diff --git a/M1/Stats learning/TP2_KNN.ipynb b/M1/Stats learning/TP2_KNN.ipynb
index da5d409..9847080 100644
--- a/M1/Stats learning/TP2_KNN.ipynb	
+++ b/M1/Stats learning/TP2_KNN.ipynb	
@@ -58,27 +58,72 @@
     "\n"
    ]
   },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## 1. K-NN classification for  `Iris`  <a class=\"anchor\" id=\"chapter1\"></a>"
-   ]
-  },
   {
    "cell_type": "code",
    "metadata": {
     "ExecuteTime": {
-     "end_time": "2025-01-29T09:18:27.429726Z",
-     "start_time": "2025-01-29T09:18:27.312729Z"
+     "end_time": "2025-02-05T09:35:48.092357Z",
+     "start_time": "2025-02-05T09:35:48.052472Z"
     }
    },
-   "source": [
-    "import numpy as np"
-   ],
+   "source": "import numpy as np",
    "outputs": [],
    "execution_count": 1
   },
+  {
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2025-02-05T09:35:50.262061Z",
+     "start_time": "2025-02-05T09:35:50.258035Z"
+    }
+   },
+   "cell_type": "code",
+   "source": [
+    "X0 = np.array([0, 0, 0])\n",
+    "X1 = np.array([0, 3, 0])\n",
+    "X2 = np.array([2, 0, 0])\n",
+    "X3 = np.array([0, 1, 3])\n",
+    "X4 = np.array([0, 1, 2])\n",
+    "X5 = np.array([-1, 0, 1])\n",
+    "X6 = np.array([1, 1, 1])\n",
+    "X = np.array([X1, X2, X3, X4, X5, X6])\n",
+    "\n",
+    "distances = []\n",
+    "for x in X:\n",
+    "    dist = np.linalg.norm(x - X0) ** 2\n",
+    "    distances.append(dist)\n",
+    "    print(dist)\n",
+    "\n",
+    "for k in [1, 3]:\n",
+    "    near = np.argsort(distances)[:k]\n",
+    "    print(f\"The nearest neighbor is the observation {near}\")\n",
+    "    print(f\"The predicted value is {near}\")"
+   ],
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "9.0\n",
+      "4.0\n",
+      "10.000000000000002\n",
+      "5.000000000000001\n",
+      "2.0000000000000004\n",
+      "2.9999999999999996\n",
+      "The nearest neighbor is the observation [4]\n",
+      "The predicted value is [4]\n",
+      "The nearest neighbor is the observation [4 5 1]\n",
+      "The predicted value is [4 5 1]\n"
+     ]
+    }
+   ],
+   "execution_count": 2
+  },
+  {
+   "metadata": {},
+   "cell_type": "markdown",
+   "source": "## 1. K-NN classification for  `Iris`  <a class=\"anchor\" id=\"chapter1\"></a>"
+  },
   {
    "attachments": {
     "Iris_setosa_versicolor_virginica.png": {
@@ -120,16 +165,21 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2025-02-05T09:35:55.259856Z",
+     "start_time": "2025-02-05T09:35:54.512643Z"
+    }
+   },
    "source": [
     "from sklearn import datasets\n",
     "\n",
     "iris = datasets.load_iris()\n",
-    "X=iris.data\n",
-    "y=iris.target\n"
-   ]
+    "X = iris.data\n",
+    "y = iris.target"
+   ],
+   "outputs": [],
+   "execution_count": 3
   },
   {
    "cell_type": "markdown",
@@ -140,18 +190,28 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2025-02-05T09:35:56.416120Z",
+     "start_time": "2025-02-05T09:35:56.413692Z"
+    }
+   },
    "source": [
     "# Answer for Exercise 2\n",
-    "\n",
-    "\n",
-    "\n",
-    "\n",
-    "\n",
-    "\n"
-   ]
+    "print(np.shape(X))\n",
+    "print(np.shape(y))"
+   ],
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "(150, 4)\n",
+      "(150,)\n"
+     ]
+    }
+   ],
+   "execution_count": 4
   },
   {
    "cell_type": "markdown",
@@ -162,14 +222,19 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2025-02-05T09:35:57.854360Z",
+     "start_time": "2025-02-05T09:35:57.797319Z"
+    }
+   },
    "source": [
     "from sklearn.model_selection import train_test_split\n",
     "\n",
     "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.33, random_state=42)"
-   ]
+   ],
+   "outputs": [],
+   "execution_count": 5
   },
   {
    "cell_type": "markdown",
@@ -182,18 +247,32 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2025-02-05T09:35:58.651809Z",
+     "start_time": "2025-02-05T09:35:58.649114Z"
+    }
+   },
    "source": [
     "#Answer for Exercise 3 \n",
-    "\n"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": []
+    "print(np.shape(X_train))\n",
+    "print(np.shape(X_test))\n",
+    "print(np.shape(y_train))\n",
+    "print(np.shape(y_test))"
+   ],
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "(100, 4)\n",
+      "(50, 4)\n",
+      "(100,)\n",
+      "(50,)\n"
+     ]
+    }
+   ],
+   "execution_count": 6
   },
   {
    "cell_type": "markdown",
@@ -213,18 +292,18 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2025-02-05T09:38:50.058465Z",
+     "start_time": "2025-02-05T09:38:50.056580Z"
+    }
+   },
    "source": [
-    "# Answer for Exercise 4\n",
-    "\n",
-    "def euc_dis(sample1,sample2):\n",
-    "    \n",
-    "     dist = # complete with your code\n",
-    "        \n",
-    "     return dist   "
-   ]
+    "def euc_dis(sample1, sample2):\n",
+    "    return np.linalg.norm(sample1 - sample2, axis=1) ** 2"
+   ],
+   "outputs": [],
+   "execution_count": 19
   },
   {
    "cell_type": "markdown",
@@ -248,46 +327,65 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2025-02-05T09:38:51.442212Z",
+     "start_time": "2025-02-05T09:38:51.439679Z"
+    }
+   },
    "source": [
     "#  np.argsort \n",
     "\n",
-    "distance_ex=np.array([4,4,4,3,3,3,2,2,2,1,1,0.5,0.2])\n",
-    "print (\"The indices where the 4 smallest digits are located are \\n\", np.argsort(distance_ex)[:4])\n",
-    "print (\"The 4 smallest digits are \", distance_ex[np.argsort(distance_ex)[:4]])\n",
+    "distance_ex = np.array([4, 4, 4, 3, 3, 3, 2, 2, 2, 1, 1, 0.5, 0.2])\n",
+    "print(\"The indices where the 4 smallest digits are located are \\n\", np.argsort(distance_ex)[:4])\n",
+    "print(\"The 4 smallest digits are \", distance_ex[np.argsort(distance_ex)[:4]])\n",
     "\n",
-    "print (\"\\n\")\n",
+    "print(\"\\n\")\n",
     "\n",
     "# counter.most_common()\n",
     "\n",
-    "from collections import Counter \n",
+    "from collections import Counter\n",
     "\n",
-    "print (\"In 'aabbbbccccccc', the frequencies of the letters are : \\n\", Counter('aabbbbccccccc').most_common())\n",
