diff --git "a/notebooks/RAG_CLAUDE.ipynb" "b/notebooks/RAG_CLAUDE.ipynb"
--- "a/notebooks/RAG_CLAUDE.ipynb"
+++ "b/notebooks/RAG_CLAUDE.ipynb"
@@ -11,32 +11,34 @@
     },
     {
       "cell_type": "code",
-      "execution_count": 24,
+      "execution_count": null,
       "metadata": {
         "colab": {
           "base_uri": "https://localhost:8080/"
         },
         "id": "YImwMQLjASiK",
-        "outputId": "1177bd7c-e220-4f5f-e75f-24c2a7556604"
+        "outputId": "16d2a28d-714a-4d88-8b1a-dc117070bcf0"
       },
       "outputs": [
         {
-          "name": "stderr",
           "output_type": "stream",
-          "text": [
-            "huggingface/tokenizers: The current process just got forked, after parallelism has already been used. Disabling parallelism to avoid deadlocks...\n",
-            "To disable this warning, you can either:\n",
-            "\t- Avoid using `tokenizers` before the fork if possible\n",
-            "\t- Explicitly set the environment variable TOKENIZERS_PARALLELISM=(true | false)\n"
-          ]
-        },
-        {
           "name": "stdout",
-          "output_type": "stream",
           "text": [
-            "\n",
-            "\u001b[1m[\u001b[0m\u001b[34;49mnotice\u001b[0m\u001b[1;39;49m]\u001b[0m\u001b[39;49m A new release of pip is available: \u001b[0m\u001b[31;49m23.2.1\u001b[0m\u001b[39;49m -> \u001b[0m\u001b[32;49m24.2\u001b[0m\n",
-            "\u001b[1m[\u001b[0m\u001b[34;49mnotice\u001b[0m\u001b[1;39;49m]\u001b[0m\u001b[39;49m To update, run: \u001b[0m\u001b[32;49mpip install --upgrade pip\u001b[0m\n"
+            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m50.4/50.4 kB\u001b[0m \u001b[31m355.1 kB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
+            "\u001b[2K   \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m27.0/27.0 MB\u001b[0m \u001b[31m18.2 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
+            "\u001b[2K   \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m227.1/227.1 kB\u001b[0m \u001b[31m4.9 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
+            "\u001b[2K   \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m1.0/1.0 MB\u001b[0m \u001b[31m16.1 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
+            "\u001b[2K   \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m2.3/2.3 MB\u001b[0m \u001b[31m36.7 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
+            "\u001b[2K   \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m891.9/891.9 kB\u001b[0m \u001b[31m10.5 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
+            "\u001b[2K   \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m76.4/76.4 kB\u001b[0m \u001b[31m3.6 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
+            "\u001b[2K   \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m77.9/77.9 kB\u001b[0m \u001b[31m2.8 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
+            "\u001b[2K   \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m318.9/318.9 kB\u001b[0m \u001b[31m8.9 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
+            "\u001b[2K   \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m396.4/396.4 kB\u001b[0m \u001b[31m15.7 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
+            "\u001b[2K   \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m290.4/290.4 kB\u001b[0m \u001b[31m12.0 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
+            "\u001b[2K   \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m49.3/49.3 kB\u001b[0m \u001b[31m2.8 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
