diff --git a/02_activities/assignments/assignment_1.ipynb b/02_activities/assignments/assignment_1.ipynb
index 28d4df017..9144ae9e2 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": 70,
    "id": "4a3485d6-ba58-4660-a983-5680821c5719",
    "metadata": {},
    "outputs": [],
@@ -56,10 +56,288 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 71,
    "id": "a431d282-f9ca-4d5d-8912-71ffc9d8ea19",
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>alcohol</th>\n",
+       "      <th>malic_acid</th>\n",
+       "      <th>ash</th>\n",
+       "      <th>alcalinity_of_ash</th>\n",
+       "      <th>magnesium</th>\n",
+       "      <th>total_phenols</th>\n",
+       "      <th>flavanoids</th>\n",
+       "      <th>nonflavanoid_phenols</th>\n",
+       "      <th>proanthocyanins</th>\n",
+       "      <th>color_intensity</th>\n",
+       "      <th>hue</th>\n",
+       "      <th>od280/od315_of_diluted_wines</th>\n",
+       "      <th>proline</th>\n",
+       "      <th>class</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>14.23</td>\n",
+       "      <td>1.71</td>\n",
+       "      <td>2.43</td>\n",
+       "      <td>15.6</td>\n",
+       "      <td>127.0</td>\n",
+       "      <td>2.80</td>\n",
+       "      <td>3.06</td>\n",
+       "      <td>0.28</td>\n",
+       "      <td>2.29</td>\n",
+       "      <td>5.64</td>\n",
+       "      <td>1.04</td>\n",
+       "      <td>3.92</td>\n",
+       "      <td>1065.0</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>13.20</td>\n",
+       "      <td>1.78</td>\n",
+       "      <td>2.14</td>\n",
+       "      <td>11.2</td>\n",
+       "      <td>100.0</td>\n",
+       "      <td>2.65</td>\n",
+       "      <td>2.76</td>\n",
+       "      <td>0.26</td>\n",
+       "      <td>1.28</td>\n",
+       "      <td>4.38</td>\n",
+       "      <td>1.05</td>\n",
+       "      <td>3.40</td>\n",
+       "      <td>1050.0</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>13.16</td>\n",
+       "      <td>2.36</td>\n",
+       "      <td>2.67</td>\n",
+       "      <td>18.6</td>\n",
+       "      <td>101.0</td>\n",
+       "      <td>2.80</td>\n",
+       "      <td>3.24</td>\n",
+       "      <td>0.30</td>\n",
+       "      <td>2.81</td>\n",
+       "      <td>5.68</td>\n",
+       "      <td>1.03</td>\n",
+       "      <td>3.17</td>\n",
+       "      <td>1185.0</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>14.37</td>\n",
+       "      <td>1.95</td>\n",
+       "      <td>2.50</td>\n",
+       "      <td>16.8</td>\n",
+       "      <td>113.0</td>\n",
+       "      <td>3.85</td>\n",
+       "      <td>3.49</td>\n",
+       "      <td>0.24</td>\n",
+       "      <td>2.18</td>\n",
+       "      <td>7.80</td>\n",
+       "      <td>0.86</td>\n",
+       "      <td>3.45</td>\n",
+       "      <td>1480.0</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>13.24</td>\n",
+       "      <td>2.59</td>\n",
+       "      <td>2.87</td>\n",
+       "      <td>21.0</td>\n",
+       "      <td>118.0</td>\n",
+       "      <td>2.80</td>\n",
+       "      <td>2.69</td>\n",
+       "      <td>0.39</td>\n",
+       "      <td>1.82</td>\n",
+       "      <td>4.32</td>\n",
+       "      <td>1.04</td>\n",
+       "      <td>2.93</td>\n",
+       "      <td>735.0</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>...</th>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>173</th>\n",
+       "      <td>13.71</td>\n",
+       "      <td>5.65</td>\n",
+       "      <td>2.45</td>\n",
+       "      <td>20.5</td>\n",
+       "      <td>95.0</td>\n",
+       "      <td>1.68</td>\n",
+       "      <td>0.61</td>\n",
+       "      <td>0.52</td>\n",
+       "      <td>1.06</td>\n",
+       "      <td>7.70</td>\n",
+       "      <td>0.64</td>\n",
+       "      <td>1.74</td>\n",
+       "      <td>740.0</td>\n",
+       "      <td>2</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>174</th>\n",
