diff --git a/02_activities/assignments/assignment_1.ipynb b/02_activities/assignments/assignment_1.ipynb
index 73d92a3ee..a32064353 100644
--- a/02_activities/assignments/assignment_1.ipynb
+++ b/02_activities/assignments/assignment_1.ipynb
@@ -34,7 +34,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 1,
    "id": "4a3485d6-ba58-4660-a983-5680821c5719",
    "metadata": {},
    "outputs": [],
@@ -56,10 +56,288 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 2,
    "id": "a431d282-f9ca-4d5d-8912-71ffc9d8ea19",
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>alcohol</th>\n",
+       "      <th>malic_acid</th>\n",
+       "      <th>ash</th>\n",
+       "      <th>alcalinity_of_ash</th>\n",
+       "      <th>magnesium</th>\n",
+       "      <th>total_phenols</th>\n",
+       "      <th>flavanoids</th>\n",
+       "      <th>nonflavanoid_phenols</th>\n",
+       "      <th>proanthocyanins</th>\n",
+       "      <th>color_intensity</th>\n",
+       "      <th>hue</th>\n",
+       "      <th>od280/od315_of_diluted_wines</th>\n",
+       "      <th>proline</th>\n",
+       "      <th>class</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>14.23</td>\n",
+       "      <td>1.71</td>\n",
+       "      <td>2.43</td>\n",
+       "      <td>15.6</td>\n",
+       "      <td>127.0</td>\n",
+       "      <td>2.80</td>\n",
+       "      <td>3.06</td>\n",
+       "      <td>0.28</td>\n",
+       "      <td>2.29</td>\n",
+       "      <td>5.64</td>\n",
+       "      <td>1.04</td>\n",
+       "      <td>3.92</td>\n",
+       "      <td>1065.0</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>13.20</td>\n",
+       "      <td>1.78</td>\n",
+       "      <td>2.14</td>\n",
+       "      <td>11.2</td>\n",
+       "      <td>100.0</td>\n",
+       "      <td>2.65</td>\n",
+       "      <td>2.76</td>\n",
+       "      <td>0.26</td>\n",
+       "      <td>1.28</td>\n",
+       "      <td>4.38</td>\n",
+       "      <td>1.05</td>\n",
+       "      <td>3.40</td>\n",
+       "      <td>1050.0</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>13.16</td>\n",
+       "      <td>2.36</td>\n",
+       "      <td>2.67</td>\n",
+       "      <td>18.6</td>\n",
+       "      <td>101.0</td>\n",
+       "      <td>2.80</td>\n",
+       "      <td>3.24</td>\n",
+       "      <td>0.30</td>\n",
+       "      <td>2.81</td>\n",
+       "      <td>5.68</td>\n",
+       "      <td>1.03</td>\n",
+       "      <td>3.17</td>\n",
+       "      <td>1185.0</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>14.37</td>\n",
+       "      <td>1.95</td>\n",
+       "      <td>2.50</td>\n",
+       "      <td>16.8</td>\n",
+       "      <td>113.0</td>\n",
+       "      <td>3.85</td>\n",
+       "      <td>3.49</td>\n",
+       "      <td>0.24</td>\n",
+       "      <td>2.18</td>\n",
+       "      <td>7.80</td>\n",
+       "      <td>0.86</td>\n",
+       "      <td>3.45</td>\n",
+       "      <td>1480.0</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>13.24</td>\n",
+       "      <td>2.59</td>\n",
+       "      <td>2.87</td>\n",
+       "      <td>21.0</td>\n",
+       "      <td>118.0</td>\n",
+       "      <td>2.80</td>\n",
+       "      <td>2.69</td>\n",
+       "      <td>0.39</td>\n",
+       "      <td>1.82</td>\n",
+       "      <td>4.32</td>\n",
+       "      <td>1.04</td>\n",
+       "      <td>2.93</td>\n",
+       "      <td>735.0</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>...</th>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>173</th>\n",
+       "      <td>13.71</td>\n",
+       "      <td>5.65</td>\n",
+       "      <td>2.45</td>\n",
+       "      <td>20.5</td>\n",
+       "      <td>95.0</td>\n",
+       "      <td>1.68</td>\n",
+       "      <td>0.61</td>\n",
+       "      <td>0.52</td>\n",
+       "      <td>1.06</td>\n",
+       "      <td>7.70</td>\n",
+       "      <td>0.64</td>\n",
+       "      <td>1.74</td>\n",
+       "      <td>740.0</td>\n",
+       "      <td>2</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>174</th>\n",
+       "      <td>13.40</td>\n",
+       "      <td>3.91</td>\n",
+       "      <td>2.48</td>\n",
+       "      <td>23.0</td>\n",
+       "      <td>102.0</td>\n",
+       "      <td>1.80</td>\n",
+       "      <td>0.75</td>\n",