-    "print (\"In 'aabbbbccccccc', The letter that repeats the most is \", Counter('aabbbbccccccc').most_common()[0][0])"
-   ]
+    "print(\"In 'aabbbbccccccc', the frequencies of the letters are : \\n\", Counter('aabbbbccccccc').most_common())\n",
+    "print(\"In 'aabbbbccccccc', The letter that repeats the most is \", Counter('aabbbbccccccc').most_common()[0][0])"
+   ],
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "The indices where the 4 smallest digits are located are \n",
+      " [12 11  9 10]\n",
+      "The 4 smallest digits are  [0.2 0.5 1.  1. ]\n",
+      "\n",
+      "\n",
+      "In 'aabbbbccccccc', the frequencies of the letters are : \n",
+      " [('c', 7), ('b', 4), ('a', 2)]\n",
+      "In 'aabbbbccccccc', The letter that repeats the most is  c\n"
+     ]
+    }
+   ],
+   "execution_count": 20
   },
   {
    "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2025-02-05T09:39:22.762643Z",
+     "start_time": "2025-02-05T09:39:22.760629Z"
+    }
+   },
    "source": [
-    "# Answer for Exercise 5\n",
+    "from collections import Counter\n",
     "\n",
-    "from collections import Counter \n",
     "\n",
-    "def knn_classifier (X_train, y_train, x_new, K):\n",
-    "    \n",
-    "\n",
-    "    # complete with your code\n",
-    "    \n",
-    "\n",
-    "    \n",
-    "    \n",
-    "    "
-   ]
+    "def knn_classifier(X_train, y_train, x_new, K):\n",
+    "    distances = euc_dis(X_train, x_new)\n",
+    "    nearest_neighbors = np.argsort(distances)[:K]\n",
+    "    return Counter(y_train[nearest_neighbors]).most_common(1)[0][0]"
+   ],
+   "outputs": [],
+   "execution_count": 23
   },
   {
    "cell_type": "markdown",
@@ -315,40 +413,105 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2025-02-05T09:36:48.588320Z",
+     "start_time": "2025-02-05T09:36:48.579414Z"
+    }
+   },
    "source": [
-    "a=np.array([[1,2],[3,4]])\n",
+    "a = np.array([[1, 2], [3, 4]])\n",
     "a"
-   ]
+   ],
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "array([[1, 2],\n",
+       "       [3, 4]])"
+      ]
+     },
+     "execution_count": 11,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "execution_count": 11
   },
   {
    "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2025-02-05T09:36:49.154413Z",
+     "start_time": "2025-02-05T09:36:49.151819Z"
+    }
+   },
    "source": [
     "a.sum()"
-   ]
+   ],
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "np.int64(10)"
+      ]
+     },
+     "execution_count": 12,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "execution_count": 12
   },
   {
    "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2025-02-05T09:36:49.710602Z",
+     "start_time": "2025-02-05T09:36:49.707120Z"
+    }
+   },
    "source": [
     "a.sum(axis=1)"
-   ]
+   ],
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "array([3, 7])"
+      ]
+     },
+     "execution_count": 13,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "execution_count": 13
   },
   {
    "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2025-02-05T09:36:50.261475Z",
+     "start_time": "2025-02-05T09:36:50.258120Z"
+    }
+   },
    "source": [
     "a.sum(axis=0)"
-   ]
+   ],
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "array([4, 6])"
+      ]
+     },
+     "execution_count": 14,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "execution_count": 14
   },
   {
    "cell_type": "markdown",
@@ -360,22 +523,62 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2025-02-05T09:36:52.007400Z",
+     "start_time": "2025-02-05T09:36:52.002191Z"
+    }
+   },
    "source": [
-    "b=np.arange(8).reshape(2,2,2)\n",
+    "b = np.arange(8).reshape(2, 2, 2)\n",
     "b"
-   ]
+   ],
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "array([[[0, 1],\n",
+       "        [2, 3]],\n",
+       "\n",
+       "       [[4, 5],\n",
+       "        [6, 7]]])"
+      ]
+     },
+     "execution_count": 15,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "execution_count": 15
   },
   {
    "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "b.sum(axis=0), b.sum(axis=1), b.sum(axis=2),b.sum()"
-   ]
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2025-02-05T09:36:52.325717Z",
+     "start_time": "2025-02-05T09:36:52.322203Z"
+    }
+   },
+   "source": "b.sum(axis=0), b.sum(axis=1), b.sum(axis=2), b.sum()",
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "(array([[ 4,  6],\n",
+       "        [ 8, 10]]),\n",
+       " array([[ 2,  4],\n",
+       "        [10, 12]]),\n",
+       " array([[ 1,  5],\n",
+       "        [ 9, 13]]),\n",
+       " np.int64(28))"
+      ]
+     },
+     "execution_count": 16,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "execution_count": 16
   },
   {
    "cell_type": "markdown",
@@ -454,9 +657,9 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "a=np.array([1,2,3,4]).reshape(2,2)\n",
-    "b=np.repeat(a,3,axis=0)\n",
-    "b           "
+    "a = np.array([1, 2, 3, 4]).reshape(2, 2)\n",
+    "b = np.repeat(a, 3, axis=0)\n",
+    "b"
    ]
   },
   {
@@ -464,9 +667,14 @@
    "execution_count": null,
    "metadata": {},
    "outputs": [],
-   "source": [
-    "np.tile(a,(2,1))"
-   ]
+   "source": "np.tile(a, (2, 1))"
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": "b.reshape(2, 3, 2)"
   },
   {
    "cell_type": "code",
@@ -474,16 +682,7 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "b.reshape(2,3,2)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "A=np.array([[[0,1],[2,2]],[[1,1],[2,3]]])\n",
+    "A = np.array([[[0, 1], [2, 2]], [[1, 1], [2, 3]]])\n",
     "A"
    ]
   },
@@ -492,9 +691,7 @@
    "execution_count": null,
    "metadata": {},
    "outputs": [],
-   "source": [
-    "np.linalg.norm(A,axis=2)**2"
-   ]
+   "source": "np.linalg.norm(A, axis=2) ** 2"
   },
   {
    "cell_type": "code",
@@ -502,7 +699,7 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "(A**2).sum(axis=2) # same, i.e. square of the euclidian norm\n",
+    "(A ** 2).sum(axis=2)  # same, i.e. square of the euclidian norm\n",
     "# of each row"
    ]
   },