+       "      <td>13.40</td>\n",
+       "      <td>3.91</td>\n",
+       "      <td>2.48</td>\n",
+       "      <td>23.0</td>\n",
+       "      <td>102.0</td>\n",
+       "      <td>1.80</td>\n",
+       "      <td>0.75</td>\n",
+       "      <td>0.43</td>\n",
+       "      <td>1.41</td>\n",
+       "      <td>7.30</td>\n",
+       "      <td>0.70</td>\n",
+       "      <td>1.56</td>\n",
+       "      <td>750.0</td>\n",
+       "      <td>2</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>175</th>\n",
+       "      <td>13.27</td>\n",
+       "      <td>4.28</td>\n",
+       "      <td>2.26</td>\n",
+       "      <td>20.0</td>\n",
+       "      <td>120.0</td>\n",
+       "      <td>1.59</td>\n",
+       "      <td>0.69</td>\n",
+       "      <td>0.43</td>\n",
+       "      <td>1.35</td>\n",
+       "      <td>10.20</td>\n",
+       "      <td>0.59</td>\n",
+       "      <td>1.56</td>\n",
+       "      <td>835.0</td>\n",
+       "      <td>2</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>176</th>\n",
+       "      <td>13.17</td>\n",
+       "      <td>2.59</td>\n",
+       "      <td>2.37</td>\n",
+       "      <td>20.0</td>\n",
+       "      <td>120.0</td>\n",
+       "      <td>1.65</td>\n",
+       "      <td>0.68</td>\n",
+       "      <td>0.53</td>\n",
+       "      <td>1.46</td>\n",
+       "      <td>9.30</td>\n",
+       "      <td>0.60</td>\n",
+       "      <td>1.62</td>\n",
+       "      <td>840.0</td>\n",
+       "      <td>2</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>177</th>\n",
+       "      <td>14.13</td>\n",
+       "      <td>4.10</td>\n",
+       "      <td>2.74</td>\n",
+       "      <td>24.5</td>\n",
+       "      <td>96.0</td>\n",
+       "      <td>2.05</td>\n",
+       "      <td>0.76</td>\n",
+       "      <td>0.56</td>\n",
+       "      <td>1.35</td>\n",
+       "      <td>9.20</td>\n",
+       "      <td>0.61</td>\n",
+       "      <td>1.60</td>\n",
+       "      <td>560.0</td>\n",
+       "      <td>2</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "<p>178 rows × 14 columns</p>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "     alcohol  malic_acid   ash  alcalinity_of_ash  magnesium  total_phenols  \\\n",
+       "0      14.23        1.71  2.43               15.6      127.0           2.80   \n",
+       "1      13.20        1.78  2.14               11.2      100.0           2.65   \n",
+       "2      13.16        2.36  2.67               18.6      101.0           2.80   \n",
+       "3      14.37        1.95  2.50               16.8      113.0           3.85   \n",
+       "4      13.24        2.59  2.87               21.0      118.0           2.80   \n",
+       "..       ...         ...   ...                ...        ...            ...   \n",
+       "173    13.71        5.65  2.45               20.5       95.0           1.68   \n",
+       "174    13.40        3.91  2.48               23.0      102.0           1.80   \n",
+       "175    13.27        4.28  2.26               20.0      120.0           1.59   \n",
+       "176    13.17        2.59  2.37               20.0      120.0           1.65   \n",
+       "177    14.13        4.10  2.74               24.5       96.0           2.05   \n",
+       "\n",
+       "     flavanoids  nonflavanoid_phenols  proanthocyanins  color_intensity   hue  \\\n",
+       "0          3.06                  0.28             2.29             5.64  1.04   \n",
+       "1          2.76                  0.26             1.28             4.38  1.05   \n",
+       "2          3.24                  0.30             2.81             5.68  1.03   \n",
+       "3          3.49                  0.24             2.18             7.80  0.86   \n",
+       "4          2.69                  0.39             1.82             4.32  1.04   \n",
+       "..          ...                   ...              ...              ...   ...   \n",