+       "      <td>0.43</td>\n",
+       "      <td>1.41</td>\n",
+       "      <td>7.30</td>\n",
+       "      <td>0.70</td>\n",
+       "      <td>1.56</td>\n",
+       "      <td>750.0</td>\n",
+       "      <td>2</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>175</th>\n",
+       "      <td>13.27</td>\n",
+       "      <td>4.28</td>\n",
+       "      <td>2.26</td>\n",
+       "      <td>20.0</td>\n",
+       "      <td>120.0</td>\n",
+       "      <td>1.59</td>\n",
+       "      <td>0.69</td>\n",
+       "      <td>0.43</td>\n",
+       "      <td>1.35</td>\n",
+       "      <td>10.20</td>\n",
+       "      <td>0.59</td>\n",
+       "      <td>1.56</td>\n",
+       "      <td>835.0</td>\n",
+       "      <td>2</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>176</th>\n",
+       "      <td>13.17</td>\n",
+       "      <td>2.59</td>\n",
+       "      <td>2.37</td>\n",
+       "      <td>20.0</td>\n",
+       "      <td>120.0</td>\n",
+       "      <td>1.65</td>\n",
+       "      <td>0.68</td>\n",
+       "      <td>0.53</td>\n",
+       "      <td>1.46</td>\n",
+       "      <td>9.30</td>\n",
+       "      <td>0.60</td>\n",
+       "      <td>1.62</td>\n",
+       "      <td>840.0</td>\n",
+       "      <td>2</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>177</th>\n",
+       "      <td>14.13</td>\n",
+       "      <td>4.10</td>\n",
+       "      <td>2.74</td>\n",
+       "      <td>24.5</td>\n",
+       "      <td>96.0</td>\n",
+       "      <td>2.05</td>\n",
+       "      <td>0.76</td>\n",
+       "      <td>0.56</td>\n",
+       "      <td>1.35</td>\n",
+       "      <td>9.20</td>\n",
+       "      <td>0.61</td>\n",
+       "      <td>1.60</td>\n",
+       "      <td>560.0</td>\n",
+       "      <td>2</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "<p>178 rows × 14 columns</p>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "     alcohol  malic_acid   ash  alcalinity_of_ash  magnesium  total_phenols  \\\n",
+       "0      14.23        1.71  2.43               15.6      127.0           2.80   \n",
+       "1      13.20        1.78  2.14               11.2      100.0           2.65   \n",
+       "2      13.16        2.36  2.67               18.6      101.0           2.80   \n",
+       "3      14.37        1.95  2.50               16.8      113.0           3.85   \n",
+       "4      13.24        2.59  2.87               21.0      118.0           2.80   \n",
+       "..       ...         ...   ...                ...        ...            ...   \n",
+       "173    13.71        5.65  2.45               20.5       95.0           1.68   \n",
+       "174    13.40        3.91  2.48               23.0      102.0           1.80   \n",
+       "175    13.27        4.28  2.26               20.0      120.0           1.59   \n",
+       "176    13.17        2.59  2.37               20.0      120.0           1.65   \n",
+       "177    14.13        4.10  2.74               24.5       96.0           2.05   \n",
+       "\n",
+       "     flavanoids  nonflavanoid_phenols  proanthocyanins  color_intensity   hue  \\\n",
+       "0          3.06                  0.28             2.29             5.64  1.04   \n",
+       "1          2.76                  0.26             1.28             4.38  1.05   \n",
+       "2          3.24                  0.30             2.81             5.68  1.03   \n",
+       "3          3.49                  0.24             2.18             7.80  0.86   \n",
+       "4          2.69                  0.39             1.82             4.32  1.04   \n",
+       "..          ...                   ...              ...              ...   ...   \n",
+       "173        0.61                  0.52             1.06             7.70  0.64   \n",
+       "174        0.75                  0.43             1.41             7.30  0.70   \n",
+       "175        0.69                  0.43             1.35            10.20  0.59   \n",
+       "176        0.68                  0.53             1.46             9.30  0.60   \n",
+       "177        0.76                  0.56             1.35             9.20  0.61   \n",
+       "\n",
+       "     od280/od315_of_diluted_wines  proline  class  \n",
+       "0                            3.92   1065.0      0  \n",
