diff --git a/01_materials/notebooks/Classification-2.ipynb b/01_materials/notebooks/Classification-2.ipynb
index 96db650b8..f6655c7c8 100644
--- a/01_materials/notebooks/Classification-2.ipynb
+++ b/01_materials/notebooks/Classification-2.ipynb
@@ -38,7 +38,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 5,
+   "execution_count": 4,
    "metadata": {},
    "outputs": [
     {
@@ -424,7 +424,7 @@
        "[569 rows x 32 columns]"
       ]
      },
-     "execution_count": 5,
+     "execution_count": 4,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -444,7 +444,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 6,
+   "execution_count": 5,
    "metadata": {},
    "outputs": [
     {
@@ -453,7 +453,7 @@
        "array(['Malignant', 'Benign'], dtype=object)"
       ]
      },
-     "execution_count": 6,
+     "execution_count": 5,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -478,7 +478,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 8,
+   "execution_count": 6,
    "metadata": {},
    "outputs": [
     {
@@ -864,7 +864,7 @@
        "[569 rows x 32 columns]"
       ]
      },
-     "execution_count": 8,
+     "execution_count": 6,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -956,7 +956,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 9,
+   "execution_count": 7,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -978,7 +978,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 10,
+   "execution_count": 8,
    "metadata": {},
    "outputs": [
     {
@@ -986,7 +986,7 @@
      "output_type": "stream",
      "text": [
       "<class 'pandas.core.frame.DataFrame'>\n",
-      "Int64Index: 426 entries, 164 to 284\n",
+      "Index: 426 entries, 164 to 284\n",
       "Data columns (total 32 columns):\n",
       " #   Column                   Non-Null Count  Dtype  \n",
       "---  ------                   --------------  -----  \n",
@@ -1033,18 +1033,19 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 11,
+   "execution_count": 9,
    "metadata": {},
    "outputs": [
     {
      "data": {
       "text/plain": [
+       "diagnosis\n",
        "Benign       0.626761\n",
        "Malignant    0.373239\n",
-       "Name: diagnosis, dtype: float64"
+       "Name: proportion, dtype: float64"
       ]
      },
-     "execution_count": 11,
+     "execution_count": 9,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -1070,19 +1071,628 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 12,
+   "execution_count": 10,
    "metadata": {},
    "outputs": [
     {
      "data": {
       "text/html": [
-       "<style>#sk-container-id-1 {color: black;background-color: white;}#sk-container-id-1 pre{padding: 0;}#sk-container-id-1 div.sk-toggleable {background-color: white;}#sk-container-id-1 label.sk-toggleable__label {cursor: pointer;display: block;width: 100%;margin-bottom: 0;padding: 0.3em;box-sizing: border-box;text-align: center;}#sk-container-id-1 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-1 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-1 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-1 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-1 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-1 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-1 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-container-id-1 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-1 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-1 input.sk-hidden--visually {border: 0;clip: rect(1px 1px 1px 1px);clip: rect(1px, 1px, 1px, 1px);height: 1px;margin: -1px;overflow: hidden;padding: 0;position: absolute;width: 1px;}#sk-container-id-1 div.sk-estimator {font-family: monospace;background-color: #f0f8ff;border: 1px dotted black;border-radius: 0.25em;box-sizing: border-box;margin-bottom: 0.5em;}#sk-container-id-1 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-1 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-1 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-1 div.sk-serial::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: 0;}#sk-container-id-1 div.sk-serial {display: flex;flex-direction: column;align-items: center;background-color: white;padding-right: 0.2em;padding-left: 0.2em;position: relative;}#sk-container-id-1 div.sk-item {position: relative;z-index: 1;}#sk-container-id-1 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-1 div.sk-item::before, #sk-container-id-1 div.sk-parallel-item::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: -1;}#sk-container-id-1 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-1 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-1 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-1 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-1 div.sk-dashed-wrapped {border: 1px dashed gray;margin: 0 0.4em 0.5em 0.4em;box-sizing: border-box;padding-bottom: 0.4em;background-color: white;}#sk-container-id-1 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-1 div.sk-label-container {text-align: center;}#sk-container-id-1 div.sk-container {/* jupyter's `normalize.less` sets `[hidden] { display: none; }` but bootstrap.min.css set `[hidden] { display: none !important; }` so we also need the `!important` here to be able to override the default hidden behavior on the sphinx rendered scikit-learn.org. See: https://github.com/scikit-learn/scikit-learn/issues/21755 */display: inline-block !important;position: relative;}#sk-container-id-1 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-1\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>KNeighborsClassifier()</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-1\" type=\"checkbox\" checked><label for=\"sk-estimator-id-1\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">KNeighborsClassifier</label><div class=\"sk-toggleable__content\"><pre>KNeighborsClassifier()</pre></div></div></div></div></div>"
+       "<style>#sk-container-id-1 {\n",
+       "  /* Definition of color scheme common for light and dark mode */\n",
+       "  --sklearn-color-text: #000;\n",
+       "  --sklearn-color-text-muted: #666;\n",
+       "  --sklearn-color-line: gray;\n",
+       "  /* Definition of color scheme for unfitted estimators */\n",
+       "  --sklearn-color-unfitted-level-0: #fff5e6;\n",
+       "  --sklearn-color-unfitted-level-1: #f6e4d2;\n",
+       "  --sklearn-color-unfitted-level-2: #ffe0b3;\n",
+       "  --sklearn-color-unfitted-level-3: chocolate;\n",
+       "  /* Definition of color scheme for fitted estimators */\n",
+       "  --sklearn-color-fitted-level-0: #f0f8ff;\n",
+       "  --sklearn-color-fitted-level-1: #d4ebff;\n",
+       "  --sklearn-color-fitted-level-2: #b3dbfd;\n",
+       "  --sklearn-color-fitted-level-3: cornflowerblue;\n",
+       "\n",
+       "  /* Specific color for light theme */\n",
+       "  --sklearn-color-text-on-default-background: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, black)));\n",
+       "  --sklearn-color-background: var(--sg-background-color, var(--theme-background, var(--jp-layout-color0, white)));\n",
+       "  --sklearn-color-border-box: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, black)));\n",
+       "  --sklearn-color-icon: #696969;\n",
+       "\n",
+       "  @media (prefers-color-scheme: dark) {\n",
+       "    /* Redefinition of color scheme for dark theme */\n",
+       "    --sklearn-color-text-on-default-background: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, white)));\n",
+       "    --sklearn-color-background: var(--sg-background-color, var(--theme-background, var(--jp-layout-color0, #111)));\n",
+       "    --sklearn-color-border-box: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, white)));\n",
+       "    --sklearn-color-icon: #878787;\n",
+       "  }\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-1 {\n",
+       "  color: var(--sklearn-color-text);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-1 pre {\n",
+       "  padding: 0;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-1 input.sk-hidden--visually {\n",
+       "  border: 0;\n",
+       "  clip: rect(1px 1px 1px 1px);\n",
+       "  clip: rect(1px, 1px, 1px, 1px);\n",
+       "  height: 1px;\n",
+       "  margin: -1px;\n",
+       "  overflow: hidden;\n",
+       "  padding: 0;\n",
+       "  position: absolute;\n",
+       "  width: 1px;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-1 div.sk-dashed-wrapped {\n",
+       "  border: 1px dashed var(--sklearn-color-line);\n",
+       "  margin: 0 0.4em 0.5em 0.4em;\n",
+       "  box-sizing: border-box;\n",
+       "  padding-bottom: 0.4em;\n",
+       "  background-color: var(--sklearn-color-background);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-1 div.sk-container {\n",
+       "  /* jupyter's `normalize.less` sets `[hidden] { display: none; }`\n",
+       "     but bootstrap.min.css set `[hidden] { display: none !important; }`\n",
+       "     so we also need the `!important` here to be able to override the\n",
+       "     default hidden behavior on the sphinx rendered scikit-learn.org.\n",
+       "     See: https://github.com/scikit-learn/scikit-learn/issues/21755 */\n",
+       "  display: inline-block !important;\n",
+       "  position: relative;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-1 div.sk-text-repr-fallback {\n",
+       "  display: none;\n",
+       "}\n",
+       "\n",
+       "div.sk-parallel-item,\n",
+       "div.sk-serial,\n",
+       "div.sk-item {\n",
+       "  /* draw centered vertical line to link estimators */\n",
+       "  background-image: linear-gradient(var(--sklearn-color-text-on-default-background), var(--sklearn-color-text-on-default-background));\n",
+       "  background-size: 2px 100%;\n",
+       "  background-repeat: no-repeat;\n",
+       "  background-position: center center;\n",
+       "}\n",
+       "\n",
+       "/* Parallel-specific style estimator block */\n",
+       "\n",
+       "#sk-container-id-1 div.sk-parallel-item::after {\n",
+       "  content: \"\";\n",
+       "  width: 100%;\n",
+       "  border-bottom: 2px solid var(--sklearn-color-text-on-default-background);\n",
+       "  flex-grow: 1;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-1 div.sk-parallel {\n",
+       "  display: flex;\n",
+       "  align-items: stretch;\n",
+       "  justify-content: center;\n",
+       "  background-color: var(--sklearn-color-background);\n",
+       "  position: relative;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-1 div.sk-parallel-item {\n",
+       "  display: flex;\n",
+       "  flex-direction: column;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-1 div.sk-parallel-item:first-child::after {\n",
+       "  align-self: flex-end;\n",
+       "  width: 50%;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-1 div.sk-parallel-item:last-child::after {\n",
+       "  align-self: flex-start;\n",
+       "  width: 50%;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-1 div.sk-parallel-item:only-child::after {\n",
+       "  width: 0;\n",
+       "}\n",
+       "\n",
+       "/* Serial-specific style estimator block */\n",
+       "\n",
+       "#sk-container-id-1 div.sk-serial {\n",
+       "  display: flex;\n",
+       "  flex-direction: column;\n",
+       "  align-items: center;\n",
+       "  background-color: var(--sklearn-color-background);\n",
+       "  padding-right: 1em;\n",
+       "  padding-left: 1em;\n",
+       "}\n",
+       "\n",
+       "\n",
+       "/* Toggleable style: style used for estimator/Pipeline/ColumnTransformer box that is\n",
+       "clickable and can be expanded/collapsed.\n",
+       "- Pipeline and ColumnTransformer use this feature and define the default style\n",
+       "- Estimators will overwrite some part of the style using the `sk-estimator` class\n",
+       "*/\n",
+       "\n",
+       "/* Pipeline and ColumnTransformer style (default) */\n",
+       "\n",
+       "#sk-container-id-1 div.sk-toggleable {\n",
+       "  /* Default theme specific background. It is overwritten whether we have a\n",
+       "  specific estimator or a Pipeline/ColumnTransformer */\n",
+       "  background-color: var(--sklearn-color-background);\n",
+       "}\n",
+       "\n",
+       "/* Toggleable label */\n",
+       "#sk-container-id-1 label.sk-toggleable__label {\n",
+       "  cursor: pointer;\n",
+       "  display: flex;\n",
+       "  width: 100%;\n",
+       "  margin-bottom: 0;\n",
+       "  padding: 0.5em;\n",
+       "  box-sizing: border-box;\n",
+       "  text-align: center;\n",
+       "  align-items: start;\n",
+       "  justify-content: space-between;\n",
+       "  gap: 0.5em;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-1 label.sk-toggleable__label .caption {\n",
+       "  font-size: 0.6rem;\n",
+       "  font-weight: lighter;\n",
+       "  color: var(--sklearn-color-text-muted);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-1 label.sk-toggleable__label-arrow:before {\n",
+       "  /* Arrow on the left of the label */\n",
+       "  content: \"▸\";\n",
+       "  float: left;\n",
+       "  margin-right: 0.25em;\n",
+       "  color: var(--sklearn-color-icon);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-1 label.sk-toggleable__label-arrow:hover:before {\n",
+       "  color: var(--sklearn-color-text);\n",
+       "}\n",
+       "\n",
+       "/* Toggleable content - dropdown */\n",
+       "\n",
+       "#sk-container-id-1 div.sk-toggleable__content {\n",
+       "  display: none;\n",
+       "  text-align: left;\n",
+       "  /* unfitted */\n",
+       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-1 div.sk-toggleable__content.fitted {\n",
+       "  /* fitted */\n",
+       "  background-color: var(--sklearn-color-fitted-level-0);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-1 div.sk-toggleable__content pre {\n",
+       "  margin: 0.2em;\n",
+       "  border-radius: 0.25em;\n",
+       "  color: var(--sklearn-color-text);\n",
+       "  /* unfitted */\n",
+       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-1 div.sk-toggleable__content.fitted pre {\n",
+       "  /* unfitted */\n",
+       "  background-color: var(--sklearn-color-fitted-level-0);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-1 input.sk-toggleable__control:checked~div.sk-toggleable__content {\n",
+       "  /* Expand drop-down */\n",
+       "  display: block;\n",
+       "  width: 100%;\n",
+       "  overflow: visible;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-1 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {\n",
+       "  content: \"▾\";\n",
+       "}\n",
+       "\n",
+       "/* Pipeline/ColumnTransformer-specific style */\n",
+       "\n",
+       "#sk-container-id-1 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
+       "  color: var(--sklearn-color-text);\n",
+       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-1 div.sk-label.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
+       "  background-color: var(--sklearn-color-fitted-level-2);\n",
+       "}\n",
+       "\n",
+       "/* Estimator-specific style */\n",
+       "\n",
+       "/* Colorize estimator box */\n",
+       "#sk-container-id-1 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
+       "  /* unfitted */\n",
+       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-1 div.sk-estimator.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
+       "  /* fitted */\n",
+       "  background-color: var(--sklearn-color-fitted-level-2);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-1 div.sk-label label.sk-toggleable__label,\n",
+       "#sk-container-id-1 div.sk-label label {\n",
+       "  /* The background is the default theme color */\n",
+       "  color: var(--sklearn-color-text-on-default-background);\n",
+       "}\n",
+       "\n",
+       "/* On hover, darken the color of the background */\n",
+       "#sk-container-id-1 div.sk-label:hover label.sk-toggleable__label {\n",
+       "  color: var(--sklearn-color-text);\n",
+       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
+       "}\n",
+       "\n",