+       "173        0.61                  0.52             1.06             7.70  0.64   \n",
+       "174        0.75                  0.43             1.41             7.30  0.70   \n",
+       "175        0.69                  0.43             1.35            10.20  0.59   \n",
+       "176        0.68                  0.53             1.46             9.30  0.60   \n",
+       "177        0.76                  0.56             1.35             9.20  0.61   \n",
+       "\n",
+       "     od280/od315_of_diluted_wines  proline  class  \n",
+       "0                            3.92   1065.0      0  \n",
+       "1                            3.40   1050.0      0  \n",
+       "2                            3.17   1185.0      0  \n",
+       "3                            3.45   1480.0      0  \n",
+       "4                            2.93    735.0      0  \n",
+       "..                            ...      ...    ...  \n",
+       "173                          1.74    740.0      2  \n",
+       "174                          1.56    750.0      2  \n",
+       "175                          1.56    835.0      2  \n",
+       "176                          1.62    840.0      2  \n",
+       "177                          1.60    560.0      2  \n",
+       "\n",
+       "[178 rows x 14 columns]"
+      ]
+     },
+     "execution_count": 71,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
    "source": [
     "from sklearn.datasets import load_wine\n",
     "\n",
@@ -91,12 +369,13 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 72,
    "id": "56916892",
    "metadata": {},
    "outputs": [],
    "source": [
-    "# Your answer here"
+    "# Your answer here\n",
+    "# 178 observations"
    ]
   },
   {
@@ -109,12 +388,13 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 73,
    "id": "df0ef103",
    "metadata": {},
    "outputs": [],
    "source": [
-    "# Your answer here"
+    "# Your answer here\n",
+    "# 14 variables"
    ]
   },
   {
@@ -127,12 +407,47 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 74,
    "id": "47989426",
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "dtype('int64')"
+      ]
+     },
+     "execution_count": 74,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# Your answer here\n",
+    "wine_df['class'].dtype\n",
+    "# int64 is the variable type"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 75,
+   "id": "682e1b8a",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "array([0, 1, 2])"
+      ]
+     },
+     "execution_count": 75,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
    "source": [
-    "# Your answer here"
+    "wine_df[\"class\"].unique()\n",
+    "# the unique values are 0, 1, and 2"
    ]
   },
   {
@@ -146,12 +461,13 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 76,
    "id": "bd7b0910",
    "metadata": {},
    "outputs": [],
    "source": [
-    "# Your answer here"
+    "# Your answer here\n",
+    "# 13 predictor variables"
    ]
   },
   {
@@ -175,10 +491,37 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 77,
    "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",
@@ -187,10 +530,237 @@
     "scaler = StandardScaler()\n",
     "predictors_standardized = pd.DataFrame(scaler.fit_transform(predictors), columns=predictors.columns)\n",
     "\n",
+    "\n",
     "# Display the head of the standardized predictors\n",
     "print(predictors_standardized.head())"
    ]
   },
+  {
+   "cell_type": "code",
+   "execution_count": 78,
+   "id": "c5a50c3c",
+   "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",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>count</th>\n",
+       "      <td>1.780000e+02</td>\n",
+       "      <td>1.780000e+02</td>\n",
+       "      <td>1.780000e+02</td>\n",
+       "      <td>1.780000e+02</td>\n",
+       "      <td>1.780000e+02</td>\n",
+       "      <td>1.780000e+02</td>\n",
+       "      <td>1.780000e+02</td>\n",
+       "      <td>1.780000e+02</td>\n",