+       "1                            3.40   1050.0      0  \n",
+       "2                            3.17   1185.0      0  \n",
+       "3                            3.45   1480.0      0  \n",
+       "4                            2.93    735.0      0  \n",
+       "..                            ...      ...    ...  \n",
+       "173                          1.74    740.0      2  \n",
+       "174                          1.56    750.0      2  \n",
+       "175                          1.56    835.0      2  \n",
+       "176                          1.62    840.0      2  \n",
+       "177                          1.60    560.0      2  \n",
+       "\n",
+       "[178 rows x 14 columns]"
+      ]
+     },
+     "execution_count": 2,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
    "source": [
     "from sklearn.datasets import load_wine\n",
     "\n",
@@ -91,12 +369,22 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 55,
    "id": "56916892",
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "178\n"
+     ]
+    }
+   ],
    "source": [
-    "# Your answer here"
+    "# Your answer here\n",
+    "num_rows = wine_df.shape[0]\n",
+    "print(num_rows)"
    ]
   },
   {
@@ -109,12 +397,26 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 53,
    "id": "df0ef103",
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Index(['alcohol', 'malic_acid', 'ash', 'alcalinity_of_ash', 'magnesium',\n",
+      "       'total_phenols', 'flavanoids', 'nonflavanoid_phenols',\n",
+      "       'proanthocyanins', 'color_intensity', 'hue',\n",
+      "       'od280/od315_of_diluted_wines', 'proline', 'class'],\n",
+      "      dtype='object')\n"
+     ]
+    }
+   ],
    "source": [
-    "# Your answer here"
+    "# Your answer here\n",
+    "columns = wine_df.columns\n",
+    "print(columns)"
    ]
   },
   {
@@ -127,12 +429,22 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 57,
    "id": "47989426",
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[0 1 2]\n"
+     ]
+    }
+   ],
    "source": [
-    "# Your answer here"
+    "# Your answer here\n",
+    "# the variable type is integer.\n",
+    "print(wine_df['class'].unique())\n"
    ]
   },
   {
@@ -146,12 +458,26 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 58,
    "id": "bd7b0910",
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Index(['alcohol', 'malic_acid', 'ash', 'alcalinity_of_ash', 'magnesium',\n",
+      "       'total_phenols', 'flavanoids', 'nonflavanoid_phenols',\n",
+      "       'proanthocyanins', 'color_intensity', 'hue',\n",
+      "       'od280/od315_of_diluted_wines', 'proline'],\n",
+      "      dtype='object')\n"
+     ]
+    }
+   ],
    "source": [
-    "# Your answer here"
+    "# Your answer here\n",
+    "predictor_variables = wine_df.drop(columns=['class'])\n",
+    "print(predictor_variables.columns)"
    ]
   },
   {
@@ -175,10 +501,58 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 42,
+   "id": "fff52b40",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "pandas.core.frame.DataFrame"
+      ]
+     },
+     "execution_count": 42,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "type (predictors_standardized)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 59,
    "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 +578,7 @@
    "id": "403ef0bb",
    "metadata": {},
    "source": [
-    "> Your answer here..."
+    "Standardizing predictor variables ensures equal contribution to the model and prevents variables with larger scales from dominating the model."
    ]
   },
   {
@@ -220,7 +594,7 @@
    "id": "fdee5a15",
    "metadata": {},
    "source": [
-    "> Your answer here..."
+    "Because it's not a predictor variable, the response variable \"class\" works as a wine category. "
    ]
   },
   {
@@ -236,7 +610,9 @@
    "id": "f0676c21",
    "metadata": {},
    "source": [
-    "> Your answer here..."
+    "Setting a seed is essential for reproducibility and consistency.\n",
+    "Ensures that the results of experiments can be replicated by anyone at any time.\n",
+    "The value is important. When comparing models or techniques, using the same seed ensures that they are evaluated under identical conditions (e.g., same training and testing splits)."
