diff --git a/user_based.ipynb b/user_based.ipynb
index fded6f506d137724a7ccf2cad09be556cf85ebc0..a113588359331ef129b21e0cd1c5e24f37842c16 100644
--- a/user_based.ipynb
+++ b/user_based.ipynb
@@ -11,19 +11,10 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 5,
+   "execution_count": 1,
    "id": "00d1b249",
    "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "The autoreload extension is already loaded. To reload it, use:\n",
-      "  %reload_ext autoreload\n"
-     ]
-    }
-   ],
+   "outputs": [],
    "source": [
     "# reloads modules automatically before entering the execution of code\n",
     "%load_ext autoreload\n",
@@ -56,53 +47,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 6,
-   "id": "aafd1712",
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Computing the msd similarity matrix...\n",
-      "Done computing similarity matrix.\n",
-      "user: 11         item: 364        r_ui = 4.00   est = 3.42   {'was_impossible': True, 'reason': 'User and/or item is unknown.'}\n"
-     ]
-    }
-   ],
-   "source": [
-    "\n",
-    "# Create Surprise Dataset from the pandas DataFrame and Reader\n",
-    "surprise_data = load_ratings(surprise_format=True)\n",
-    "\n",
-    "trainset = surprise_data.build_full_trainset()\n",
-    "\n",
-    "\n",
-    "testset = trainset.build_anti_testset()\n",
-    "\n",
-    "\n",
-    "sim_options = {\n",
-    "    'name': 'msd',  # Mean Squared Difference (Mean Square Error)\n",
-    "    'user_based': True,  # User-based collaborative filtering\n",
-    "    'min_support': 3  # Minimum number of common ratings required\n",
-    "}\n",
-    "\n",
-    "\n",
-    "# Build an algorithm, and train it.\n",
-    "algo = KNNWithMeans(sim_options=sim_options, k=3, min_k=2)\n",
-    "algo.fit(trainset)\n",
-    "algo.test(testset)\n",
-    "\n",
-    "\n",
-    "uid = str(11)  # raw user id (as in the ratings file). They are **strings**!\n",
-    "iid = str(364) \n",
-    "\n",
-    "pred = algo.predict(uid, iid, r_ui=4, verbose=True)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 7,
+   "execution_count": 2,
    "id": "cf3ccdc0",
    "metadata": {},
    "outputs": [],
@@ -132,7 +77,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 8,
+   "execution_count": 3,
    "id": "e6fb78b7",
    "metadata": {},
    "outputs": [
@@ -169,7 +114,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 9,
+   "execution_count": 4,
    "id": "ffe89c56",
    "metadata": {},
    "outputs": [
@@ -330,7 +275,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 10,
+   "execution_count": 5,
    "id": "cc806424",
    "metadata": {},
    "outputs": [
@@ -482,7 +427,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 11,
+   "execution_count": 6,
    "id": "d03ed9eb",
    "metadata": {},
    "outputs": [
@@ -626,7 +571,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 12,
+   "execution_count": 7,
    "id": "be53ae27",
    "metadata": {},
    "outputs": [
@@ -683,7 +628,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 13,
+   "execution_count": 8,
    "id": "c20d8e19",
    "metadata": {},
    "outputs": [
@@ -695,10 +640,10 @@
       "Done computing similarity matrix.\n",
       "Computing the cosine similarity matrix...\n",
       "Done computing similarity matrix.\n",
-      "RMSE: 0.9501\n",
-      "RMSE: 0.9613\n",
-      "RMSE with MSD similarity: 0.9500902346226462\n",
-      "RMSE with Jaccard similarity: 0.9612909313186003\n"
+      "RMSE: 0.9683\n",
+      "RMSE: 0.9824\n",
+      "RMSE with MSD similarity: 0.9682664011125741\n",
+      "RMSE with Jaccard similarity: 0.9824127884570012\n"
      ]
     }
    ],