+       "/* Label box, darken color on hover, fitted */\n",
+       "#sk-container-id-1 div.sk-label.fitted:hover label.sk-toggleable__label.fitted {\n",
+       "  color: var(--sklearn-color-text);\n",
+       "  background-color: var(--sklearn-color-fitted-level-2);\n",
+       "}\n",
+       "\n",
+       "/* Estimator label */\n",
+       "\n",
+       "#sk-container-id-1 div.sk-label label {\n",
+       "  font-family: monospace;\n",
+       "  font-weight: bold;\n",
+       "  display: inline-block;\n",
+       "  line-height: 1.2em;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-1 div.sk-label-container {\n",
+       "  text-align: center;\n",
+       "}\n",
+       "\n",
+       "/* Estimator-specific */\n",
+       "#sk-container-id-1 div.sk-estimator {\n",
+       "  font-family: monospace;\n",
+       "  border: 1px dotted var(--sklearn-color-border-box);\n",
+       "  border-radius: 0.25em;\n",
+       "  box-sizing: border-box;\n",
+       "  margin-bottom: 0.5em;\n",
+       "  /* unfitted */\n",
+       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-1 div.sk-estimator.fitted {\n",
+       "  /* fitted */\n",
+       "  background-color: var(--sklearn-color-fitted-level-0);\n",
+       "}\n",
+       "\n",
+       "/* on hover */\n",
+       "#sk-container-id-1 div.sk-estimator:hover {\n",
+       "  /* unfitted */\n",
+       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-1 div.sk-estimator.fitted:hover {\n",
+       "  /* fitted */\n",
+       "  background-color: var(--sklearn-color-fitted-level-2);\n",
+       "}\n",
+       "\n",
+       "/* Specification for estimator info (e.g. \"i\" and \"?\") */\n",
+       "\n",
+       "/* Common style for \"i\" and \"?\" */\n",
+       "\n",
+       ".sk-estimator-doc-link,\n",
+       "a:link.sk-estimator-doc-link,\n",
+       "a:visited.sk-estimator-doc-link {\n",
+       "  float: right;\n",
+       "  font-size: smaller;\n",
+       "  line-height: 1em;\n",
+       "  font-family: monospace;\n",
+       "  background-color: var(--sklearn-color-background);\n",
+       "  border-radius: 1em;\n",
+       "  height: 1em;\n",
+       "  width: 1em;\n",
+       "  text-decoration: none !important;\n",
+       "  margin-left: 0.5em;\n",
+       "  text-align: center;\n",
+       "  /* unfitted */\n",
+       "  border: var(--sklearn-color-unfitted-level-1) 1pt solid;\n",
+       "  color: var(--sklearn-color-unfitted-level-1);\n",
+       "}\n",
+       "\n",
+       ".sk-estimator-doc-link.fitted,\n",
+       "a:link.sk-estimator-doc-link.fitted,\n",
+       "a:visited.sk-estimator-doc-link.fitted {\n",
+       "  /* fitted */\n",
+       "  border: var(--sklearn-color-fitted-level-1) 1pt solid;\n",
+       "  color: var(--sklearn-color-fitted-level-1);\n",
+       "}\n",
+       "\n",
+       "/* On hover */\n",
+       "div.sk-estimator:hover .sk-estimator-doc-link:hover,\n",
+       ".sk-estimator-doc-link:hover,\n",
+       "div.sk-label-container:hover .sk-estimator-doc-link:hover,\n",
+       ".sk-estimator-doc-link:hover {\n",
+       "  /* unfitted */\n",
+       "  background-color: var(--sklearn-color-unfitted-level-3);\n",
+       "  color: var(--sklearn-color-background);\n",
+       "  text-decoration: none;\n",
+       "}\n",
+       "\n",
+       "div.sk-estimator.fitted:hover .sk-estimator-doc-link.fitted:hover,\n",
+       ".sk-estimator-doc-link.fitted:hover,\n",
+       "div.sk-label-container:hover .sk-estimator-doc-link.fitted:hover,\n",
+       ".sk-estimator-doc-link.fitted:hover {\n",
+       "  /* fitted */\n",
+       "  background-color: var(--sklearn-color-fitted-level-3);\n",
+       "  color: var(--sklearn-color-background);\n",
+       "  text-decoration: none;\n",
+       "}\n",
+       "\n",
+       "/* Span, style for the box shown on hovering the info icon */\n",
+       ".sk-estimator-doc-link span {\n",
+       "  display: none;\n",
+       "  z-index: 9999;\n",
+       "  position: relative;\n",
+       "  font-weight: normal;\n",
+       "  right: .2ex;\n",
+       "  padding: .5ex;\n",
+       "  margin: .5ex;\n",
+       "  width: min-content;\n",
+       "  min-width: 20ex;\n",
+       "  max-width: 50ex;\n",
+       "  color: var(--sklearn-color-text);\n",
+       "  box-shadow: 2pt 2pt 4pt #999;\n",
+       "  /* unfitted */\n",
+       "  background: var(--sklearn-color-unfitted-level-0);\n",
+       "  border: .5pt solid var(--sklearn-color-unfitted-level-3);\n",
+       "}\n",
+       "\n",
+       ".sk-estimator-doc-link.fitted span {\n",
+       "  /* fitted */\n",
+       "  background: var(--sklearn-color-fitted-level-0);\n",
+       "  border: var(--sklearn-color-fitted-level-3);\n",
+       "}\n",
+       "\n",
+       ".sk-estimator-doc-link:hover span {\n",
+       "  display: block;\n",
+       "}\n",
+       "\n",
+       "/* \"?\"-specific style due to the `<a>` HTML tag */\n",
+       "\n",
+       "#sk-container-id-1 a.estimator_doc_link {\n",
+       "  float: right;\n",
+       "  font-size: 1rem;\n",
+       "  line-height: 1em;\n",
+       "  font-family: monospace;\n",
+       "  background-color: var(--sklearn-color-background);\n",
+       "  border-radius: 1rem;\n",
+       "  height: 1rem;\n",
+       "  width: 1rem;\n",
+       "  text-decoration: none;\n",
+       "  /* unfitted */\n",
+       "  color: var(--sklearn-color-unfitted-level-1);\n",
+       "  border: var(--sklearn-color-unfitted-level-1) 1pt solid;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-1 a.estimator_doc_link.fitted {\n",
+       "  /* fitted */\n",
+       "  border: var(--sklearn-color-fitted-level-1) 1pt solid;\n",
+       "  color: var(--sklearn-color-fitted-level-1);\n",
+       "}\n",
+       "\n",
+       "/* On hover */\n",
+       "#sk-container-id-1 a.estimator_doc_link:hover {\n",
+       "  /* unfitted */\n",
+       "  background-color: var(--sklearn-color-unfitted-level-3);\n",
+       "  color: var(--sklearn-color-background);\n",
+       "  text-decoration: none;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-1 a.estimator_doc_link.fitted:hover {\n",
+       "  /* fitted */\n",
+       "  background-color: var(--sklearn-color-fitted-level-3);\n",
+       "}\n",
+       "\n",
+       ".estimator-table summary {\n",
+       "    padding: .5rem;\n",
+       "    font-family: monospace;\n",
+       "    cursor: pointer;\n",
+       "}\n",
+       "\n",
+       ".estimator-table details[open] {\n",
+       "    padding-left: 0.1rem;\n",
+       "    padding-right: 0.1rem;\n",
+       "    padding-bottom: 0.3rem;\n",
+       "}\n",
+       "\n",
+       ".estimator-table .parameters-table {\n",
+       "    margin-left: auto !important;\n",
+       "    margin-right: auto !important;\n",
+       "}\n",
+       "\n",
+       ".estimator-table .parameters-table tr:nth-child(odd) {\n",
+       "    background-color: #fff;\n",
+       "}\n",
+       "\n",
+       ".estimator-table .parameters-table tr:nth-child(even) {\n",
+       "    background-color: #f6f6f6;\n",
+       "}\n",
+       "\n",
+       ".estimator-table .parameters-table tr:hover {\n",
+       "    background-color: #e0e0e0;\n",
+       "}\n",
+       "\n",
+       ".estimator-table table td {\n",
+       "    border: 1px solid rgba(106, 105, 104, 0.232);\n",
+       "}\n",
+       "\n",
+       ".user-set td {\n",
+       "    color:rgb(255, 94, 0);\n",
+       "    text-align: left;\n",
+       "}\n",
+       "\n",
+       ".user-set td.value pre {\n",
+       "    color:rgb(255, 94, 0) !important;\n",
+       "    background-color: transparent !important;\n",
+       "}\n",
+       "\n",
+       ".default td {\n",
+       "    color: black;\n",
+       "    text-align: left;\n",
+       "}\n",
+       "\n",
+       ".user-set td i,\n",
+       ".default td i {\n",
+       "    color: black;\n",
+       "}\n",
+       "\n",
+       ".copy-paste-icon {\n",
+       "    background-image: url(data:image/svg+xml;base64,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);\n",
+       "    background-repeat: no-repeat;\n",
+       "    background-size: 14px 14px;\n",
+       "    background-position: 0;\n",
+       "    display: inline-block;\n",
+       "    width: 14px;\n",
+       "    height: 14px;\n",
+       "    cursor: pointer;\n",
+       "}\n",
+       "</style><body><div id=\"sk-container-id-1\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>KNeighborsClassifier()</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item\"><div class=\"sk-estimator  sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-1\" type=\"checkbox\" checked><label for=\"sk-estimator-id-1\" class=\"sk-toggleable__label  sk-toggleable__label-arrow\"><div><div>KNeighborsClassifier</div></div><div><a class=\"sk-estimator-doc-link \" rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.7/modules/generated/sklearn.neighbors.KNeighborsClassifier.html\">?<span>Documentation for KNeighborsClassifier</span></a><span class=\"sk-estimator-doc-link \">i<span>Not fitted</span></span></div></label><div class=\"sk-toggleable__content \" data-param-prefix=\"\">\n",
+       "        <div class=\"estimator-table\">\n",
+       "            <details>\n",
+       "                <summary>Parameters</summary>\n",
+       "                <table class=\"parameters-table\">\n",
+       "                  <tbody>\n",
+       "                    \n",
+       "        <tr class=\"default\">\n",
+       "            <td><i class=\"copy-paste-icon\"\n",
+       "                 onclick=\"copyToClipboard('n_neighbors',\n",
+       "                          this.parentElement.nextElementSibling)\"\n",
+       "            ></i></td>\n",
+       "            <td class=\"param\">n_neighbors&nbsp;</td>\n",
+       "            <td class=\"value\">5</td>\n",
+       "        </tr>\n",
+       "    \n",
+       "\n",
+       "        <tr class=\"default\">\n",
+       "            <td><i class=\"copy-paste-icon\"\n",
+       "                 onclick=\"copyToClipboard('weights',\n",
+       "                          this.parentElement.nextElementSibling)\"\n",
+       "            ></i></td>\n",
+       "            <td class=\"param\">weights&nbsp;</td>\n",
+       "            <td class=\"value\">&#x27;uniform&#x27;</td>\n",
+       "        </tr>\n",
+       "    \n",
+       "\n",
+       "        <tr class=\"default\">\n",
+       "            <td><i class=\"copy-paste-icon\"\n",
+       "                 onclick=\"copyToClipboard('algorithm',\n",
+       "                          this.parentElement.nextElementSibling)\"\n",
+       "            ></i></td>\n",
+       "            <td class=\"param\">algorithm&nbsp;</td>\n",
+       "            <td class=\"value\">&#x27;auto&#x27;</td>\n",
+       "        </tr>\n",
+       "    \n",
+       "\n",
+       "        <tr class=\"default\">\n",
+       "            <td><i class=\"copy-paste-icon\"\n",
+       "                 onclick=\"copyToClipboard('leaf_size',\n",
+       "                          this.parentElement.nextElementSibling)\"\n",
+       "            ></i></td>\n",
+       "            <td class=\"param\">leaf_size&nbsp;</td>\n",
+       "            <td class=\"value\">30</td>\n",
+       "        </tr>\n",
+       "    \n",
+       "\n",
+       "        <tr class=\"default\">\n",
+       "            <td><i class=\"copy-paste-icon\"\n",
+       "                 onclick=\"copyToClipboard('p',\n",
+       "                          this.parentElement.nextElementSibling)\"\n",
+       "            ></i></td>\n",
+       "            <td class=\"param\">p&nbsp;</td>\n",
+       "            <td class=\"value\">2</td>\n",
+       "        </tr>\n",
+       "    \n",
+       "\n",
+       "        <tr class=\"default\">\n",
+       "            <td><i class=\"copy-paste-icon\"\n",
+       "                 onclick=\"copyToClipboard('metric',\n",
+       "                          this.parentElement.nextElementSibling)\"\n",
+       "            ></i></td>\n",
+       "            <td class=\"param\">metric&nbsp;</td>\n",
+       "            <td class=\"value\">&#x27;minkowski&#x27;</td>\n",
+       "        </tr>\n",
+       "    \n",
+       "\n",
+       "        <tr class=\"default\">\n",
+       "            <td><i class=\"copy-paste-icon\"\n",
+       "                 onclick=\"copyToClipboard('metric_params',\n",
+       "                          this.parentElement.nextElementSibling)\"\n",
+       "            ></i></td>\n",
+       "            <td class=\"param\">metric_params&nbsp;</td>\n",
+       "            <td class=\"value\">None</td>\n",
+       "        </tr>\n",
+       "    \n",
+       "\n",
+       "        <tr class=\"default\">\n",
+       "            <td><i class=\"copy-paste-icon\"\n",
+       "                 onclick=\"copyToClipboard('n_jobs',\n",
+       "                          this.parentElement.nextElementSibling)\"\n",
+       "            ></i></td>\n",
+       "            <td class=\"param\">n_jobs&nbsp;</td>\n",
+       "            <td class=\"value\">None</td>\n",
+       "        </tr>\n",
+       "    \n",
+       "                  </tbody>\n",
+       "                </table>\n",
+       "            </details>\n",
+       "        </div>\n",
+       "    </div></div></div></div></div><script>function copyToClipboard(text, element) {\n",
+       "    // Get the parameter prefix from the closest toggleable content\n",
+       "    const toggleableContent = element.closest('.sk-toggleable__content');\n",
+       "    const paramPrefix = toggleableContent ? toggleableContent.dataset.paramPrefix : '';\n",
+       "    const fullParamName = paramPrefix ? `${paramPrefix}${text}` : text;\n",
+       "\n",
+       "    const originalStyle = element.style;\n",
+       "    const computedStyle = window.getComputedStyle(element);\n",
+       "    const originalWidth = computedStyle.width;\n",
+       "    const originalHTML = element.innerHTML.replace('Copied!', '');\n",
+       "\n",
+       "    navigator.clipboard.writeText(fullParamName)\n",
+       "        .then(() => {\n",
+       "            element.style.width = originalWidth;\n",
+       "            element.style.color = 'green';\n",
+       "            element.innerHTML = \"Copied!\";\n",
+       "\n",
+       "            setTimeout(() => {\n",
+       "                element.innerHTML = originalHTML;\n",
+       "                element.style = originalStyle;\n",
+       "            }, 2000);\n",
+       "        })\n",
+       "        .catch(err => {\n",
+       "            console.error('Failed to copy:', err);\n",
+       "            element.style.color = 'red';\n",
+       "            element.innerHTML = \"Failed!\";\n",
+       "            setTimeout(() => {\n",
+       "                element.innerHTML = originalHTML;\n",
+       "                element.style = originalStyle;\n",
+       "            }, 2000);\n",
+       "        });\n",
+       "    return false;\n",
+       "}\n",
+       "\n",
+       "document.querySelectorAll('.fa-regular.fa-copy').forEach(function(element) {\n",
+       "    const toggleableContent = element.closest('.sk-toggleable__content');\n",
+       "    const paramPrefix = toggleableContent ? toggleableContent.dataset.paramPrefix : '';\n",
+       "    const paramName = element.parentElement.nextElementSibling.textContent.trim();\n",
+       "    const fullParamName = paramPrefix ? `${paramPrefix}${paramName}` : paramName;\n",
+       "\n",
+       "    element.setAttribute('title', fullParamName);\n",
+       "});\n",
+       "</script></body>"
       ],
       "text/plain": [
        "KNeighborsClassifier()"
       ]
      },
-     "execution_count": 12,
+     "execution_count": 10,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -1094,19 +1704,628 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 13,
+   "execution_count": 11,
    "metadata": {},
    "outputs": [
     {
      "data": {
       "text/html": [