    ]
   },
   {
@@ -252,7 +628,7 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "id": "72c101f2",
+   "id": "4b72dbac",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -260,8 +636,280 @@
     "np.random.seed(123)\n",
     "\n",
     "# split the data into a training and testing set. hint: use train_test_split !\n",
+    "wine_df_train, wine_df_test = train_test_split(\n",
+    "    wine_df, train_size = 0.75, shuffle = True, random_state=123\n",
+    ")\n",
+    "train_x = wine_df_train.iloc[:, :-1]\n",
+    "train_y = wine_df_train['class']\n",
+    "test_x = wine_df_test['class']\n",
+    "test_y = wine_df_test.iloc[:, :-1]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 65,
+   "id": "72c101f2",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "(      alcohol  malic_acid       ash  alcalinity_of_ash  magnesium  \\\n",
+       " 28   1.073917   -0.391694  1.584529          -0.028510   0.509638   \n",
+       " 13   2.160950   -0.544297  0.085839          -2.430790  -0.613775   \n",
+       " 88  -1.680724   -0.248068  0.341713           0.632117  -1.105268   \n",
+       " 24   0.616869   -0.472483  0.890014           0.151661  -0.262708   \n",
+       " 100 -1.137207   -0.230114 -2.436346          -0.599052  -0.192495   \n",
+       " ..        ...         ...       ...                ...        ...   \n",
+       " 17   1.024507   -0.687923  0.926567           0.151661   1.071345   \n",
+       " 98  -0.778980   -1.136754 -0.974210          -0.298767  -0.824415   \n",
+       " 66   0.135116   -1.190614 -2.436346          -1.349764  -1.526548   \n",
+       " 126 -0.704864   -0.723829 -0.279695           0.602088  -0.964842   \n",
+       " 109 -1.717782   -0.885409  1.218995           0.151661  -0.403135   \n",
+       " \n",
+       "      total_phenols  flavanoids  nonflavanoid_phenols  proanthocyanins  \\\n",
+       " 28        1.049347    0.944462              0.065639         0.296279   \n",
+       " 13        1.289697    1.667318              0.549108         2.135968   \n",
+       " 88       -0.552984   -0.340615              0.951998        -0.422075   \n",
+       " 24        0.376368    0.583034             -0.659563         0.121071   \n",
+       " 100      -0.104332    0.141289             -0.820719        -0.334471   \n",
+       " ..             ...         ...                   ...              ...   \n",
+       " 17        1.049347    1.376168              0.307374         0.226196   \n",
+       " 98        1.962676    1.727556             -0.981875         0.629175   \n",
+       " 66        1.097417    1.155295             -0.820719         1.207363   \n",
+       " 126       0.712858    1.125176              0.226796         0.313800   \n",
+       " 109       0.712858    0.894264             -0.578985         1.575301   \n",
+       " \n",
+       "      color_intensity       hue  od280/od315_of_diluted_wines   proline  \n",
+       " 28         -0.241413  1.283518                      1.113449  0.535335  \n",
+       " 13          0.147900  1.283518                      0.167113  1.283691  \n",
+       " 88         -0.976782  0.186684                      0.195361 -0.213021  \n",
+       " 24         -0.665332  0.713164                      1.706675  0.312420  \n",
+       " 100        -0.760497  1.371265                      0.491974 -0.117486  \n",
+       " ..               ...       ...                           ...       ...  \n",
+       " 17          0.666984  0.757038                     -0.058878  1.220001  \n",
+       " 98         -0.241413  0.362177                      0.223610 -0.276711  \n",
+       " 66          0.104643  0.713164                      0.802712 -0.779861  \n",
+       " 126        -0.483652 -1.173391                      0.322481 -1.257535  \n",
+       " 109        -1.041667  0.011190                      0.915707 -0.213021  \n",
+       " \n",
+       " [133 rows x 13 columns],\n",
+       "       alcohol  malic_acid       ash  alcalinity_of_ash  magnesium  \\\n",
+       " 138  0.604516    1.125357 -0.645229           0.001518  -0.824415   \n",
+       " 60  -0.828391   -1.109824 -0.316249          -1.049479   0.088358   \n",
+       " 137 -0.581338    2.848870  0.999674           1.653086  -0.262708   \n",
+       " 77  -1.433671    0.496993 -0.499016          -0.448909   0.860705   \n",