+            "\u001b[2K   \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m141.9/141.9 kB\u001b[0m \u001b[31m8.7 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
+            "\u001b[2K   \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m58.3/58.3 kB\u001b[0m \u001b[31m3.4 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
+            "\u001b[?25h"
           ]
         }
       ],
@@ -46,46 +48,48 @@
     },
     {
       "cell_type": "code",
-      "execution_count": 25,
-      "metadata": {},
+      "execution_count": null,
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "Axccge0wAajT",
+        "outputId": "2e28f056-293f-412e-adb8-104133755487"
+      },
       "outputs": [
         {
-          "name": "stderr",
           "output_type": "stream",
-          "text": [
-            "huggingface/tokenizers: The current process just got forked, after parallelism has already been used. Disabling parallelism to avoid deadlocks...\n",
-            "To disable this warning, you can either:\n",
-            "\t- Avoid using `tokenizers` before the fork if possible\n",
-            "\t- Explicitly set the environment variable TOKENIZERS_PARALLELISM=(true | false)\n"
-          ]
-        },
-        {
           "name": "stdout",
-          "output_type": "stream",
           "text": [
-            "Requirement already satisfied: google-api-python-client in /Users/HUAWEI/Coding/0Project-summarizer/myenv/lib/python3.11/site-packages (2.144.0)\n",
-            "Requirement already satisfied: httplib2<1.dev0,>=0.19.0 in /Users/HUAWEI/Coding/0Project-summarizer/myenv/lib/python3.11/site-packages (from google-api-python-client) (0.22.0)\n",
-            "Requirement already satisfied: google-auth!=2.24.0,!=2.25.0,<3.0.0.dev0,>=1.32.0 in /Users/HUAWEI/Coding/0Project-summarizer/myenv/lib/python3.11/site-packages (from google-api-python-client) (2.34.0)\n",
-            "Requirement already satisfied: google-auth-httplib2<1.0.0,>=0.2.0 in /Users/HUAWEI/Coding/0Project-summarizer/myenv/lib/python3.11/site-packages (from google-api-python-client) (0.2.0)\n",
-            "Requirement already satisfied: google-api-core!=2.0.*,!=2.1.*,!=2.2.*,!=2.3.0,<3.0.0.dev0,>=1.31.5 in /Users/HUAWEI/Coding/0Project-summarizer/myenv/lib/python3.11/site-packages (from google-api-python-client) (2.19.2)\n",
-            "Requirement already satisfied: uritemplate<5,>=3.0.1 in /Users/HUAWEI/Coding/0Project-summarizer/myenv/lib/python3.11/site-packages (from google-api-python-client) (4.1.1)\n",
-            "Requirement already satisfied: googleapis-common-protos<2.0.dev0,>=1.56.2 in /Users/HUAWEI/Coding/0Project-summarizer/myenv/lib/python3.11/site-packages (from google-api-core!=2.0.*,!=2.1.*,!=2.2.*,!=2.3.0,<3.0.0.dev0,>=1.31.5->google-api-python-client) (1.65.0)\n",
-            "Requirement already satisfied: protobuf!=3.20.0,!=3.20.1,!=4.21.0,!=4.21.1,!=4.21.2,!=4.21.3,!=4.21.4,!=4.21.5,<6.0.0.dev0,>=3.19.5 in /Users/HUAWEI/Coding/0Project-summarizer/myenv/lib/python3.11/site-packages (from google-api-core!=2.0.*,!=2.1.*,!=2.2.*,!=2.3.0,<3.0.0.dev0,>=1.31.5->google-api-python-client) (5.28.0)\n",
-            "Requirement already satisfied: proto-plus<2.0.0dev,>=1.22.3 in /Users/HUAWEI/Coding/0Project-summarizer/myenv/lib/python3.11/site-packages (from google-api-core!=2.0.*,!=2.1.*,!=2.2.*,!=2.3.0,<3.0.0.dev0,>=1.31.5->google-api-python-client) (1.24.0)\n",
