diff --git a/02_activities/assignments/assignment_1.ipynb b/02_activities/assignments/assignment_1.ipynb
index 28d4df017..49a94963c 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 +369,30 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 3,
    "id": "56916892",
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "The dataset contains 178 rows.\n"
+     ]
+    }
+   ],
    "source": [
-    "# Your answer here"
+    "# Your answer here\n",
+    "\n",
+    "\n",
+    "from sklearn.datasets import load_wine\n",
+    "\n",
+    "# Load Wine dataset\n",
+    "wine = load_wine()\n",
+    "\n",
+    "# Get number of rows\n",
+    "num_rows = wine.data.shape[0]\n",
+    "print(f\"The dataset contains {num_rows} rows.\")\n"
    ]
   },
   {
@@ -109,12 +405,30 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 4,
    "id": "df0ef103",
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "The dataset contains 13 columns.\n"
+     ]
+    }
+   ],
    "source": [
-    "# Your answer here"
+    "# Your answer here\n",
+    "\n",
+    "\n",
+    "from sklearn.datasets import load_wine\n",
+    "\n",
+    "# Load Wine dataset\n",
+    "wine = load_wine()\n",
+    "\n",
+    "# Get number of columns\n",
+    "num_columns = wine.data.shape[1]\n",
+    "print(f\"The dataset contains {num_columns} columns.\")"
    ]
   },
   {
@@ -127,12 +441,46 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 6,
    "id": "47989426",
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "pandas dtype of 'class': int64\n",
+      "NumPy dtype of 'class': int64\n",
+      "Python type of elements: <class 'numpy.int64'>\n",
+      "Levels (unique values) of 'class': [np.int64(0), np.int64(1), np.int64(2)]\n"
+     ]
+    }
+   ],
    "source": [
-    "# Your answer here"
+    "# Your answer here\n",
+    "\n",
+    "from sklearn.datasets import load_wine\n",
+    "import pandas as pd\n",
+    "import numpy as np\n",
+    "\n",
+    "# Load Wine dataset\n",
+    "wine = load_wine()\n",
+    "\n",
+    "# Create a pandas Series for the response variable 'class'\n",
+    "y = pd.Series(wine.target, name='class')\n",
+    "\n",
+    "# Query the variable type (dtype)\n",
+    "print(f\"pandas dtype of 'class': {y.dtype}\")           # e.g., int64\n",
+    "\n",
+    "# Also show the underlying NumPy dtype and Python type of elements, should be the same\n",
+    "print(f\"NumPy dtype of 'class': {y.to_numpy().dtype}\") \n",
+    "print(f\"Python type of elements: {type(y.iloc[0])}\")   \n",
+    "\n",
+    "# (b) Query the levels (unique values)\n",
+    "levels = sorted(y.unique())\n",
+    "print(f\"Levels (unique values) of 'class': {levels}\")\n",
+    "\n",
+    "\n"
    ]
   },
   {
@@ -146,12 +494,28 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 7,
    "id": "bd7b0910",
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "The dataset has 13 predictor variables.\n"
+     ]
+    }
+   ],
    "source": [
-    "# Your answer here"
+    "# Your answer here\n",
+    "from sklearn.datasets import load_wine\n",
+    "\n",
+    "# Load Wine dataset\n",
+    "wine = load_wine()\n",
+    "\n",
+    "# Number of predictor variables = number of columns in data\n",
+    "num_predictors = wine.data.shape[1]\n",
+    "print(f\"The dataset has {num_predictors} predictor variables.\")\n"
    ]
   },
   {
@@ -175,10 +539,37 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 8,
    "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",
@@ -196,7 +587,7 @@
    "id": "9981ca48",
    "metadata": {},
    "source": [
-    "(i) Why is it important to standardize the predictor variables?"
+    "(i) Why is it important to standardize the predictor variables?\n"
    ]
   },
   {
@@ -204,7 +595,7 @@
    "id": "403ef0bb",
    "metadata": {},
    "source": [
-    "> Your answer here..."
+    "Standardization is important because KNN relies on distances, and features with larger scales would otherwise dominate the calculation."