-       "<style>#sk-container-id-2 {color: black;background-color: white;}#sk-container-id-2 pre{padding: 0;}#sk-container-id-2 div.sk-toggleable {background-color: white;}#sk-container-id-2 label.sk-toggleable__label {cursor: pointer;display: block;width: 100%;margin-bottom: 0;padding: 0.3em;box-sizing: border-box;text-align: center;}#sk-container-id-2 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-2 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-2 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-2 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-2 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-2 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-2 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-container-id-2 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-2 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-2 input.sk-hidden--visually {border: 0;clip: rect(1px 1px 1px 1px);clip: rect(1px, 1px, 1px, 1px);height: 1px;margin: -1px;overflow: hidden;padding: 0;position: absolute;width: 1px;}#sk-container-id-2 div.sk-estimator {font-family: monospace;background-color: #f0f8ff;border: 1px dotted black;border-radius: 0.25em;box-sizing: border-box;margin-bottom: 0.5em;}#sk-container-id-2 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-2 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-2 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-2 div.sk-serial::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: 0;}#sk-container-id-2 div.sk-serial {display: flex;flex-direction: column;align-items: center;background-color: white;padding-right: 0.2em;padding-left: 0.2em;position: relative;}#sk-container-id-2 div.sk-item {position: relative;z-index: 1;}#sk-container-id-2 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-2 div.sk-item::before, #sk-container-id-2 div.sk-parallel-item::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: -1;}#sk-container-id-2 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-2 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-2 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-2 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-2 div.sk-dashed-wrapped {border: 1px dashed gray;margin: 0 0.4em 0.5em 0.4em;box-sizing: border-box;padding-bottom: 0.4em;background-color: white;}#sk-container-id-2 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-2 div.sk-label-container {text-align: center;}#sk-container-id-2 div.sk-container {/* jupyter's `normalize.less` sets `[hidden] { display: none; }` but bootstrap.min.css set `[hidden] { display: none !important; }` so we also need the `!important` here to be able to override the default hidden behavior on the sphinx rendered scikit-learn.org. See: https://github.com/scikit-learn/scikit-learn/issues/21755 */display: inline-block !important;position: relative;}#sk-container-id-2 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-2\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>KNeighborsClassifier()</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-2\" type=\"checkbox\" checked><label for=\"sk-estimator-id-2\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">KNeighborsClassifier</label><div class=\"sk-toggleable__content\"><pre>KNeighborsClassifier()</pre></div></div></div></div></div>"
+       "<style>#sk-container-id-2 {\n",
+       "  /* Definition of color scheme common for light and dark mode */\n",
+       "  --sklearn-color-text: #000;\n",
+       "  --sklearn-color-text-muted: #666;\n",
+       "  --sklearn-color-line: gray;\n",
+       "  /* Definition of color scheme for unfitted estimators */\n",
+       "  --sklearn-color-unfitted-level-0: #fff5e6;\n",
+       "  --sklearn-color-unfitted-level-1: #f6e4d2;\n",
+       "  --sklearn-color-unfitted-level-2: #ffe0b3;\n",
+       "  --sklearn-color-unfitted-level-3: chocolate;\n",
+       "  /* Definition of color scheme for fitted estimators */\n",
+       "  --sklearn-color-fitted-level-0: #f0f8ff;\n",
+       "  --sklearn-color-fitted-level-1: #d4ebff;\n",
+       "  --sklearn-color-fitted-level-2: #b3dbfd;\n",
+       "  --sklearn-color-fitted-level-3: cornflowerblue;\n",
+       "\n",
+       "  /* Specific color for light theme */\n",
+       "  --sklearn-color-text-on-default-background: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, black)));\n",
+       "  --sklearn-color-background: var(--sg-background-color, var(--theme-background, var(--jp-layout-color0, white)));\n",
+       "  --sklearn-color-border-box: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, black)));\n",
+       "  --sklearn-color-icon: #696969;\n",
+       "\n",
+       "  @media (prefers-color-scheme: dark) {\n",
+       "    /* Redefinition of color scheme for dark theme */\n",
+       "    --sklearn-color-text-on-default-background: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, white)));\n",
+       "    --sklearn-color-background: var(--sg-background-color, var(--theme-background, var(--jp-layout-color0, #111)));\n",
+       "    --sklearn-color-border-box: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, white)));\n",
+       "    --sklearn-color-icon: #878787;\n",
+       "  }\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-2 {\n",
+       "  color: var(--sklearn-color-text);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-2 pre {\n",
+       "  padding: 0;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-2 input.sk-hidden--visually {\n",
+       "  border: 0;\n",
+       "  clip: rect(1px 1px 1px 1px);\n",
+       "  clip: rect(1px, 1px, 1px, 1px);\n",
+       "  height: 1px;\n",
+       "  margin: -1px;\n",
+       "  overflow: hidden;\n",
+       "  padding: 0;\n",
+       "  position: absolute;\n",
+       "  width: 1px;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-2 div.sk-dashed-wrapped {\n",
+       "  border: 1px dashed var(--sklearn-color-line);\n",
+       "  margin: 0 0.4em 0.5em 0.4em;\n",
+       "  box-sizing: border-box;\n",
+       "  padding-bottom: 0.4em;\n",
+       "  background-color: var(--sklearn-color-background);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-2 div.sk-container {\n",
+       "  /* jupyter's `normalize.less` sets `[hidden] { display: none; }`\n",
+       "     but bootstrap.min.css set `[hidden] { display: none !important; }`\n",
+       "     so we also need the `!important` here to be able to override the\n",
+       "     default hidden behavior on the sphinx rendered scikit-learn.org.\n",
+       "     See: https://github.com/scikit-learn/scikit-learn/issues/21755 */\n",
+       "  display: inline-block !important;\n",
+       "  position: relative;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-2 div.sk-text-repr-fallback {\n",
+       "  display: none;\n",
+       "}\n",
+       "\n",
+       "div.sk-parallel-item,\n",
+       "div.sk-serial,\n",
+       "div.sk-item {\n",
+       "  /* draw centered vertical line to link estimators */\n",
+       "  background-image: linear-gradient(var(--sklearn-color-text-on-default-background), var(--sklearn-color-text-on-default-background));\n",
+       "  background-size: 2px 100%;\n",
+       "  background-repeat: no-repeat;\n",
+       "  background-position: center center;\n",
+       "}\n",
+       "\n",
+       "/* Parallel-specific style estimator block */\n",
+       "\n",
+       "#sk-container-id-2 div.sk-parallel-item::after {\n",
+       "  content: \"\";\n",
+       "  width: 100%;\n",
+       "  border-bottom: 2px solid var(--sklearn-color-text-on-default-background);\n",
+       "  flex-grow: 1;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-2 div.sk-parallel {\n",
+       "  display: flex;\n",
+       "  align-items: stretch;\n",
+       "  justify-content: center;\n",
+       "  background-color: var(--sklearn-color-background);\n",
+       "  position: relative;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-2 div.sk-parallel-item {\n",
+       "  display: flex;\n",
+       "  flex-direction: column;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-2 div.sk-parallel-item:first-child::after {\n",
+       "  align-self: flex-end;\n",
+       "  width: 50%;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-2 div.sk-parallel-item:last-child::after {\n",
+       "  align-self: flex-start;\n",
+       "  width: 50%;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-2 div.sk-parallel-item:only-child::after {\n",
+       "  width: 0;\n",
+       "}\n",
+       "\n",
+       "/* Serial-specific style estimator block */\n",
+       "\n",
+       "#sk-container-id-2 div.sk-serial {\n",
+       "  display: flex;\n",
+       "  flex-direction: column;\n",
+       "  align-items: center;\n",
+       "  background-color: var(--sklearn-color-background);\n",
+       "  padding-right: 1em;\n",
+       "  padding-left: 1em;\n",
+       "}\n",
+       "\n",
+       "\n",
+       "/* Toggleable style: style used for estimator/Pipeline/ColumnTransformer box that is\n",
+       "clickable and can be expanded/collapsed.\n",
+       "- Pipeline and ColumnTransformer use this feature and define the default style\n",
+       "- Estimators will overwrite some part of the style using the `sk-estimator` class\n",
+       "*/\n",
+       "\n",
+       "/* Pipeline and ColumnTransformer style (default) */\n",
+       "\n",
+       "#sk-container-id-2 div.sk-toggleable {\n",
+       "  /* Default theme specific background. It is overwritten whether we have a\n",
+       "  specific estimator or a Pipeline/ColumnTransformer */\n",
+       "  background-color: var(--sklearn-color-background);\n",
+       "}\n",
+       "\n",
+       "/* Toggleable label */\n",
+       "#sk-container-id-2 label.sk-toggleable__label {\n",
+       "  cursor: pointer;\n",
+       "  display: flex;\n",
+       "  width: 100%;\n",
+       "  margin-bottom: 0;\n",
+       "  padding: 0.5em;\n",
+       "  box-sizing: border-box;\n",
+       "  text-align: center;\n",
+       "  align-items: start;\n",
+       "  justify-content: space-between;\n",
+       "  gap: 0.5em;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-2 label.sk-toggleable__label .caption {\n",
+       "  font-size: 0.6rem;\n",
+       "  font-weight: lighter;\n",
+       "  color: var(--sklearn-color-text-muted);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-2 label.sk-toggleable__label-arrow:before {\n",
+       "  /* Arrow on the left of the label */\n",
+       "  content: \"▸\";\n",
+       "  float: left;\n",
+       "  margin-right: 0.25em;\n",
+       "  color: var(--sklearn-color-icon);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-2 label.sk-toggleable__label-arrow:hover:before {\n",
+       "  color: var(--sklearn-color-text);\n",
+       "}\n",
+       "\n",
+       "/* Toggleable content - dropdown */\n",
+       "\n",
+       "#sk-container-id-2 div.sk-toggleable__content {\n",
+       "  display: none;\n",
+       "  text-align: left;\n",
+       "  /* unfitted */\n",
+       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-2 div.sk-toggleable__content.fitted {\n",
+       "  /* fitted */\n",
+       "  background-color: var(--sklearn-color-fitted-level-0);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-2 div.sk-toggleable__content pre {\n",
+       "  margin: 0.2em;\n",
+       "  border-radius: 0.25em;\n",
+       "  color: var(--sklearn-color-text);\n",
+       "  /* unfitted */\n",
+       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-2 div.sk-toggleable__content.fitted pre {\n",
+       "  /* unfitted */\n",
+       "  background-color: var(--sklearn-color-fitted-level-0);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-2 input.sk-toggleable__control:checked~div.sk-toggleable__content {\n",
+       "  /* Expand drop-down */\n",
+       "  display: block;\n",
+       "  width: 100%;\n",
+       "  overflow: visible;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-2 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {\n",
+       "  content: \"▾\";\n",
+       "}\n",
+       "\n",
+       "/* Pipeline/ColumnTransformer-specific style */\n",
+       "\n",
+       "#sk-container-id-2 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
+       "  color: var(--sklearn-color-text);\n",
+       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-2 div.sk-label.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
+       "  background-color: var(--sklearn-color-fitted-level-2);\n",
+       "}\n",
+       "\n",
+       "/* Estimator-specific style */\n",
+       "\n",
+       "/* Colorize estimator box */\n",
+       "#sk-container-id-2 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
+       "  /* unfitted */\n",
+       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-2 div.sk-estimator.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
+       "  /* fitted */\n",
+       "  background-color: var(--sklearn-color-fitted-level-2);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-2 div.sk-label label.sk-toggleable__label,\n",
+       "#sk-container-id-2 div.sk-label label {\n",
+       "  /* The background is the default theme color */\n",
+       "  color: var(--sklearn-color-text-on-default-background);\n",
+       "}\n",
+       "\n",
+       "/* On hover, darken the color of the background */\n",
+       "#sk-container-id-2 div.sk-label:hover label.sk-toggleable__label {\n",
+       "  color: var(--sklearn-color-text);\n",
+       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
+       "}\n",
+       "\n",
+       "/* Label box, darken color on hover, fitted */\n",
+       "#sk-container-id-2 div.sk-label.fitted:hover label.sk-toggleable__label.fitted {\n",
+       "  color: var(--sklearn-color-text);\n",
+       "  background-color: var(--sklearn-color-fitted-level-2);\n",
+       "}\n",
+       "\n",
+       "/* Estimator label */\n",
+       "\n",
+       "#sk-container-id-2 div.sk-label label {\n",
+       "  font-family: monospace;\n",
+       "  font-weight: bold;\n",
+       "  display: inline-block;\n",
+       "  line-height: 1.2em;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-2 div.sk-label-container {\n",
+       "  text-align: center;\n",
+       "}\n",
+       "\n",
+       "/* Estimator-specific */\n",
+       "#sk-container-id-2 div.sk-estimator {\n",
+       "  font-family: monospace;\n",
+       "  border: 1px dotted var(--sklearn-color-border-box);\n",
+       "  border-radius: 0.25em;\n",
+       "  box-sizing: border-box;\n",
+       "  margin-bottom: 0.5em;\n",
+       "  /* unfitted */\n",
+       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-2 div.sk-estimator.fitted {\n",
+       "  /* fitted */\n",
+       "  background-color: var(--sklearn-color-fitted-level-0);\n",
+       "}\n",
+       "\n",
+       "/* on hover */\n",
+       "#sk-container-id-2 div.sk-estimator:hover {\n",
+       "  /* unfitted */\n",
+       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-2 div.sk-estimator.fitted:hover {\n",
+       "  /* fitted */\n",
+       "  background-color: var(--sklearn-color-fitted-level-2);\n",
+       "}\n",
+       "\n",
+       "/* Specification for estimator info (e.g. \"i\" and \"?\") */\n",
+       "\n",
+       "/* Common style for \"i\" and \"?\" */\n",
+       "\n",
+       ".sk-estimator-doc-link,\n",
+       "a:link.sk-estimator-doc-link,\n",
+       "a:visited.sk-estimator-doc-link {\n",
+       "  float: right;\n",
+       "  font-size: smaller;\n",
+       "  line-height: 1em;\n",
+       "  font-family: monospace;\n",
+       "  background-color: var(--sklearn-color-background);\n",
+       "  border-radius: 1em;\n",
+       "  height: 1em;\n",
+       "  width: 1em;\n",
+       "  text-decoration: none !important;\n",
+       "  margin-left: 0.5em;\n",
+       "  text-align: center;\n",
+       "  /* unfitted */\n",
+       "  border: var(--sklearn-color-unfitted-level-1) 1pt solid;\n",
+       "  color: var(--sklearn-color-unfitted-level-1);\n",
+       "}\n",
+       "\n",
+       ".sk-estimator-doc-link.fitted,\n",
+       "a:link.sk-estimator-doc-link.fitted,\n",
+       "a:visited.sk-estimator-doc-link.fitted {\n",
+       "  /* fitted */\n",
+       "  border: var(--sklearn-color-fitted-level-1) 1pt solid;\n",