-            "Requirement already satisfied: requests<3.0.0.dev0,>=2.18.0 in /Users/HUAWEI/Coding/0Project-summarizer/myenv/lib/python3.11/site-packages (from google-api-core!=2.0.*,!=2.1.*,!=2.2.*,!=2.3.0,<3.0.0.dev0,>=1.31.5->google-api-python-client) (2.32.3)\n",
-            "Requirement already satisfied: cachetools<6.0,>=2.0.0 in /Users/HUAWEI/Coding/0Project-summarizer/myenv/lib/python3.11/site-packages (from google-auth!=2.24.0,!=2.25.0,<3.0.0.dev0,>=1.32.0->google-api-python-client) (5.5.0)\n",
-            "Requirement already satisfied: pyasn1-modules>=0.2.1 in /Users/HUAWEI/Coding/0Project-summarizer/myenv/lib/python3.11/site-packages (from google-auth!=2.24.0,!=2.25.0,<3.0.0.dev0,>=1.32.0->google-api-python-client) (0.4.0)\n",
-            "Requirement already satisfied: rsa<5,>=3.1.4 in /Users/HUAWEI/Coding/0Project-summarizer/myenv/lib/python3.11/site-packages (from google-auth!=2.24.0,!=2.25.0,<3.0.0.dev0,>=1.32.0->google-api-python-client) (4.9)\n",
-            "Requirement already satisfied: pyparsing!=3.0.0,!=3.0.1,!=3.0.2,!=3.0.3,<4,>=2.4.2 in /Users/HUAWEI/Coding/0Project-summarizer/myenv/lib/python3.11/site-packages (from httplib2<1.dev0,>=0.19.0->google-api-python-client) (3.1.4)\n",
-            "Requirement already satisfied: pyasn1<0.7.0,>=0.4.6 in /Users/HUAWEI/Coding/0Project-summarizer/myenv/lib/python3.11/site-packages (from pyasn1-modules>=0.2.1->google-auth!=2.24.0,!=2.25.0,<3.0.0.dev0,>=1.32.0->google-api-python-client) (0.6.0)\n",
-            "Requirement already satisfied: charset-normalizer<4,>=2 in /Users/HUAWEI/Coding/0Project-summarizer/myenv/lib/python3.11/site-packages (from requests<3.0.0.dev0,>=2.18.0->google-api-core!=2.0.*,!=2.1.*,!=2.2.*,!=2.3.0,<3.0.0.dev0,>=1.31.5->google-api-python-client) (3.3.2)\n",
-            "Requirement already satisfied: idna<4,>=2.5 in /Users/HUAWEI/Coding/0Project-summarizer/myenv/lib/python3.11/site-packages (from requests<3.0.0.dev0,>=2.18.0->google-api-core!=2.0.*,!=2.1.*,!=2.2.*,!=2.3.0,<3.0.0.dev0,>=1.31.5->google-api-python-client) (3.8)\n",
-            "Requirement already satisfied: urllib3<3,>=1.21.1 in /Users/HUAWEI/Coding/0Project-summarizer/myenv/lib/python3.11/site-packages (from requests<3.0.0.dev0,>=2.18.0->google-api-core!=2.0.*,!=2.1.*,!=2.2.*,!=2.3.0,<3.0.0.dev0,>=1.31.5->google-api-python-client) (2.2.2)\n",
-            "Requirement already satisfied: certifi>=2017.4.17 in /Users/HUAWEI/Coding/0Project-summarizer/myenv/lib/python3.11/site-packages (from requests<3.0.0.dev0,>=2.18.0->google-api-core!=2.0.*,!=2.1.*,!=2.2.*,!=2.3.0,<3.0.0.dev0,>=1.31.5->google-api-python-client) (2024.8.30)\n",
-            "\n",
-            "\u001b[1m[\u001b[0m\u001b[34;49mnotice\u001b[0m\u001b[1;39;49m]\u001b[0m\u001b[39;49m A new release of pip is available: \u001b[0m\u001b[31;49m23.2.1\u001b[0m\u001b[39;49m -> \u001b[0m\u001b[32;49m24.2\u001b[0m\n",
-            "\u001b[1m[\u001b[0m\u001b[34;49mnotice\u001b[0m\u001b[1;39;49m]\u001b[0m\u001b[39;49m To update, run: \u001b[0m\u001b[32;49mpip install --upgrade pip\u001b[0m\n",
-            "Note: you may need to restart the kernel to use updated packages.\n"
+            "Requirement already satisfied: google-api-python-client in /usr/local/lib/python3.10/dist-packages (2.137.0)\n",
+            "Collecting google-api-python-client\n",
+            "  Downloading google_api_python_client-2.144.0-py2.py3-none-any.whl.metadata (6.7 kB)\n",
+            "Requirement already satisfied: httplib2<1.dev0,>=0.19.0 in /usr/local/lib/python3.10/dist-packages (from google-api-python-client) (0.22.0)\n",
