwjNthmwkmalh1E4yEK6kb03YzDMPEC2wzwTiOVTfBSLiSujFtN8MwTLzA0xyM41h1E4xE+G03pu1mGIZxOzzNwUQNq2m1I5EaPRLXCNU9UjU9OMMwjNvgaQ4mLMAN8N57jbdhvb+boB33SKtE4hqo19NPE2Xy+2XhO9abuUfOmuXRasCAs00bzye+Yz3DMIzb4WkOJixocSPMMOu87bhHWiWc10Dnf//95tMcRvEWzI7RtBkco4FhmGjBrqEWG4JxjkjYJ7gRO2m4OXU3wzBuhm0mmLhOQe5G7KTh5tTdDMPEA2wzwThOJGI6uBE7cSY4NgXDMPEACxOM40QipoMbsRNngmNTMAwTD7ABJuM4iW4zYSXORKRjU3DeDIZh4s5m4vvvv6d77rmHUlNTKSkpiebMmeOzXQhBAwcOpGLFilHOnDmpYcOGtHXr1qiVl3FPTAc3YifOhJ3U6HZh91OGYcJFVIWJ06dPU9WqVem9994z3D58+HB6++23acyYMfTrr79S7ty5qXHjxnTu3LmIl5VxX0wHN+LWNNya+6m/gSg0IljP8SwYhomLaQ5oJmbPnk3NmzeX31EsaCz69u1LTyPaD5FUsxQpUoQmTJhADz30kNJ52TU0ukQiboQbUZ1OiIRrKLufMgxjF9U+NAu5lB07dtCBAwfk1IYGKlSzZk1avny5qTBx/vx5uegbgokeEBzspvSOZdDxq6QZt+IaajdteSSuwTBMYuNabw4IEgCaCD34rm0zYtiwYVLo0JYSJUqEvawMY5dIuIay+ynDMAkrTNilf//+Uh2jLXsw5GIYlxIJ11B2P2UYJmGFiaJFi8rPgwcP+qzHd22bEdmzZ5fzOvqFYdwKbClgE+Hv/aGB9VCuYT83X4NhmMTGtcLEVVddJYWGxYsX+9g/wKujVq1aUS0bwzhFqGnL3XINxgOnkWcSlagKE6dOnaI1a9bIRTO6xP+7d++W3h29evWil19+mebNm0fr16+ndu3aSQ8PzeODYeKBSLiTutVlNZ7gOB5MIhNV19ClS5dSvXr1Mqxv3769dP9E0QYNGkRjx46lY8eO0a233kqjR4+ma665Rvka7BrKxAqRiE7JETDDA6eRZ+IVTkFusSEYhmHswHE8mHgmJsJpMwzDxDqcRp5hWJhgGIYJCY7jwTAsTDAMw4QEx/FgGBYmGIZhQoLjeDAMCxMMwzAhwXE8GIaFCYZhmJDhOB5MouParKEMwzCxJlA0axb+WCEM40ZYmGAYholw6nmGiTc4zgTDMAzDMCHBwgTDMAzDMCHBwgTDMAzDMCHBwgTDMAzDMCHBwgTDMAzDMCHBwgTDMAzDMCER966hQghvGlWGYRiGYdTR+k6tL01YYeLkyZPys0SJEtEuCsMwDMPEbF+aN29e0+1JIpi4EeOkp6fTvn37KE+ePJSUlGQodUHQ2LNnD11xxRWUSHDd+b7zM5848O+df+9X2OjjICJAkEhNTaVMmTIlrmYClb/yyiuD7odGTrQXqwbXne97osHPPD/zicYVIfRxgTQSGmyAyTAMwzBMSLAwwTAMwzBMSCS8MJE9e3YaNGiQ/Ew0uO583xMNfub5mU80skeoj4t7A0yGYRiGYcJLwmsmGIZhGIYJDRYmGIZhGIYJCRYmGIZhGIYJCRYmGIZhGIYJibgXJkqXLi0jX/ov3bp1M9x/woQJGfbNkSMHxQLff/893XPPPTJSGco9Z84cn+2wtR04cCAVK1aMcubMSQ0bNqStW7cGPe97770n2xHtULNmTfrtt98oVup98eJFevbZZ6ly5