+       "  color: var(--sklearn-color-fitted-level-1);\n",
+       "}\n",
+       "\n",
+       "/* On hover */\n",
+       "div.sk-estimator:hover .sk-estimator-doc-link:hover,\n",
+       ".sk-estimator-doc-link:hover,\n",
+       "div.sk-label-container:hover .sk-estimator-doc-link:hover,\n",
+       ".sk-estimator-doc-link:hover {\n",
+       "  /* unfitted */\n",
+       "  background-color: var(--sklearn-color-unfitted-level-3);\n",
+       "  color: var(--sklearn-color-background);\n",
+       "  text-decoration: none;\n",
+       "}\n",
+       "\n",
+       "div.sk-estimator.fitted:hover .sk-estimator-doc-link.fitted:hover,\n",
+       ".sk-estimator-doc-link.fitted:hover,\n",
+       "div.sk-label-container:hover .sk-estimator-doc-link.fitted:hover,\n",
+       ".sk-estimator-doc-link.fitted:hover {\n",
+       "  /* fitted */\n",
+       "  background-color: var(--sklearn-color-fitted-level-3);\n",
+       "  color: var(--sklearn-color-background);\n",
+       "  text-decoration: none;\n",
+       "}\n",
+       "\n",
+       "/* Span, style for the box shown on hovering the info icon */\n",
+       ".sk-estimator-doc-link span {\n",
+       "  display: none;\n",
+       "  z-index: 9999;\n",
+       "  position: relative;\n",
+       "  font-weight: normal;\n",
+       "  right: .2ex;\n",
+       "  padding: .5ex;\n",
+       "  margin: .5ex;\n",
+       "  width: min-content;\n",
+       "  min-width: 20ex;\n",
+       "  max-width: 50ex;\n",
+       "  color: var(--sklearn-color-text);\n",
+       "  box-shadow: 2pt 2pt 4pt #999;\n",
+       "  /* unfitted */\n",
+       "  background: var(--sklearn-color-unfitted-level-0);\n",
+       "  border: .5pt solid var(--sklearn-color-unfitted-level-3);\n",
+       "}\n",
+       "\n",
+       ".sk-estimator-doc-link.fitted span {\n",
+       "  /* fitted */\n",
+       "  background: var(--sklearn-color-fitted-level-0);\n",
+       "  border: var(--sklearn-color-fitted-level-3);\n",
+       "}\n",
+       "\n",
+       ".sk-estimator-doc-link:hover span {\n",
+       "  display: block;\n",
+       "}\n",
+       "\n",
+       "/* \"?\"-specific style due to the `<a>` HTML tag */\n",
+       "\n",
+       "#sk-container-id-2 a.estimator_doc_link {\n",
+       "  float: right;\n",
+       "  font-size: 1rem;\n",
+       "  line-height: 1em;\n",
+       "  font-family: monospace;\n",
+       "  background-color: var(--sklearn-color-background);\n",
+       "  border-radius: 1rem;\n",
+       "  height: 1rem;\n",
+       "  width: 1rem;\n",
+       "  text-decoration: none;\n",
+       "  /* unfitted */\n",
+       "  color: var(--sklearn-color-unfitted-level-1);\n",
+       "  border: var(--sklearn-color-unfitted-level-1) 1pt solid;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-2 a.estimator_doc_link.fitted {\n",
+       "  /* fitted */\n",
+       "  border: var(--sklearn-color-fitted-level-1) 1pt solid;\n",
+       "  color: var(--sklearn-color-fitted-level-1);\n",
+       "}\n",
+       "\n",
+       "/* On hover */\n",
+       "#sk-container-id-2 a.estimator_doc_link:hover {\n",
+       "  /* unfitted */\n",
+       "  background-color: var(--sklearn-color-unfitted-level-3);\n",
+       "  color: var(--sklearn-color-background);\n",
+       "  text-decoration: none;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-2 a.estimator_doc_link.fitted:hover {\n",
+       "  /* fitted */\n",
+       "  background-color: var(--sklearn-color-fitted-level-3);\n",
+       "}\n",
+       "\n",
+       ".estimator-table summary {\n",
+       "    padding: .5rem;\n",
+       "    font-family: monospace;\n",
+       "    cursor: pointer;\n",
+       "}\n",
+       "\n",
+       ".estimator-table details[open] {\n",
+       "    padding-left: 0.1rem;\n",
+       "    padding-right: 0.1rem;\n",
+       "    padding-bottom: 0.3rem;\n",
+       "}\n",
+       "\n",
+       ".estimator-table .parameters-table {\n",
+       "    margin-left: auto !important;\n",
+       "    margin-right: auto !important;\n",
+       "}\n",
+       "\n",
+       ".estimator-table .parameters-table tr:nth-child(odd) {\n",
+       "    background-color: #fff;\n",
+       "}\n",
+       "\n",
+       ".estimator-table .parameters-table tr:nth-child(even) {\n",
+       "    background-color: #f6f6f6;\n",
+       "}\n",
+       "\n",
+       ".estimator-table .parameters-table tr:hover {\n",
+       "    background-color: #e0e0e0;\n",
+       "}\n",
+       "\n",
+       ".estimator-table table td {\n",
+       "    border: 1px solid rgba(106, 105, 104, 0.232);\n",
+       "}\n",
+       "\n",
+       ".user-set td {\n",
+       "    color:rgb(255, 94, 0);\n",
+       "    text-align: left;\n",
+       "}\n",
+       "\n",
+       ".user-set td.value pre {\n",
+       "    color:rgb(255, 94, 0) !important;\n",
+       "    background-color: transparent !important;\n",
+       "}\n",
+       "\n",
+       ".default td {\n",
+       "    color: black;\n",
+       "    text-align: left;\n",
+       "}\n",
+       "\n",
+       ".user-set td i,\n",
+       ".default td i {\n",
+       "    color: black;\n",
+       "}\n",
+       "\n",
+       ".copy-paste-icon {\n",
+       "    background-image: url(data:image/svg+xml;base64,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);\n",
+       "    background-repeat: no-repeat;\n",
+       "    background-size: 14px 14px;\n",
+       "    background-position: 0;\n",
+       "    display: inline-block;\n",
+       "    width: 14px;\n",
+       "    height: 14px;\n",
+       "    cursor: pointer;\n",
+       "}\n",
+       "</style><body><div id=\"sk-container-id-2\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>KNeighborsClassifier()</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item\"><div class=\"sk-estimator fitted sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-2\" type=\"checkbox\" checked><label for=\"sk-estimator-id-2\" class=\"sk-toggleable__label fitted sk-toggleable__label-arrow\"><div><div>KNeighborsClassifier</div></div><div><a class=\"sk-estimator-doc-link fitted\" rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.7/modules/generated/sklearn.neighbors.KNeighborsClassifier.html\">?<span>Documentation for KNeighborsClassifier</span></a><span class=\"sk-estimator-doc-link fitted\">i<span>Fitted</span></span></div></label><div class=\"sk-toggleable__content fitted\" data-param-prefix=\"\">\n",
+       "        <div class=\"estimator-table\">\n",
+       "            <details>\n",
+       "                <summary>Parameters</summary>\n",
+       "                <table class=\"parameters-table\">\n",
+       "                  <tbody>\n",
+       "                    \n",
+       "        <tr class=\"default\">\n",
+       "            <td><i class=\"copy-paste-icon\"\n",
+       "                 onclick=\"copyToClipboard('n_neighbors',\n",
+       "                          this.parentElement.nextElementSibling)\"\n",
+       "            ></i></td>\n",
+       "            <td class=\"param\">n_neighbors&nbsp;</td>\n",
+       "            <td class=\"value\">5</td>\n",
+       "        </tr>\n",
+       "    \n",
+       "\n",
+       "        <tr class=\"default\">\n",
+       "            <td><i class=\"copy-paste-icon\"\n",
+       "                 onclick=\"copyToClipboard('weights',\n",
+       "                          this.parentElement.nextElementSibling)\"\n",
+       "            ></i></td>\n",
+       "            <td class=\"param\">weights&nbsp;</td>\n",
+       "            <td class=\"value\">&#x27;uniform&#x27;</td>\n",
+       "        </tr>\n",
+       "    \n",
+       "\n",
+       "        <tr class=\"default\">\n",
+       "            <td><i class=\"copy-paste-icon\"\n",
+       "                 onclick=\"copyToClipboard('algorithm',\n",
+       "                          this.parentElement.nextElementSibling)\"\n",
+       "            ></i></td>\n",
+       "            <td class=\"param\">algorithm&nbsp;</td>\n",
+       "            <td class=\"value\">&#x27;auto&#x27;</td>\n",
+       "        </tr>\n",
+       "    \n",
+       "\n",
+       "        <tr class=\"default\">\n",
+       "            <td><i class=\"copy-paste-icon\"\n",
+       "                 onclick=\"copyToClipboard('leaf_size',\n",
+       "                          this.parentElement.nextElementSibling)\"\n",
+       "            ></i></td>\n",
+       "            <td class=\"param\">leaf_size&nbsp;</td>\n",
+       "            <td class=\"value\">30</td>\n",
+       "        </tr>\n",
+       "    \n",
+       "\n",
+       "        <tr class=\"default\">\n",
+       "            <td><i class=\"copy-paste-icon\"\n",
+       "                 onclick=\"copyToClipboard('p',\n",
+       "                          this.parentElement.nextElementSibling)\"\n",
+       "            ></i></td>\n",
+       "            <td class=\"param\">p&nbsp;</td>\n",
+       "            <td class=\"value\">2</td>\n",
+       "        </tr>\n",
+       "    \n",
+       "\n",
+       "        <tr class=\"default\">\n",
+       "            <td><i class=\"copy-paste-icon\"\n",
+       "                 onclick=\"copyToClipboard('metric',\n",
+       "                          this.parentElement.nextElementSibling)\"\n",
+       "            ></i></td>\n",
+       "            <td class=\"param\">metric&nbsp;</td>\n",
+       "            <td class=\"value\">&#x27;minkowski&#x27;</td>\n",
+       "        </tr>\n",
+       "    \n",
+       "\n",
+       "        <tr class=\"default\">\n",
+       "            <td><i class=\"copy-paste-icon\"\n",
+       "                 onclick=\"copyToClipboard('metric_params',\n",
+       "                          this.parentElement.nextElementSibling)\"\n",
+       "            ></i></td>\n",
+       "            <td class=\"param\">metric_params&nbsp;</td>\n",
+       "            <td class=\"value\">None</td>\n",
+       "        </tr>\n",
+       "    \n",
+       "\n",
+       "        <tr class=\"default\">\n",
+       "            <td><i class=\"copy-paste-icon\"\n",
+       "                 onclick=\"copyToClipboard('n_jobs',\n",
+       "                          this.parentElement.nextElementSibling)\"\n",
+       "            ></i></td>\n",
+       "            <td class=\"param\">n_jobs&nbsp;</td>\n",
+       "            <td class=\"value\">None</td>\n",
+       "        </tr>\n",
+       "    \n",
+       "                  </tbody>\n",
+       "                </table>\n",
+       "            </details>\n",
+       "        </div>\n",
+       "    </div></div></div></div></div><script>function copyToClipboard(text, element) {\n",
+       "    // Get the parameter prefix from the closest toggleable content\n",
+       "    const toggleableContent = element.closest('.sk-toggleable__content');\n",
+       "    const paramPrefix = toggleableContent ? toggleableContent.dataset.paramPrefix : '';\n",
+       "    const fullParamName = paramPrefix ? `${paramPrefix}${text}` : text;\n",
+       "\n",
+       "    const originalStyle = element.style;\n",
+       "    const computedStyle = window.getComputedStyle(element);\n",
+       "    const originalWidth = computedStyle.width;\n",
+       "    const originalHTML = element.innerHTML.replace('Copied!', '');\n",
+       "\n",
+       "    navigator.clipboard.writeText(fullParamName)\n",
+       "        .then(() => {\n",
+       "            element.style.width = originalWidth;\n",
+       "            element.style.color = 'green';\n",
+       "            element.innerHTML = \"Copied!\";\n",
+       "\n",
+       "            setTimeout(() => {\n",
+       "                element.innerHTML = originalHTML;\n",
+       "                element.style = originalStyle;\n",
+       "            }, 2000);\n",
+       "        })\n",
+       "        .catch(err => {\n",
+       "            console.error('Failed to copy:', err);\n",
+       "            element.style.color = 'red';\n",
+       "            element.innerHTML = \"Failed!\";\n",
+       "            setTimeout(() => {\n",
+       "                element.innerHTML = originalHTML;\n",
+       "                element.style = originalStyle;\n",
+       "            }, 2000);\n",
+       "        });\n",
+       "    return false;\n",
+       "}\n",
+       "\n",
+       "document.querySelectorAll('.fa-regular.fa-copy').forEach(function(element) {\n",
+       "    const toggleableContent = element.closest('.sk-toggleable__content');\n",
+       "    const paramPrefix = toggleableContent ? toggleableContent.dataset.paramPrefix : '';\n",
+       "    const paramName = element.parentElement.nextElementSibling.textContent.trim();\n",
+       "    const fullParamName = paramPrefix ? `${paramPrefix}${paramName}` : paramName;\n",
+       "\n",
+       "    element.setAttribute('title', fullParamName);\n",
+       "});\n",
+       "</script></body>"
       ],
       "text/plain": [
        "KNeighborsClassifier()"
       ]
      },
-     "execution_count": 13,
+     "execution_count": 11,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -1124,7 +2343,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 14,
+   "execution_count": 12,
    "metadata": {},
    "outputs": [
     {
@@ -1242,7 +2461,7 @@
        "[143 rows x 3 columns]"
       ]
      },
-     "execution_count": 14,
+     "execution_count": 12,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -1277,7 +2496,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 15,
+   "execution_count": 13,
    "metadata": {},
    "outputs": [
     {
@@ -1286,7 +2505,7 @@
        "0.9230769230769231"
       ]
      },
-     "execution_count": 15,
+     "execution_count": 13,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -1363,7 +2582,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 16,
+   "execution_count": 14,
    "metadata": {},
    "outputs": [
     {
@@ -1418,7 +2637,7 @@
        "Malignant       9         44"
       ]
      },
-     "execution_count": 16,
+     "execution_count": 14,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -1488,7 +2707,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 17,
+   "execution_count": 15,
    "metadata": {},
    "outputs": [
     {
@@ -1497,7 +2716,7 @@
        "0.9565217391304348"
       ]
      },
-     "execution_count": 17,
+     "execution_count": 15,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -1523,7 +2742,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 18,
+   "execution_count": 16,
    "metadata": {},
    "outputs": [
     {
@@ -1532,7 +2751,7 @@
        "0.8301886792452831"
       ]
      },
-     "execution_count": 18,
+     "execution_count": 16,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -1592,7 +2811,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 19,
+   "execution_count": 17,
    "metadata": {},
    "outputs": [
     {
@@ -1601,7 +2820,7 @@
        "0.8785046728971962"
       ]
      },
-     "execution_count": 19,
+     "execution_count": 17,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -1671,7 +2890,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 20,
+   "execution_count": 18,
    "metadata": {},
    "outputs": [
     {
@@ -1703,32 +2922,32 @@
        "  <tbody>\n",
        "    <tr>\n",
        "      <th>0</th>\n",
-       "      <td>0.002303</td>\n",
-       "      <td>0.004767</td>\n",
+       "      <td>0.002793</td>\n",
+       "      <td>0.003942</td>\n",
        "      <td>0.930233</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>1</th>\n",
-       "      <td>0.001559</td>\n",
-       "      <td>0.003668</td>\n",
+       "      <td>0.002390</td>\n",
+       "      <td>0.002543</td>\n",
        "      <td>0.894118</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>2</th>\n",
-       "      <td>0.001143</td>\n",
-       "      <td>0.002115</td>\n",
+       "      <td>0.001581</td>\n",
+       "      <td>0.002322</td>\n",
        "      <td>0.870588</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>3</th>\n",
-       "      <td>0.001001</td>\n",
-       "      <td>0.001845</td>\n",