+            "Requirement already satisfied: google-auth!=2.24.0,!=2.25.0,<3.0.0.dev0,>=1.32.0 in /usr/local/lib/python3.10/dist-packages (from google-api-python-client) (2.27.0)\n",
+            "Requirement already satisfied: google-auth-httplib2<1.0.0,>=0.2.0 in /usr/local/lib/python3.10/dist-packages (from google-api-python-client) (0.2.0)\n",
+            "Requirement already satisfied: google-api-core!=2.0.*,!=2.1.*,!=2.2.*,!=2.3.0,<3.0.0.dev0,>=1.31.5 in /usr/local/lib/python3.10/dist-packages (from google-api-python-client) (2.19.2)\n",
+            "Requirement already satisfied: uritemplate<5,>=3.0.1 in /usr/local/lib/python3.10/dist-packages (from google-api-python-client) (4.1.1)\n",
+            "Requirement already satisfied: googleapis-common-protos<2.0.dev0,>=1.56.2 in /usr/local/lib/python3.10/dist-packages (from google-api-core!=2.0.*,!=2.1.*,!=2.2.*,!=2.3.0,<3.0.0.dev0,>=1.31.5->google-api-python-client) (1.65.0)\n",
+            "Requirement already satisfied: protobuf!=3.20.0,!=3.20.1,!=4.21.0,!=4.21.1,!=4.21.2,!=4.21.3,!=4.21.4,!=4.21.5,<6.0.0.dev0,>=3.19.5 in /usr/local/lib/python3.10/dist-packages (from google-api-core!=2.0.*,!=2.1.*,!=2.2.*,!=2.3.0,<3.0.0.dev0,>=1.31.5->google-api-python-client) (3.20.3)\n",
+            "Requirement already satisfied: proto-plus<2.0.0dev,>=1.22.3 in /usr/local/lib/python3.10/dist-packages (from google-api-core!=2.0.*,!=2.1.*,!=2.2.*,!=2.3.0,<3.0.0.dev0,>=1.31.5->google-api-python-client) (1.24.0)\n",
+            "Requirement already satisfied: requests<3.0.0.dev0,>=2.18.0 in /usr/local/lib/python3.10/dist-packages (from google-api-core!=2.0.*,!=2.1.*,!=2.2.*,!=2.3.0,<3.0.0.dev0,>=1.31.5->google-api-python-client) (2.32.3)\n",
+            "Requirement already satisfied: cachetools<6.0,>=2.0.0 in /usr/local/lib/python3.10/dist-packages (from google-auth!=2.24.0,!=2.25.0,<3.0.0.dev0,>=1.32.0->google-api-python-client) (5.5.0)\n",
+            "Requirement already satisfied: pyasn1-modules>=0.2.1 in /usr/local/lib/python3.10/dist-packages (from google-auth!=2.24.0,!=2.25.0,<3.0.0.dev0,>=1.32.0->google-api-python-client) (0.4.0)\n",
+            "Requirement already satisfied: rsa<5,>=3.1.4 in /usr/local/lib/python3.10/dist-packages (from google-auth!=2.24.0,!=2.25.0,<3.0.0.dev0,>=1.32.0->google-api-python-client) (4.9)\n",
+            "Requirement already satisfied: pyparsing!=3.0.0,!=3.0.1,!=3.0.2,!=3.0.3,<4,>=2.4.2 in /usr/local/lib/python3.10/dist-packages (from httplib2<1.dev0,>=0.19.0->google-api-python-client) (3.1.4)\n",
+            "Requirement already satisfied: pyasn1<0.7.0,>=0.4.6 in /usr/local/lib/python3.10/dist-packages (from pyasn1-modules>=0.2.1->google-auth!=2.24.0,!=2.25.0,<3.0.0.dev0,>=1.32.0->google-api-python-client) (0.6.0)\n",
+            "Requirement already satisfied: charset-normalizer<4,>=2 in /usr/local/lib/python3.10/dist-packages (from requests<3.0.0.dev0,>=2.18.0->google-api-core!=2.0.*,!=2.1.*,!=2.2.*,!=2.3.0,<3.0.0.dev0,>=1.31.5->google-api-python-client) (3.3.2)\n",
+            "Requirement already satisfied: idna<4,>=2.5 in /usr/local/lib/python3.10/dist-packages (from requests<3.0.0.dev0,>=2.18.0->google-api-core!=2.0.*,!=2.1.*,!=2.2.*,!=2.3.0,<3.0.0.dev0,>=1.31.5->google-api-python-client) (3.8)\n",
+            "Requirement already satisfied: urllib3<3,>=1.21.1 in /usr/local/lib/python3.10/dist-packages (from requests<3.0.0.dev0,>=2.18.0->google-api-core!=2.0.*,!=2.1.*,!=2.2.*,!=2.3.0,<3.0.0.dev0,>=1.31.5->google-api-python-client) (2.0.7)\n",
+            "Requirement already satisfied: certifi>=2017.4.17 in /usr/local/lib/python3.10/dist-packages (from requests<3.0.0.dev0,>=2.18.0->google-api-core!=2.0.*,!=2.1.*,!=2.2.*,!=2.3.0,<3.0.0.dev0,>=1.31.5->google-api-python-client) (2024.8.30)\n",
+            "Downloading google_api_python_client-2.144.0-py2.py3-none-any.whl (12.2 MB)\n",
+            "\u001b[2K   \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m12.2/12.2 MB\u001b[0m \u001b[31m66.0 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
+            "\u001b[?25hInstalling collected packages: google-api-python-client\n",
+            "  Attempting uninstall: google-api-python-client\n",
+            "    Found existing installation: google-api-python-client 2.137.0\n",
+            "    Uninstalling google-api-python-client-2.137.0:\n",
+            "      Successfully uninstalled google-api-python-client-2.137.0\n",
+            "Successfully installed google-api-python-client-2.144.0\n"
           ]
         }
       ],
@@ -119,7 +123,7 @@
     },
     {
       "cell_type": "code",
-      "execution_count": 26,
+      "execution_count": null,
       "metadata": {
         "id": "sFYWNeL-4xIr"
       },
@@ -128,7 +132,7 @@
         "from youtube_transcript_api import YouTubeTranscriptApi\n",
         "from googleapiclient.discovery import build\n",
         "\n",
-        "api_key = \"Youtube_api\"\n",
+        "api_key = \"AIzaSyBzwEw3l9Or_l3ZfFniGBXJH7cK-s6-oQo\"\n",
         "\n",
         "\n",
         "def get_playlist_video_ids(playlist_id, api_key):\n",
@@ -142,7 +146,7 @@
         "        request = youtube.playlistItems().list(\n",
         "            part=\"contentDetails\",\n",
         "            playlistId=playlist_id,\n",
-        "            maxResults=50,  # Максимальное количество видео, которое можно получить за один запрос\n",
+        "            maxResults=100,  # Максимальное количество видео, которое можно получить за один запрос\n",
         "            pageToken=next_page_token\n",
         "        )\n",
         "        response = request.execute()\n",
@@ -152,7 +156,7 @@
         "\n",
         "        # pagination\n",
         "        next_page_token = response.get('nextPageToken')\n",
-        "        \n",
+        "\n",
         "        if not next_page_token:\n",
         "            break\n",
         "\n",
@@ -161,10 +165,17 @@
     },
     {
       "cell_type": "code",
-      "execution_count": 27,
-      "metadata": {},
+      "execution_count": null,
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "bnMIT3apAajU",
+        "outputId": "f5387bde-46e1-43c1-b57d-923e2b77cb6e"
+      },
       "outputs": [
         {
+          "output_type": "execute_result",
           "data": {
             "text/plain": [
               "{'kind': 'youtube#playlistItemListResponse',\n",
@@ -267,9 +278,8 @@
               " 'pageInfo': {'totalResults': 19, 'resultsPerPage': 50}}"
             ]
           },
-          "execution_count": 27,
           "metadata": {},
-          "output_type": "execute_result"
+          "execution_count": 6
         }
       ],
       "source": [
@@ -278,7 +288,7 @@
         "request = youtube.playlistItems().list(\n",
         "            part=\"contentDetails\",\n",
         "            playlistId='PLYSHtNPbAINnbqXjIbN-c7DorjCT6eYOQ',\n",
-        "            maxResults=50,  # Максимальное количество видео, которое можно получить за один запрос\n",
+        "            maxResults=100,  # Максимальное количество видео, которое можно получить за один запрос\n",
         "            # pageToken=response.get('nextPageToken')\n",
         "        )\n",
         "result1 = request.execute()\n",