+       "      <td>1.780000e+02</td>\n",
+       "      <td>1.780000e+02</td>\n",
+       "      <td>1.780000e+02</td>\n",
+       "      <td>1.780000e+02</td>\n",
+       "      <td>1.780000e+02</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>mean</th>\n",
+       "      <td>7.943708e-15</td>\n",
+       "      <td>3.592632e-16</td>\n",
+       "      <td>-4.066660e-15</td>\n",
+       "      <td>-7.983626e-17</td>\n",
+       "      <td>-7.983626e-17</td>\n",
+       "      <td>-3.991813e-17</td>\n",
+       "      <td>9.979533e-16</td>\n",
+       "      <td>-5.588538e-16</td>\n",
+       "      <td>-1.656602e-15</td>\n",
+       "      <td>-3.442939e-16</td>\n",
+       "      <td>1.636643e-15</td>\n",
+       "      <td>2.235415e-15</td>\n",
+       "      <td>-1.197544e-16</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>std</th>\n",
+       "      <td>1.002821e+00</td>\n",
+       "      <td>1.002821e+00</td>\n",
+       "      <td>1.002821e+00</td>\n",
+       "      <td>1.002821e+00</td>\n",
+       "      <td>1.002821e+00</td>\n",
+       "      <td>1.002821e+00</td>\n",
+       "      <td>1.002821e+00</td>\n",
+       "      <td>1.002821e+00</td>\n",
+       "      <td>1.002821e+00</td>\n",
+       "      <td>1.002821e+00</td>\n",
+       "      <td>1.002821e+00</td>\n",
+       "      <td>1.002821e+00</td>\n",
+       "      <td>1.002821e+00</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>min</th>\n",
+       "      <td>-2.434235e+00</td>\n",
+       "      <td>-1.432983e+00</td>\n",
+       "      <td>-3.679162e+00</td>\n",
+       "      <td>-2.671018e+00</td>\n",
+       "      <td>-2.088255e+00</td>\n",
+       "      <td>-2.107246e+00</td>\n",
+       "      <td>-1.695971e+00</td>\n",
+       "      <td>-1.868234e+00</td>\n",
+       "      <td>-2.069034e+00</td>\n",
+       "      <td>-1.634288e+00</td>\n",
+       "      <td>-2.094732e+00</td>\n",
+       "      <td>-1.895054e+00</td>\n",
+       "      <td>-1.493188e+00</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>25%</th>\n",
+       "      <td>-7.882448e-01</td>\n",
+       "      <td>-6.587486e-01</td>\n",
+       "      <td>-5.721225e-01</td>\n",
+       "      <td>-6.891372e-01</td>\n",
+       "      <td>-8.244151e-01</td>\n",
+       "      <td>-8.854682e-01</td>\n",
+       "      <td>-8.275393e-01</td>\n",
+       "      <td>-7.401412e-01</td>\n",
+       "      <td>-5.972835e-01</td>\n",
+       "      <td>-7.951025e-01</td>\n",
+       "      <td>-7.675624e-01</td>\n",
+       "      <td>-9.522483e-01</td>\n",
+       "      <td>-7.846378e-01</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>50%</th>\n",
+       "      <td>6.099988e-02</td>\n",
+       "      <td>-4.231120e-01</td>\n",
+       "      <td>-2.382132e-02</td>\n",
+       "      <td>1.518295e-03</td>\n",
+       "      <td>-1.222817e-01</td>\n",
+       "      <td>9.595986e-02</td>\n",
+       "      <td>1.061497e-01</td>\n",
+       "      <td>-1.760948e-01</td>\n",
+       "      <td>-6.289785e-02</td>\n",
+       "      <td>-1.592246e-01</td>\n",
+       "      <td>3.312687e-02</td>\n",
+       "      <td>2.377348e-01</td>\n",
+       "      <td>-2.337204e-01</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>75%</th>\n",
+       "      <td>8.361286e-01</td>\n",
+       "      <td>6.697929e-01</td>\n",
+       "      <td>6.981085e-01</td>\n",
+       "      <td>6.020883e-01</td>\n",
+       "      <td>5.096384e-01</td>\n",
+       "      <td>8.089974e-01</td>\n",
+       "      <td>8.490851e-01</td>\n",
+       "      <td>6.095413e-01</td>\n",
+       "      <td>6.291754e-01</td>\n",
+       "      <td>4.939560e-01</td>\n",
+       "      <td>7.131644e-01</td>\n",
+       "      <td>7.885875e-01</td>\n",
+       "      <td>7.582494e-01</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>max</th>\n",
+       "      <td>2.259772e+00</td>\n",
+       "      <td>3.109192e+00</td>\n",