@@ -530,16 +727,27 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2025-02-05T09:44:40.315263Z",
+     "start_time": "2025-02-05T09:44:40.312108Z"
+    }
+   },
    "source": [
     "predictions = [knn_classifier(X_train, y_train, data, 3) for data in X_test]\n",
     "\n",
-    "# Display the accuracy rate :\n",
-    "\n",
-    "print (\"The accuracy rate of our classifier is : \" #complete with your code)"
-   ]
+    "print(f\"The accuracy rate of our classifier is {np.sum(predictions) / len(predictions) * 100}%\")"
+   ],
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "The accuracy rate of our classifier is 92.0%\n"
+     ]
+    }
+   ],
+   "execution_count": 41
   },
   {
    "cell_type": "markdown",
@@ -557,13 +765,19 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2025-02-05T09:42:30.248172Z",
+     "start_time": "2025-02-05T09:42:29.888107Z"
+    }
+   },
    "source": [
     "from sklearn.neighbors import KNeighborsClassifier\n",
+    "\n",
     "knn_classifier_2 = KNeighborsClassifier(n_neighbors=3)"
-   ]
+   ],
+   "outputs": [],
+   "execution_count": 30
   },
   {
    "cell_type": "markdown",
@@ -574,13 +788,444 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "\n",
-    "\n"
-   ]
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2025-02-05T09:42:41.408162Z",
+     "start_time": "2025-02-05T09:42:41.401181Z"
+    }
+   },
+   "source": "knn_classifier_2.fit(X_train, y_train)",
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "KNeighborsClassifier(n_neighbors=3)"
+      ],
+      "text/html": [
+       "<style>#sk-container-id-1 {\n",
+       "  /* Definition of color scheme common for light and dark mode */\n",
+       "  --sklearn-color-text: #000;\n",
+       "  --sklearn-color-text-muted: #666;\n",
+       "  --sklearn-color-line: gray;\n",
+       "  /* Definition of color scheme for unfitted estimators */\n",
+       "  --sklearn-color-unfitted-level-0: #fff5e6;\n",
+       "  --sklearn-color-unfitted-level-1: #f6e4d2;\n",
+       "  --sklearn-color-unfitted-level-2: #ffe0b3;\n",
+       "  --sklearn-color-unfitted-level-3: chocolate;\n",
+       "  /* Definition of color scheme for fitted estimators */\n",
+       "  --sklearn-color-fitted-level-0: #f0f8ff;\n",
+       "  --sklearn-color-fitted-level-1: #d4ebff;\n",
+       "  --sklearn-color-fitted-level-2: #b3dbfd;\n",
+       "  --sklearn-color-fitted-level-3: cornflowerblue;\n",
+       "\n",
+       "  /* Specific color for light theme */\n",
+       "  --sklearn-color-text-on-default-background: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, black)));\n",
+       "  --sklearn-color-background: var(--sg-background-color, var(--theme-background, var(--jp-layout-color0, white)));\n",
+       "  --sklearn-color-border-box: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, black)));\n",
+       "  --sklearn-color-icon: #696969;\n",
+       "\n",
+       "  @media (prefers-color-scheme: dark) {\n",
+       "    /* Redefinition of color scheme for dark theme */\n",
+       "    --sklearn-color-text-on-default-background: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, white)));\n",
+       "    --sklearn-color-background: var(--sg-background-color, var(--theme-background, var(--jp-layout-color0, #111)));\n",
+       "    --sklearn-color-border-box: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, white)));\n",
+       "    --sklearn-color-icon: #878787;\n",
+       "  }\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-1 {\n",
+       "  color: var(--sklearn-color-text);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-1 pre {\n",
+       "  padding: 0;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-1 input.sk-hidden--visually {\n",
+       "  border: 0;\n",
+       "  clip: rect(1px 1px 1px 1px);\n",
+       "  clip: rect(1px, 1px, 1px, 1px);\n",
+       "  height: 1px;\n",
+       "  margin: -1px;\n",
+       "  overflow: hidden;\n",
+       "  padding: 0;\n",
+       "  position: absolute;\n",
+       "  width: 1px;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-1 div.sk-dashed-wrapped {\n",
+       "  border: 1px dashed var(--sklearn-color-line);\n",
+       "  margin: 0 0.4em 0.5em 0.4em;\n",
+       "  box-sizing: border-box;\n",
+       "  padding-bottom: 0.4em;\n",
+       "  background-color: var(--sklearn-color-background);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-1 div.sk-container {\n",
+       "  /* jupyter's `normalize.less` sets `[hidden] { display: none; }`\n",
+       "     but bootstrap.min.css set `[hidden] { display: none !important; }`\n",
+       "     so we also need the `!important` here to be able to override the\n",
+       "     default hidden behavior on the sphinx rendered scikit-learn.org.\n",
+       "     See: https://github.com/scikit-learn/scikit-learn/issues/21755 */\n",
+       "  display: inline-block !important;\n",
+       "  position: relative;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-1 div.sk-text-repr-fallback {\n",
+       "  display: none;\n",
+       "}\n",
+       "\n",
+       "div.sk-parallel-item,\n",
+       "div.sk-serial,\n",
+       "div.sk-item {\n",
+       "  /* draw centered vertical line to link estimators */\n",
+       "  background-image: linear-gradient(var(--sklearn-color-text-on-default-background), var(--sklearn-color-text-on-default-background));\n",
+       "  background-size: 2px 100%;\n",
+       "  background-repeat: no-repeat;\n",
+       "  background-position: center center;\n",
+       "}\n",
+       "\n",
+       "/* Parallel-specific style estimator block */\n",
+       "\n",
+       "#sk-container-id-1 div.sk-parallel-item::after {\n",
+       "  content: \"\";\n",
+       "  width: 100%;\n",
+       "  border-bottom: 2px solid var(--sklearn-color-text-on-default-background);\n",
+       "  flex-grow: 1;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-1 div.sk-parallel {\n",
+       "  display: flex;\n",
+       "  align-items: stretch;\n",
+       "  justify-content: center;\n",
+       "  background-color: var(--sklearn-color-background);\n",