+       "      <td>0.001555</td>\n",
+       "      <td>0.002171</td>\n",
        "      <td>0.952941</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>4</th>\n",
-       "      <td>0.000851</td>\n",
-       "      <td>0.001786</td>\n",
+       "      <td>0.001550</td>\n",
+       "      <td>0.002333</td>\n",
        "      <td>0.917647</td>\n",
        "    </tr>\n",
        "  </tbody>\n",
@@ -1737,14 +2956,14 @@
       ],
       "text/plain": [
        "   fit_time  score_time  test_score\n",
-       "0  0.002303    0.004767    0.930233\n",
-       "1  0.001559    0.003668    0.894118\n",
-       "2  0.001143    0.002115    0.870588\n",
-       "3  0.001001    0.001845    0.952941\n",
-       "4  0.000851    0.001786    0.917647"
+       "0  0.002793    0.003942    0.930233\n",
+       "1  0.002390    0.002543    0.894118\n",
+       "2  0.001581    0.002322    0.870588\n",
+       "3  0.001555    0.002171    0.952941\n",
+       "4  0.001550    0.002333    0.917647"
       ]
      },
-     "execution_count": 20,
+     "execution_count": 18,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -1768,7 +2987,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 21,
+   "execution_count": 19,
    "metadata": {},
    "outputs": [
     {
@@ -1800,14 +3019,14 @@
        "  <tbody>\n",
        "    <tr>\n",
        "      <th>mean</th>\n",
-       "      <td>0.001371</td>\n",
-       "      <td>0.002836</td>\n",
+       "      <td>0.001974</td>\n",
+       "      <td>0.002662</td>\n",
        "      <td>0.913105</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>sem</th>\n",
-       "      <td>0.000261</td>\n",
-       "      <td>0.000593</td>\n",
+       "      <td>0.000260</td>\n",
+       "      <td>0.000325</td>\n",
        "      <td>0.014264</td>\n",
        "    </tr>\n",
        "  </tbody>\n",
@@ -1816,11 +3035,11 @@
       ],
       "text/plain": [
        "      fit_time  score_time  test_score\n",
-       "mean  0.001371    0.002836    0.913105\n",
-       "sem   0.000261    0.000593    0.014264"
+       "mean  0.001974    0.002662    0.913105\n",
+       "sem   0.000260    0.000325    0.014264"
       ]
      },
-     "execution_count": 21,
+     "execution_count": 19,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -1884,7 +3103,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 22,
+   "execution_count": 20,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -1903,7 +3122,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 23,
+   "execution_count": 21,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -1916,7 +3135,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 24,
+   "execution_count": 22,
    "metadata": {},
    "outputs": [
     {
@@ -1964,10 +3183,10 @@
        "  <tbody>\n",
        "    <tr>\n",
        "      <th>0</th>\n",
-       "      <td>0.001236</td>\n",
-       "      <td>0.000532</td>\n",
-       "      <td>0.001921</td>\n",
-       "      <td>0.001362</td>\n",
+       "      <td>0.001896</td>\n",
+       "      <td>0.000519</td>\n",
+       "      <td>0.002995</td>\n",
+       "      <td>0.002168</td>\n",
        "      <td>1</td>\n",
        "      <td>{'n_neighbors': 1}</td>\n",
        "      <td>0.953488</td>\n",
@@ -1986,10 +3205,10 @@
        "    </tr>\n",
        "    <tr>\n",
        "      <th>1</th>\n",
-       "      <td>0.000739</td>\n",
-       "      <td>0.000039</td>\n",
-       "      <td>0.001109</td>\n",
-       "      <td>0.000077</td>\n",
+       "      <td>0.001637</td>\n",
+       "      <td>0.000327</td>\n",
+       "      <td>0.002368</td>\n",
+       "      <td>0.000441</td>\n",
        "      <td>6</td>\n",
        "      <td>{'n_neighbors': 6}</td>\n",
        "      <td>0.930233</td>\n",
@@ -2008,10 +3227,10 @@
        "    </tr>\n",
        "    <tr>\n",
        "      <th>2</th>\n",
-       "      <td>0.000642</td>\n",
-       "      <td>0.000028</td>\n",
-       "      <td>0.000983</td>\n",
-       "      <td>0.000054</td>\n",
+       "      <td>0.001541</td>\n",
+       "      <td>0.000021</td>\n",
+       "      <td>0.002211</td>\n",
+       "      <td>0.000102</td>\n",
        "      <td>11</td>\n",
        "      <td>{'n_neighbors': 11}</td>\n",
        "      <td>0.906977</td>\n",
@@ -2030,10 +3249,10 @@
        "    </tr>\n",
        "    <tr>\n",
        "      <th>3</th>\n",
-       "      <td>0.000591</td>\n",
-       "      <td>0.000042</td>\n",
-       "      <td>0.000902</td>\n",
-       "      <td>0.000028</td>\n",
+       "      <td>0.001547</td>\n",
+       "      <td>0.000027</td>\n",
+       "      <td>0.002193</td>\n",
+       "      <td>0.000043</td>\n",
        "      <td>16</td>\n",
        "      <td>{'n_neighbors': 16}</td>\n",
        "      <td>0.906977</td>\n",
@@ -2052,10 +3271,10 @@
        "    </tr>\n",
        "    <tr>\n",
        "      <th>4</th>\n",
-       "      <td>0.000560</td>\n",
-       "      <td>0.000055</td>\n",
-       "      <td>0.000879</td>\n",
-       "      <td>0.000042</td>\n",
+       "      <td>0.001529</td>\n",
+       "      <td>0.000015</td>\n",
+       "      <td>0.002230</td>\n",
+       "      <td>0.000029</td>\n",
        "      <td>21</td>\n",
        "      <td>{'n_neighbors': 21}</td>\n",
        "      <td>0.906977</td>\n",
@@ -2074,10 +3293,10 @@
        "    </tr>\n",
        "    <tr>\n",
        "      <th>5</th>\n",
-       "      <td>0.000539</td>\n",
-       "      <td>0.000011</td>\n",
-       "      <td>0.000888</td>\n",
-       "      <td>0.000066</td>\n",
+       "      <td>0.001694</td>\n",
+       "      <td>0.000361</td>\n",
+       "      <td>0.002454</td>\n",
+       "      <td>0.000522</td>\n",
        "      <td>26</td>\n",
        "      <td>{'n_neighbors': 26}</td>\n",
        "      <td>0.906977</td>\n",
@@ -2096,10 +3315,10 @@
        "    </tr>\n",
        "    <tr>\n",
        "      <th>6</th>\n",
-       "      <td>0.000553</td>\n",
-       "      <td>0.000030</td>\n",
-       "      <td>0.000899</td>\n",
-       "      <td>0.000049</td>\n",
+       "      <td>0.001602</td>\n",
+       "      <td>0.000108</td>\n",
+       "      <td>0.002455</td>\n",
+       "      <td>0.000422</td>\n",
        "      <td>31</td>\n",
        "      <td>{'n_neighbors': 31}</td>\n",
        "      <td>0.906977</td>\n",
@@ -2118,10 +3337,10 @@
        "    </tr>\n",
        "    <tr>\n",
        "      <th>7</th>\n",
-       "      <td>0.000532</td>\n",
-       "      <td>0.000011</td>\n",
-       "      <td>0.000890</td>\n",
-       "      <td>0.000015</td>\n",
+       "      <td>0.001536</td>\n",
+       "      <td>0.000010</td>\n",
+       "      <td>0.002269</td>\n",
+       "      <td>0.000028</td>\n",
        "      <td>36</td>\n",
        "      <td>{'n_neighbors': 36}</td>\n",
        "      <td>0.906977</td>\n",
@@ -2140,10 +3359,10 @@
        "    </tr>\n",
        "    <tr>\n",
        "      <th>8</th>\n",
-       "      <td>0.000541</td>\n",
-       "      <td>0.000023</td>\n",
-       "      <td>0.000918</td>\n",
-       "      <td>0.000048</td>\n",
+       "      <td>0.001527</td>\n",
+       "      <td>0.000021</td>\n",
+       "      <td>0.002312</td>\n",
+       "      <td>0.000047</td>\n",
        "      <td>41</td>\n",
        "      <td>{'n_neighbors': 41}</td>\n",
        "      <td>0.906977</td>\n",
@@ -2162,10 +3381,10 @@
        "    </tr>\n",
        "    <tr>\n",
        "      <th>9</th>\n",
-       "      <td>0.000551</td>\n",
-       "      <td>0.000041</td>\n",
-       "      <td>0.000936</td>\n",
-       "      <td>0.000028</td>\n",
+       "      <td>0.001526</td>\n",
+       "      <td>0.000017</td>\n",
+       "      <td>0.002338</td>\n",
+       "      <td>0.000043</td>\n",
        "      <td>46</td>\n",
        "      <td>{'n_neighbors': 46}</td>\n",
        "      <td>0.906977</td>\n",
@@ -2184,10 +3403,10 @@
        "    </tr>\n",
        "    <tr>\n",
        "      <th>10</th>\n",
+       "      <td>0.001715</td>\n",
+       "      <td>0.000345</td>\n",
+       "      <td>0.002650</td>\n",
        "      <td>0.000554</td>\n",
-       "      <td>0.000036</td>\n",
-       "      <td>0.000965</td>\n",
-       "      <td>0.000057</td>\n",
        "      <td>51</td>\n",
        "      <td>{'n_neighbors': 51}</td>\n",
        "      <td>0.906977</td>\n",
@@ -2206,10 +3425,10 @@
        "    </tr>\n",
        "    <tr>\n",
        "      <th>11</th>\n",
-       "      <td>0.000553</td>\n",
-       "      <td>0.000047</td>\n",
-       "      <td>0.000977</td>\n",
-       "      <td>0.000046</td>\n",
+       "      <td>0.001647</td>\n",
+       "      <td>0.000324</td>\n",
+       "      <td>0.002456</td>\n",
+       "      <td>0.000176</td>\n",
        "      <td>56</td>\n",
        "      <td>{'n_neighbors': 56}</td>\n",
        "      <td>0.906977</td>\n",
@@ -2228,10 +3447,10 @@
        "    </tr>\n",
        "    <tr>\n",
        "      <th>12</th>\n",
-       "      <td>0.000552</td>\n",
-       "      <td>0.000017</td>\n",
-       "      <td>0.001015</td>\n",
-       "      <td>0.000091</td>\n",
+       "      <td>0.001686</td>\n",
+       "      <td>0.000326</td>\n",
+       "      <td>0.002637</td>\n",
+       "      <td>0.000498</td>\n",
        "      <td>61</td>\n",
        "      <td>{'n_neighbors': 61}</td>\n",
        "      <td>0.906977</td>\n",
@@ -2250,10 +3469,10 @@
        "    </tr>\n",
        "    <tr>\n",
        "      <th>13</th>\n",
-       "      <td>0.000573</td>\n",
-       "      <td>0.000072</td>\n",
-       "      <td>0.001017</td>\n",
-       "      <td>0.000080</td>\n",
+       "      <td>0.002051</td>\n",
+       "      <td>0.000447</td>\n",
+       "      <td>0.003003</td>\n",
+       "      <td>0.000582</td>\n",
        "      <td>66</td>\n",
        "      <td>{'n_neighbors': 66}</td>\n",
        "      <td>0.930233</td>\n",
@@ -2272,10 +3491,10 @@
        "    </tr>\n",
        "    <tr>\n",
        "      <th>14</th>\n",
-       "      <td>0.000530</td>\n",
-       "      <td>0.000008</td>\n",
-       "      <td>0.001025</td>\n",
-       "      <td>0.000093</td>\n",
+       "      <td>0.002825</td>\n",
+       "      <td>0.001358</td>\n",
+       "      <td>0.003944</td>\n",
+       "      <td>0.001173</td>\n",
        "      <td>71</td>\n",
        "      <td>{'n_neighbors': 71}</td>\n",
        "      <td>0.930233</td>\n",
@@ -2294,10 +3513,10 @@
        "    </tr>\n",
        "    <tr>\n",
        "      <th>15</th>\n",
-       "      <td>0.000552</td>\n",
-       "      <td>0.000045</td>\n",
-       "      <td>0.001024</td>\n",
-       "      <td>0.000052</td>\n",
+       "      <td>0.002050</td>\n",
+       "      <td>0.000467</td>\n",
+       "      <td>0.003184</td>\n",
+       "      <td>0.000704</td>\n",
        "      <td>76</td>\n",
        "      <td>{'n_neighbors': 76}</td>\n",
        "      <td>0.930233</td>\n",
@@ -2316,10 +3535,10 @@
        "    </tr>\n",
        "    <tr>\n",
        "      <th>16</th>\n",
-       "      <td>0.000528</td>\n",
-       "      <td>0.000015</td>\n",
-       "      <td>0.001038</td>\n",
-       "      <td>0.000071</td>\n",
+       "      <td>0.002425</td>\n",
+       "      <td>0.000447</td>\n",
+       "      <td>0.003826</td>\n",
+       "      <td>0.000559</td>\n",
        "      <td>81</td>\n",
        "      <td>{'n_neighbors': 81}</td>\n",
        "      <td>0.930233</td>\n",
@@ -2338,10 +3557,10 @@
        "    </tr>\n",
        "    <tr>\n",
        "      <th>17</th>\n",
-       "      <td>0.000538</td>\n",
-       "      <td>0.000022</td>\n",
-       "      <td>0.001039</td>\n",
-       "      <td>0.000036</td>\n",
+       "      <td>0.003370</td>\n",
+       "      <td>0.001417</td>\n",
+       "      <td>0.004490</td>\n",
+       "      <td>0.002347</td>\n",
        "      <td>86</td>\n",
        "      <td>{'n_neighbors': 86}</td>\n",
        "      <td>0.906977</td>\n",
@@ -2360,10 +3579,10 @@
        "    </tr>\n",
        "    <tr>\n",
        "      <th>18</th>\n",
-       "      <td>0.000535</td>\n",
-       "      <td>0.000017</td>\n",
-       "      <td>0.001055</td>\n",
-       "      <td>0.000070</td>\n",
+       "      <td>0.001956</td>\n",
+       "      <td>0.000502</td>\n",
+       "      <td>0.012455</td>\n",
+       "      <td>0.027865</td>\n",
        "      <td>91</td>\n",
        "      <td>{'n_neighbors': 91}</td>\n",
        "      <td>0.906977</td>\n",
@@ -2382,10 +3601,10 @@
        "    </tr>\n",
        "    <tr>\n",
        "      <th>19</th>\n",
-       "      <td>0.000548</td>\n",
-       "      <td>0.000048</td>\n",
+       "      <td>0.003192</td>\n",
        "      <td>0.001064</td>\n",
-       "      <td>0.000042</td>\n",
+       "      <td>0.006115</td>\n",
+       "      <td>0.002523</td>\n",
        "      <td>96</td>\n",
        "      <td>{'n_neighbors': 96}</td>\n",
        "      <td>0.906977</td>\n",
@@ -2408,48 +3627,48 @@
       ],
       "text/plain": [
        "    mean_fit_time  std_fit_time  mean_score_time  std_score_time  \\\n",
-       "0        0.001236      0.000532         0.001921        0.001362   \n",
-       "1        0.000739      0.000039         0.001109        0.000077   \n",
-       "2        0.000642      0.000028         0.000983        0.000054   \n",
-       "3        0.000591      0.000042         0.000902        0.000028   \n",
-       "4        0.000560      0.000055         0.000879        0.000042   \n",
-       "5        0.000539      0.000011         0.000888        0.000066   \n",
-       "6        0.000553      0.000030         0.000899        0.000049   \n",
-       "7        0.000532      0.000011         0.000890        0.000015   \n",
-       "8        0.000541      0.000023         0.000918        0.000048   \n",
-       "9        0.000551      0.000041         0.000936        0.000028   \n",
-       "10       0.000554      0.000036         0.000965        0.000057   \n",
-       "11       0.000553      0.000047         0.000977        0.000046   \n",
-       "12       0.000552      0.000017         0.001015        0.000091   \n",
-       "13       0.000573      0.000072         0.001017        0.000080   \n",
-       "14       0.000530      0.000008         0.001025        0.000093   \n",
-       "15       0.000552      0.000045         0.001024        0.000052   \n",
-       "16       0.000528      0.000015         0.001038        0.000071   \n",
-       "17       0.000538      0.000022         0.001039        0.000036   \n",
-       "18       0.000535      0.000017         0.001055        0.000070   \n",
-       "19       0.000548      0.000048         0.001064        0.000042   \n",
-       "\n",
-       "   param_n_neighbors               params  split0_test_score  \\\n",
-       "0                  1   {'n_neighbors': 1}           0.953488   \n",
-       "1                  6   {'n_neighbors': 6}           0.930233   \n",
-       "2                 11  {'n_neighbors': 11}           0.906977   \n",
-       "3                 16  {'n_neighbors': 16}           0.906977   \n",
-       "4                 21  {'n_neighbors': 21}           0.906977   \n",
-       "5                 26  {'n_neighbors': 26}           0.906977   \n",
-       "6                 31  {'n_neighbors': 31}           0.906977   \n",
-       "7                 36  {'n_neighbors': 36}           0.906977   \n",
-       "8                 41  {'n_neighbors': 41}           0.906977   \n",
-       "9                 46  {'n_neighbors': 46}           0.906977   \n",
-       "10                51  {'n_neighbors': 51}           0.906977   \n",
-       "11                56  {'n_neighbors': 56}           0.906977   \n",
-       "12                61  {'n_neighbors': 61}           0.906977   \n",
-       "13                66  {'n_neighbors': 66}           0.930233   \n",
-       "14                71  {'n_neighbors': 71}           0.930233   \n",
-       "15                76  {'n_neighbors': 76}           0.930233   \n",
-       "16                81  {'n_neighbors': 81}           0.930233   \n",
-       "17                86  {'n_neighbors': 86}           0.906977   \n",