+       "      <td>3.156325e+00</td>\n",
+       "      <td>3.154511e+00</td>\n",
+       "      <td>4.371372e+00</td>\n",
+       "      <td>2.539515e+00</td>\n",
+       "      <td>3.062832e+00</td>\n",
+       "      <td>2.402403e+00</td>\n",
+       "      <td>3.485073e+00</td>\n",
+       "      <td>3.435432e+00</td>\n",
+       "      <td>3.301694e+00</td>\n",
+       "      <td>1.960915e+00</td>\n",
+       "      <td>2.971473e+00</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "            alcohol    malic_acid           ash  alcalinity_of_ash  \\\n",
+       "count  1.780000e+02  1.780000e+02  1.780000e+02       1.780000e+02   \n",
+       "mean   7.943708e-15  3.592632e-16 -4.066660e-15      -7.983626e-17   \n",
+       "std    1.002821e+00  1.002821e+00  1.002821e+00       1.002821e+00   \n",
+       "min   -2.434235e+00 -1.432983e+00 -3.679162e+00      -2.671018e+00   \n",
+       "25%   -7.882448e-01 -6.587486e-01 -5.721225e-01      -6.891372e-01   \n",
+       "50%    6.099988e-02 -4.231120e-01 -2.382132e-02       1.518295e-03   \n",
+       "75%    8.361286e-01  6.697929e-01  6.981085e-01       6.020883e-01   \n",
+       "max    2.259772e+00  3.109192e+00  3.156325e+00       3.154511e+00   \n",
+       "\n",
+       "          magnesium  total_phenols    flavanoids  nonflavanoid_phenols  \\\n",
+       "count  1.780000e+02   1.780000e+02  1.780000e+02          1.780000e+02   \n",
+       "mean  -7.983626e-17  -3.991813e-17  9.979533e-16         -5.588538e-16   \n",
+       "std    1.002821e+00   1.002821e+00  1.002821e+00          1.002821e+00   \n",
+       "min   -2.088255e+00  -2.107246e+00 -1.695971e+00         -1.868234e+00   \n",
+       "25%   -8.244151e-01  -8.854682e-01 -8.275393e-01         -7.401412e-01   \n",
+       "50%   -1.222817e-01   9.595986e-02  1.061497e-01         -1.760948e-01   \n",
+       "75%    5.096384e-01   8.089974e-01  8.490851e-01          6.095413e-01   \n",
+       "max    4.371372e+00   2.539515e+00  3.062832e+00          2.402403e+00   \n",
+       "\n",
+       "       proanthocyanins  color_intensity           hue  \\\n",
+       "count     1.780000e+02     1.780000e+02  1.780000e+02   \n",
+       "mean     -1.656602e-15    -3.442939e-16  1.636643e-15   \n",
+       "std       1.002821e+00     1.002821e+00  1.002821e+00   \n",
+       "min      -2.069034e+00    -1.634288e+00 -2.094732e+00   \n",
+       "25%      -5.972835e-01    -7.951025e-01 -7.675624e-01   \n",
+       "50%      -6.289785e-02    -1.592246e-01  3.312687e-02   \n",
+       "75%       6.291754e-01     4.939560e-01  7.131644e-01   \n",
+       "max       3.485073e+00     3.435432e+00  3.301694e+00   \n",
+       "\n",
+       "       od280/od315_of_diluted_wines       proline  \n",
+       "count                  1.780000e+02  1.780000e+02  \n",
+       "mean                   2.235415e-15 -1.197544e-16  \n",
+       "std                    1.002821e+00  1.002821e+00  \n",
+       "min                   -1.895054e+00 -1.493188e+00  \n",
+       "25%                   -9.522483e-01 -7.846378e-01  \n",
+       "50%                    2.377348e-01 -2.337204e-01  \n",
+       "75%                    7.885875e-01  7.582494e-01  \n",
+       "max                    1.960915e+00  2.971473e+00  "
+      ]
+     },
+     "execution_count": 78,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "predictors_standardized.describe()"
+   ]
+  },
   {
    "cell_type": "markdown",
    "id": "9981ca48",
@@ -204,7 +774,7 @@
    "id": "403ef0bb",
    "metadata": {},
    "source": [
-    "> Your answer here..."
+    "> Your answer here: so that your variables are not weighted differently and are balanced. Final results will be more reliable. Without standardization, models will be dominated by varaibles with higher scales."
    ]
   },
   {
@@ -220,7 +790,7 @@
    "id": "fdee5a15",
    "metadata": {},
    "source": [
-    "> Your answer here..."