    ]
   },
   {
@@ -220,7 +611,8 @@
    "id": "fdee5a15",
    "metadata": {},
    "source": [
-    "> Your answer here..."
+    "\n",
+    "Standardization is only meaningful for continuous numeric predictors but 'class' contains discrete categories.If we standardized 'class', it would distort its categorical meaning and break classification.KNN uses 'class' only for labeling, not for distance calculations."
    ]
   },
   {
@@ -236,7 +628,10 @@
    "id": "f0676c21",
    "metadata": {},
    "source": [
-    "> Your answer here..."
+    "\n",
+    "random.seed(42)      \n",
+    "np.random.seed(42)  \n",
+    " It makes results reproducible across runs and machines (e.g.same train/test split), which is important for debugging, comparison, and auditability."
    ]
   },
   {
@@ -254,14 +649,42 @@
    "execution_count": null,
    "id": "72c101f2",
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Training set size: 133 samples\n",
+      "Testing set size: 45 samples\n"
+     ]
+    }
+   ],
    "source": [
     "# set a seed for reproducibility\n",
     "np.random.seed(123)\n",
     "\n",
     "# split the data into a training and testing set. hint: use train_test_split !\n",
     "\n",
-    "# Your code here ..."
+    "# Your code here ...\n",
+    "\n",
+    "import numpy as np\n",
+    "from sklearn.model_selection import train_test_split\n",
+    "\n",
+    "# Set a seed for reproducibility\n",
+    "np.random.seed(123)\n",
+    "RANDOM_STATE = 123  # use the same random_state in sklearn\n",
+    "\n",
+    "\n",
+    "# 75% train / 25% test split (stratified to preserve class proportions)\n",
+    "X_train, X_test, y_train, y_test = train_test_split(\n",
+    "    predictors_standardized,\n",
+    "    wine_df['class'],\n",
+    "    test_size=0.25,\n",
+    "    stratify=wine_df['class'],\n",
+    "    random_state=RANDOM_STATE\n",
+    ")\n",
+    "print(f\"Training set size: {X_train.shape[0]} samples\")\n",
+    "print(f\"Testing set size: {X_test.shape[0]} samples\")\n"
    ]
   },
   {
@@ -284,12 +707,74 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 13,
    "id": "08818c64",
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "KNeighborsClassifier()\n",
+      "{'n_neighbors': [1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33, 34, 35, 36, 37, 38, 39, 40, 41, 42, 43, 44, 45, 46, 47, 48, 49, 50]}\n",
+      "Best n_neighbors: 13\n",
+      "Best 10-fold CV accuracy: 0.9851648351648352\n",
+      "Test accuracy with best model: 0.9111111111111111\n"
+     ]
+    }
+   ],
    "source": [
-    "# Your code here..."
+    "# Your code here...\n",
+    "\n",
+    "\n",
+    "# Step 1: Initialize the KNN classifier\n",
+    "knn = KNeighborsClassifier()    # Leave default values          \n",
+    "print(knn)\n",
+    "\n",
+    "\n",
+    "# Step 2: Define the parameter grid for n_neighbors\n",
+    "param_grid = {\n",
+    "    'n_neighbors': list(range(1, 51))  # Values from 1 to 50\n",
+    "}\n",
+    "\n",
+    "print(param_grid)\n",
+    "\n",
+    "\n",
+    "# 3) Build a pipeline: Standardize features -> KNN classifier\n",
+    "from sklearn.pipeline import Pipeline\n",
+    "pipe = Pipeline([\n",
+    "    (\"scaler\", StandardScaler()),\n",
+    "    (\"knn\", KNeighborsClassifier())\n",
+    "])\n",
+    "\n",
+    "# 4) Parameter grid for n_neighbors from 1 to 50\n",
+    "param_grid = {\n",
+    "    \"knn__n_neighbors\": list(range(1, 51))\n",
+    "}\n",
+    "\n",
+    "# 5) Grid search with 10-fold CV, optimize accuracy\n",
+    "grid = GridSearchCV(\n",
+    "    estimator=pipe,\n",
+    "    param_grid=param_grid,\n",
+    "    cv=10,\n",
+    "    scoring=\"accuracy\",\n",
+    "    n_jobs=-1,\n",
+    "    return_train_score=False\n",
+    ")\n",
+    "\n",
+    "# 6) Fit on training data\n",
+    "grid.fit(X_train, y_train)\n",
+    "\n",
+    "best_k = grid.best_params_[\"knn__n_neighbors\"]\n",
+    "best_cv_score = grid.best_score_\n",
+    "\n",
+    "# 7) Evaluate best model on the held-out test set\n",
+    "best_model = grid.best_estimator_\n",
+    "test_accuracy = accuracy_score(y_test, best_model.predict(X_test))\n",
+    "\n",
+    "print(\"Best n_neighbors:\", best_k)\n",
+    "print(\"Best 10-fold CV accuracy:\", best_cv_score)\n",
+    "print(\"Test accuracy with best model:\", test_accuracy)\n"