+       " 90  -1.137207   -0.454530 -0.170035          -0.298767  -1.315908   \n",
+       " 159  0.592164   -0.598156  0.999674           0.902373  -0.754202   \n",
+       " 41   0.505695    1.349773 -0.901103          -0.208681  -0.683988   \n",
+       " 136 -0.927212    2.139716  0.634140           0.451946  -0.754202   \n",
+       " 166  0.555106    1.224100  0.853460           1.052516   0.790492   \n",
+       " 93  -0.877801    0.443133 -0.535569          -0.448909  -0.824415   \n",
+       " 154 -0.519575   -0.939268 -0.974210           0.151661   0.228785   \n",
+       " 141  0.443932    0.200764 -0.060375           0.151661  -0.754202   \n",
+       " 164  0.962743    0.380297 -0.243142           0.752231  -0.683988   \n",
+       " 33   0.938038   -0.723829  1.218995           0.001518   2.264972   \n",
+       " 31   0.715690   -0.607133 -0.023821          -0.118596   0.439425   \n",
+       " 152  0.135116   -0.391694  1.401762           1.803228   1.141558   \n",
+       " 82  -1.137207   -1.082894  0.524480           1.352801  -1.526548   \n",
+       " 122 -0.717217    1.879394  1.328655           2.103513   0.158572   \n",
+       " 42   1.086270   -0.400670  0.816907          -1.349764   0.088358   \n",
+       " 87  -1.668371   -0.598156  0.926567           1.953371  -0.824415   \n",
+       " 139 -0.198406    0.559829  0.890014           1.352801   0.088358   \n",
+       " 147 -0.161348    2.040973  0.414820           0.602088  -0.964842   \n",
+       " 151 -0.260169    0.299507  0.414820           0.752231   0.860705   \n",
+       " 162 -0.186053    0.838105  0.780354           0.752231   0.439425   \n",
+       " 79  -0.371343    1.376703  0.122392           1.052516   0.088358   \n",
+       " 169  0.493343    2.031997  1.803849           1.653086   0.860705   \n",
+       " 157 -0.680159    0.622666  0.999674           2.253656  -0.192495   \n",
+       " 95  -0.655454   -0.732806 -0.608676          -0.148624   4.371372   \n",
+       " 53   0.950391   -0.391694  1.145888          -0.719166   1.071345   \n",
+       " 10   1.358028   -0.158301 -0.243142          -0.448909   0.369212   \n",
+       " 23  -0.186053   -0.660993  0.561033          -0.508966  -0.332922   \n",
+       " 37   0.061000   -0.616110  0.670693          -0.448909  -0.122282   \n",
+       " 134 -0.606043   -0.984151 -0.425909          -0.599052  -1.035055   \n",
+       " 110 -1.903071    1.260006 -1.997705           0.001518   0.509638   \n",
+       " 167 -0.223111    0.927871 -0.243142           0.001518  -0.824415   \n",
+       " 120 -1.915424    0.057138  0.195499           0.151661  -0.262708   \n",
+       " 142  0.641574    0.748338  1.292101           1.202658  -0.192495   \n",
+       " 8    2.259772   -0.625086 -0.718336          -1.650049  -0.192495   \n",
+       " 72   0.604516   -0.607133 -0.462462           1.352801  -0.894628   \n",
+       " 114 -1.137207   -0.849502  0.487926           0.902373  -1.105268   \n",
+       " 148  0.394521    0.811175  0.049285           0.602088  -0.543562   \n",
+       " 163 -0.050174    0.999684 -0.060375          -0.298767   0.439425   \n",
+       " 36   0.345111   -0.625086  1.730742          -1.199622   0.720278   \n",
+       " 20   1.308617   -0.634063 -0.316249          -1.049479   1.843692   \n",
+       " 121 -1.779545   -0.257044  3.156325           2.704083   1.352198   \n",
+       " \n",
+       "      total_phenols  flavanoids  nonflavanoid_phenols  proanthocyanins  \\\n",
+       " 138      -1.081754   -1.555415              1.757779        -1.245555   \n",
+       " 60       -0.392751   -0.942995              2.160669        -2.069034   \n",
+       " 137      -0.809357   -1.434939              2.160669        -0.860096   \n",
+       " 77       -0.921521   -0.712083              0.549108        -1.122909   \n",
+       " 90       -1.113800   -0.531369              1.274310         0.086029   \n",
+       " 159       0.488531   -0.932956              1.274310         1.224884   \n",
+       " 41        0.248181    0.653312             -0.740141        -0.194304   \n",
+       " 136      -1.466313   -1.565455              1.354888        -1.385721   \n",
+       " 166      -0.953567   -1.113670              0.549108        -0.229346   \n",
+       " 93        0.248181    0.221606             -0.901297         0.699259   \n",