+    "> Your answer here: That is because we intend to determine the Class variable of our wine."
    ]
   },
   {
@@ -236,7 +806,7 @@
    "id": "f0676c21",
    "metadata": {},
    "source": [
-    "> Your answer here..."
+    "> Your answer here: It is important to set a seed to randomize our function the same way each time - so that it is reprodicible. No, the particular seed value is not important since using the same value is what is important to remain consistent in our tests and training."
    ]
   },
   {
@@ -251,7 +821,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 79,
    "id": "72c101f2",
    "metadata": {},
    "outputs": [],
@@ -261,7 +831,18 @@
     "\n",
     "# split the data into a training and testing set. hint: use train_test_split !\n",
     "\n",
-    "# Your code here ..."
+    "# Your code here ...\n",
+    "X = predictors_standardized \n",
+    "y = wine_df['class']\n",
+    "\n",
+    "X_train, X_test, y_train, y_test = train_test_split(\n",
+    "    X,\n",
+    "    y,\n",
+    "    train_size= 0.75, \n",
+    "    shuffle= True, \n",
+    "    stratify = y,\n",
+    "    random_state = 123\n",
+    "    )"
    ]
   },
   {
@@ -284,12 +865,32 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 80,
    "id": "08818c64",
    "metadata": {},
    "outputs": [],
    "source": [
-    "# Your code here..."
+    "# Your code here...\n",
+    "\n",
+    "knn = KNeighborsClassifier(n_neighbors = 50)\n",
+    "\n",
+    "parameter_grid = {\n",
+    "    \"n_neighbors\" : range(1, 51)\n",
+    "}"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 81,
+   "id": "0417a55a",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "wine_tune_grid = GridSearchCV(\n",
+    "    estimator = knn,\n",
+    "    param_grid = parameter_grid,\n",
+    "    cv = 10\n",
+    ")"
    ]
   },
   {
@@ -305,12 +906,257 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 82,
+   "id": "63650cd6",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "(pandas.core.frame.DataFrame, pandas.core.series.Series)"
+      ]
+     },
+     "execution_count": 82,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "type(X_train), type(y_train)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 83,
+   "id": "8100cdb7",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "((133, 13), (133,))"
+      ]
+     },
+     "execution_count": 83,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "X_train.shape, y_train.shape"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 84,
+   "id": "ac8eeb30",
+   "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",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>78</th>\n",
+       "      <td>-0.828391</td>\n",
+       "      <td>-1.208567</td>\n",
+       "      <td>-1.522511</td>\n",
+       "      <td>-1.409821</td>\n",
+       "      <td>2.545825</td>\n",
+       "      <td>-0.633101</td>\n",
+       "      <td>-0.179981</td>\n",
+       "      <td>-0.095517</td>\n",
+       "      <td>2.048364</td>\n",
+       "      <td>-0.717240</td>\n",
+       "      <td>0.449924</td>\n",
+       "      <td>-0.426113</td>\n",
+       "      <td>0.009893</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>1.518613</td>\n",
+       "      <td>-0.562250</td>\n",
+       "      <td>0.232053</td>\n",
+       "      <td>-1.169593</td>\n",
+       "      <td>1.913905</td>\n",
+       "      <td>0.808997</td>\n",
+       "      <td>1.034819</td>\n",
+       "      <td>-0.659563</td>\n",
+       "      <td>1.224884</td>\n",
+       "      <td>0.251717</td>\n",
+       "      <td>0.362177</td>\n",
+       "      <td>1.847920</td>\n",
+       "      <td>1.013009</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>15</th>\n",
+       "      <td>0.777454</td>\n",
+       "      <td>-0.472483</td>\n",
+       "      <td>1.218995</td>\n",
+       "      <td>-0.689137</td>\n",
+       "      <td>0.860705</td>\n",
+       "      <td>0.889114</td>\n",
+       "      <td>0.884224</td>\n",
+       "      <td>-0.498407</td>\n",
+       "      <td>-0.229346</td>\n",
+       "      <td>0.969783</td>\n",
+       "      <td>1.415139</td>\n",
+       "      <td>0.378979</td>\n",