+       "  position: relative;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-1 div.sk-parallel-item {\n",
+       "  display: flex;\n",
+       "  flex-direction: column;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-1 div.sk-parallel-item:first-child::after {\n",
+       "  align-self: flex-end;\n",
+       "  width: 50%;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-1 div.sk-parallel-item:last-child::after {\n",
+       "  align-self: flex-start;\n",
+       "  width: 50%;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-1 div.sk-parallel-item:only-child::after {\n",
+       "  width: 0;\n",
+       "}\n",
+       "\n",
+       "/* Serial-specific style estimator block */\n",
+       "\n",
+       "#sk-container-id-1 div.sk-serial {\n",
+       "  display: flex;\n",
+       "  flex-direction: column;\n",
+       "  align-items: center;\n",
+       "  background-color: var(--sklearn-color-background);\n",
+       "  padding-right: 1em;\n",
+       "  padding-left: 1em;\n",
+       "}\n",
+       "\n",
+       "\n",
+       "/* Toggleable style: style used for estimator/Pipeline/ColumnTransformer box that is\n",
+       "clickable and can be expanded/collapsed.\n",
+       "- Pipeline and ColumnTransformer use this feature and define the default style\n",
+       "- Estimators will overwrite some part of the style using the `sk-estimator` class\n",
+       "*/\n",
+       "\n",
+       "/* Pipeline and ColumnTransformer style (default) */\n",
+       "\n",
+       "#sk-container-id-1 div.sk-toggleable {\n",
+       "  /* Default theme specific background. It is overwritten whether we have a\n",
+       "  specific estimator or a Pipeline/ColumnTransformer */\n",
+       "  background-color: var(--sklearn-color-background);\n",
+       "}\n",
+       "\n",
+       "/* Toggleable label */\n",
+       "#sk-container-id-1 label.sk-toggleable__label {\n",
+       "  cursor: pointer;\n",
+       "  display: flex;\n",
+       "  width: 100%;\n",
+       "  margin-bottom: 0;\n",
+       "  padding: 0.5em;\n",
+       "  box-sizing: border-box;\n",
+       "  text-align: center;\n",
+       "  align-items: start;\n",
+       "  justify-content: space-between;\n",
+       "  gap: 0.5em;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-1 label.sk-toggleable__label .caption {\n",
+       "  font-size: 0.6rem;\n",
+       "  font-weight: lighter;\n",
+       "  color: var(--sklearn-color-text-muted);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-1 label.sk-toggleable__label-arrow:before {\n",
+       "  /* Arrow on the left of the label */\n",
+       "  content: \"▸\";\n",
+       "  float: left;\n",
+       "  margin-right: 0.25em;\n",
+       "  color: var(--sklearn-color-icon);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-1 label.sk-toggleable__label-arrow:hover:before {\n",
+       "  color: var(--sklearn-color-text);\n",
+       "}\n",
+       "\n",
+       "/* Toggleable content - dropdown */\n",
+       "\n",
+       "#sk-container-id-1 div.sk-toggleable__content {\n",
+       "  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: 0.5em;\n",
+       "  text-align: center;\n",
+       "  /* unfitted */\n",
+       "  border: var(--sklearn-color-unfitted-level-1) 1pt solid;\n",
+       "  color: var(--sklearn-color-unfitted-level-1);\n",
+       "}\n",
+       "\n",
+       ".sk-estimator-doc-link.fitted,\n",
+       "a:link.sk-estimator-doc-link.fitted,\n",
+       "a:visited.sk-estimator-doc-link.fitted {\n",
+       "  /* fitted */\n",
+       "  border: var(--sklearn-color-fitted-level-1) 1pt solid;\n",
+       "  color: var(--sklearn-color-fitted-level-1);\n",
+       "}\n",
+       "\n",
+       "/* On hover */\n",
+       "div.sk-estimator:hover .sk-estimator-doc-link:hover,\n",
+       ".sk-estimator-doc-link:hover,\n",
+       "div.sk-label-container:hover .sk-estimator-doc-link:hover,\n",
+       ".sk-estimator-doc-link:hover {\n",
+       "  /* unfitted */\n",
+       "  background-color: var(--sklearn-color-unfitted-level-3);\n",
+       "  color: var(--sklearn-color-background);\n",
+       "  text-decoration: none;\n",
+       "}\n",
+       "\n",
+       "div.sk-estimator.fitted:hover .sk-estimator-doc-link.fitted:hover,\n",
+       ".sk-estimator-doc-link.fitted:hover,\n",
+       "div.sk-label-container:hover .sk-estimator-doc-link.fitted:hover,\n",
+       ".sk-estimator-doc-link.fitted:hover {\n",
+       "  /* fitted */\n",
+       "  background-color: var(--sklearn-color-fitted-level-3);\n",
+       "  color: var(--sklearn-color-background);\n",
+       "  text-decoration: none;\n",
+       "}\n",
+       "\n",
+       "/* Span, style for the box shown on hovering the info icon */\n",
+       ".sk-estimator-doc-link span {\n",
+       "  display: none;\n",
+       "  z-index: 9999;\n",
+       "  position: relative;\n",
+       "  font-weight: normal;\n",
+       "  right: .2ex;\n",
+       "  padding: .5ex;\n",
+       "  margin: .5ex;\n",
+       "  width: min-content;\n",
+       "  min-width: 20ex;\n",
+       "  max-width: 50ex;\n",
+       "  color: var(--sklearn-color-text);\n",
+       "  box-shadow: 2pt 2pt 4pt #999;\n",
+       "  /* unfitted */\n",
+       "  background: var(--sklearn-color-unfitted-level-0);\n",
+       "  border: .5pt solid var(--sklearn-color-unfitted-level-3);\n",
+       "}\n",
+       "\n",
+       ".sk-estimator-doc-link.fitted span {\n",
+       "  /* fitted */\n",
+       "  background: var(--sklearn-color-fitted-level-0);\n",
+       "  border: var(--sklearn-color-fitted-level-3);\n",
+       "}\n",
+       "\n",
+       ".sk-estimator-doc-link:hover span {\n",
+       "  display: block;\n",
+       "}\n",
+       "\n",
+       "/* \"?\"-specific style due to the `<a>` HTML tag */\n",
+       "\n",
+       "#sk-container-id-1 a.estimator_doc_link {\n",
+       "  float: right;\n",
+       "  font-size: 1rem;\n",
+       "  line-height: 1em;\n",
+       "  font-family: monospace;\n",
+       "  background-color: var(--sklearn-color-background);\n",
+       "  border-radius: 1rem;\n",