-       "18                91  {'n_neighbors': 91}           0.906977   \n",
-       "19                96  {'n_neighbors': 96}           0.906977   \n",
+       "0        0.001896      0.000519         0.002995        0.002168   \n",
+       "1        0.001637      0.000327         0.002368        0.000441   \n",
+       "2        0.001541      0.000021         0.002211        0.000102   \n",
+       "3        0.001547      0.000027         0.002193        0.000043   \n",
+       "4        0.001529      0.000015         0.002230        0.000029   \n",
+       "5        0.001694      0.000361         0.002454        0.000522   \n",
+       "6        0.001602      0.000108         0.002455        0.000422   \n",
+       "7        0.001536      0.000010         0.002269        0.000028   \n",
+       "8        0.001527      0.000021         0.002312        0.000047   \n",
+       "9        0.001526      0.000017         0.002338        0.000043   \n",
+       "10       0.001715      0.000345         0.002650        0.000554   \n",
+       "11       0.001647      0.000324         0.002456        0.000176   \n",
+       "12       0.001686      0.000326         0.002637        0.000498   \n",
+       "13       0.002051      0.000447         0.003003        0.000582   \n",
+       "14       0.002825      0.001358         0.003944        0.001173   \n",
+       "15       0.002050      0.000467         0.003184        0.000704   \n",
+       "16       0.002425      0.000447         0.003826        0.000559   \n",
+       "17       0.003370      0.001417         0.004490        0.002347   \n",
+       "18       0.001956      0.000502         0.012455        0.027865   \n",
+       "19       0.003192      0.001064         0.006115        0.002523   \n",
+       "\n",
+       "    param_n_neighbors               params  split0_test_score  \\\n",
+       "0                   1   {'n_neighbors': 1}           0.953488   \n",
+       "1                   6   {'n_neighbors': 6}           0.930233   \n",
+       "2                  11  {'n_neighbors': 11}           0.906977   \n",
+       "3                  16  {'n_neighbors': 16}           0.906977   \n",
+       "4                  21  {'n_neighbors': 21}           0.906977   \n",
+       "5                  26  {'n_neighbors': 26}           0.906977   \n",
+       "6                  31  {'n_neighbors': 31}           0.906977   \n",
+       "7                  36  {'n_neighbors': 36}           0.906977   \n",
+       "8                  41  {'n_neighbors': 41}           0.906977   \n",
+       "9                  46  {'n_neighbors': 46}           0.906977   \n",
+       "10                 51  {'n_neighbors': 51}           0.906977   \n",
+       "11                 56  {'n_neighbors': 56}           0.906977   \n",
+       "12                 61  {'n_neighbors': 61}           0.906977   \n",
+       "13                 66  {'n_neighbors': 66}           0.930233   \n",
+       "14                 71  {'n_neighbors': 71}           0.930233   \n",
+       "15                 76  {'n_neighbors': 76}           0.930233   \n",
+       "16                 81  {'n_neighbors': 81}           0.930233   \n",
+       "17                 86  {'n_neighbors': 86}           0.906977   \n",
+       "18                 91  {'n_neighbors': 91}           0.906977   \n",
+       "19                 96  {'n_neighbors': 96}           0.906977   \n",
        "\n",
        "    split1_test_score  split2_test_score  split3_test_score  \\\n",
        "0            0.837209           0.906977           0.860465   \n",
@@ -2540,7 +3759,7 @@
        "19        0.047625               14  "
       ]
      },
-     "execution_count": 24,
+     "execution_count": 22,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -2576,7 +3795,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 23,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -2601,12 +3820,12 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 25,
+   "execution_count": 24,
    "metadata": {},
    "outputs": [
     {
      "data": {
-      "image/png": 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",
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",
       "text/plain": [
        "<Figure size 1000x600 with 1 Axes>"
       ]
@@ -2641,7 +3860,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 26,
+   "execution_count": 25,
    "metadata": {},
    "outputs": [
     {
@@ -2650,7 +3869,7 @@
        "{'n_neighbors': 16}"
       ]
      },
-     "execution_count": 26,
+     "execution_count": 25,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -2683,12 +3902,12 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 27,
+   "execution_count": 26,
    "metadata": {},
    "outputs": [
     {
      "data": {
-      "image/png": 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",
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bJ7ZLze3aTu2YpHWsDhw4oEaNGqlKlSqaMGGCwsPD5e/vr0WLFumdd95J9l1x17FPTExUjRo1NGHChBQfT2+PGQDwNoRuAPAyderU0cKFC9WyZUs1adJEq1atUmhoqJYuXeqwX0RERKptDBkyRF988UWySbkkqXz58jIMQ2XLllWlSpVSbWP79u3666+/NHPmTHXr1s2+/dY6bqd8+fL65ZdfdP/99zsVpO69917de++9GjVqlL766it17dpVs2fP1pNPPul0qLqd/v37a+LEiRoxYoTDxF1JtUpSsWLF1LhxY5faLV26tBITExUdHe1wNnf//v0ZrjnpDPzN/vrrL+XJk8f+R4s8efJo7969yfbbs2ePfHx8nA5fxYsX13PPPafnnntOp06d0l133aVRo0Y5Fbpvx9mfufRI+uz37dunO+64w7795MmTOn/+vEqXLu3W10vJ999/rytXrui7775zOIudke7d5cuX144dO9Lc588//1SjRo3c8v0AAG/FmG4A8EKNGjXSrFmztH//fjVr1kxXr15V48aNHS5J3ZFTUr58eT322GP68MMPdeLECYfH2rVrJ19fX40YMSLZmTXDMHT27FlJN87G3byPYRgpdllPzaOPPqqEhAS98cYbyR67fv26zp8/L8nsUn1rLbVq1ZIk+9JHSbOhJz0nPZLOdn/77bfaunWrw2NRUVEKCgrS6NGjUxxXfmv3/lufK0kffPCBw/b33nsv3bUmWbt2rcOyXUeOHNG3336rpk2bytfXV76+vmratKm+/fZbHTp0yL7fyZMn9dVXX6levXq37Rovmb0abh0uUKxYMZUoUcItS085+zOXHi1atJAkTZw40WF70tlfZ5fcy4iUviuxsbGaPn16utts3769/vzzz2Szr9/8Oo8++qiOHTumadOmJdvn8uXLbluDHQByOs50A4CXatu2raZNm6aePXuqdevWWrJkiQIDA51+/muvvabPP/9ce/fudRjPW758eb355psaPHiwDh06pDZt2ih//vyKjo7WggUL9PTTT+vFF19UlSpVVL58eb344os6duyYgoKC9M033yQb2307DzzwgJ555hmNGTNGW7duVdOmTZUrVy7t27dPc+fO1aRJk9ShQwfNnDlTH3zwgdq2bavy5cvrwoULmjZtmoKCguyhKnfu3KpatarmzJmjSpUqqVChQqpevbp92SxnJY3t/vPPP5U3b1779qCgIE2ZMkWPP/647rrrLnXq1ElFixbV4cOH9eOPP+r+++/X+++/n2KbtWvXVvv27TVx4kSdPXvWvmTYX3/9Jcn5rs8pqV69uqKiohyWDJPM9cqTvPnmm1q6dKnq1aun5557Tn5+fvrwww915coVjR8/Ps3XuHDhgsLCwtShQwdFREQoX758+uWXX/THH3/o7bffTnftSZz9mUuPiIgIde/eXR999JHOnz+vBx54QBs2bNDMmTPVpk0bPfjggxmuPy1NmzaVv7+/WrVqpWeeeUYXL17UtGnTVKxYsVQnNEzLSy+9pHnz5umRRx5Rz549Vbt2bZ07d07fffedpk6dqoiICD3++OP6+uuv9eyzz2r58uW6//77lZCQoD179ujrr7+2r0cOALg9QjcAeLEePXro3LlzevHFF/XII49owYIFTo8trlChgh577DHNnDkz2WODBg1SpUqV9M4779jDW3h4uJo2barWrVtLMie3+v7779W3b1+NGTNGgYGBatu2rfr06XPbru23mjp1qmrXrq0PP/xQr776qvz8/FSmTBk99thjuv/++yXJHpRmz56tkydPKjg4WHXq1NGXX37pMJb9448/1vPPP68XXnhBV69e1bBhw1wO3QUKFFD//v0dQmuSLl26qESJEho7dqz+97//6cqVKypZsqTq16+f5mzan332mUJDQzVr1iwtWLBAjRs31pw5c1S5cmWX/lhyqwceeEB169bViBEjdPjwYVWtWlUzZsxwWKO8WrVqWrVqlQYPHqwxY8YoMTFRkZGR+uKLL5Kt0Z2SPHny6LnnntPPP/9sn2m8QoUK+uCDD5LNSJ9ezvzMpdfHH3+scuXKacaMGVqwYIFCQ0M1ePBgDRs2zB2lp6ly5cqaN2+ehgwZohdffFGhoaHq1auXihYtmmzmc2fly5dPq1at0rBhw7RgwQLNnDlTxYoVU6NGjewTFvr4+GjhwoV655139Nlnn2nBggXKkyePypUrp379+rm9Kz8A5FQ2IzNmVAEAAG63detW3Xnnnfriiy/UtWtXq8sBAAApYEw3AADZwOXLl5Ntmzhxonx8fNSgQQMLKgIAAM6gezkAANnA+PHjtWnTJj344IPy8/PT4sWLtXjxYj399NMs3QQAQBZG93IAALKBpUuXasSIEdq1a5cuXryoUqVK6fHHH9drr73m9jW+AQCA+xC6AQAAAADwEMZ0AwAAAADgIYRuAAAAAAA8hEFgKUhMTNTx48eVP39+2Ww2q8sBAAAAAHiQYRi6cOGCSpQoIR8f956bJnSn4Pjx48wECwAAAABe5siRIwoLC3Nrm4TuFOTPn1+S+YEHBQVZXA0AAAAAwJPi4uIUHh5uz4LuROhOQVKX8qCgIEI3AAAAAHgJTwwvZiI1AAAAAAA8hNANAAAAAICHELoBAAAAAPAQQjcAAAAAAB5C6AYAAAAAwEMI3QAAAAAAeAihGwAAAAAADyF0AwAAAADgIYRuAAAAAAA8hNANAAAAAICHELoBAAAAAPAQQjcAAAAAAB5C6AYAAAAAwEMI3QAAAAAAeAihGwAAAAAAD/GzugAAWUNCgrRqlRQTIxUvLtWvL/n6Zr02AQAAgOyE0A1A8+dL/fpJR4/e2BYWJk2aJLVrl3XaBAAAALIbupcDXm7+fKlDB8dwLEnHjpnb58/PGm0CAAAA2ZHNMAzD6iKymri4OAUHBys2NlZBQUFWlwN4TEKCVKZM8nCcxGYzz05HRzvfLdwTbQIAAACe5MkMSPdyIBty11jpn39OPRxLkmFIR45INWpIzv7uiYtzrs1Vq6SGDV0qFwAAAMh2CN1ANpOesdKXL0u7d0s7d5qXHTvM60OHnHvN3bszXHYyMTHubxMAAADIagjdQDaSNFb61kEhSWOlZ82S7rjDMVjv3CkdOJD8Oa4YNco82+2M7dul115Le7/oaLMmmy39dQEAAABZHWO6U8CYbmRFaY2VTkvhwlL16lK1ajeuq1SRatc2Q3tKvwkyMqY7tTZvVqeO9OabUuPGhG8AAABYhzHdAPTbb84F7rx5pVq1bgTrpEuxYikH20mTzLPkNptjSE7ad+JE18aL+/qm3WbbttKSJdKGDVLTplKDBtIbb5jXAAAAQE7CkmFAFrdjhzR0qNSli3P7f/SRtHq1NHWq9Pzz0kMPSSEhqZ9JbtdOmjdPKlnScXtYmLk9PWtqp9XmN99IBw9KL7wgBQSYf1B44AEpKsoM4gAAAEBOQffyFNC9HFbbs0f6+mtpzhxp1y7Xnrt8efpmBXfXjOiutnnsmDlmfNo06fp1c1vr1tLIkVJERMZeHwAAAHCGJzMgoTsFhG73sSrIZRWu1Lp/vxmyv/5a2rbtxnZ/f6lZM+mRR6RXXjHbctf466wkOtoM2p99JiUmmtsefVQaPtycHO5m3v5zBQAAAPcidGcyQrd7pGdpKyva9BRnao2ONkP2119Lmzff2M/Pzxzr3LGj9PDDUnDwjTY7dDBvpzRWOr3dwbOSvXulESOk2bPN9+jjIz32mDRsmFSuHD9XAAAAcD9CdyYjdGdcaktbZSQceqJNT0mr1m7dzLWvbx6/7OsrNWpkBu02baRChVJv+9aAGB5uTniWVd6/O2zfbo5lX7jQvO/nJz34oPTLL977cwUAAADPIHRnMkJ3xqS1tFVGlqFyZ5ue4srSXj4+5vjrjh3NYFekiPOv4S1doTduNMP34sW33y+n/1wBAADAcwjdmYzQnTErVphnJNNSsaKUP79zbV64IO3bl/Z+6Z1ETHJPkDUMae5cM0SnpV8/afBgc2ZxpO2996S+fdPe7557zDXJnXH2rPTHH2nvl5GfKwAAAGR9rNONbCUmxrn9nAnRrmrdWrrrruRrVKcVwtIzpvfsWbML9I4d5iXpdlycc7VGRhK4XeFsLwBnQrSrDh4kdAMAACB9CN1wu/h45/YbN06qWdO5fbdtM2fuTsuFC9LKleblZqGhZvi+NYwHB6c+pvfYMXP7F19IFSokD9cnTqRcg4/Pjdm3b6d48bT3wQ3Ofl6DBiWf7Tw1u3dLY8emvd+zz0qLFkmdOkktWkh58jjXPgAAAED38hTQvTx9rl83g/Tw4TfWW05JRsbeHjuW+nJZJUtK33xjrnG9Y4e0c6d5+fvv1NstWVI6c0a6csW5Om5VtqwZ5GvUMK+rVzcDeqVKt6+VccKuc+ZnwN0/V5I5gdvNP89585ozynfqZM4wHxDg6jsBAABAVsOY7kxG6Hbdnj3mjNxJXXvr1Llx211LW6V3uawLF6Rdu8wAfnMYP3bM+dcuWPBGt/WkgF21aupj0r1haS8reOJzTavNuXPNpcrmzDGXMbv5jzgFCkht25oB/KGHzIB+q+y0prg3TdAHAABwM49mQAPJxMbGGpKM2NhYq0vJ8hISDOOddwwjMNAwJMMIDjaMzz83jMREw/jmG8MICzO3J13Cw83t6eXONs+dM4xhwxzbSu3y1VfW1oobrPy5Skw0jLVrDaN/f8MoXtxx/yJFDOPZZw1jxQrDuH499XbDwtxfa0bb9GS7AAAA2YEnMyBnulPAmW7nREdLPXrcGD/dtKn0ySdmF98kWf0sn7Mzrad39mrOHHpGVvi5SkiQVq82z4DPnWsOU0hSooR0553Sjz8mf15WXFOctcoBAIC3o3t5JiN0355hSB9/LA0YIF28aI5xfest6ZlnbvwnPbvwxDhheJ/r16VffzUD+Pz50vnzaT+nUCHp7bfNifeckZhofuf++cd9bTrTLt8BAADgDQjdmYzQnbrjx6Unn5QWLzbv16snzZghlS9vaVkZwvhruNOVK2bwfe01qytxL9YqBwAAORnrdMNyhiHNmiX16WOeEQsIkEaNkvr3z/5nv9q1M4N1Sut0T5xI4IZrAgLMWe2dUbOm2RXdGcePm0vnubNNV9r9/HNzAkFn10sHAACAiTPdKeBMt6PTp6VevczluCSpdm3ps8/M2btzEsZfw108MVeAp+YfcLZdyZydPSpK6tJFat1aypfP+dcBAADIyuhensm8MXSnFji//VZ6+mnp1CnzP9yvvy4NHizlymV1xUDWZcWa4ukde+1Mu8HB5j5bt97YniePGby7dDGDuL9/6u3zxywAAJDVeTIDujDdDnKq+fPN/1A/+KD5H+gHH5RKlTLPlrVpYwbuatWk9euloUMJ3EBafH2lSZPM27dOLph0f+JE18KnJ9p0tt1PPpG2bJF27zZ/B1SoIF26ZK5b3rq1FBpq/nFuxQpzYrYkKf1uKVPG3A4AAOAtONOdAm86053aUkE3e/llaeRIc6wqAOfNn598roDw8IzNFeCJNl1t1zCkjRulr74yZ2yPibnxWMmSUseOZhB/5RWWIQMAANkD3cszmbeE7qRupTf/J/tWxYqZEy3RHRRIn6ywprgn201IkFauNAP4vHlSbGzar8MyZAAAIKshdGcybwndnpqYCYB3unLFXE5w0iTz90tafv3V+UncbsVYcQAA4E4sGQaPuLlLqDv2A+DdAgLMeSAuX3YudLdsKd15p1SjhrkcWfXq5u3ChW//vJS6woeFmWGfLusAACCrIXR7seLF3bsfAEjO/864fFn6/XfzcrPQ0ORBvGpVKW/e1OehOHbM3M5YcQAAkNVYPnv55MmTVaZMGQUGBioyMlIbNmxIdd9r165p5MiRKl++vAIDAxUREaElS5ZkqE1vVr++eXYoNTabOZFS/fqZVxOA7C/pd8uts6EnSRrTvWWLORb81VelVq2ksmXNx0+ckJYuld55R/rvf6U6daT8+aVy5aSuXVOe+DFpW//+ZtdzAACArMLS0D1nzhwNGDBAw4YN0+bNmxUREaGoqCidOnUqxf2HDBmiDz/8UO+995527dqlZ599Vm3bttWWLVvS3aY3u3mpoFtlZAkiAN7NmWXIJk2SatWSOneWRo2SvvtOOnhQunBBWrdO+vhjM0A3aiSFhJihOjpa+vff1F/XMKQjR8yx3gAAAFmFpROpRUZG6p577tH7778vSUpMTFR4eLief/55DRo0KNn+JUqU0GuvvabevXvbt7Vv3165c+fWF198ka42U+ItE6lJ0o4dZtfNW7ljCSIA3s2dy5udPm0G9VGj0t73q6/MMA8AAOAsT2ZAy850X716VZs2bVLjxo1vFOPjo8aNG2vt2rUpPufKlSsKDAx02JY7d26tXr063W0mtRsXF+dw8RajR5vX7dubs5R/9ZV5HR1N4AaQMe3aSYcOued3S9Gi0k2/2m/rzBnX2wcAAPAUy0L3mTNnlJCQoJCQEIftISEhOnHiRIrPiYqK0oQJE7Rv3z4lJiZq6dKlmj9/vmL+f3rt9LQpSWPGjFFwcLD9Eh4ensF3lz389Zc0Z455e8gQc1mwzp3Na7qUA3AHX1/3/W5Ja6x4kr59pebNpY0b0/9aAAAA7mL5RGqumDRpkipWrKgqVarI399fffr0UY8ePeTjk7G3MXjwYMXGxtovR44ccVPFWdvYsVJiovSf/5hjKwEgK0trrLjNJjVpIvn5SUuWSPfcI7VtK23fnvm1AgAAJLEsdBcpUkS+vr46efKkw/aTJ08qNDQ0xecULVpUCxcuVHx8vP7++2/t2bNH+fLlU7ly5dLdpiQFBAQoKCjI4ZLTHTokff65eXvIEEtLAQCntWtnLgtWsqTj9rAwc/vPP0t790rdu0s+PtLChVJEhHmmfe9eS0oGAABezrLQ7e/vr9q1a2vZsmX2bYmJiVq2bJnq1q172+cGBgaqZMmSun79ur755hs9/PDDGW7T24wbJ12/bp4Vioy0uhoAcF5aY8XLlZNmzJB27pQ6djRnNZ8921zru0cPc18AAIDMYuns5XPmzFH37t314Ycfqk6dOpo4caK+/vpr7dmzRyEhIerWrZtKliypMWPGSJLWr1+vY8eOqVatWjp27JiGDx+u6Ohobd68WQUKFHCqTWfk9NnLjx0z/1N69aq0cqXUoIHVFQGA5/z5pzR0qLksmWR2P3/ySem118wz5EkSEszlxmJipOLFzTHkzG8BAIB38GQG9HNray7q2LGjTp8+raFDh+rEiROqVauWlixZYg/Hhw8fdhiv/e+//2rIkCE6ePCg8uXLpxYtWujzzz+3B25n2oT01ltm4K5fn8ANIOeLiJC+/VbasMEM3z/9JE2dKk2fLj37rDR4sLRmTfLlzcLCzDHkrOQAAAAywtIz3VlVTj7TfeqUVKaMdPmy+R/Ppk2trggAMteqVeZcFr/9Zt739zf/EHmrpMna5s0jeAMAkNPlyHW6YY133jED9z33mOO5AcDb1K8vrVhhTrp2zz0pB27JHAsuSf37m13PAQAA0sPS7uXIXOfOSe+/b94eMiTttW4BIKe6eXmxhx5KfT/DkI4cMVd76NpVypXL9ddirDgAAN6N0O1F3ntPunhRqlnTXJsbALzdiRPO7dejh/T001KlSlL16lK1aualenWpfPnUQ/T8+YwVBwDA2xG6vURcnPmfPMmcsdeHgQUAoOLFndsvMFD6919zGbKdOx0fCwiQqlRJHsY3b5YeffRGN/Ukx45JHTowVhwAAG/BRGopyIkTqY0bJw0aJFWubP6Hka6NAGB2/S5TxgzCKf1raLOZZ6YPHjT3SQrdO3aY17t2mfNkpMRmS7nNm9uNjub3MQAAWUGOXTIMmePSJentt83br77Kf/AAIImvr9kLqEOH5CE5ad6LiRPNsd+lS5uXFi1u7JOYaAbnW8P4zp3S9eupv27SWPFVq6SGDT3xzgAAQFZBJ2Mv8NFH0unTUtmyUufOVlcDAFlLu3ZmV++SJR23h4Wl3QXcx8cc0926tbne95dfSlu3mmuAOyMmJt1lAwCAbIIz3Tncv/9K//ufeXvw4PTNvAsAOV27dtLDD7tvlvGwMOf2c3ZMOQAAyL4I3TncjBnS8ePmfwC7dbO6GgDIunx93dfVu3598/duamPFJSl/fun++93zegAAIOuie3kOdu2aNHasefvll80ZdgEAnpc0Vly6MTb8VhcumGt/pzYRGwAAyBkI3TnYl19Kf/8tFSsmPfmk1dUAgHdJbax4eLj0/PPmcJ+5c6UHH5ROnrSmRgAA4HmE7hwqIUEaPdq8/eKLUu7c1tYDAN6oXTvp0CFp+XLpq6/M6+ho6d13paVLpYIFpfXrpXvvTb7+NwAAyBlYpzsFOWGd7tmzzZnKCxUy/8OXP7/VFQEAbvXXX1LLltL+/VJQkHlmvEkTq6sCAMD7eDIDcqY7B0pMlEaNMm/370/gBoCsqlIlad06c+K1uDipeXNzmUcAAJBzELpzoO++k3bsMM+aPP+81dUAAG6ncGGzq/ljj5lDg555RnrpJfMPqAAAIPsjdOcwhiG9+aZ5u08fqUABS8sBADghIED67DNp5Ejz/ltvSe3bS/Hx1tYFAAAyjtCdw/z0k7Rpk5Qnj9m1HACQPdhs0uuvmxOu+ftLCxdKDzwgxcRYXRkAAMgIQncOYhjSG2+Yt3v1kooWtbYeAIDrOneWfv1VKlLE/CNqZKS0bZvVVQEAgPQidOcgK1ZIv/9udlMcONDqagAA6XX//eYEa5UrS0eOmPcXLbK6KgAAkB6E7hwkaSz3k09KxYtbWwsAIGPKl5fWrpUefFC6eFFq1UqaPNl8LCHB/EPrrFnmdUKClZVmPm9//wCA7IV1ulOQHdfp/v1380yIn5904IBUqpTVFQEA3OHqVXPI0KefmvdbtpT+/FM6evTGPmFh0qRJUrt21tSYmebPl/r18973DwDwDNbpRpqS1uXu3p3ADQA5ib+/9PHH0pgx5v0ff3QMnJJ07JjUoYMZSHOy+fPN9+mt7x8AkD1xpjsF2e1M9+bNUu3ako+PtHevVKGC1RUBANwtIUEKCZHOnk35cZvNPOMbHS35+mZubZkhIUEqUyZ54E6S098/AMCzONON20o6y925M4EbAHKqVatSD9ySuYLFkSPmet/XrrnevqfGSbuj3cRE6euvUw/c0o33v2pVeisFAMAz/KwuABmzc+eN7nSvvmptLQAAz3F2ve6ePaVnnpGqVJGqV5dq1DCvq1eXSpc2e0XdylPjpF1t1zDMfXfuNC87dpjXu3ZJ8fHOveaKFVKDBim/TwAArED38hRkp+7lXbtKX30ltW8vzZtndTUAAE9ZscKcyTwtuXNLly+n/Fi+fFK1ajdCeI0a0t9/m6te3Pq/AZvNvJ43L33BO2n8dWrtTptm/hHg5nC9c6cUF5dye35+0vXrzr12WJjUsaN5ufvuG68JAEBqPJkBCd0pyOqhOyHB7D63ZYu5HrdhmOO677zT6soAAJ6SNKb52LHkQVa6Mab54EFznx07pO3bzesdO6Tdu82Z0F1VqJD01lvmOGmbLe2Lj4/ZHbxXr9t3h0+Nr69UqZL5R4Fq1W78kaBsWXMIVWrvX5Ly5DGff+HCjW3lykmdOpkBvEaN2wfwpH9fY2LMpTfr12d8OAB4C0J3JsvKoTulrnqBgdKXX7JUCgDkdElnjyXH4OnMWelr16T9+x3D+IYNZoi1SsmSUp06juG6UiVzxvaUOPP+W7SQliyRZs+Wvv9eunTpxn533HEjgFeunLxtliIDAO9F6M5kWTV0p9ZVTzL/w5HeLoAAgOwjpXAYHi5NnOj6vwGzZklduqS9X82a5plfw3DucuqUuZpGWr76ypwE1BWuvP/4eOmHH8wAvnixdOXKjcdq1TID+KOPmj3HbtcVnn9fASDnI3RnsqwYulkqBQCQxF3doJ0dJ758udSwofXtJknP+4+Nlb791gzgS5c6jg/390+96z3/vgKAdyB0Z7KsGLo9/R8YAID3cXacuKuB01PtusvZs+YZ89mzzX83nfmfUGb+gQAAkPlYpxtOLxXj7H4AAPj6mmOWpeQTjCXdnzjR9ZDoqXbdpXBh6amnpGXLpMmTnXvO8OHShAnmWfKYGOeC+vz55h8fHnzQ7Mb/4IPm/aSlPgEA3oHQnU0UL+7e/QAAkMyxyvPmmZOa3SwsLGNjmT3VrrvdcYdz+61caa4Y0rSpVKKEVLSoeea7Tx9p6lRpzRrp/Pkb+yfNw3LrsLBjx8ztBG8A8B50L09BVuxentW76gEAsjdPdYPO6t2rnfn3tVAh6bnnpF27zJnf9+83l0VLSViYOQv76tXSxYsp78O/2QCQ9TCmO5NlxdAtZWypGAAAkDJX/329fFnas8dxHfTt21Of7DQ1P/8sNWmSvpqz+h8zACC7IXRnsqwauiX3LhUDAABM7vj39fx5aedO6dNPzUtafHzM7u03r1NerZpUvrzk5+darawpDgAZQ+jOZFk5dEv8dRsAAE/I7KXYUhMQIFWpkjyMly0rLVzImuIA4AmE7kyW1UM3AADIupydh2XlSrOb+o4d5hnynTvNceOXLqXcbmCg2fa1ayk/zlhxAEg/T2bA23ReAgAAgKuSlkzr0MEMwimNE5840TxzXbas1Lz5jccTE6VDh8wAfnMY371b+vff27+uYUhHjphn69OzpjgAwDNYMgwAAMDN0rtkmo+PVK6c1KqVNHiw9MUX0pYt5kzob7/t3GvHxGSsdgCAe3GmGwAAwAPatZMeftg948T9/KS77nJu3+LFXW8fAOA5hG4AAAAP8fV1X1fv+vXNM+VpjRWvX989rwcAcA+6lwMAAGQDSWPFpRtjw281cSKTqAFAVkPoBgAAyCZSGysuSePHs1wYAGRFhG4AAIBspF07c4bz5culr76SHnrI3L52raVlAQBSwZhuAACAbObmseI1a0rVq0vz55vLjFWvbmlpAIBbcKYbAAAgG6tWzVwTXJLefNPaWgAAyRG6AQAAsrkhQ8zrr7+W9uyxthYAgCNCNwAAQDYXESG1bm0uJTZ6tNXVAABuRugGAADIAV5/3bz+8ktp/35rawEA3EDoBgAAyAHuvltq3lxKTJTGjLG6GgBAEkI3AABADpF0tvuzz8xlxQAA1iN0AwAA5BB160qNG0vXr0tjx1pdDQBAInQDAADkKElnu6dPl44etbYWAAChGwAAIEdp0MC8XL0qjR9vdTUAAEI3AABADjN0qHn90UdSTIy1tQCAtyN0AwAA5DAPPWSO775yRXrrLaurAQDvRugGAADIYWy2G2O7p06VTp2yth4A8GaEbgAAgByoWTNz7e5Ll6QJE6yuBgC8F6EbAAAgB7r5bPfkydLZs9bWAwDeitANAACQQ7VqJUVESBcvSpMmWV0NAHgnQjcAAEAOZbNJQ4aYt999Vzp/3tJyAMArEboBAABysHbtpKpVpdhY6b33rK4GALwPoRsAACAH8/G5cbZ74kTpwgVLywEAr0PoBgAAyOEefVSqVEk6d0764AOrqwEA70LoBgAAyOF8faXXXjNvv/WWFB9vbT0A4E0I3QAAAF6gSxepXDnpzBnpww+trgYAvIfloXvy5MkqU6aMAgMDFRkZqQ0bNtx2/4kTJ6py5crKnTu3wsPD9cILL+jff/+1Pz58+HDZbDaHS5UqVTz9NgAAALI0Pz9p8GDz9v/+J12+bG09AOAtLA3dc+bM0YABAzRs2DBt3rxZERERioqK0qlTp1Lc/6uvvtKgQYM0bNgw7d69W5988onmzJmjV1991WG/atWqKSYmxn5ZvXp1ZrwdAACALK1bN6lUKenECenjj62uBgC8g6Whe8KECXrqqafUo0cPVa1aVVOnTlWePHn06aefprj/77//rvvvv19dunRRmTJl1LRpU3Xu3DnZ2XE/Pz+FhobaL0WKFMmMtwMAAJCl+ftLgwaZt8eNk65csbYeAPAGloXuq1evatOmTWrcuPGNYnx81LhxY61duzbF59x3333atGmTPWQfPHhQixYtUosWLRz227dvn0qUKKFy5cqpa9euOnz48G1ruXLliuLi4hwuAAAAOVGPHlKJEtKxY9KMGVZXAwA5n2Wh+8yZM0pISFBISIjD9pCQEJ04cSLF53Tp0kUjR45UvXr1lCtXLpUvX14NGzZ06F4eGRmpGTNmaMmSJZoyZYqio6NVv359XbjNopRjxoxRcHCw/RIeHu6eNwkAAJDFBAZKr7xi3h4zRrp2zdp6ACCns3wiNVesWLFCo0eP1gcffKDNmzdr/vz5+vHHH/XGG2/Y92nevLkeeeQR1axZU1FRUVq0aJHOnz+vr7/+OtV2Bw8erNjYWPvlyJEjmfF2AAAALPHUU1JIiPT339Lnn1tdDQDkbJaF7iJFisjX11cnT5502H7y5EmFhoam+JzXX39djz/+uJ588knVqFFDbdu21ejRozVmzBglJiam+JwCBQqoUqVK2r9/f6q1BAQEKCgoyOECAACQU+XOLb34onl79Gjp+nVr6wGAnMyy0O3v76/atWtr2bJl9m2JiYlatmyZ6tatm+JzLl26JB8fx5J9fX0lSYZhpPicixcv6sCBAypevLibKgcAAMj+nn1WKlJEOnBAmjXL6moAIOeytHv5gAEDNG3aNM2cOVO7d+9Wr169FB8frx49ekiSunXrpsFJC0pKatWqlaZMmaLZs2crOjpaS5cu1euvv65WrVrZw/eLL76olStX6tChQ/r999/Vtm1b+fr6qnPnzpa8RwAAgKwoXz5pwADz9qhRUkKCtfUAQE7lZ+WLd+zYUadPn9bQoUN14sQJ1apVS0uWLLFPrnb48GGHM9tDhgyRzWbTkCFDdOzYMRUtWlStWrXSqFGj7PscPXpUnTt31tmzZ1W0aFHVq1dP69atU9GiRTP9/QEAAGRlvXtL//uftHevNG+e1LGj1RUBQM5jM1Lrl+3F4uLiFBwcrNjYWMZ3AwCAHG3kSGnYMKlaNWnbNsknW02zCwDu4ckMyK9VAAAAL9a3rxQUJO3cKb35pjm+e8UKupsDgLsQugEAALxYgQJSkybm7WHDpC5dpAcflMqUkebPt7IyAMgZCN0AAABebP78lMP1sWNShw4EbwDIKEI3AACAl0pIkPr1k1Ka4SdpW//+dDUHgIwgdAMAAHipVauko0dTf9wwpCNHzP0AAOlD6AYAAPBSMTHu3Q8AkByhGwAAwEsVL+7e/QAAyRG6AQAAvFT9+lJYmGSzpfy4zSaFh5v7AQDSh9ANAADgpXx9pUmTzNspBW/DkCZONPcDAKQPoRsAAMCLtWsnzZsnlSyZ8uOFCmVuPQCQ0xC6AQAAvFy7dtKhQ9Ly5dJXX5nXzzxjPta3r3T9uqXlAUC25md1AQAAALCer6/UsOGN+zVqSHPnStu3Sx9+KPXubVlpAJCtcaYbAAAAyRQuLL35pnn79delM2esrQcAsitCNwAAAFL09NNSRIT0zz/SkCFWVwMA2ROhGwAAACny9ZXefde8/dFH0pYt1tYDANkRoRsAAACpatBA6tTJXD7s+efNawCA8wjdAAAAuK3x46U8eaQ1a6RZs6yuBgCyF0I3AAAAbis8XHr1VfP2Sy9JFy9aWw8AZCeEbgAAAKRp4ECpXDnp+HFp1CirqwGA7IPQDQAAgDQFBkrvvGPenjBB2rfP2noAILsgdAMAAMAprVpJUVHS1avSgAFWVwMA2QOhGwAAAE6x2aSJEyU/P+mHH6RFi6yuCACyPkI3AAAAnFalitSvn3m7f3/zrDcAIHWEbgAAALhk6FApJMQc1z1pktXVAEDWRugGAACAS4KCpHHjzNsjR5ozmgMAUkboBgAAgMsef1yKjDTX7B40yOpqACDrInQDAADAZT4+0nvvmbc//1z6/Xdr6wGArIrQDQAAgHS55x6pZ0/zdt++UkKCtfUAQFZE6AYAAEC6jRljjvHetEmaPt3qagAg6yF0AwAAIN2KFZNGjDBvDx4s/fOPtfUAQFZD6AYAAECG9O4t3XGHdOaMNHy41dUAQNZC6AYAAECG5Mp1Y73uyZOlHTusrQcAshJCNwAAADKsSROpbVtzMrV+/STDsLoiAMgaCN0AAABwiwkTpMBA6ddfpfnzra4GALIGQjcAAADcokwZ6eWXzdsDBkiXLllaDgBkCYRuAAAAuM0rr0jh4dLhw9L48VZXAwDWI3QDAADAbfLkkd5+27w9bpx04IC0YoU0a5Z5nZBgZXUAkPn8rC4AAAAAOUuHDtKDD0rLl0s1akiXL994LCzMnOm8XTvr6gOAzMSZbgAAALiVzSa1amXevjlwS9KxY2YoZ6I1AN6C0A0AAAC3SkgwZzJPSdJSYv3709UcgHcgdAMAAMCtVq2Sjh5N/XHDkI4cMfcDgJyO0A0AAAC3iolx734AkJ0RugEAAOBWxYu7dz8AyM4I3QAAAHCr+vXNWcpttpQft9nMtbzr18/cugDACoRuAAAAuJWvr7ksmJQ8eCfdnzjR3A8AcjpCNwAAANyuXTtp3jypZEnH7SVLmttZpxuAtyB0AwAAwCPatZMOHZJ+/VUqUMDcNm0agRuAdyF0AwAAwGN8faUHH5TatzfvL1libT0AkNkI3QAAAPC4Fi3M6x9/tLYOAMhs6Q7d+/fv108//aTLly9LkgzDcFtRAAAAyFmaNJFy5ZL275f++svqagAg87gcus+ePavGjRurUqVKatGihWJiYiRJ//3vfzVw4EC3FwgAAIDsL39+qUED8/aiRdbWAgCZyeXQ/cILL8jPz0+HDx9Wnjx57Ns7duyoJQzSAQAAQCpatjSv6WIOwJu4HLp//vlnjRs3TmFhYQ7bK1asqL///ttthQEAACBnSRrXvXKldOGCtbUAQGZxOXTHx8c7nOFOcu7cOQUEBLilKAAAAOQ8lSpJ5ctL165Jv/xidTUAkDlcDt3169fXZ599Zr9vs9mUmJio8ePH68EHH3RrcQAAAMg5bLYbXcwZ1w3AW/i5+oTx48erUaNG2rhxo65evaqXX35ZO3fu1Llz57RmzRpP1AgAAIAcomVL6d13zdBtGGYQB4CczOUz3dWrV9dff/2levXq6eGHH1Z8fLzatWunLVu2qHz58p6oEQAAADlEgwZSnjzS8ePS1q1WVwMAnufyme7Dhw8rPDxcr732WoqPlSpVyi2FAQAAIOcJDJQaN5a++86cxfzOO62uCAA8y+Uz3WXLltXp06eTbT979qzKli3rlqIAAACQczGuG4A3cTl0G4YhWwqDby5evKjAwEC3FAUAAICcK2npsHXrpDNnrK0FADzN6e7lAwYMkGTOVv766687LBuWkJCg9evXq1atWm4vEAAAADlLWJhUs6a0bZu0ZIn02GNWVwQAnuN06N6yZYsk80z39u3b5e/vb3/M399fERERevHFF91fIQAAAHKcli3N0P3jj4RuADmbzTAMw5Un9OjRQ5MmTVJQUJCnarJcXFycgoODFRsbm6PfJwAAgFXWrJHq1ZMKFpROnZL8XJ7eFwDcx5MZ0OUx3dOnTyeIAgAAIEPuvVcqVEj65x9zbDcA5FTp+pvixo0b9fXXX+vw4cO6evWqw2Pz5893S2EAAADIuXx9pagoadYss4t5vXpWVwQAnuHyme7Zs2frvvvu0+7du7VgwQJdu3ZNO3fu1K+//qrg4GBP1AgAAIAciKXDAHgDl0P36NGj9c477+j777+Xv7+/Jk2apD179ujRRx9VqVKlPFEjAAAAcqBmzSQfH3NCtSNHrK4GADzD5dB94MABtfz/P0v6+/srPj5eNptNL7zwgj766COXC5g8ebLKlCmjwMBARUZGasOGDbfdf+LEiapcubJy586t8PBwvfDCC/r3338z1CYAAAAyX+HC5thuibPdAHIul0N3wYIFdeHCBUlSyZIltWPHDknS+fPndenSJZfamjNnjgYMGKBhw4Zp8+bNioiIUFRUlE6dOpXi/l999ZUGDRqkYcOGaffu3frkk080Z84cvfrqq+luEwAAANZp0cK8/vFHa+sAAE9xOXQ3aNBAS5culSQ98sgj6tevn5566il17txZjRo1cqmtCRMm6KmnnlKPHj1UtWpVTZ06VXny5NGnn36a4v6///677r//fnXp0kVlypRR06ZN1blzZ4cz2a62CQAAAOskjetetky6pfMiAOQILofu999/X506dZIkvfbaaxowYIBOnjyp9u3b65NPPnG6natXr2rTpk1q3LjxjWJ8fNS4cWOtXbs2xefcd9992rRpkz1kHzx4UIsWLVKL//8TaXralKQrV64oLi7O4QIAAADPi4iQSpaULl2SVq60uhoAcD+XlwwrVKiQ/baPj48GDRqUrhc+c+aMEhISFBIS4rA9JCREe/bsSfE5Xbp00ZkzZ1SvXj0ZhqHr16/r2WeftXcvT0+bkjRmzBiNGDEiXe8DAAAA6WezmV3Mp00zu5hHRVldEQC4l8tnupOcOnVKO3bs0LZt2xwunrRixQqNHj1aH3zwgTZv3qz58+frxx9/1BtvvJGhdgcPHqzY2Fj75QjTZwIAAGSam8d1G4a1tQCAu7l8pnvTpk3q3r27du/eLeOW34o2m00JCQlOtVOkSBH5+vrq5MmTDttPnjyp0NDQFJ/z+uuv6/HHH9eTTz4pSapRo4bi4+P19NNP67XXXktXm5IUEBCggIAAp+oGAACAezVuLPn7SwcPSn/9JVWubHVFAOA+Lp/p7tmzpypVqqTff/9dBw8eVHR0tP1y8OBBp9vx9/dX7dq1tWzZMvu2xMRELVu2THXr1k3xOZcuXZKPj2PJvr6+kiTDMNLVJgAAAKyVL5/0wAPmbWYxB5DTuHym++DBg/rmm29UoUKFDL/4gAED1L17d919992qU6eOJk6cqPj4ePXo0UOS1K1bN5UsWVJjxoyRJLVq1UoTJkzQnXfeqcjISO3fv1+vv/66WrVqZQ/fabUJAACArKdlS2npUjN0DxhgdTUA4D4uh+5GjRrpzz//dEvo7tixo06fPq2hQ4fqxIkTqlWrlpYsWWKfCO3w4cMOZ7aHDBkim82mIUOG6NixYypatKhatWqlUaNGOd0mAAAAsp4WLaT+/aXffpPi4qSgIKsrAgD3sBm3DsxOw5kzZ9S9e3fVqVNH1atXV65cuRweb926tVsLtEJcXJyCg4MVGxurIH7jAwAAZIpKlaR9+6RvvpHatbO6GgDexJMZ0OUz3WvXrtWaNWu0ePHiZI+5MpEaAAAAcLOWLaWJE80u5oRuADmFyxOpPf/883rssccUExOjxMREhwuBGwAAAOnVsqV5vWiRlJhobS0A4C4uh+6zZ8/qhRdeYIw0AAAA3Kp+fSlvXunECWnLFqurAQD3cDl0t2vXTsuXL/dELQAAAPBiAQFSkybm7UWLrK0FANzF5THdlSpV0uDBg7V69WrVqFEj2URqffv2dVtxAAAA8C4tW0oLF5rjul9/3epqACDjXJ69vGzZsqk3ZrPp4MGDGS7KasxeDgAAYI3jx6WSJSWbTTp5Uipa1OqKAHiDLDV7eXR0tFsLAAAAAJKUKCHVqiVt3SotXix162Z1RQCQMS6P6QYAAAA86eZZzAEgu3PqTPeAAQP0xhtvKG/evBowYMBt950wYYJbCgMAAIB3atlSGjVK+ukn6fp1yc/lvpkAkHU49Stsy5Ytunbtmv02AAAA4Cl16kiFC0tnz0q//y41aGB1RQCQfk6F7puXCGO5MAAAAHiSr6/UrJn05ZfmLOaEbgDZmctjunv27KkLFy4k2x4fH6+ePXu6pSgAAAB4N8Z1A8gpXF4yzNfXVzExMSpWrJjD9jNnzig0NFTXr193a4FWYMkwAAAAa507Zy4Xlpgo/f23VKqU1RUByMk8mQGdPtMdFxen2NhYGYahCxcuKC4uzn75559/tGjRomRBHAAAAEiPQoWk++4zb//4o7W1AEBGOD0XZIECBWSz2WSz2VSpUqVkj9tsNo0YMcKtxQEAAMB7tWghrV5thu5evayuBgDSx+nQvXz5chmGoYceekjffPONChUqZH/M399fpUuXVokSJTxSJAAAALxPy5bSq69Kv/4qXb4s5c5tdUUA4DqnQ/cDDzwgSYqOjlapUqVks9k8VhQAAABQo4YUFiYdPSqtWCE1b251RQDgOpdnL9+9e7fWrFljvz958mTVqlVLXbp00T///OPW4gAAAOC9bLYbs5gzrhtAduVy6H7ppZcUFxcnSdq+fbsGDBigFi1aKDo6WgMGDHB7gQAAAPBeLVqY1z/+KLm25g4AZA1Ody9PEh0drapVq0qSvvnmG7Vq1UqjR4/W5s2b1SLptyIAAADgBo0aSQEB0qFD0p490h13WF0RALjG5TPd/v7+unTpkiTpl19+UdOmTSVJhQoVsp8BBwAAANwhb16pYUPzNl3MAWRHLofuevXqacCAAXrjjTe0YcMGtfz/gTZ//fWXwsLC3F4gAAAAvBvjugFkZy6H7vfff19+fn6aN2+epkyZopIlS0qSFi9erGbNmrm9QAAAAHi3pBGMq1dLsbHW1gIArrIZBlNS3CouLk7BwcGKjY1VUFCQ1eUAAAB4vSpVpL17pblzpQ4drK4GQE7jyQzo8pluSTpw4ICGDBmizp0769SpU5LMM907d+50a3EAAACARBdzANmXy6F75cqVqlGjhtavX6/58+fr4sWLkqQ///xTw4YNc3uBAAAAQFLoXrRISky0thYAcIXLoXvQoEF68803tXTpUvn7+9u3P/TQQ1q3bp1biwMAAAAkqV49KX9+6dQpafNmq6sBAOe5HLq3b9+utm3bJtterFgxnTlzxi1FAQAAADfz95eaNDFv08UcQHbicuguUKCAYmJikm3fsmWLfSZzAAAAwN0Y1w0gO3I5dHfq1EmvvPKKTpw4IZvNpsTERK1Zs0YvvviiunXr5okaAQAAADVvbl7/8Yd08qS1tQCAs1wO3aNHj1aVKlUUHh6uixcvqmrVqmrQoIHuu+8+DRkyxBM1AgAAACpeXLrrLvP2kiXW1gIAznI5dPv7+2vatGk6ePCgfvjhB33xxRfas2ePPv/8c/n6+nqiRgAAAEASXcwBZD82wzAMq4vIajy5MDoAAADSb/166d57pTx5pKlTpfBwqX59iXM/ADLCkxnQ5TPdAAAAgFWOHJF8fKRLl6Ru3aQHH5TKlJHmz7e6MgBIGaEbAAAA2cL8+dKjj0qJiY7bjx2TOnQgeAPImgjdAAAAyPISEqR+/aSUBkYmbevf39wPALISQjcAAACyvFWrpKNHU3/cMMyu56tWZV5NAOAMl0N3mTJlNHLkSB0+fNgT9QAAAADJxMS4dz8AyCwuh+7+/ftr/vz5KleunJo0aaLZs2frypUrnqgNAAAAkGSu0e3O/QAgs6QrdG/dulUbNmzQHXfcoeeff17FixdXnz59tHnzZk/UCAAAAC9Xv74UFibZbCk/brPdWD4MALKSdI/pvuuuu/Tuu+/q+PHjGjZsmD7++GPdc889qlWrlj799FOx/DcAAADcxddXmjTJvH1r8E66P3Ei63UDyHrSHbqvXbumr7/+Wq1bt9bAgQN199136+OPP1b79u316quvqmvXru6sEwAAAF6uXTtp3jypZEnH7cWKmdvbtbOmLgC4HZvh4inpzZs3a/r06Zo1a5Z8fHzUrVs3Pfnkk6pSpYp9nx07duiee+7R5cuX3V5wZoiLi1NwcLBiY2MVFBRkdTkAAAC4SUKCOUt5//7Sn3+aZ8D79rW6KgDZmSczoJ+rT7jnnnvUpEkTTZkyRW3atFGuXLmS7VO2bFl16tTJLQUCAAAAN/P1lRo2lNq2NUP3H39YXREApM7l0H3w4EGVLl36tvvkzZtX06dPT3dRAAAAQFruvde8XrfO2joA4HZcHtN96tQprV+/Ptn29evXa+PGjW4pCgAAAEhLnTrm9f790tmz1tYCAKlxOXT37t1bR44cSbb92LFj6t27t1uKAgAAANJSsKBUubJ5O4VzQgCQJbgcunft2qW77ror2fY777xTu3btcktRAAAAgDMiI81rQjeArMrl0B0QEKCTJ08m2x4TEyM/P5eHiAMAAADpxrhuAFmdy6G7adOmGjx4sGJjY+3bzp8/r1dffVVNmjRxa3EAAADA7SSd6d6wQUpMtLYWAEiJy+t0Hzt2TA0aNNDZs2d15513SpK2bt2qkJAQLV26VOHh4R4pNDOxTjcAAED2cO2aFBwsXb4s7d4tValidUUAsiNPZkCXz3SXLFlS27Zt0/jx41W1alXVrl1bkyZN0vbt23NE4AYAAED2kSuXdPfd5m26mAPIitI1CDtv3rx6+umn3V0LAAAA4LLISGnVKnMytSeesLoaAHCU7pnPdu3apcOHD+vq1asO21u3bp3hogAAAABnMZkagKzM5dB98OBBtW3bVtu3b5fNZlPSkHCbzSZJSkhIcG+FAAAAwG0kTaa2fbsUHy/lzWttPQBwM5fHdPfr109ly5bVqVOnlCdPHu3cuVO//fab7r77bq1YscIDJQIAAACpCwuTSpaUEhKkTZusrgYAHLkcuteuXauRI0eqSJEi8vHxkY+Pj+rVq6cxY8aob9++nqgRAAAAuK2ks93r11tbBwDcyuXQnZCQoPz580uSihQpouPHj0uSSpcurb1797q3OgAAAMAJjOsGkFW5PKa7evXq+vPPP1W2bFlFRkZq/Pjx8vf310cffaRy5cp5okYAAADgtjjTDSCrcjl0DxkyRPHx8ZKkkSNH6j//+Y/q16+vwoULa86cOW4vEAAAAEhL7dqSr6907Jh09Kg5zhsAsgKXQ3dUVJT9doUKFbRnzx6dO3dOBQsWtM9gDgAAAGSmvHmlGjWkrVvNs92EbgBZhUtjuq9duyY/Pz/t2LHDYXuhQoUI3AAAALAU47oBZEUuhe5cuXKpVKlSrMUNAACALIdx3QCyIpdnL3/ttdf06quv6ty5c56oBwAAAEiXpDPdGzdK165ZWwsAJHF5TPf777+v/fv3q0SJEipdurTy5s3r8PjmzZvdVhwAAADgrEqVpOBgKTZW2r5duusuqysCgHSE7jZt2nigDAAAACBjfHzMLuY//2x2MSd0A8gKXA7dw4YN80QdAAAAQIbde68Zutetk3r1sroaAEjHmG5PmDx5ssqUKaPAwEBFRkZqw4YNqe7bsGFD2Wy2ZJeWLVva93niiSeSPd6sWbPMeCsAAACwEJOpAchqXD7T7ePjc9vlwVyd2XzOnDkaMGCApk6dqsjISE2cOFFRUVHau3evihUrlmz/+fPn6+rVq/b7Z8+eVUREhB555BGH/Zo1a6bp06fb7wcEBLhUFwAAALKfpNC9d6/0zz9SwYLW1gMALofuBQsWONy/du2atmzZopkzZ2rEiBEuFzBhwgQ99dRT6tGjhyRp6tSp+vHHH/Xpp59q0KBByfYvVKiQw/3Zs2crT548yUJ3QECAQkNDXa4HAAAA2VfhwlKFCtL+/dKGDVJUlNUVAfB2Lofuhx9+ONm2Dh06qFq1apozZ47++9//Ot3W1atXtWnTJg0ePNi+zcfHR40bN9batWudauOTTz5Rp06dks2ivmLFChUrVkwFCxbUQw89pDfffFOFCxdOsY0rV67oypUr9vtxcXFOvwcAAABkLffea4budesI3QCs57Yx3ffee6+WLVvm0nPOnDmjhIQEhYSEOGwPCQnRiRMn0nz+hg0btGPHDj355JMO25s1a6bPPvtMy5Yt07hx47Ry5Uo1b9481a7vY8aMUXBwsP0SHh7u0vsAAABA1sG4bgBZictnulNy+fJlvfvuuypZsqQ7mnPaJ598oho1aqhOnToO2zt16mS/XaNGDdWsWVPly5fXihUr1KhRo2TtDB48WAMGDLDfj4uLI3gDAABkU/fea16vXy8ZhnSb6YgAwONcDt0FCxZ0mEjNMAxduHBBefLk0RdffOFSW0WKFJGvr69OnjzpsP3kyZNpjseOj4/X7NmzNXLkyDRfp1y5cipSpIj279+fYugOCAhgojUAAIAcomZNKSBAOnfO7GZesaLVFQHwZi6H7nfeecchdPv4+Kho0aKKjIxUQRenh/T391ft2rW1bNkytWnTRpKUmJioZcuWqU+fPrd97ty5c3XlyhU99thjab7O0aNHdfbsWRUvXtyl+gAAAJD9+PtLtWtLv/9ujusmdAOwksuh+4knnnBrAQMGDFD37t119913q06dOpo4caLi4+Pts5l369ZNJUuW1JgxYxye98knn6hNmzbJJke7ePGiRowYofbt2ys0NFQHDhzQyy+/rAoVKiiKmTQAAAC8QmSkGbrXr5cef9zqagB4M5dD9/Tp05UvX75kS3TNnTtXly5dUvfu3V1qr2PHjjp9+rSGDh2qEydOqFatWlqyZIl9crXDhw/Lx8dxvre9e/dq9erV+vnnn5O15+vrq23btmnmzJk6f/68SpQooaZNm+qNN96gCzkAAICXSBrXvW6dtXUAgM0wDMOVJ1SqVEkffvihHnzwQYftK1eu1NNPP629e/e6tUArxMXFKTg4WLGxsQoKCrK6HAAAALjo77+lMmUkPz8pLk7KndvqigBkZZ7MgC4vGXb48GGVLVs22fbSpUvr8OHDbikKAAAAyIhSpaTQUOn6dWnzZqurAeDNXA7dxYoV07Zt25Jt//PPP5ONrwYAAACsYLPRxRxA1uBy6O7cubP69u2r5cuXKyEhQQkJCfr111/Vr18/h/WxAQAAACtFRprX69dbWwcA7+byRGpvvPGGDh06pEaNGsnPz3x6YmKiunXrptGjR7u9QAAAACA9ONMNICtweSK1JPv27dPWrVuVO3du1ahRQ6VLl3Z3bZZhIjUAAIDs7+JFKThYSkyUjh2TSpSwuiIAWZUnM6DLZ7qTVKxYURUrVnRnLQAAAIDb5MsnVa8ubdtmdjFv29bqigB4I5fHdLdv317jxo1Ltn38+PHJ1u4GAAAArMS4bgBWczl0//bbb2rRokWy7c2bN9dvv/3mlqIAAAAAd2BcNwCruRy6L168KH9//2Tbc+XKpbi4OLcUBQAAALhD0pnujRvNNbsBILO5HLpr1KihOXPmJNs+e/ZsVa1a1S1FAQAAAO5wxx1SUJAUHy/t3Gl1NQC8kcsTqb3++utq166dDhw4oIceekiStGzZMs2aNUtz5851e4EAAABAevn4SPfcIy1bZo7rjoiwuiIA3sblM92tWrXSwoULtX//fj333HMaOHCgjh49ql9++UVt2rTxQIkAAABA+jGuG4CV0rVkWMuWLdWyZctk23fs2KHq1atnuCgAAADAXZLGdRO6AVjB5TPdt7pw4YI++ugj1alTRxH01wEAAEAWkxS6d++Wzp+3tBQAXijdofu3335Tt27dVLx4cb311lt66KGHtI4/HwIAACCLKVZMKlvWvP3HH9bWAsD7uNS9/MSJE5oxY4Y++eQTxcXF6dFHH9WVK1e0cOFCZi4HAABAlnXvvVJ0tDmZWpMmVlcDwJs4faa7VatWqly5srZt26aJEyfq+PHjeu+99zxZGwAAAOAWTKYGwCpOn+levHix+vbtq169eqlixYqerAkAAABwq6Rx3evXS4Yh2WzW1gPAezh9pnv16tW6cOGCateurcjISL3//vs6c+aMJ2sDAAAA3KJWLcnfXzpzRjp40OpqAHgTp0P3vffeq2nTpikmJkbPPPOMZs+erRIlSigxMVFLly7VhQsXPFknAAAAkG4BAdKdd5q316+3thYA3sXl2cvz5s2rnj17avXq1dq+fbsGDhyosWPHqlixYmrdurUnagQAAAAyjHHdAKyQoXW6K1eurPHjx+vo0aOaNWuWu2oCAAAA3O7mcd0AkFlshmEYVheR1cTFxSk4OFixsbEKCgqyuhwAAAC4QXS0VK6clCuXFBcnBQZaXRGArMKTGTBDZ7oBAACA7KJMGaloUenaNWnrVqurAeAtCN0AAADwCjYb47oBZD5CNwAAALxG0rhuQjeAzELoBgAAgNdIOtPNZGoAMguhGwAAAF7jnnvMbuaHDkknT1pdDQBvQOgGAACA1wgKkqpWNW9zthtAZiB0AwAAwKswrhtAZiJ0AwAAwKswrhtAZiJ0AwAAwKskhe4NG6SEBGtrAZDzEboBAADgVapWlfLlky5elHbvtroaADkdoRsAAABexdfXnMVcYlw3AM8jdAMAAMDrJE2mxrhuAJ5G6AYAAIDXSRrXzZluAJ5G6AYAAIDXSTrTvXOndOGCtbUAyNkI3QAAAPA6oaFS6dKSYUh//GF1NQByMkI3AAAAvFLS2W66mAPwJEI3AAAAvFLSuG4mUwPgSYRuAAAAeKWbz3QbhrW1AMi5CN0AAADwSnfeKeXKJZ06Jf39t9XVAMipCN0AAADwSrlzSxER5m3GdQPwFEI3AAAAvBbjugF4GqEbAAAAXosZzAF4GqEbAAAAXivpTPeWLdKVK9bWAiBnInQDAADAa5UvLxUubAbuP/+0uhoAORGhGwAAAF7LZrvRxZxx3QA8gdANAAAAr5bUxZxx3QA8gdANAAAAr8aZbgCeROgGAACAV6tTx7w+cEA6fdraWgDkPIRuAAAAeLUCBaQqVczbnO0G4G6EbgAAAHi9pHHdhG4A7kboBgAAgNdLGte9aJE0a5a0YoWUkGBpSQByCEI3AAAAvF58vHm9ebPUpYv04INSmTLS/PmWlgUgByB0AwAAwKvNny+99FLy7ceOSR06ELwBZAyhGwAAAF4rIUHq108yjOSPJW3r35+u5gDSj9ANAAAAr7VqlXT0aOqPG4Z05Ii5HwCkB6EbAAAAXismxr37AcCtCN0AAADwWsWLu3c/ALgVoRsAAABeq359KSxMstlSftxmk8LDzf0AID0I3QAAAPBavr7SpEnm7VuDd9L9iRPN/QAgPQjdAAAA8Grt2knz5kklSzpuDwszt7drZ01dAHIGQjcAAAC8Xrt20qFDUteu5v3//EeKjiZwA8g4QjcAAAAgswt5ixbm7X/+oUs5APcgdAMAAAD/r1o183rXLnONbgDIKEI3AAAA8P8qV5Z8fMwz3SdOWF0NgJwgS4TuyZMnq0yZMgoMDFRkZKQ2bNiQ6r4NGzaUzWZLdmnZsqV9H8MwNHToUBUvXly5c+dW48aNtW/fvsx4KwAAAMjGAgOl8uXN2zt3WlsLgJzB8tA9Z84cDRgwQMOGDdPmzZsVERGhqKgonTp1KsX958+fr5iYGPtlx44d8vX11SOPPGLfZ/z48Xr33Xc1depUrV+/Xnnz5lVUVJT+/fffzHpbAAAAyKaSupgTugG4g+Whe8KECXrqqafUo0cPVa1aVVOnTlWePHn06aefprh/oUKFFBoaar8sXbpUefLksYduwzA0ceJEDRkyRA8//LBq1qypzz77TMePH9fChQsz8Z0BAAAgOyJ0A3AnS0P31atXtWnTJjVu3Ni+zcfHR40bN9batWudauOTTz5Rp06dlDdvXklSdHS0Tpw44dBmcHCwIiMjU23zypUriouLc7gAAADAOxG6AbiTpaH7zJkzSkhIUEhIiMP2kJAQnXBi5ooNGzZox44devLJJ+3bkp7nSptjxoxRcHCw/RIeHu7qWwEAAEAOwQzmANzJ8u7lGfHJJ5+oRo0aqlOnTobaGTx4sGJjY+2XI0eOuKlCAAAAZDdJM5ifPy/FxFhdDYDsztLQXaRIEfn6+urkyZMO20+ePKnQ0NDbPjc+Pl6zZ8/Wf//7X4ftSc9zpc2AgAAFBQU5XAAAAOCdAgKkChXM23QxB5BRloZuf39/1a5dW8uWLbNvS0xM1LJly1S3bt3bPnfu3Lm6cuWKHnvsMYftZcuWVWhoqEObcXFxWr9+fZptAgAAABLjugG4j+XdywcMGKBp06Zp5syZ2r17t3r16qX4+Hj16NFDktStWzcNHjw42fM++eQTtWnTRoULF3bYbrPZ1L9/f7355pv67rvvtH37dnXr1k0lSpRQmzZtMuMtAQAAIJsjdANwFz+rC+jYsaNOnz6toUOH6sSJE6pVq5aWLFlinwjt8OHD8vFx/NvA3r17tXr1av38888ptvnyyy8rPj5eTz/9tM6fP6969eppyZIlCgwM9Pj7AQAAQPZH6AbgLjbDYE7GW8XFxSk4OFixsbGM7wYAAPBC27dLNWtKQUHmhGo2m9UVAfAkT2ZAy7uXAwAAAFlNpUqSr68UFycdP251NQCyM0I3AAAAcAtmMAfgLoRuAAAAIAWM6wbgDoRuAAAAIAWEbgDuQOgGAAAAUkDoBuAOhG4AAAAgBUmhe9cuifV+AKQXoRsAAABIQaVKkp+fOYP5sWNWVwMguyJ0AwAAACnw95cqVjRv08UcQHoRugEAAIBUVK1qXhO6AaQXoRsAAABIBZOpAcgoQjcAAACQCkI3gIwidAMAAACpYAZzABlF6AYAAABSUbGiOYP5hQvSkSNWVwMgOyJ0AwAAAKnw9zeXDpPMs90A4CpCNwAAAHAbzGAOICMI3QAAAMBtMJkagIwgdAMAAAC3QegGkBGEbgAAAOA2mMEcQEYQugEAAIDbqFhRypVLunhROnzY6moAZDeEbgAAAOA2cuViBnMA6UfoBgAAANLADOYA0ovQDQAAAKSBydQApBehGwAAAEgDoRtAehG6AQAAgDTcPIN5YqK1tQDIXgjdAAAAQBoqVDAnVIuPZwZzAK4hdAMAAABpyJVLqlzZvE0XcwCuIHQDAAAATri5izkAOIvQDQAAADiBZcMApAehGwAAAHACM5gDSA9CNwAAAOAEZjAHkB6EbgAAAMAJFSpI/v7SpUvS339bXQ2A7ILQDQAAADjBz48ZzAG4jtANAAAAOIkZzAG4itANAAAAOIkZzAG4itANAAAAOIkZzAG4itANAAAAOCkpdO/ezQzmAJxD6AYAAACcVL78jRnMDx2yuhoA2QGhGwAAAHCSn59UpYp5my7mAJxB6AYAAABcwLhuAK4gdAMAAAAuYNkwAK4gdAMAAAAuYNkwAK4gdAMAAAAuYAZzAK4gdAMAAAAuKF9eCgiQLl+WoqOtrgZAVkfoBgAAAFzg68sM5gCcR+gGAAAAXMQM5gCcRegGAAAAXMQM5gCcRegGAAAAXMQM5gCcRegGAAAAXHTzDOYJCdbWAiBrI3QDAAAALipXTgoMlP79lxnMAdweoRsAAABwETOYA3AWoRsAAABIB2YwB+AMQjcAAACQDoRuAM4gdAMAAADpwLJhAJxB6AYAAADSIWnZsD17mMEcQOoI3QAAAEA6lC17YwbzgwetrgZAVkXoBgAAANLB11e64w7zNuO6AaSG0A0AAACkE5