+       "      <td>1.793210</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>13</th>\n",
+       "      <td>2.160950</td>\n",
+       "      <td>-0.544297</td>\n",
+       "      <td>0.085839</td>\n",
+       "      <td>-2.430790</td>\n",
+       "      <td>-0.613775</td>\n",
+       "      <td>1.289697</td>\n",
+       "      <td>1.667318</td>\n",
+       "      <td>0.549108</td>\n",
+       "      <td>2.135968</td>\n",
+       "      <td>0.147900</td>\n",
+       "      <td>1.283518</td>\n",
+       "      <td>0.167113</td>\n",
+       "      <td>1.283691</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>14</th>\n",
+       "      <td>1.703902</td>\n",
+       "      <td>-0.418624</td>\n",
+       "      <td>0.049285</td>\n",
+       "      <td>-2.250619</td>\n",
+       "      <td>0.158572</td>\n",
+       "      <td>1.610163</td>\n",
+       "      <td>1.617120</td>\n",
+       "      <td>-0.578985</td>\n",
+       "      <td>2.398780</td>\n",
+       "      <td>1.056297</td>\n",
+       "      <td>1.064151</td>\n",
+       "      <td>0.548472</td>\n",
+       "      <td>2.547935</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "     alcohol  malic_acid       ash  alcalinity_of_ash  magnesium  \\\n",
+       "78 -0.828391   -1.208567 -1.522511          -1.409821   2.545825   \n",
+       "0   1.518613   -0.562250  0.232053          -1.169593   1.913905   \n",
+       "15  0.777454   -0.472483  1.218995          -0.689137   0.860705   \n",
+       "13  2.160950   -0.544297  0.085839          -2.430790  -0.613775   \n",
+       "14  1.703902   -0.418624  0.049285          -2.250619   0.158572   \n",
+       "\n",
+       "    total_phenols  flavanoids  nonflavanoid_phenols  proanthocyanins  \\\n",
+       "78      -0.633101   -0.179981             -0.095517         2.048364   \n",
+       "0        0.808997    1.034819             -0.659563         1.224884   \n",
+       "15       0.889114    0.884224             -0.498407        -0.229346   \n",
+       "13       1.289697    1.667318              0.549108         2.135968   \n",
+       "14       1.610163    1.617120             -0.578985         2.398780   \n",
+       "\n",
+       "    color_intensity       hue  od280/od315_of_diluted_wines   proline  \n",
+       "78        -0.717240  0.449924                     -0.426113  0.009893  \n",
+       "0          0.251717  0.362177                      1.847920  1.013009  \n",
+       "15         0.969783  1.415139                      0.378979  1.793210  \n",
+       "13         0.147900  1.283518                      0.167113  1.283691  \n",
+       "14         1.056297  1.064151                      0.548472  2.547935  "
+      ]
+     },
+     "execution_count": 84,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "X_train.head()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 85,
+   "id": "0766251e",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "78    1\n",
+       "0     0\n",
+       "15    0\n",
+       "13    0\n",
+       "14    0\n",
+       "Name: class, dtype: int64"
+      ]
+     },
+     "execution_count": 85,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "y_train.head()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 87,
    "id": "ffefa9f2",
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "0.9333333333333333"
+      ]
+     },
+     "execution_count": 87,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
    "source": [
-    "# Your code here..."
+    "# Your code here...\n",
+    "wine_tune_grid.fit(X_train, y_train)\n",
+    "\n",
+    "y_pred = wine_tune_grid.best_estimator_.predict(X_test)\n",
+    "\n",
+    "accuracy = accuracy_score(y_test, y_pred)\n",
+    "\n",
+    "accuracy"
    ]
   },
   {
@@ -365,7 +1211,7 @@
  ],
  "metadata": {
   "kernelspec": {
-   "display_name": "Python 3.10.4",
+   "display_name": "lcr-env (3.11.14)",
    "language": "python",
    "name": "python3"
   },
@@ -379,12 +1225,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.9.19"
-  },
-  "vscode": {
-   "interpreter": {
-    "hash": "497a84dc8fec8cf8d24e7e87b6d954c9a18a327edc66feb9b9ea7e9e72cc5c7e"
-   }
+   "version": "3.11.14"
   }
  },
  "nbformat": 4,