+       "  height: 1rem;\n",
+       "  width: 1rem;\n",
+       "  text-decoration: none;\n",
+       "  /* unfitted */\n",
+       "  color: var(--sklearn-color-unfitted-level-1);\n",
+       "  border: var(--sklearn-color-unfitted-level-1) 1pt solid;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-1 a.estimator_doc_link.fitted {\n",
+       "  /* fitted */\n",
+       "  border: var(--sklearn-color-fitted-level-1) 1pt solid;\n",
+       "  color: var(--sklearn-color-fitted-level-1);\n",
+       "}\n",
+       "\n",
+       "/* On hover */\n",
+       "#sk-container-id-1 a.estimator_doc_link:hover {\n",
+       "  /* unfitted */\n",
+       "  background-color: var(--sklearn-color-unfitted-level-3);\n",
+       "  color: var(--sklearn-color-background);\n",
+       "  text-decoration: none;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-1 a.estimator_doc_link.fitted:hover {\n",
+       "  /* fitted */\n",
+       "  background-color: var(--sklearn-color-fitted-level-3);\n",
+       "}\n",
+       "</style><div id=\"sk-container-id-1\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>KNeighborsClassifier(n_neighbors=3)</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\"><div><div>KNeighborsClassifier</div></div><div><a class=\"sk-estimator-doc-link fitted\" rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.6/modules/generated/sklearn.neighbors.KNeighborsClassifier.html\">?<span>Documentation for KNeighborsClassifier</span></a><span class=\"sk-estimator-doc-link fitted\">i<span>Fitted</span></span></div></label><div class=\"sk-toggleable__content fitted\"><pre>KNeighborsClassifier(n_neighbors=3)</pre></div> </div></div></div></div>"
+      ]
+     },
+     "execution_count": 31,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "execution_count": 31
   },
   {
    "cell_type": "markdown",
@@ -591,22 +1236,26 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "\n",
-    "\n",
-    "\n",
-    "\n"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": []
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2025-02-05T09:42:58.024511Z",
+     "start_time": "2025-02-05T09:42:58.019583Z"
+    }
+   },
+   "source": "knn_classifier_2.score(X_test, y_test)",
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "0.98"
+      ]
+     },
+     "execution_count": 33,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "execution_count": 33
   },
   {
    "cell_type": "markdown",
@@ -652,43 +1301,87 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2025-02-05T09:45:15.636638Z",
+     "start_time": "2025-02-05T09:45:15.633980Z"
+    }
+   },
    "source": [
     "import matplotlib.pyplot as plt\n",
     "import numpy as np\n",
     "from itertools import product\n",
     "from sklearn.neighbors import KNeighborsClassifier"
-   ]
+   ],
+   "outputs": [],
+   "execution_count": 44
   },
   {
    "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2025-02-05T09:45:16.962479Z",
+     "start_time": "2025-02-05T09:45:16.958858Z"
+    }
+   },
    "source": [
     "rng = np.random.default_rng(seed=12)\n",
     "num_observations = 100\n",
     "\n",
-    "xx1=rng.multivariate_normal([0,0],[[1,0.6],[0.6,1]], num_observations )\n",
-    "xx2=rng.multivariate_normal([1,2],[[1,0.6],[0.6,1]], num_observations )\n",
+    "xx1 = rng.multivariate_normal([0, 0], [[1, 0.6], [0.6, 1]], num_observations)\n",
+    "xx2 = rng.multivariate_normal([1, 2], [[1, 0.6], [0.6, 1]], num_observations)\n",
     "\n",
-    "X2= np.vstack((xx1, xx2)).astype(np.float32)\n",
-    "Y2= np.hstack((np.zeros(num_observations),np.ones(num_observations)))\n",
+    "X2 = np.vstack((xx1, xx2)).astype(np.float32)\n",
+    "Y2 = np.hstack((np.zeros(num_observations), np.ones(num_observations)))\n",
     "\n",
-    "print (X2.shape, Y2.shape)"
-   ]
+    "print(X2.shape, Y2.shape)"
+   ],
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "(200, 2) (200,)\n"
+     ]
+    }
+   ],
+   "execution_count": 45
   },
   {
    "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2025-02-05T09:45:17.604633Z",
+     "start_time": "2025-02-05T09:45:17.524418Z"
+    }
+   },
    "source": [
-    "plt.figure(figsize=(8,6))\n",
-    "plt.scatter(X2[:,0], X2[:,1],c=Y2,alpha=0.4)"
-   ]
+    "plt.figure(figsize=(8, 6))\n",
+    "plt.scatter(X2[:, 0], X2[:, 1], c=Y2, alpha=0.4)"
+   ],
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "<matplotlib.collections.PathCollection at 0x10da5d4f0>"
+      ]
+     },
+     "execution_count": 46,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "text/plain": [
+       "<Figure size 800x600 with 1 Axes>"
+      ],
+      "image/png": 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"
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "execution_count": 46
   },
   {
    "cell_type": "markdown",
@@ -699,21 +1392,24 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2025-02-05T09:46:55.685593Z",
+     "start_time": "2025-02-05T09:46:55.680045Z"
+    }
+   },
    "source": [
-    "# Answer for Exercise 10\n",
+    "nb_neighbors = [1, 3, 5, 7, 9, 11]\n",
     "\n",
-    "nb_neighbors=[1,3,5,7,9,11]\n",
+    "KNNs = []\n",
     "\n",
-    "KNNs=[]\n",
-    "\n",
-    "for i in range(len(nb_neighbors)):\n",
-    "    KNNs.append(#complete here)\n",
-    "\n",
-    "\n"
-   ]
+    "for k in nb_neighbors:\n",
+    "    knn_classifier_k = KNeighborsClassifier(n_neighbors=k)\n",
+    "    knn_classifier_k.fit(X2, Y2)\n",
+    "    KNNs.append(knn_classifier_k)"
+   ],
+   "outputs": [],
+   "execution_count": 50
   },
   {
    "cell_type": "markdown",
@@ -724,27 +1420,43 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2025-02-05T09:46:57.940547Z",
+     "start_time": "2025-02-05T09:46:57.035448Z"
+    }
+   },
    "source": [
-    "x_min, x_max = X2[:,0].min()-1, X2[:,0].max()+1\n",
-    "y_min, y_max = X2[:,1].min()-1, X2[:,1].max()+1\n",
+    "x_min, x_max = X2[:, 0].min() - 1, X2[:, 0].max() + 1\n",
+    "y_min, y_max = X2[:, 1].min() - 1, X2[:, 1].max() + 1\n",
     "\n",
-    "xx,yy = np.meshgrid(np.arange(x_min,x_max,0.1), np.arange(y_min, y_max,0.1))\n",