+       " 154      -1.306080   -1.455019              1.354888        -0.334471   \n",
+       " 141      -1.434267   -1.535336              0.065639        -1.666055   \n",
+       " 164      -1.514383   -1.354622              0.387952        -0.982742   \n",
+       " 33        1.049347    0.713550              1.113154        -0.422075   \n",
+       " 31        0.905137    1.165335             -1.143031         0.629175   \n",
+       " 152      -0.152402   -0.752242             -0.820719        -0.054137   \n",
+       " 82       -0.472868   -0.451052              0.307374        -0.334471   \n",
+       " 122      -0.152402    0.101130              0.549108         0.208675   \n",
+       " 42        1.530047    1.536802             -1.545922         0.191154   \n",
+       " 87       -0.601054   -0.420933              0.307374        -0.439596   \n",
+       " 139       0.039878   -1.434939              1.354888        -1.368201   \n",
+       " 147      -0.953567   -1.384741              0.871420        -1.280596   \n",
+       " 151      -1.306080   -0.671924             -0.981875        -0.579763   \n",
+       " 162      -1.033684   -1.434939              1.918935        -1.105388   \n",
+       " 79        0.857067    0.522796              0.549108         0.629175   \n",
+       " 169      -0.504914   -1.073511             -0.740141        -0.842575   \n",
+       " 157      -0.633101   -1.455019              2.160669        -0.790013   \n",
+       " 95        0.328298    0.241685             -0.337251         2.959447   \n",
+       " 53        1.129464    0.763748              0.226796         0.156113   \n",
+       " 10        1.049347    1.295850             -1.143031         1.382572   \n",
+       " 23        0.296251    0.342082             -0.820719        -0.229346   \n",
+       " 37        0.248181    0.402320             -0.578985        -0.264388   \n",
+       " 134      -0.472868   -1.455019              1.918935        -0.597284   \n",
+       " 110       1.417883    0.552915             -0.981875         3.485073   \n",
+       " 167      -1.306080   -1.374701              0.307374        -1.087867   \n",
+       " 120       0.969231    0.763748             -0.337251         0.418925   \n",
+       " 142      -1.193917   -1.515257              1.113154        -1.823742   \n",
+       " 8         0.808997    0.954502             -0.578985         0.681738   \n",
+       " 72       -0.665148   -0.190020             -0.740141        -0.982742   \n",
+       " 114       0.424438    0.261765              0.549108        -0.965221   \n",
+       " 148      -0.585031   -1.274305              0.710264        -0.597284   \n",
+       " 163      -1.450290   -1.334543              0.307374        -1.140430   \n",
+       " 36        0.488531    0.653312             -0.176095        -0.404554   \n",
+       " 20        1.129464    1.145255             -0.981875         0.891988   \n",
+       " 121       1.417883    3.062832              0.871420         0.489009   \n",
+       " \n",
+       "      color_intensity       hue  od280/od315_of_diluted_wines   proline  \n",
+       " 138         0.277671 -0.646911                     -1.118210 -0.531471  \n",
+       " 60         -0.773474  1.283518                     -1.330077 -0.213021  \n",
+       " 137        -0.025128 -0.603037                     -1.301828 -0.738463  \n",
+       " 77         -1.041667  0.011190                     -0.129500 -0.786230  \n",
+       " 90         -1.149810  0.537671                     -0.482611 -0.849920  \n",
+       " 159         2.894719 -1.699872                     -1.174708 -0.404091  \n",
+       " 41         -0.336578 -0.208177                      0.548472  0.917474  \n",
+       " 136        -0.522583 -0.910151                     -1.895054 -0.085641  \n",
+       " 166         2.431870 -0.471417                     -1.485445 -0.165254  \n",
+       " 93         -1.257952  0.844785                      0.972205 -1.454974  \n",
+       " 154         1.099554 -1.655999                     -1.499570 -0.340401  \n",
+       " 141         0.234414 -1.129518                     -0.200123  0.105428  \n",
+       " 164         1.956043 -1.129518                     -1.315952 -0.420013  \n",