    ]
   },
   {
@@ -365,7 +850,7 @@
  ],
  "metadata": {
   "kernelspec": {
-   "display_name": "Python 3.10.4",
+   "display_name": "lcr-env",
    "language": "python",
    "name": "python3"
   },
@@ -379,12 +864,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.9.19"
-  },
-  "vscode": {
-   "interpreter": {
-    "hash": "497a84dc8fec8cf8d24e7e87b6d954c9a18a327edc66feb9b9ea7e9e72cc5c7e"
-   }
+   "version": "3.11.14"
   }
  },
  "nbformat": 4,
diff --git a/02_activities/assignments/assignment_2.ipynb b/02_activities/assignments/assignment_2.ipynb
index a05da5cd3..c98f7121f 100644
--- a/02_activities/assignments/assignment_2.ipynb
+++ b/02_activities/assignments/assignment_2.ipynb
@@ -34,7 +34,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 63,
+   "execution_count": 1,
    "id": "4a3485d6-ba58-4660-a983-5680821c5719",
    "metadata": {},
    "outputs": [],
@@ -50,10 +50,128 @@
   },
   {
    "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>mpg</th>\n",
+       "      <th>cylinders</th>\n",
+       "      <th>displacement</th>\n",
+       "      <th>horsepower</th>\n",
+       "      <th>weight</th>\n",
+       "      <th>acceleration</th>\n",
+       "      <th>model_year</th>\n",
+       "      <th>origin</th>\n",
+       "      <th>name</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>18.0</td>\n",
+       "      <td>8</td>\n",
+       "      <td>307.0</td>\n",
+       "      <td>130.0</td>\n",
+       "      <td>3504</td>\n",
+       "      <td>12.0</td>\n",
+       "      <td>70</td>\n",
+       "      <td>usa</td>\n",
+       "      <td>chevrolet chevelle malibu</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>15.0</td>\n",
+       "      <td>8</td>\n",
+       "      <td>350.0</td>\n",
+       "      <td>165.0</td>\n",
+       "      <td>3693</td>\n",
+       "      <td>11.5</td>\n",
+       "      <td>70</td>\n",
+       "      <td>usa</td>\n",
+       "      <td>buick skylark 320</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>18.0</td>\n",
+       "      <td>8</td>\n",
+       "      <td>318.0</td>\n",
+       "      <td>150.0</td>\n",
+       "      <td>3436</td>\n",
+       "      <td>11.0</td>\n",
+       "      <td>70</td>\n",
+       "      <td>usa</td>\n",
+       "      <td>plymouth satellite</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>16.0</td>\n",
+       "      <td>8</td>\n",
+       "      <td>304.0</td>\n",
+       "      <td>150.0</td>\n",
+       "      <td>3433</td>\n",
+       "      <td>12.0</td>\n",
+       "      <td>70</td>\n",
+       "      <td>usa</td>\n",
+       "      <td>amc rebel sst</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>17.0</td>\n",
+       "      <td>8</td>\n",
+       "      <td>302.0</td>\n",
+       "      <td>140.0</td>\n",
+       "      <td>3449</td>\n",
+       "      <td>10.5</td>\n",
+       "      <td>70</td>\n",
+       "      <td>usa</td>\n",
+       "      <td>ford torino</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "    mpg  cylinders  displacement  horsepower  weight  acceleration  \\\n",
+       "0  18.0          8         307.0       130.0    3504          12.0   \n",
+       "1  15.0          8         350.0       165.0    3693          11.5   \n",
+       "2  18.0          8         318.0       150.0    3436          11.0   \n",
+       "3  16.0          8         304.0       150.0    3433          12.0   \n",
+       "4  17.0          8         302.0       140.0    3449          10.5   \n",
+       "\n",
+       "   model_year origin                       name  \n",
+       "0          70    usa  chevrolet chevelle malibu  \n",
+       "1          70    usa          buick skylark 320  \n",
+       "2          70    usa         plymouth satellite  \n",
+       "3          70    usa              amc rebel sst  \n",
+       "4          70    usa                ford torino  "
+      ]
+     },
+     "execution_count": 2,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
    "source": [
     "import seaborn as sns\n",
     "\n",
@@ -82,12 +200,23 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 5,
    "id": "5d79f1cf",
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "The dataset contains 392 observations (rows) and 9 variables (columns).\n"
+     ]
+    }
+   ],
    "source": [
-    "# Your answer here..."