+    "xx, yy = np.meshgrid(np.arange(x_min, x_max, 0.1), np.arange(y_min, y_max, 0.1))\n",
     "\n",
-    "f, axarr = plt.subplots(2,3, sharex=\"col\", sharey=\"row\",figsize=(15,12))\n",
+    "f, axarr = plt.subplots(2, 3, sharex=\"col\", sharey=\"row\", figsize=(15, 12))\n",
     "\n",
-    "for idx,clf,tt in zip(product([0,1,2],[0,1,2]),KNNs, [f\"KNN (k={k})\" for k in nb_neighbors]):\n",
-    "    Z = clf.predict(np.c_[xx.ravel(),yy.ravel()])\n",
-    "    Z = Z.reshape(xx.shape)\n",
-    "    \n",
-    "    axarr[idx[0],idx[1]].contourf(xx,yy,Z,alpha = 0.4)\n",
-    "    axarr[idx[0],idx[1]].scatter(X2[:,0],X2[:,1], c=Y2,s=20,edgecolor=\"k\")\n",
-    "    axarr[idx[0],idx[1]].set_title(tt)\n",
-    "    \n",
-    "plt.show()\n"
-   ]
+    "for idx, clf, tt in zip(product([0, 1, 2], [0, 1, 2]), KNNs, [f\"KNN (k={k})\" for k in nb_neighbors]):\n",
+    "    Z = clf.predict(np.c_[xx.ravel(), yy.ravel()])\n",
+    "Z = Z.reshape(xx.shape)\n",
+    "\n",
+    "axarr[idx[0], idx[1]].contourf(xx, yy, Z, alpha=0.4)\n",
+    "axarr[idx[0], idx[1]].scatter(X2[:, 0], X2[:, 1], c=Y2, s=20, edgecolor=\"k\")\n",
+    "axarr[idx[0], idx[1]].set_title(tt)\n",
+    "\n",
+    "plt.show()"
+   ],
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "<Figure size 1500x1200 with 6 Axes>"
+      ],
+      "image/png": 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"
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "execution_count": 51
   },
   {
    "cell_type": "markdown",
@@ -783,13 +1495,19 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2025-02-05T09:48:21.055727Z",
+     "start_time": "2025-02-05T09:48:18.802198Z"
+    }
+   },
    "source": [
     "from sklearn.datasets import fetch_openml\n",
+    "\n",
     "mnist = fetch_openml('mnist_784')"
-   ]
+   ],
+   "outputs": [],
+   "execution_count": 52
   },
   {
    "cell_type": "markdown",
@@ -800,48 +1518,478 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2025-02-05T09:48:22.477736Z",
+     "start_time": "2025-02-05T09:48:22.465303Z"
+    }
+   },
    "source": [
     "mnist.data"
-   ]
+   ],
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "       pixel1  pixel2  pixel3  pixel4  pixel5  pixel6  pixel7  pixel8  pixel9  \\\n",
+       "0           0       0       0       0       0       0       0       0       0   \n",
+       "1           0       0       0       0       0       0       0       0       0   \n",
+       "2           0       0       0       0       0       0       0       0       0   \n",
+       "3           0       0       0       0       0       0       0       0       0   \n",
+       "4           0       0       0       0       0       0       0       0       0   \n",
+       "...       ...     ...     ...     ...     ...     ...     ...     ...     ...   \n",
+       "69995       0       0       0       0       0       0       0       0       0   \n",
+       "69996       0       0       0       0       0       0       0       0       0   \n",
+       "69997       0       0       0       0       0       0       0       0       0   \n",
+       "69998       0       0       0       0       0       0       0       0       0   \n",
+       "69999       0       0       0       0       0       0       0       0       0   \n",
+       "\n",
+       "       pixel10  ...  pixel775  pixel776  pixel777  pixel778  pixel779  \\\n",
+       "0            0  ...         0         0         0         0         0   \n",
+       "1            0  ...         0         0         0         0         0   \n",
+       "2            0  ...         0         0         0         0         0   \n",
+       "3            0  ...         0         0         0         0         0   \n",
+       "4            0  ...         0         0         0         0         0   \n",
+       "...        ...  ...       ...       ...       ...       ...       ...   \n",
+       "69995        0  ...         0         0         0         0         0   \n",
+       "69996        0  ...         0         0         0         0         0   \n",
+       "69997        0  ...         0         0         0         0         0   \n",
+       "69998        0  ...         0         0         0         0         0   \n",
+       "69999        0  ...         0         0         0         0         0   \n",
+       "\n",
+       "       pixel780  pixel781  pixel782  pixel783  pixel784  \n",
+       "0             0         0         0         0         0  \n",
+       "1             0         0         0         0         0  \n",
+       "2             0         0         0         0         0  \n",
+       "3             0         0         0         0         0  \n",
+       "4             0         0         0         0         0  \n",
+       "...         ...       ...       ...       ...       ...  \n",
+       "69995         0         0         0         0         0  \n",
+       "69996         0         0         0         0         0  \n",
+       "69997         0         0         0         0         0  \n",
+       "69998         0         0         0         0         0  \n",
+       "69999         0         0         0         0         0  \n",
+       "\n",
+       "[70000 rows x 784 columns]"
+      ],
+      "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",
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+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>pixel1</th>\n",
+       "      <th>pixel2</th>\n",
+       "      <th>pixel3</th>\n",
+       "      <th>pixel4</th>\n",
+       "      <th>pixel5</th>\n",
+       "      <th>pixel6</th>\n",
+       "      <th>pixel7</th>\n",
+       "      <th>pixel8</th>\n",
+       "      <th>pixel9</th>\n",
+       "      <th>pixel10</th>\n",
+       "      <th>...</th>\n",
+       "      <th>pixel775</th>\n",
+       "      <th>pixel776</th>\n",
+       "      <th>pixel777</th>\n",
+       "      <th>pixel778</th>\n",
+       "      <th>pixel779</th>\n",
+       "      <th>pixel780</th>\n",
+       "      <th>pixel781</th>\n",
+       "      <th>pixel782</th>\n",