cqUO3duuU+7du1kVNRADB48OMNzUKFCBYq1e96hQ4cM9WjSpEnM33OVuhv97rGMGDEi5u/7sGHDqEaNGjKyb+HChal58+a0efNmn33OnTsn33MFChSg5ORkatmyJR08eDDgee2+I9xU96NHj9JTTz1F5cuXl3UoWbIk9ejRg44fPx7wvHZ/K26657fffnuGOnTu3Dki9zzuhYnff/+d9u/f712+/fZbuf6BBx4wPQZRwvTH7Nq1i2KB06dPU9WqVWVHYMTw4cPp7bffpjFjxtCvv/4qO9fGjRvLl44Zn332GfXp00e6Fq1atUqeH8ccOnSIYqHeZ86ckeV+8cUX5eesWbPkD/Dee+8Net7rrrvO5zn48ccfKdbuOcALUV+PqVOnBjxnLNxzlbrr64xl3Lhx8uWKTjXW7/uyZcukoPDLL7/IdxqE5kaNGsk20ejduzd98cUXNGPGDLk/BOgWLVoEPK+dd4Tb6o56YnnjjTdow4YNcoC4YMEC6tixY9BzW/2tuO2eg06dOvnUAfc0IvdcJBg9e/YUV199tUhPTzfcPn78eJE3b14R6+DWzp492/sd9S1atKgYMWKEd92xY8dE9uzZxdSpU03Pc9NNN4lu3bp5v6elpYnU1FQxbNgwEQv1NuK3336T++3atct0n0GDBomqVauKWMKo7u3btxfNmjWzdJ5Yu+eq9x3tUL9+/YD7xOJ9B4cOHZJtsGzZMu9vO2vWrGLGjBneff7880+5z/Llyw3PYfcd4ba6GzF9+nSRLVs2cfHiRdN97PxW3FbvunXryj5OFSfvedxrJvRcuHCBJk2aRI899phh0i+NU6dOUalSpWQCsGbNmtEff/xBsc6OHTvowIEDUoWlj7cOFfby5ctN22vlypU+xyDXCb6bHRMLQN2J+58vX76A+0HVBxV6mTJlqG3btrR7926KRZYuXSrVolD7dunShY4cOWK6b7zec6j358+frzQ6jcX7rqnwU1JS5CfuIUau+vuI6Rqo/M3uo513hBvrbrYPNM5ZsmRx7Lfi1npPnjyZChYsSJUqVaL+/ftL7awZTt7zuE/0pQdzqseOHZNzY2bgIYI6tEqVKvJmQVV2yy23SIFCJWGYW8EDA4oUKeKzHt+1bf4cPnyY0tLSDI/ZtGkTxSJQ3cGGonXr1gGT3uDHBPUongeoCocMGUK33XabVJtizjJWgNoWqu2rrrqKtm/fTgMGDKA777xTvigyZ86cEPccfPLJJ/K+BVPzx+J9R2bkXr16Ue3atWUHAvCbzpYtWwaBOdDv3c47wo11N3qmhw4dSk888YSjvxU31rtNmzZyIAxheN26dfJdh2ldTO+G+54nlDDx8ccfy4cDDW1GrVq15KIBQeLaa6+lDz74QD6QTOyCkdqDDz4oDY7ef//9gPviOdGAYIlOBj/S6dOnK41u3cJDDz3k/R9GqKjL1VdfLUdgDRo0oEQBAwRoGYIZU8fifcc8OoQdN9p2RLvuSLt+9913U8WKFaVxbbz8VrqZ1FsvMKEOMKpE2SEcoS7hJGGmOWBEuWjRInr88cctHZc1a1aqVq0abdu2jWKZokWLyk9/a25817b5A1UZJHIrx7hdkMBzAOMlq6l4McK75pprYv45gOoe99WsHvF0zzV++OEHOTqz+tuPhfvevXt3+vLLL2nJkiU+mlPcK0xZQROreh/tvCPcWHeNkydPSm0DNEqzZ8+W73InfyturbceCMPArA5O3vOEESbGjx8v58IgpVoBKt/169dLCS+WgeoOD8fixYt9pHZY7+o1MXqgJr3hhht8