@@ -289,15 +299,19 @@
     },
     {
       "cell_type": "markdown",
-      "metadata": {},
+      "metadata": {
+        "id": "5yDtqgsjAajV"
+      },
       "source": [
         "### EN transcripts all"
       ]
     },
     {
       "cell_type": "code",
-      "execution_count": 29,
-      "metadata": {},
+      "execution_count": null,
+      "metadata": {
+        "id": "NfYgich3AajV"
+      },
       "outputs": [],
       "source": [
         "\n",
@@ -345,22 +359,24 @@
     },
     {
       "cell_type": "code",
-      "execution_count": 32,
-      "metadata": {},
+      "execution_count": null,
+      "metadata": {
+        "id": "w_6MYrKTAajV"
+      },
       "outputs": [],
       "source": [
         "# Sources:\n",
-        "playlist_ml_en = \"https://www.youtube.com/watch?v=Gv9_4yMHFhI&list=PLblh5JKOoLUICTaGLRoHQDuF_7q2GfuJF\" \n",
-        "playlist_logistic_en = \"https://www.youtube.com/watch?v=yIYKR4sgzI8&list=PLblh5JKOoLUKxzEP5HA2d-Li7IJkHfXSe\" \n",
-        "playlist_nn_en = \"https://www.youtube.com/watch?v=zxagGtF9MeU&list=PLblh5JKOoLUIxGDQs4LFFD--41Vzf-ME1\" \n",
-        "playlist_stat_en = \"https://www.youtube.com/watch?v=qBigTkBLU6g&list=PLblh5JKOoLUK0FLuzwntyYI10UQFUhsY9\" \n",
-        "playlist_nn2_en = \"https://www.youtube.com/playlist?list=PLZHQObOWTQDNU6R1_67000Dx_ZCJB-3pi\" \n",
-        "playlist_linal2_en = \"https://www.youtube.com/playlist?list=PLZHQObOWTQDPD3MizzM2xVFitgF8hE_ab\" \n"
+        "playlist_ml_en = \"https://www.youtube.com/watch?v=Gv9_4yMHFhI&list=PLblh5JKOoLUICTaGLRoHQDuF_7q2GfuJF\"\n",
+        "playlist_logistic_en = \"https://www.youtube.com/watch?v=yIYKR4sgzI8&list=PLblh5JKOoLUKxzEP5HA2d-Li7IJkHfXSe\"\n",
+        "playlist_nn_en = \"https://www.youtube.com/watch?v=zxagGtF9MeU&list=PLblh5JKOoLUIxGDQs4LFFD--41Vzf-ME1\"\n",
+        "playlist_stat_en = \"https://www.youtube.com/watch?v=qBigTkBLU6g&list=PLblh5JKOoLUK0FLuzwntyYI10UQFUhsY9\"\n",
+        "playlist_nn2_en = \"https://www.youtube.com/playlist?list=PLZHQObOWTQDNU6R1_67000Dx_ZCJB-3pi\"\n",
+        "playlist_linal2_en = \"https://www.youtube.com/playlist?list=PLZHQObOWTQDPD3MizzM2xVFitgF8hE_ab\"\n"
       ]
     },
     {
       "cell_type": "code",
-      "execution_count": 31,
+      "execution_count": null,
       "metadata": {
         "id": "QYmaFtk_5O6C"
       },
@@ -372,8 +388,36 @@
     },
     {
       "cell_type": "code",
-      "execution_count": 33,
-      "metadata": {},
+      "source": [
+        "len(transcripts_ML_en)"
+      ],
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "Sko_ef_2BUP6",
+        "outputId": "b47cfbf2-6196-466d-fc28-b6db8ef8a959"
+      },
+      "execution_count": null,
+      "outputs": [
+        {
+          "output_type": "execute_result",
+          "data": {
+            "text/plain": [
+              "99"
+            ]
+          },
+          "metadata": {},
+          "execution_count": 14
+        }
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "id": "Cd5jJYKdAajV"
+      },
       "outputs": [],
       "source": [
         "# tier 2\n",
@@ -384,8 +428,10 @@
     },
     {
       "cell_type": "code",
-      "execution_count": 34,
-      "metadata": {},
+      "execution_count": null,
+      "metadata": {
+        "id": "1m-VG8K9AajV"
+      },
       "outputs": [],
       "source": [
         "# tier 3\n",
@@ -395,15 +441,19 @@
     },
     {
       "cell_type": "markdown",
-      "metadata": {},
+      "metadata": {
+        "id": "wpCh1vb2AajW"
+      },
       "source": [
         "### RU transcripts all"
       ]