+    "# Your answer here...\n",
+    "num_rows, num_cols = mpg_data.shape\n",
+    "\n",
+    "print(f\"The dataset contains {num_rows} observations (rows) and {num_cols} variables (columns).\")"
    ]
   },
   {
@@ -103,9 +232,20 @@
    "execution_count": null,
    "id": "ac306190",
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "The data type of 'mpg' is: float64\n"
+     ]
+    }
+   ],
    "source": [
-    "# Your answer here..."
+    "# Miles per gallon is a numeric value for fuel efficiency and can take on any real number value \n",
+    "\n",
+    "mpg_dtype = mpg_data['mpg'].dtype\n",
+    "print(f\"The data type of 'mpg' is: {mpg_dtype}\")\n"
    ]
   },
   {
@@ -126,12 +266,38 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 7,
    "id": "9f034a5d",
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Top 5 rows with the greatest horsepower:\n",
+      "      mpg  cylinders  displacement  horsepower  weight  acceleration  \\\n",
+      "116  16.0          8         400.0       230.0    4278           9.5   \n",
+      "95   12.0          8         455.0       225.0    4951          11.0   \n",
+      "8    14.0          8         455.0       225.0    4425          10.0   \n",
+      "13   14.0          8         455.0       225.0    3086          10.0   \n",
+      "6    14.0          8         454.0       220.0    4354           9.0   \n",
+      "\n",
+      "     model_year origin                      name  \n",
+      "116          73    usa        pontiac grand prix  \n",
+      "95           73    usa  buick electra 225 custom  \n",
+      "8            70    usa          pontiac catalina  \n",
+      "13           70    usa   buick estate wagon (sw)  \n",
+      "6            70    usa          chevrolet impala  \n"
+     ]
+    }
+   ],
    "source": [
-    "# Your answer here... "
+    "# Your answer here... \n",
+    "\n",
+    "top_5_hp = mpg_data.sort_values(by='horsepower', ascending=False).head(5)\n",
+    "\n",
+    "print(\"Top 5 rows with the greatest horsepower:\")\n",
+    "print(top_5_hp)"
    ]
   },
   {
@@ -144,12 +310,25 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 8,
    "id": "1b91233e",
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "The dataset has 8 predictor variables.\n"
+     ]
+    }
+   ],
    "source": [
-    "# Your answer here..."