joF6Dd/NjnGzIIG5cAiUcJWzCuxoIN3H+nPw999/y3lgs3rEyz3310iiTvD8iJf7Du0aOhV0kt999538fetBfdF56u8jBCrYf5jdRzvvCDfWXSs3PB3wPM+bN8+We3+w34ob6+3PmjVr5KdZHRy95yIBgDV6yZIlxbPPPpth2yOPPCKee+457/chQ4aIb775Rmzfvl2sXLlSPPTQQyJHjhzijz/+EG7n5MmTYvXq1XLBrR05cqT8X/NaeO2110S+fPnE3Llzxbp166Tl8lVXXSXOnj3rPQes3d955x3v92nTpknL3gkTJoiNGzeKJ554Qp7jwIEDIhbqfeHCBXHvvfeKK6+8UqxZs0bs37/fu5w/f9603n379hVLly4VO3bsED/99JNo2LChKFiwoLSgdhOB6o5tTz/9tLTeRz0WLVokqlevLsqVKyfOnTsX0/dc5XkHx48fF7ly5RLvv/++4Tli9b536dJFep2hrPpn+syZM959OnfuLN973333nVixYoWoVauWXPSUL19ezJo1y/td5R3h9rrjntesWVNUrlxZbNu2zWefS5cuGdZd9bfi5npv27ZNvPTSS/Jeow64h2XKlBF16tSJyD1PCGECwgFeNps3b86wDa40cAnS6NWrl/wBwo2oSJEi4q677hKrVq0SscCSJUtkPf0XrX5wA3rxxRdlvdBZNGjQIEOblCpVSrrH6cHLVmsTuA3+8ssvIlbqjR+V0TYsOM6s3q1atRLFihWTdS5evLj8jh+r2whUd7xkGjVqJAoVKiTdBFHHTp06ZRAKYvGeqzzv4IMPPhA5c+aU7m5GxOp9N3um4dqugc6ga9euIn/+/FKguu+++2Tn438e/TEq7wi3193sucCC94H+PNoxqr8VN9d79+7dUnBISUmR965s2bKiX79+UriKxD3nFOQMwzAMw4REwthMMAzDMAwTHliYYBiGYRgmJFiYYBiGYRgmJFiYYBiGYRgmJFiYYBiGYRgmJFiYYBiGYRgmJFiYYBiGYRgmJFiYYBiGYRiGhQmGYdwHUnn7p8COJDt37qSkpCRvfgKGYcJHQqUgZxgmPunQoYPMkDlnzhzvuhIlStD+/ftl5keGYcILT3MwDOPqjK92QSp1ZETMkoXHTAwTbliYYJg4ZsGCBXTrrbfK6QakXm/atKlMqa1Ps9y6dWtKSUmh3Llz04033ijTD2t88cUXVKNGDZnCGSP8++67z7vt/Pnz9PTTT1Px4sXlsTVr1qSlS5cGLM/cuXOpevXq8nxlypShIUOG0KVLl7zbMS3x/vvv07333ivP+corr1BaWhp17NhRpkvOmTMnlS9fnv73v/95jxk8eDB98skn8tw4HgvKYTTNsWzZMrrpppsoe/bsMi3zc88953P922+/nXr06EHPPPOMbBMIIzg/wzCBYWGCYeKY06dPU58+fWjFihW0ePFiypQpkxQI0tPT6dSpU1S3bl3au3cvzZs3j9auXSs7UWwD8+fPl/veddddtHr1ank8OmKN7t270/Lly2natGm0bt06euCBB6hJkya0detWw7L88MMP1K5dO+rZsydt3LiRPvjgA2lXAYFBDzpvXHf9+vX02GOPyfJceeWVNGPGDHncwIEDacCAATR9+nS5PwSaBx98UF4b0xpYbrnllgzXRz1RFwhHqCuElo8//phefvlln/0gmECQgVA1fPhweumll+jbb7915H4wTNwSemJUhmFihX/++UemIF6/fr1Mz50nTx5x5MgRw31r1aol2rZta7ht165dInPmzGLv3r0+65G+uH///vJ/pDnOmzevz7ZXX33VZ/9PP/1UpvzWQNl69eoVtB7dunUTLVu29H5H2vFmzZr57KOln1+9erX8PmDAAFG+fHmZclnjvffeE8nJySItLU1+r1u3rrj11lt9zlOjRg3x7LPPBi0TwyQyPJnIMHEMtAQYyWOUffjwYa/WYffu3VL9X61aNanONwLbO3XqZLgNWgNMP1xzzTU+6zH1gekUI6AN+Omnn3w0ETjHuXPn6MyZM5QrVy65DlMt/rz33ns0btw4We6zZ8/ShQsX6Prrr7fQEkR//vkn1apVS059aNSuXVtqaDDdU7JkSbmuSpUqPsdhOuTQoUOWrsUwiQYLEwwTx9xzzz1UqlQp+vDDDyk1NVUKE5UqVZKdMewPAhFoOzpgGDiuXLlSfupJTk42PQY2Ei1atMiwDTYUGphi0INpFExlvPnmm1IYyJMnD40YMcLHtsNJsmbN6vMdwocmhDEMYwwLEwwTpxw5coQ2b94sBYnbbrtNrvvxxx+92zEC/+ijj+jo0aOG2glsh53Eo48+mmEbNBrQKmDErp07GDC8RHnKli1rqR7QZsAGomvXrt51eiNSkC1bNlmeQFx77bX0+eefY2rXq53AuSGcwCaDYRj7sAEmw8Qp+fPnl1MOY8eOpW3bttF3330njTE14MUBb4XmzZvLTvWvv/6SnS2MKsGgQYNo6tSp8hNTBJjaeP311+U2TG+0bdtWGlTOmjWLduzYQb/99hsNGzZMGm4agemWiRMnSu3EH3/8Ic8JrcMLL7wQsB7lypWTBqTffPMNbdmyhV588UX6/fffffYpXbq0NAKFsILpHCOXUggje/bsoaeeeoo2bdokvT9QN7QJDFMZhrEP/4IYJk5BB4nOGlMRmNro3bu3nB7Qj+YXLlxIhQsXll4OlStXptdee807bQE3SXhQwNMD9gn169eXAoPG+PHjpTDRt29f6a4JoQSdvGZ74E/jxo3pyy+/lNeER8XNN99Mo0aNktMwgXjyySfl1EirVq2k+yk0LnotBYBtB8oAe4tChQpJ4cgfuLB+9dVXsg5Vq1alzp07S5fTYMIMwzDBSYIVpsJ+DMMwDMMwhrBmgmEYhmGYkGBhgmEYhmGYkGBhgmEYhmGYkGBhgmEYhmGYkGBhgmEYhmGYkGBhgmEYhmGYkGBhgmEYhmGYkGBhgmEYhmGYkGBhgmEYhmGYkGBhgmEYhmGYkGBhgmEYhmEYCoX/Ax73tis1nU6LAAAAAElFTkSuQmCC",
+      "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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",
+      "text/plain": [
+       "<Figure size 600x400 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
    "source": [
     "# Exclude the 'mpg' (target variable) and non-numeric columns from the feature names\n",
     "feature_names = mpg_data.select_dtypes(include=[float, int]).columns.difference(['mpg'])\n",
@@ -225,7 +466,11 @@
    "id": "f67e57ab",
    "metadata": {},
    "source": [
-    "> Your answer here..."