+       " 33          0.147900  1.283518                      0.548472  1.554373  \n",
+       " 31          0.796755  0.581544                      0.378979  2.446031  \n",
+       " 152         0.883269 -1.524378                     -1.810307 -1.025067  \n",
+       " 82         -1.236324  1.546759                      0.152988 -0.372246  \n",
+       " 122        -1.288232 -0.164303                      0.717965 -1.216137  \n",
+       " 42          0.160877 -0.339797                      1.339440  1.108544  \n",
+       " 87         -1.063296  1.766126                      0.845085 -0.588791  \n",
+       " 139        -0.059734 -0.295924                     -0.652104 -0.499626  \n",
+       " 147         1.121183 -1.831492                     -1.061713 -0.388168  \n",
+       " 151         2.483778 -2.094732                     -1.612565 -0.849920  \n",
+       " 162         0.225763 -0.383670                     -0.708602 -0.563315  \n",
+       " 79         -1.076273  1.020278                      0.732090 -0.904056  \n",
+       " 169         1.488867 -1.261138                     -0.976966 -0.372246  \n",
+       " 157         1.056297 -1.261138                     -1.245330  0.423878  \n",
+       " 95         -1.063296  0.888658                      0.025868  0.605394  \n",
+       " 53          0.537213  0.757038                      0.449601  2.000202  \n",
+       " 10          0.299300  1.283518                      0.788587  2.430109  \n",
+       " 23         -0.487978  0.581544                      1.438311  0.853784  \n",
+       " 37         -0.349555  0.713164                     -0.143625  1.140389  \n",
+       " 134         0.169529 -0.910151                     -1.556068 -0.308556  \n",
+       " 110        -0.933525 -0.910151                      0.280108 -0.588791  \n",
+       " 167         2.250190 -1.041771                     -1.217081 -0.197099  \n",
+       " 120        -0.782125 -0.690784                      1.099325 -0.388168  \n",
+       " 142        -0.306298 -0.295924                     -0.779224 -0.722540  \n",
+       " 8           0.061386  0.537671                      0.336606  0.949319  \n",
+       " 72         -0.570166  0.098937                      0.237735 -0.875396  \n",
+       " 114        -0.933525 -0.120430                      0.816836 -1.152447  \n",
+       " 148         1.454261 -1.787619                     -1.400699 -0.308556  \n",
+       " 163         0.095992 -1.217265                     -1.217081 -0.228944  \n",
+       " 36         -0.198156  0.581544                      0.237735  0.423878  \n",
+       " 20          0.256043  0.581544                      1.551307  0.105428  \n",
+       " 121         0.407442 -0.120430                      1.523058 -0.897687  ,\n",
+       " 28     0\n",
+       " 13     0\n",
+       " 88     1\n",
+       " 24     0\n",
+       " 100    1\n",
+       "       ..\n",
+       " 17     0\n",
+       " 98     1\n",
+       " 66     1\n",
+       " 126    1\n",
+       " 109    1\n",
+       " Name: class, Length: 133, dtype: int64,\n",
+       " 138    2\n",
+       " 60     1\n",
+       " 137    2\n",
+       " 77     1\n",
+       " 90     1\n",
+       " 159    2\n",
+       " 41     0\n",
+       " 136    2\n",
+       " 166    2\n",
+       " 93     1\n",
+       " 154    2\n",
+       " 141    2\n",
+       " 164    2\n",
+       " 33     0\n",
+       " 31     0\n",
+       " 152    2\n",
+       " 82     1\n",
+       " 122    1\n",
+       " 42     0\n",
+       " 87     1\n",
+       " 139    2\n",
+       " 147    2\n",
+       " 151    2\n",
+       " 162    2\n",
+       " 79     1\n",
+       " 169    2\n",
+       " 157    2\n",
+       " 95     1\n",
+       " 53     0\n",
+       " 10     0\n",
+       " 23     0\n",
+       " 37     0\n",
+       " 134    2\n",
+       " 110    1\n",
+       " 167    2\n",
+       " 120    1\n",
+       " 142    2\n",
+       " 8      0\n",
+       " 72     1\n",
+       " 114    1\n",
+       " 148    2\n",
+       " 163    2\n",
+       " 36     0\n",
+       " 20     0\n",
+       " 121    1\n",
+       " Name: class, dtype: int64)"
+      ]
+     },
+     "execution_count": 65,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# set a seed for reproducibility\n",
+    "np.random.seed(123)\n",
     "\n",
-    "# Your code here ..."