+    "# Your answer here...\n",
+    "# Count predictor variables (all columns except 'mpg')\n",
+    "predictor_vars = [col for col in mpg_data.columns if col != 'mpg']\n",
+    "num_predictors = len(predictor_vars)\n",
+    "\n",
+    "print(f\"The dataset has {num_predictors} predictor variables.\")\n"
    ]
   },
   {
@@ -173,10 +352,71 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 9,
    "id": "732784d8",
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "data": {
+      "image/png": "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",
+      "text/plain": [
+       "<Figure size 600x400 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/png": "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",
+      "text/plain": [
+       "<Figure size 600x400 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/png": "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",
+      "text/plain": [
+       "<Figure size 600x400 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/png": "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",
+      "text/plain": [
+       "<Figure size 600x400 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/png": "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",
+      "text/plain": [
+       "<Figure size 600x400 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/png": "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",
+      "text/plain": [
+       "<Figure size 600x400 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
    "source": [
     "# Exclude the 'mpg' (target variable) and non-numeric columns from the feature names\n",
     "feature_names = mpg_data.select_dtypes(include=[float, int]).columns.difference(['mpg'])\n",
@@ -220,12 +460,61 @@
     "_(i)_ Describe the associations being plotted ? (i.e., positive association, negative association, no association)"
    ]
   },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "id": "ac2cdf70",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Association of each numeric predictor with mpg:\n",
+      "     feature  correlation          association\n",
+      "  model_year     0.580541 positive association\n",
+      "acceleration     0.423329 positive association\n",
+      "   cylinders    -0.777618 negative association\n",
+      "  horsepower    -0.778427 negative association\n",
+      "displacement    -0.805127 negative association\n",
+      "      weight    -0.832244 negative association\n"
+     ]
+    }
+   ],
+   "source": [
+    "# Select numeric predictors excluding the target 'mpg'\n",
+    "feature_names = mpg_data.select_dtypes(include=[float, int]).columns.difference(['mpg'])\n",
+    "\n",
+    "associations = []\n",
+    "for feature in feature_names:\n",
+    "    corr = np.corrcoef(mpg_data[feature], mpg_data['mpg'])[0, 1]\n",
+    "    # Label association by the sign and magnitude of correlation\n",
+    "    if abs(corr) < 0.1:\n",
+    "        assoc = 'no association'\n",
+    "    elif corr > 0:\n",
+    "        assoc = 'positive association'\n",
+    "    else:\n",
+    "        assoc = 'negative association'\n",
+    "    associations.append({'feature': feature, 'correlation': corr, 'association': assoc})\n",
+    "\n",
+    "assoc_df = pd.DataFrame(associations).sort_values(by='correlation', ascending=False)\n",
+    "\n",
+    "print(\"Association of each numeric predictor with mpg:\")\n",
+    "print(assoc_df.to_string(index=False))\n"
+   ]
+  },
   {
    "cell_type": "markdown",
    "id": "f67e57ab",
    "metadata": {},
    "source": [
-    "> Your answer here..."
+    ">See above associations generated.\n",
+    "1. Model year - the newer the car the higher the mpg\n",
+    "2. accelration - the higher the acceleration the higher the mpg\n",
+    "3. cyinders - the more cylinders, the lower mpg\n",
+    "4. horsepower - higher horse power, the lower the mpg\n",
+    "5. displacement - bigger the engine, the lower the mpg\n",
+    "6. weigh - higher weigh, lower mpg\n"
    ]
   },
   {
@@ -241,7 +530,7 @@
    "id": "843f9eef",
    "metadata": {},
    "source": [
-    "> Your answer here..."
+    ">A simple linear regression model. It represents the line of best fit through the data points. It is calculated using the least squares method y^​=β0​+β1​x"
    ]
   },
   {
@@ -257,7 +546,7 @@
    "id": "2ea782fc",
    "metadata": {},
    "source": [
-    "> Your answer here..."
+    ">No because the red line is the linear regression fit, which assumes a straight-line relationship between the predictor and mpg. Real-world data often has variation due to other factors not included in the simple model (e.g., other car characteristics, measurement noise). These deviations are called residuals and represent the difference between the actual mpg and the predicted value from the line."
    ]
   },
   {
@@ -274,12 +563,37 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 11,
    "id": "399225f4",
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Total samples: 392\n",
+      "Train samples: 294\n",
+      "Test samples:  98\n"
+     ]
+    }
+   ],
    "source": [
-    "# Your answer here..."