+    "No Association : Number of Cylinders and Model_year have no association with MPG \n",
+    "\n",
+    "Negative Association : Weight, displacement and Horsepower has negative association with MPG - as per the plot, as the weight, displacement and horsepower increases, MPG decreses\n",
+    "\n",
+    "Positive Association : Acceleration has positive association with MOG - as the acceleration increases, MPG increases\n"
    ]
   },
   {
@@ -241,7 +486,7 @@
    "id": "843f9eef",
    "metadata": {},
    "source": [
-    "> Your answer here..."
+    "The regression line shows linear relationship between response and predictor variables. It shows how much the dependent variable changes with change in independent variable."
    ]
   },
   {
@@ -257,7 +502,7 @@
    "id": "2ea782fc",
    "metadata": {},
    "source": [
-    "> Your answer here..."
+    "No, not all data points fall on the regression line. Regression line can not predict all points perfectly and these deviations are called residuals. The aim of the linear regression is to minimize these residuals - best fitted line is the one that has minimum residuals across all obs in the training dataset. Residual is the difference between Actual value and predicted value of the response variable."
    ]
   },
   {
@@ -274,12 +519,14 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 27,
    "id": "399225f4",
    "metadata": {},
    "outputs": [],
    "source": [
-    "# Your answer here..."
+    "mpg_data_train, mpg_data_test = train_test_split(mpg_data, train_size= 0.75, random_state= 42)\n",
+    "#mpg_data_test.shape\n",
+    "#mpg_data_train.shape"
    ]
   },
   {
@@ -292,14 +539,37 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 29,
    "id": "ac1e1117",
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "      predictor     slope  intercept\n",
+      "0     cylinders -0.160143 -15.047371\n",
+      "1  displacement  0.000373 -15.047371\n",
+      "2    horsepower -0.001899 -15.047371\n",
+      "3        weight -0.006457 -15.047371\n",
+      "4  acceleration  0.057588 -15.047371\n",
+      "5    model_year  0.762270 -15.047371\n"
+     ]
+    }
+   ],
    "source": [
     "# Your code here ...\n",
     "\n",
-    "numeric_predictors = 🤷‍♂️\n",
+    "# Step 1 : Define response and predictor variables\n",
+    "X_train = mpg_data_train.drop([\"mpg\"], axis=1) # droping response variable so we have only predictor variable\n",
+    "y_train = mpg_data_train[\"mpg\"] # keeping only response variable\n",
+    "numeric_predictors = X_train.select_dtypes(include=[np.number]) # keepping only numeric variables as predictors\n",
+    "\n",
+    "# Step 2 : Initialize a linera regression\n",
+    "lm = LinearRegression()\n",
+    "\n",
+    "# step 3 : Fit model to our data\n",
+    "lm.fit(numeric_predictors, y_train)\n",
     "\n",
     "\n",
     "# Create a DataFrame containing the slope (coefficients) and intercept\n",
@@ -330,12 +600,31 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 33,
    "id": "ffefa9f2",
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "3.1854749513391583"
+      ]
+     },
+     "execution_count": 33,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
    "source": [
-    "# Your code here ..."
+    "# make predictions on test data\n",
+    "\n",
+    "mpg_data_test[\"predicted\"] = lm.predict(mpg_data_test[numeric_predictors.columns])\n",
+    "\n",
+    "# calculate RMSPE\n",
+    "mean_squared_error(\n",
+    "    y_true= mpg_data_test[\"mpg\"],\n",
+    "    y_pred= mpg_data_test[\"predicted\"]\n",
+    ")**(1/2)"
    ]
   },
   {
@@ -386,7 +675,7 @@
  ],
  "metadata": {
   "kernelspec": {
-   "display_name": "Python 3 (ipykernel)",
+   "display_name": "lcr-env",
    "language": "python",
    "name": "python3"
   },
@@ -400,12 +689,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.12.3"
-  },
-  "vscode": {
-   "interpreter": {
-    "hash": "497a84dc8fec8cf8d24e7e87b6d954c9a18a327edc66feb9b9ea7e9e72cc5c7e"
-   }
+   "version": "3.11.14"
   }
  },
  "nbformat": 4,