OpAUgLoRsAAABIJ0I3gLQQugEAAIB0YgZzAGkhdAMAAADpxAzmANJC6AYAAADSqWxZKXdu6coV6cABq6sBkBURugEAAIB08vFhBnMAt0foBgAAADKAydQA3A6hGwAAAMgAQjeA2yF0AwAAABlA6AZwO4RuAAAAIAOSQvfevdL169bWAiDrIXQDAAAAGVC6tJQnj3T1KjOYA0jO8tA9efJklSlTRoGBgYqMjNSGDRtuu//58+fVu3dvFS9eXAEBAapUqZIWLVpkf3z48OGy2WwOlypVqnj6bQAAAMBLMYM5gNuxNHTPmTNHAwYM0LBhw7R582ZFREQoKipKp06dSnH/q1evqkmTJjp06JDmzZunvXv3atq0aSpZsqTDftWqVVNMTIz9snr16sx4OwAAAPBSjOsGkBo/K198woQJeuqpp9SjRw9J0tSpU/Xjjz/q008/1aBBg5Lt/+mnn+rcuXP6/ffflStXLklSmTJlku3n5+en0NBQj9YOAAAAJCF0A0iNZWe6r169qk2bNqlx48Y3ivHxUePGjbV27doUn/Pdd9+pbt266t27t0JCQlS9enWNHj1aCQkJDvvt27dPJUqUULly5dS1a1cdPnz4trVcuXJFcXFxDhcAAADAWYRuAKmxLHSfOXNGCQkJCgkJcdgeEhKiEydOpPicgwcPat68eUpISNCiRYv0+uuv6+2339abb75p3ycyMlIzZszQkiVLNGXKFEVHR6t+/fq6cOFCqrWMGTNGwcHB9kt4eLh73iQAAAC8AjOYA0iN5ROpuSIxMVHFihXTRx99pNq1a6tjx4567bXXNHXqVPs+zZs31yOPPKKaNWsqKipKixYt0vnz5/X111+n2u7gwYMVGxtrvxw5ciQz3g4AAAByiFKlzBnMr12T9u+3uhoAWYllY7qLFCkiX19fnTx50mH7yZMnUx2PXbx4ceXKlUu+vr72bXfccYdOnDihq1evyt/fP9lzChQooEqVKmn/bX77BQQEKCAgIJ3vBAAAAN7Ox0eqWlXauNHsYs7iOQCSWHam29/fX7Vr19ayZcvs2xITE7Vs2TLVrVs3xefcf//92r9/vxITE+3b/vrrLxUvXjzFwC1JFy9e1IEDB1S8eHH3vgEAAADgJozrBpASS7uXDxgwQNOmTdPMmTO1e/du9erVS/Hx8fbZzLt166bBgwfb9+/Vq5fOnTunfv366a+//tKPP/6o0aNHq3fv3vZ9XnzxRa1cuVKHDh3S77//rrZt28rX11edO3fO9PcHAAAA70HoBpASS5cM69ixo06fPq2hQ4fqxIkTqlWrlpYsWWKfXO3w4cPy8bnxd4Hw8HD99NNPeuGFF1SzZk2VLFlS/fr10yuvvGLf5+jRo+rcubPOnj2rokWLql69elq3bp2KFi2a6e8PAAAA3oPQDSAlNsMwDKuLyGri4uIUHBys2NhYBQUFWV0OAAAAsoG//5bKlJFy5ZLi481rANmDJzNgtpq9HAAAAMiqwsOlvHmZwRyAI0I3AAAA4AZJM5hLdDEHcAOhGwAAAHATxnUDuBWhGwAAAHATQjeAWxG6AQAAADchdAO4FaEbAAAAcJOk0P3XX9LVq9bWAiBrIHQDAAAAbhIeLuXLJ12/zgzmAEyEbgAAAMBNbDZmMAfgiNANAAAAuBHjugHcjNANAAAAuBGhG8DNCN0AAACAGxG6AdyM0A0AAAC4UVLo3rePGcwBELoBAAAAtwoLk/LnN2cw/+svq6sBYDVCNwAAAOBGN89gvmuXtbUAsB6hGwAAAHAzxnUDSELoBgAAANyM0A0gCaEbAAAAcDNCN4AkhG4AAADAzW6ewfzKFWtrAWAtQjcAAADgZiVLSkFBUkICM5gD3o7QDQAAALiZzXbjbDczmAPejdANAAAAeEDSsmGM6wa8G6EbAAAA8AAmUwMgEboBAAAAjyB0A5AI3QAAAIBHJIXu/fuZwRzwZoRuAAAAwANKlJCCg80ZzPfutboaAFbxs7oAAAAAICey2czJ1NaulaZNk9q3l+rXl3x9M9ZuQoK0apUUEyMVL+6eNj3VLrVSq6dqzVYMJBMbG2tIMmJjY60uBQAAANnUN98YRt68hiHduISFmdsz0mZYmHvb9FS71EqtnqrVEzyZAW2GYRhWB/+sJi4uTsHBwYqNjVVQUJDV5QAAACCbmT9f6tDBjBk3s9nM63nzpHbtrG+TWqk1u9XqKZ7MgITuFBC6AQAAkF4JCVKZMtLRo6nvU6yYNGuW891sExKkTp2k06fd16an2qVWak2rTZtNCguToqOzTldzQncmI3QDAAAgvVaskB580OoqgKxv+XKpYUOrqzB5MgMykRoAAADgRjExzu2XNLu5M2JjpePH3dump9qlVmp1tk1nvyvZHaEbAAAAcKPixZ3b78svnT/L5+zZc1fa9FS71Eqtzrbp7Hclu6N7eQroXg4AAID0ShrTfexY8kmkpPSNZ/VEm9RKrdmtVk/yZAb0cWtrAAAAgJfz9ZUmTTJvJ83UnCTp/sSJroUNT7RJrdSa3WrNrgjdAAAAgJu1a2cuiVSypOP2sLD0L5XkiTaplVqzW63ZEd3LU0D3cgAAALhDQoK0apU5YVTx4lL9+hk/u+eJNqmVWrNbre7GkmGZjNANAAAAAN6DMd0AAAAAAGRDhG4AAAAAADyE0A0AAAAAgIcQugEAAAAA8BBCNwAAAAAAHkLoBgAAAADAQwjdAAAAAAB4CKEbAAAAAAAPIXQDAAAAAOAhhG4AAAAAADyE0A0AAAAAgIcQugEAAAAA8BBCNwAAAAAAHkLoBgAAAADAQwjdAAAAAAB4iJ/VBWRFhmFIkuLi4iyuBAAAAADgaUnZLykLuhOhOwUXLlyQJIWHh1tcCQAAAAAgs5w9e1bBwcFubdNmeCLKZ3OJiYk6fvy48ufPL5vNZnU5DuLi4hQeHq4jR44oKCjI6nJwGxyr7INjlX1wrLIXjlf2wbHKPjhW2QvHK/uIjY1VqVKl9M8//6hAgQJubZsz3Snw8fFRWFiY1WXcVlBQEF/cbIJjlX1wrLIPjlX2wvHKPjhW2QfHKnvheGUfPj7un/aMidQAAAAAAPAQQjcAAAAAAB5C6M5mAgICNGzYMAUEBFhdCtLAsco+OFbZB8cqe+F4ZR8cq+yDY5W9cLyyD08eKyZSAwAAAADAQzjTDQAAAACAhxC6AQAAAADwEEI3AAAAAAAeQujORiZPnqwyZcooMDBQkZGR2rBhg9Uleb3hw4fLZrM5XKpUqWJ//N9//1Xv3r1VuHBh5cuXT+3bt9fJkyctrNi7/Pbbb2rVqpVKlCghm82mhQsXOjxuGIaGDh2q4sWLK3fu3GrcuLH27dvnsM+5c+fUtWtXBQUFqUCBAvrvf/+rixcvZuK78A5pHasnnngi2XetWbNmDvtwrDLHmDFjdM899yh//vwqVqyY2rRpo7179zrs48zvvsOHD6tly5bKkyePihUrppdeeknXr1/PzLeS4zlzrBo2bJjsu/Xss8867MOx8rwpU6aoZs2a9rWc69atq8WLF9sf5zuVtaR1vPheZU1jx46VzWZT//797dsy67tF6M4m5syZowEDBmjYsGHavHmzIiIiFBUVpVOnTlldmterVq2aYmJi7JfVq1fbH3vhhRf0/fffa+7cuVq5cqWOHz+udu3aWVitd4mPj1dERIQmT56c4uPjx4/Xu+++q6lTp2r9+vXKmzevoqKi9O+//9r36dq1q3bu3KmlS5fqhx9+0G+//aann346s96C10jrWElSs2bNHL5rs2bNcnicY5U5Vq5cqd69e2vdunVaunSprl27pqZNmyo+Pt6+T1q/+xISEtSyZUtdvXpVv//+u2bOnKkZM2Zo6NChVrylHMuZYyVJTz31lMN3a/z48fbHOFaZIywsTGPHjtWmTZu0ceNGPfTQQ3r44Ye1c+dOSXynspq0jpfE9yqr+eOPP/Thhx+qZs2aDtsz7btlIFuoU6eO0bt3b/v9hIQEo0SJEsaYMWMsrArDhg0zIiIiUnzs/PnzRq5cuYy5c+fat+3evduQZKxduzaTKkQSScaCBQvs9xMTE43Q0FDjf//7n33b+fPnjYCAAGPWrFmGYRjGrl27DEnGH3/8Yd9n8eLFhs1mM44dO5ZptXubW4+VYRhG9+7djYcffjjV53CsrHPq1ClDkrFy5UrDMJz73bdo0SLDx8fHOHHihH2fKVOmGEFBQcaVK1cy9w14kVuPlWEYxgMPPGD069cv1edwrKxTsGBB4+OPP+Y7lU0kHS/D4HuV1Vy4cMGoWLGisXTpUodjk5nfLc50ZwNXr17Vpk2b1LhxY/s2Hx8fNW7cWGvXrrWwMkjSvn37VKJECZUrV05du3bV4cOHJUmbNm3StWvXHI5blSpVVKpUKY5bFhAdHa0TJ044HJ/g4GBFRkbaj8/atWtVoEAB3X333fZ9GjduLB8fH61fvz7Ta/Z2K1asULFixVS5cmX16tVLZ8+etT/GsbJObGysJKlQoUKSnPvdt3btWtWoUUMhISH2faKiohQXF+dwpgjudeuxSvLll1+qSJEiql69ugYPHqxLly7ZH+NYZb6EhATNnj1b8fHxqlu3Lt+pLO7W45WE71XW0bt3b7Vs2dLhOyRl7r9Xfhl8D8gEZ86cUUJCgsPBlqSQkBDt2bPHoqogSZGRkZoxY4YqV66smJgYjRgxQvXr19eOHTt04sQJ+fv7q0CBAg7PCQkJ0YkTJ6wpGHZJxyCl71XSYydOnFCxYsUcHvfz81OhQoU4hpmsWbNmateuncqWLasDBw7o1VdfVfPmzbV27Vr5+vpyrCySmJio/v376/7771f16tUlyanffSdOnEjxu5f0GNwvpWMlSV26dFHp0qVVokQJbdu2Ta+88or27t2r+fPnS+JYZabt27erbt26+vfff5UvXz4tWLBAVatW1datW/lOZUGpHS+J71VWMnv2bG3evFl//PFHsscy898rQjeQAc2bN7ffrlmzpiIjI1W6dGl9/fXXyp07t4WVATlLp06d7Ldr1KihmjVrqnz58lqxYoUaNWpkYWXerXfv3tqxY4fDXBbImlI7VjfPe1CjRg0VL15cjRo10oEDB1S+fPnMLtOrVa5cWVu3blVsbKzmzZun7t27a+XKlVaXhVSkdryqVq3K9yqLOHLkiPr166elS5cqMDDQ0lroXp4NFClSRL6+vslm0jt58qRCQ0MtqgopKVCggCpVqqT9+/crNDRUV69e1fnz5x324bhlDUnH4Hbfq9DQ0GSTFV6/fl3nzp3jGFqsXLlyKlKkiPbv3y+JY2WFPn366IcfftDy5csVFhZm3+7M777Q0NAUv3tJj8G9UjtWKYmMjJQkh+8Wxypz+Pv7q0KFCqpdu7bGjBmjiIgITZo0ie9UFpXa8UoJ3ytrbNq0SadOndJdd90lPz8/+fn5aeXKlXr33Xfl5+enkJCQTPtuEbqzAX9/f9WuXVvLli2zb0tMTNSyZcscxo7AehcvXtSBAwdUvHhx1a5dW7ly5XI4bnv37tXhw4c5bllA2bJlFRoa6nB84uLitH79evvxqVu3rs6fP69NmzbZ9/n111+VmJho/wcU1jh69KjOnj2r4sWLS+JYZSbDMNSnTx8tWLBAv/76q8qWLevwuDO/++rWravt27c7/KFk6dKlCgoKsnfPRMaldaxSsnXrVkly+G5xrKyRmJioK1eu8J3KJpKOV0r4XlmjUaNG2r59u7Zu3Wq/3H333eratav9dqZ9t9wxIxw8b/bs2UZAQIAxY8YMY9euXcbTTz9tFChQwGEmPWS+gQMHGitWrDCio6ONNWvWGI0bNzaKFClinDp1yjAMw3j22WeNUqVKGb/++quxceNGo27dukbdunUtrtp7XLhwwdiyZYuxZcsWQ5IxYcIEY8uWLcbff/9tGIZhjB071ihQoIDx7bffGtu2bTMefvhho2zZssbly5ftbTRr1sy48847jfXr1xurV682KlasaHTu3Nmqt5Rj3e5YXbhwwXjxxReNtWvXGtHR0cYvv/xi3HXXXUbFihWNf//9194Gxypz9OrVywgODjZWrFhhxMTE2C+XLl2y75PW777r168b1atXN5o2bWps3brVWLJkiVG0aFFj8ODBVrylHCutY7V//35j5MiRxsaNG43o6Gjj22+/NcqVK2c0aNDA3gbHKnMMGjTIWLlypREdHW1s27bNGDRokGGz2Yyff/7ZMAy+U1nN7Y4X36us7daZ5TPru0Xozkbee+89o1SpUoa/v79Rp04dY926dVaX5PU6duxoFC9e3PD39zdKlixpdOzY0di/f7/98cuXLxvPPfecUbBgQSNPnjxG27ZtjZiYGAsr9i7Lly83JCW7dO/e3TAMc9mw119/3QgJCTECAgKMRo0aGXv37nVo4+zZs0bnzp2NfPnyGUFBQUaPHj2MCxcuWPBucrbbHatLly4ZTZs2NYoWLWrkypXLKF26tPHUU08l+6MjxypzpHScJBnTp0+37+PM775Dhw4ZzZs3N3Lnzm0UKVLEGDhwoHHt2rVMfjc5W1rH6vDhw0aDBg2MQoUKGQEBAUaFChWMl156yYiNjXVoh2PleT179jRKly5t+Pv7G0WLFjUaNWpkD9yGwXcqq7nd8eJ7lbXdGroz67tlMwzDcPlcPQAAAAAASBNjugEAAAAA8BBCNwAAAAAAHkLoBgAAAADAQwjdAAAAAAB4CKEbAAAAAAAPIXQDAAAAAOAhhG4AAAAAADyE0A0AAAAAgIcQugEAyCSHDh2SzWbT1q1brS7Fbs+ePbr33nsVGBioWrVqefz1ypQpo4kTJzq9vzOf2YwZM1SgQIEM1wYAgCcQugEAXuOJJ56QzWbT2LFjHbYvXLhQNpvNoqqsNWzYMOXNm1d79+7VsmXLUtzHnZ/bH3/8oaeffjrd9QIAkN0QugEAXiUwMFDjxo3TP//8Y3UpbnP16tV0P/fAgQOqV6+eSpcurcKFC6e6n7s+t6JFiypPnjwZaiOzXLt2zeoSAAA5AKEbAOBVGjdurNDQUI0ZMybVfYYPH56sq/XEiRNVpkwZ+/0nnnhCbdq00ejRoxUSEqICBQpo5MiRun79ul566SUVKlRIYWFhmj59erL29+zZo/vuu0+BgYGqXr26Vq5c6fD4jh071Lx5c+XLl08hISF6/PHHdebMGfvjDRs2VJ8+fdS/f38VKVJEUVFRKb6PxMREjRw5UmFhYQoICFCtWrW0ZMkS++M2m02bNm3SyJEjZbPZNHz48Ax9bpK0evVq1a9fX7lz51Z4eLj69u2r+Ph4++O3di/fs2eP6tWrp8DAQFWtWlW//PKLbDabFi5c6NDuwYMH9eCDDypPnjyKiIjQ2rVrk732woULVbFiRQUGBioqKkpHjhxxeHzKlCkqX768/P39VblyZX3++ecOj9tsNk2ZMkWtW7dW3rx5NWrUKP3zzz/q2rWrihYtqty5c6tixYopHlMAAFJD6AYAeBVfX1+NHj1a7733no4ePZqhtn799VcdP35cv/32myZMmKBhw4bpP//5jwoWLKj169fr2Wef1TPPPJPsdV566SUNHDhQW7ZsUd26ddWqVSudPXtWknT+/Hk99NBDuvPOO7Vx40YtWbJEJ0+e1KOPPurQxsyZM+Xv7681a9Zo6tSpKdY3adIkvf3223rrrbe0bds2RUVFqXXr1tq3b58kKSYmRtWqVdPAgQMVExOjF198MdX36sznduDAATVr1kzt27fXtm3bNGfOHK1evVp9+vRJcf+EhAS1adNGefLk0fr16/XRRx/ptddeS3Hf1157TS+++KK2bt2qSpUqqXPnzrp+/br98UuXLmnUqFH67LPPtGbNGp0/f16dOnWyP75gwQL169dPAwcO1I4dO/TMM8+oR48eWr58ucPrDB8+XG3bttX27dvVs2dPvf7669q1a5cWL16s3bt3a8qUKSpSpEiqnxMAAMkYAAB4ie7duxsPP/ywYRiGce+99xo9e/Y0DMMwFixYYNz8T+KwYcOMiIgIh+e+8847RunSpR3aKl26tJGQkGDfVrlyZaN+/fr2+9evXzfy5s1rzJo1yzAMw4iOjjYkGWPHjrXvc+3aNSMsLMwYN26cYRiG8cYbbxhNmzZ1eO0jR44Ykoy9e/cahmEYDzzwgHHnnXem+X5LlChhjBo1ymHbPffcYzz33HP2+xEREcawYcNu246zn9t///tf4+mnn3Z47qpVqwwfHx/j8uXLhmEYRunSpY133nnHMAzDWLx4seHn52fExMTY91+6dKkhyViwYIFhGDc+s48//ti+z86dOw1Jxu7duw3DMIzp06cbkox169bZ99m9e7chyVi/fr1hGIZx3333GU899ZRDbY888ojRokUL+31JRv/+/R32adWqldGjR4/bfj4AANwOZ7oBAF5p3Lhxmjlzpnbv3p3uNqpVqyYfnxv/lIaEhKhGjRr2+76+vipcuLBOnTrl8Ly6devab/v5+enuu++21/Hnn39q+fLlypcvn/1SpUoVSeaZ5CS1a9e+bW1xcXE6fvy47r//foft999/f4be8+0+tz///FMzZsxwqD0qKkqJiYmKjo5Otv/evXsVHh6u0NBQ+7Y6deqk+Lo1a9a03y5evLgkOXyufn5+uueee+z3q1SpogIFCtjr3L17t1Ofxd133+1wv1evXpo9e7Zq1aqll19+Wb///nuK9QEAkBpCNwDAKzVo0EBRUVEaPHhwssd8fHxkGIbDtpQm1cqVK5fDfZvNluK2xMREp+u6ePGiWrVqpa1btzpc9u3bpwYNGtj3y5s3r9NtutPtPreLFy/qmWeecaj7zz//1L59+1S+fPkMve7Nn2vSjOmufK7OuvVzbd68uf7++2+98MILOn78uBo1anTbbvgAANyK0A0A8Fpjx47V999/n2xSrqJFi+rEiRMOwduda2uvW7fOfvv69evatGmT7rjjDknSXXfdpZ07d6pMmTKqUKGCw8WVoB0UFKQSJUpozZo1DtvXrFmjqlWrZqj+1D63u+66S7t27UpWd4UKFeTv75+sncqVK+vIkSM6efKkfdsff/yRrpquX7+ujRs32u/v3btX58+ft3+ud9xxR7o/i6JFi6p79+764osvNHHiRH300UfpqhEA4J0I3QAAr1WjRg117dpV7777rsP2hg0b6vTp0xo/frwOHDigyZMna/HixW573cmTJ2vBggXas2ePevfurX/++Uc9e/aUJPXu3Vvnzp1T586d9ccff+jAgQP66aef1KNHDyUkJLj0Oi+99JLGjRunOXPmaO/evRo0aJC2bt2qfv36Zaj+1D63V155Rb///rv69OljPzv/7bffpjqRWpMmTVS+fHl1795d27Zt05o1azRkyBBJcnn971y5cun555/X+vXrtWnTJj3xxBO699577d3VX3rpJc2YMUNTpkzRvn37NGHCBM2fPz/Ns9ZDhw7Vt99+q/3792vnzp364Ycf7EEeAABnELoBAF5t5MiRybop33HHHfrggw80efJkRUREaMOGDW7tUjx27FiNHTtWERERWr16tb777jv7jNhJZ6cTEhLUtGlT1ahRQ/3791eBAgUcxo87o2/fvhowYIAGDhyoGjVqaMmSJfruu+9UsWLFDL+HlD63mjVrauXKlfrrr79Uv3593XnnnRo6dKhKlCiRYhu+vr5auHChLl68qHvuuUdPPvmkffbywMBAl+rJkyePXnnlFXXp0kX333+/8uXLpzlz5tgfb9OmjSZNmqS33npL1apV04cffqjp06erYcOGt23X399fgwcPVs2aNdWgQQP5+vpq9uzZLtUGAPBuNuPWQWsAAAAWWbNmjerVq6f9+/dneBw4AABZAaEbAABYZsGCBcqXL58qVqyo/fv3q1+/fipYsKBWr15tdWkAALiFn9UFAAAA73XhwgW98sorOnz4sIoUKaLGjRvr7bfftrosAADchjPdAAAAAAB4CBOpAQAAAADgIYRuAAAAAAA8hNANAAAAAICHELoBAAAAAPAQQjcAAAAAAB5C6AYAAAAAwEMI3QAAAAAAeAihGwAAAAAADyF0AwAAAADgIf8HUvRgp8qzhn0AAAAASUVORK5CYII=",
       "text/plain": [
        "<Figure size 1000x600 with 1 Axes>"
       ]
@@ -2789,7 +4008,7 @@
  ],
  "metadata": {
   "kernelspec": {
-   "display_name": "base",
+   "display_name": "Python 3",
    "language": "python",
    "name": "python3"
   },
@@ -2803,7 +4022,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.10.10"
+   "version": "3.12.1"
   }
  },
  "nbformat": 4,
diff --git a/02_activities/assignments/assignment_1.ipynb b/02_activities/assignments/assignment_1.ipynb
index e50cc66eb..f122cd699 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": [],
@@ -57,9 +57,295 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "id": "a431d282-f9ca-4d5d-8912-71ffc9d8ea19",
+   "id": "6593c79f",
    "metadata": {},
    "outputs": [],
+   "source": []
+  },
+  {
+   "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",
@@ -73,7 +359,9 @@
     "wine_df['class'] = wine_data.target\n",
     "\n",
     "# Display the DataFrame\n",
-    "wine_df\n"
+    "wine_df\n",
+    "\n",
+    "# wine_df['class'].unique()\n"
    ]
   },
   {
@@ -91,12 +379,23 @@
   },
   {
    "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"
+    "178"
    ]
   },
   {
@@ -109,12 +408,23 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 4,
    "id": "df0ef103",
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "14"
+      ]
+     },
+     "execution_count": 4,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
    "source": [
-    "# Your answer here"
+    "14"
    ]
   },
   {
@@ -127,12 +437,23 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 5,
    "id": "47989426",
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "(int, [0, 1, 2])"
+      ]
+     },
+     "execution_count": 5,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
    "source": [
-    "# Your answer here"
+    "int, [0, 1, 2]\n"
    ]
   },
   {
@@ -146,12 +467,23 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 6,
    "id": "bd7b0910",
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "13"
+      ]
+     },
+     "execution_count": 6,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
    "source": [
-    "# Your answer here"
+    "13"
    ]
   },
   {
@@ -175,10 +507,37 @@
   },
   {
    "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",
@@ -204,7 +563,7 @@
    "id": "403ef0bb",
    "metadata": {},
    "source": [
-    "> Your answer here..."
+    "Standardization allows comparison of variables on different measurement systems in a single, dimensionless system. This means that variables measured on systems that vary in their gredation by orders of magnitude can be compared in a meaningful way."
    ]
   },
   {
@@ -220,7 +579,7 @@
    "id": "fdee5a15",
    "metadata": {},
    "source": [
-    "> Your answer here..."
+    "It is not a predictor variable, nor is it a quantitative variable. Although it uses numbers, the values are ternery, not continuous, and assigned to qualitative bins. Standardization really only makes sense for quantitative variables."
    ]
   },
   {
@@ -236,7 +595,7 @@
    "id": "f0676c21",
    "metadata": {},
    "source": [
-    "> Your answer here..."
+    "When building code that uses the random function, if you don't set a seed, you will get different values every time (the seed is set by default using the system clock). This will make it very difficult to write unit tests or see by inspection whether the code is doing what you want it to do. The seed is usually removed once the code is confirmed to work, before it goes to production. "
    ]
   },
   {
@@ -251,17 +610,17 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 8,
    "id": "72c101f2",
    "metadata": {},
    "outputs": [],
    "source": [
     "# set a seed for reproducibility\n",
-    "np.random.seed(123)\n",
+    "np.random.seed(42)\n",
     "\n",
-    "# split the data into a training and testing set. hint: use train_test_split !\n",
+    "# split the data into a training and testing set. hint: use train_test_split \n",
     "\n",
-    "# Your code here ..."
+    "[train, test] = train_test_split(wine_df, test_size=0.25, random_state=42)"
    ]
   },
   {
@@ -284,12 +643,34 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 9,
    "id": "08818c64",
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "13"
+      ]
+     },
+     "execution_count": 9,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
    "source": [
-    "# Your code here..."
+    "knn = KNeighborsClassifier()\n",
+    "\n",
+    "# set up the parameter grid for n_neighbors from 2 to 50, to avoid n_neighbors=1 being chosen as that wouldn't make sense\n",
+    "parameter_grid = {\n",
+    "    \"n_neighbors\": range(2, 50),\n",
+    "}\n",
+    "grid_search = GridSearchCV(knn, parameter_grid, cv=10)\n",
+    "# grid_search.fit(train.iloc[:, :-1], train['class'])\n",
+    "# pd.DataFrame(grid_search.cv_results_)\n",
+    "grid_search.fit(train.iloc[:, :-1], train['class'])\n",
+    "grid_search.best_params_['n_neighbors']\n",
+    "\n"
    ]
   },
   {
@@ -305,12 +686,23 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
-   "id": "ffefa9f2",
+   "execution_count": 11,
+   "id": "8fd40daa",
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "0.7111111111111111\n"
+     ]
+    }
+   ],
    "source": [
-    "# Your code here..."
+    "# fit the model with the best n_neighbors value\n",
+    "knn = KNeighborsClassifier(n_neighbors=grid_search.best_params_['n_neighbors'])\n",
+    "knn.fit(train.iloc[:, :-1], train['class'])\n",
+    "print(accuracy_score(test['class'], knn.predict(test.iloc[:, :-1])))\n"
    ]
   },
   {
@@ -365,7 +757,7 @@
  ],
  "metadata": {
   "kernelspec": {
-   "display_name": "Python 3.10.4",
+   "display_name": "Python 3",
    "language": "python",
    "name": "python3"
   },
@@ -379,12 +771,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.9.19"
-  },
-  "vscode": {
-   "interpreter": {
-    "hash": "497a84dc8fec8cf8d24e7e87b6d954c9a18a327edc66feb9b9ea7e9e72cc5c7e"
-   }
+   "version": "3.12.1"
   }
  },
  "nbformat": 4,