+    "# split the data into a training and testing set. hint: use train_test_split !\n",
+    " \n",
+    "Wine_train_X, Wine_test_X, Wine_train_y, Wine_test_y = train_test_split(\n",
+    "    predictors_standardized, wine_df['class'], train_size = 0.75, shuffle = True,\n",
+    "    random_state=123\n",
+    ")\n",
+    "Wine_train_X, Wine_test_X, Wine_train_y, Wine_test_y"
    ]
   },
   {
@@ -287,9 +935,458 @@
    "execution_count": null,
    "id": "08818c64",
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "{'n_neighbors': array([ 1,  2,  3,  4,  5,  6,  7,  8,  9, 10, 11, 12, 13, 14, 15, 16, 17,\n",
+      "       18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33, 34,\n",
+      "       35, 36, 37, 38, 39, 40, 41, 42, 43, 44, 45, 46, 47, 48, 49, 50])}\n",
+      "Optimal number of neighbors: 15\n"
+     ]
+    },
+    {
+     "data": {
+      "text/html": [
+       "<style>#sk-container-id-1 {\n",
+       "  /* Definition of color scheme common for light and dark mode */\n",
+       "  --sklearn-color-text: black;\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: block;\n",
+       "  width: 100%;\n",
+       "  margin-bottom: 0;\n",
+       "  padding: 0.5em;\n",
+       "  box-sizing: border-box;\n",
+       "  text-align: center;\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",
+       "  max-height: 0;\n",
+       "  max-width: 0;\n",
+       "  overflow: hidden;\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",
+       "  max-height: 200px;\n",
+       "  max-width: 100%;\n",
+       "  overflow: auto;\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: 1ex;\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",
+       "</style><div id=\"sk-container-id-1\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>KNeighborsClassifier(n_neighbors=10)</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-1\" type=\"checkbox\" checked><label for=\"sk-estimator-id-1\" class=\"sk-toggleable__label fitted sk-toggleable__label-arrow fitted\">&nbsp;&nbsp;KNeighborsClassifier<a class=\"sk-estimator-doc-link fitted\" rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.5/modules/generated/sklearn.neighbors.KNeighborsClassifier.html\">?<span>Documentation for KNeighborsClassifier</span></a><span class=\"sk-estimator-doc-link fitted\">i<span>Fitted</span></span></label><div class=\"sk-toggleable__content fitted\"><pre>KNeighborsClassifier(n_neighbors=10)</pre></div> </div></div></div></div>"
+      ],
+      "text/plain": [
+       "KNeighborsClassifier(n_neighbors=10)"
+      ]
+     },
+     "execution_count": 77,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
    "source": [
-    "# Your code here..."
+    "# Your code here...\n",
+    "knn = KNeighborsClassifier(n_neighbors=10)\n",
+    "\n",
+    "import numpy as np\n",
+    "\n",
+    "# Define the parameter grid for n_neighbors\n",
+    "param_grid = {'n_neighbors': np.arange(1, 51)}\n",
+    "\n",
+    "print(param_grid)\n",
+    "\n",
+    "# GridSearchCV with 10-fold cross-validation\n",
+    "grid_search = GridSearchCV(estimator=knn, param_grid=param_grid, cv=10, n_jobs=-1)\n",
+    "\n",
+    "# Fitting grid search to the training data\n",
+    "grid_search.fit(Wine_train_X, Wine_train_y)\n",
+    "\n",
+    "# Printing optimal n_neighbors\n",
+    "print(f\"Optimal number of neighbors: {grid_search.best_params_['n_neighbors']}\")\n",
+    "\n",
+    "#fitting our model to the train data\n",
+    "\n",
+    "knn.fit(Wine_train_X, Wine_train_y)\n"
    ]
   },
   {
@@ -305,12 +1402,36 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 78,
    "id": "ffefa9f2",
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Accuracy on the test set: 0.9333333333333333\n"
+     ]
+    }
+   ],
    "source": [
-    "# Your code here..."
+    "from sklearn.metrics import accuracy_score\n",
+    "\n",
+    "# Get the optimal number of neighbors from the grid search\n",
+    "best_n_neighbors = grid_search.best_params_['n_neighbors']\n",
+    "\n",
+    "# Initialize the KNN classifier with the best n_neighbors\n",
+    "knn_best = KNeighborsClassifier(n_neighbors=best_n_neighbors)\n",
+    "\n",
+    "# Fit the model on the training data\n",
+    "knn_best.fit(Wine_train_X, Wine_train_y)\n",
+    "\n",
+    "# Make predictions on the test set\n",
+    "Wine_test_pred = knn_best.predict(Wine_test_X)\n",
+    "\n",
+    "# Evaluate the model using accuracy_score\n",
+    "accuracy = accuracy_score(Wine_test_y, Wine_test_pred)\n",
+    "print(f\"Accuracy on the test set: {accuracy}\")\n"
    ]
   },
   {
@@ -365,7 +1486,7 @@
  ],
  "metadata": {
   "kernelspec": {
-   "display_name": "Python 3.10.4",
+   "display_name": "dsi_participant",
    "language": "python",
    "name": "python3"
   },
@@ -379,12 +1500,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.9.19"
-  },
-  "vscode": {
-   "interpreter": {
-    "hash": "497a84dc8fec8cf8d24e7e87b6d954c9a18a327edc66feb9b9ea7e9e72cc5c7e"
-   }
+   "version": "3.9.20"
   }
  },
  "nbformat": 4,