+    "# Your answer here...\n",
+    "from sklearn.model_selection import train_test_split\n",
+    "\n",
+    "# Define features (X) and target (y)\n",
+    "X = mpg_data.drop(columns=['mpg'])\n",
+    "y = mpg_data['mpg']\n",
+    "\n",
+    "# 75-25 split (25% test), reproducible with random_state=42\n",
+    "X_train, X_test, y_train, y_test = train_test_split(\n",
+    "    X, y, test_size=0.25, random_state=42\n",
+    ")\n",
+    "\n",
+    "# Optional: show the split sizes\n",
+    "print(f\"Total samples: {len(mpg_data)}\")\n",
+    "print(f\"Train samples: {len(X_train)}\")\n",
+    "print(f\"Test samples:  {len(X_test)}\")\n"
    ]
   },
   {
@@ -292,28 +606,55 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 29,
    "id": "ac1e1117",
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Test RMSE: 3.185\n",
+      "      predictor     slope  intercept\n",
+      "0     cylinders -0.160143 -15.047371\n",
+      "1  displacement  0.000373 -15.047371\n",
+      "2    horsepower -0.001899 -15.047371\n",
+      "3        weight -0.006457 -15.047371\n",
+      "4  acceleration  0.057588 -15.047371\n",
+      "5    model_year  0.762270 -15.047371\n"
+     ]
+    }
+   ],
    "source": [
-    "# Your code here ...\n",
     "\n",
-    "numeric_predictors = 🤷‍♂️\n",
     "\n",
+    "# Drop rows where the target is missing\n",
+    "mpg_data = mpg_data.dropna(subset=['mpg'])\n",
     "\n",
-    "# Create a DataFrame containing the slope (coefficients) and intercept\n",
+    "# Select numeric columns only and drop rows with any NaNs in these\n",
+    "mpg_numeric = mpg_data.select_dtypes(include=[float, int]).dropna()\n",
+    "\n",
+    "# Features & target\n",
+    "numeric_predictors = mpg_numeric.drop(columns=['mpg'])\n",
+    "y = mpg_numeric['mpg']\n",
+    "\n",
+    "\n",
+    "# Fit\n",
+    "lm = LinearRegression()\n",
+    "lm.fit(X_train, y_train)\n",
+    "\n",
+    "# Evaluate\n",
+    "y_pred = lm.predict(X_test)\n",
+    "rmse = np.sqrt(mean_squared_error(y_test, y_pred))\n",
+    "print(f\"Test RMSE: {rmse:.3f}\")\n",
+    "\n",
+    "# Coefficients DataFrame\n",
     "coefficients_df = pd.DataFrame({\n",
     "    \"predictor\": numeric_predictors.columns,\n",
     "    \"slope\": lm.coef_,\n",
     "    \"intercept\": [lm.intercept_] * len(lm.coef_)\n",
     "})\n",
-    "\n",
-    "# Display the coefficients DataFrame\n",
-    "print(coefficients_df)\n",
-    "\n",
-    "# lm.coef_ gives the coefficients for each predictor (change in miles per gallon per unit change in each predictor variable)\n",
-    "# lm.intercept_ gives the intercept b_0 (the predicted miles per gallon when all predictors are set to 0)"
+    "print(coefficients_df)\n"
    ]
   },
   {
@@ -333,9 +674,25 @@
    "execution_count": null,
    "id": "ffefa9f2",
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Test RMSPE: 16.05%\n"
+     ]
+    }
+   ],
    "source": [
-    "# Your code here ..."
+    "# Predict on the test set\n",
+    "y_pred = lm.predict(X_test)\n",
+    "\n",
+    "# Compute RMSPE (percentage)\n",
+    "# Protect against division by zero if target might contain zeros\n",
+    "nonzero_mask = y_test != 0\n",
+    "rmspe = np.sqrt(np.mean(((y_test[nonzero_mask] - y_pred[nonzero_mask]) / y_test[nonzero_mask]) ** 2)) * 100\n",
+    "\n",
+    "print(f\"Test RMSPE: {rmspe:.2f}%\")\n"
    ]
   },
   {
@@ -386,7 +743,7 @@
  ],
  "metadata": {
   "kernelspec": {
-   "display_name": "Python 3 (ipykernel)",
+   "display_name": "lcr-env",
    "language": "python",
    "name": "python3"
   },
@@ -400,12 +757,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.12.3"
-  },
-  "vscode": {
-   "interpreter": {
-    "hash": "497a84dc8fec8cf8d24e7e87b6d954c9a18a327edc66feb9b9ea7e9e72cc5c7e"
-   }
+   "version": "3.11.14"
   }
  },
  "nbformat": 4,