+       "      <th>pixel783</th>\n",
+       "      <th>pixel784</th>\n",
+       "    </tr>\n",
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+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>...</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>...</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>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",
+       "      <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>69995</th>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>...</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>69996</th>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>...</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>69997</th>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>...</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>69998</th>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>...</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>69999</th>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>...</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "<p>70000 rows × 784 columns</p>\n",
+       "</div>"
+      ]
+     },
+     "execution_count": 53,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "execution_count": 53
   },
   {
    "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2025-02-05T09:48:23.448971Z",
+     "start_time": "2025-02-05T09:48:23.445653Z"
+    }
+   },
    "source": [
     "mnist.target"
-   ]
+   ],
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "0        5\n",
+       "1        0\n",
+       "2        4\n",
+       "3        1\n",
+       "4        9\n",
+       "        ..\n",
+       "69995    2\n",
+       "69996    3\n",
+       "69997    4\n",
+       "69998    5\n",
+       "69999    6\n",
+       "Name: class, Length: 70000, dtype: category\n",
+       "Categories (10, object): ['0', '1', '2', '3', ..., '6', '7', '8', '9']"
+      ]
+     },
+     "execution_count": 54,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "execution_count": 54
   },
   {
    "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2025-02-05T09:48:24.144564Z",
+     "start_time": "2025-02-05T09:48:24.142427Z"
+    }
+   },
+   "source": "X, y = mnist.data, mnist.target",
    "outputs": [],
-   "source": [
-    "X,y=mnist.data,mnist.target"
-   ]
+   "execution_count": 55
   },
   {
    "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2025-02-05T09:48:24.514332Z",
+     "start_time": "2025-02-05T09:48:24.511486Z"
+    }
+   },
    "source": [
     "type(X)"
-   ]
+   ],
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "pandas.core.frame.DataFrame"
+      ]
+     },
+     "execution_count": 56,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "execution_count": 56
   },
   {
    "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2025-02-05T09:48:24.886667Z",
+     "start_time": "2025-02-05T09:48:24.884478Z"
+    }
+   },
    "source": [
     "type(y)"
-   ]
+   ],
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "pandas.core.series.Series"
+      ]
+     },
+     "execution_count": 57,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "execution_count": 57
   },
   {
    "cell_type": "markdown",
@@ -852,22 +2000,52 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2025-02-05T09:48:31.763896Z",
+     "start_time": "2025-02-05T09:48:31.760241Z"
+    }
+   },
    "source": [
-    "X,y=X.to_numpy(), y.to_numpy()\n",
-    "type(X),type(y)"
-   ]
+    "X, y = X.to_numpy(), y.to_numpy()\n",
+    "type(X), type(y)"
+   ],
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "(numpy.ndarray, numpy.ndarray)"
+      ]
+     },
+     "execution_count": 58,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "execution_count": 58
   },
   {
    "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "X.shape,y.shape"
-   ]
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2025-02-05T09:48:32.298865Z",
+     "start_time": "2025-02-05T09:48:32.295472Z"
+    }
+   },
+   "source": "X.shape, y.shape",
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "((70000, 784), (70000,))"
+      ]
+     },
+     "execution_count": 59,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "execution_count": 59
   },
   {
    "cell_type": "markdown",
@@ -878,34 +2056,72 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2025-02-05T09:48:34.139776Z",
+     "start_time": "2025-02-05T09:48:34.085344Z"
+    }
+   },
    "source": [
     "import matplotlib.pyplot as plt\n",
-    "first_figure=X[0].reshape(28,28)\n",
-    "plt.imshow(first_figure,cmap=plt.cm.gray_r,interpolation='nearest')\n",
+    "\n",
+    "first_figure = X[0].reshape(28, 28)\n",
+    "plt.imshow(first_figure, cmap=plt.cm.gray_r, interpolation='nearest')\n",
     "plt.show()"
-   ]
+   ],
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ],
+      "image/png": "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"
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "execution_count": 60
   },
   {
    "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2025-02-05T09:48:34.972262Z",
+     "start_time": "2025-02-05T09:48:34.969534Z"
+    }
+   },
    "source": [
     "# we recognised a \"5\" \n",
     "y[0]"
-   ]
+   ],
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "'5'"
+      ]
+     },
+     "execution_count": 61,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "execution_count": 61
   },
   {
    "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2025-02-05T09:48:36.165952Z",
+     "start_time": "2025-02-05T09:48:35.753636Z"
+    }
+   },
    "source": [
     "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.33, random_state=42)"
-   ]
+   ],
+   "outputs": [],
+   "execution_count": 62
   },
   {
    "cell_type": "markdown",
@@ -914,9 +2130,45 @@
     "**Exercise 12**\n",
     "Display the accuracy rate on the test set : again we will use the K-NN classifier with K=3.\n",
     "\n",
-    "Warning : do not use your own classifier as it will probably fail. Use sklearn's classifier. "
+    "Warning : do not use your own classifier as it will probably fail. Use sklearn's classifier."
    ]
   },
+  {
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2025-02-05T09:55:57.835820Z",
+     "start_time": "2025-02-05T09:55:02.002908Z"
+    }
+   },
+   "cell_type": "code",
+   "source": [
+    "knn_classifier_3 = KNeighborsClassifier(n_neighbors=3)\n",
+    "knn_classifier_3.fit(X_train, y_train)\n",
+    "print(f\"Accuracy score: {knn_classifier_3.score(X_test, y_test)}\")\n",
+    "predictions = knn_classifier_3.predict(X)\n",
+    "predictions[0]"
+   ],
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Accuracy score: 0.9693506493506493\n"
+     ]
+    },
+    {
+     "data": {
+      "text/plain": [
+       "'5'"
+      ]
+     },
+     "execution_count": 74,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "execution_count": 74
+  },
   {
    "cell_type": "markdown",
    "metadata": {},
@@ -934,31 +2186,69 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2025-02-05T09:56:15.719499Z",
+     "start_time": "2025-02-05T09:56:02.483518Z"
+    }
+   },
    "source": [
     "import tensorflow as tf\n",
+    "\n",
     "(X_train, y_train), (X_test, y_test) = tf.keras.datasets.cifar10.load_data()"
-   ]
+   ],
+   "outputs": [],
+   "execution_count": 75
   },
   {
    "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2025-02-05T09:56:25.552086Z",
+     "start_time": "2025-02-05T09:56:25.549610Z"
+    }
+   },
    "source": [
     "type(X_train)"
-   ]
+   ],
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "numpy.ndarray"
+      ]
+     },
+     "execution_count": 76,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "execution_count": 76
   },
   {
    "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2025-02-05T09:56:27.171373Z",
+     "start_time": "2025-02-05T09:56:27.168777Z"
+    }
+   },
    "source": [
     "X_train.shape, y_train.shape"
-   ]
+   ],
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "((50000, 32, 32, 3), (50000, 1))"
+      ]
+     },
+     "execution_count": 77,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "execution_count": 77
   },
   {
    "cell_type": "markdown",
@@ -976,43 +2266,104 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2025-02-05T09:56:28.805816Z",
+     "start_time": "2025-02-05T09:56:28.802933Z"
+    }
+   },
    "source": [
-    "y=y_train.ravel()\n",
+    "y = y_train.ravel()\n",
     "y.shape"
-   ]
+   ],
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "(50000,)"
+      ]
+     },
+     "execution_count": 78,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "execution_count": 78
   },
   {
    "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2025-02-05T09:56:29.943407Z",
+     "start_time": "2025-02-05T09:56:29.941183Z"
+    }
+   },
    "source": [
     "y[0:10]"
-   ]
+   ],
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "array([6, 9, 9, 4, 1, 1, 2, 7, 8, 3], dtype=uint8)"
+      ]
+     },
+     "execution_count": 79,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "execution_count": 79
   },
   {
    "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2025-02-05T09:56:36.560293Z",
+     "start_time": "2025-02-05T09:56:36.510888Z"
+    }
+   },
    "source": [
     "plt.figure()\n",
-    "plt.imshow(x_train[3],interpolation='nearest')\n",
-    "plt.show()# the image is very blurred, do not worry..."
-   ]
+    "plt.imshow(X_train[3], interpolation='nearest')\n",
+    "plt.show()  # the image is very blurred, do not worry..."
+   ],
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ],
+      "image/png": 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"
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "execution_count": 81
   },
   {
    "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2025-02-05T09:56:38.807788Z",
+     "start_time": "2025-02-05T09:56:38.805719Z"
+    }
+   },
    "source": [
     "class_names = ['plane', 'car', 'bird', 'cat', 'deer', 'dog', 'frog', 'horse', 'ship', 'truck']\n",
     "print(class_names[y[3]])"
-   ]
+   ],
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "deer\n"
+     ]
+    }
+   ],
+   "execution_count": 82
   },
   {
    "cell_type": "markdown",
@@ -1023,13 +2374,29 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2025-02-05T09:56:40.790210Z",
+     "start_time": "2025-02-05T09:56:40.539638Z"
+    }
+   },
    "source": [
-    "X=X_train.reshape(X_train.shape[0],-1)\n",
+    "X = X_train.reshape(X_train.shape[0], -1)\n",
     "X.shape"
-   ]
+   ],
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "(50000, 3072)"
+      ]
+     },
+     "execution_count": 83,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "execution_count": 83
   },
   {
    "cell_type": "markdown",
@@ -1044,10 +2411,47 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2025-02-05T10:01:24.378825Z",
+     "start_time": "2025-02-05T10:01:07.927639Z"
+    }
+   },
+   "source": [
+    "X1, y1 = X_test.reshape(X_test.shape[0], -1), y_test.ravel()\n",
+    "print(X1.shape, y1.shape)\n",
+    "\n",
+    "knn_classifier_4 = KNeighborsClassifier(n_neighbors=3)\n",
+    "knn_classifier_4.fit(X, y)\n",
+    "knn_classifier_4.score(X1, y1)"
+   ],
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "(10000, 3072) (10000,)\n"
+     ]
+    },
+    {
+     "data": {
+      "text/plain": [
+       "0.3303"
+      ]
+     },
+     "execution_count": 93,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "execution_count": 93
+  },
+  {
    "metadata": {},
+   "cell_type": "code",
    "outputs": [],
-   "source": []
+   "execution_count": null,
+   "source": ""
   }
  ],
  "metadata": {