     },
     {
       "cell_type": "code",
-      "execution_count": 36,
-      "metadata": {},
+      "execution_count": null,
+      "metadata": {
+        "id": "b6WypAXMAajW"
+      },
       "outputs": [],
       "source": [
         "\n",
@@ -450,12 +500,14 @@
     },
     {
       "cell_type": "code",
-      "execution_count": 40,
-      "metadata": {},
+      "execution_count": null,
+      "metadata": {
+        "id": "5990TRMnAajW"
+      },
       "outputs": [],
       "source": [
-        "# Sources Elbrus \n",
-        "playlist_phase_1_url = \"https://www.youtube.com/playlist?list=PLYSHtNPbAINnbqXjIbN-c7DorjCT6eYOQ\" \n",
+        "# Sources Elbrus\n",
+        "playlist_phase_1_url = \"https://www.youtube.com/playlist?list=PLYSHtNPbAINnbqXjIbN-c7DorjCT6eYOQ\"\n",
         "playlist_phase_2_url = 'https://www.youtube.com/playlist?list=PLYSHtNPbAINnNvDXtGNmC7-F1QRH7qTgb'\n",
         "playlist_phase_3_url = 'https://www.youtube.com/playlist?list=PLYSHtNPbAINlmyNNmTaqcn3BsaY8v1xgV'\n",
         "\n",
@@ -468,7 +520,7 @@
     },
     {
       "cell_type": "code",
-      "execution_count": 38,
+      "execution_count": null,
       "metadata": {
         "id": "SojIu0NP5VNt"
       },
@@ -482,8 +534,10 @@
     },
     {
       "cell_type": "code",
-      "execution_count": 41,
-      "metadata": {},
+      "execution_count": null,
+      "metadata": {
+        "id": "VEDGiNEHAajW"
+      },
       "outputs": [],
       "source": [
         "# other Ru\n",
@@ -497,15 +551,19 @@
     },
     {
       "cell_type": "markdown",
-      "metadata": {},
+      "metadata": {
+        "id": "KPB9GcSfAajW"
+      },
       "source": [
         "### Aggregate all Knowledge Base"
       ]
     },
     {
       "cell_type": "code",
-      "execution_count": 42,
-      "metadata": {},
+      "execution_count": null,
+      "metadata": {
+        "id": "fp1Jb8BiAajW"
+      },
       "outputs": [],
       "source": [
         "transcripts_all = [transcripts_phase_1, transcripts_phase_2, transcripts_phase_3, transcripts_NN_ru, transcripts_OOP_ru, transcripts_linal_ru, transcripts_docker_ru, \\\n",
@@ -523,11 +581,24 @@
     },
     {
       "cell_type": "code",
-      "execution_count": 55,
+      "execution_count": null,
       "metadata": {
-        "id": "gbbajjDK5niN"
+        "id": "gbbajjDK5niN",
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "outputId": "40059afc-575f-4d7a-d8c8-ff1cfafbc69e"
       },
-      "outputs": [],
+      "outputs": [
+        {
+          "output_type": "stream",
+          "name": "stderr",
+          "text": [
+            "/usr/local/lib/python3.10/dist-packages/sentence_transformers/cross_encoder/CrossEncoder.py:11: TqdmExperimentalWarning: Using `tqdm.autonotebook.tqdm` in notebook mode. Use `tqdm.tqdm` instead to force console mode (e.g. in jupyter console)\n",
+            "  from tqdm.autonotebook import tqdm, trange\n"
+          ]
+        }
+      ],
       "source": [
         "from langchain_core.documents import Document\n",
         "from langchain.text_splitter import RecursiveCharacterTextSplitter\n",
@@ -543,10 +614,44 @@
         "            docs.append(Document(page_content=transcript, metadata={\"title\": title}))\n",
         "\n",
         "# Split documents into chunks\n",
-        "text_splitter = RecursiveCharacterTextSplitter(chunk_size=1500, chunk_overlap=200)\n",
-        "split_docs = text_splitter.split_documents(docs)\n",
-        "\n",
-        "# Setup the new embeddings model\n",
+        "text_splitter = RecursiveCharacterTextSplitter(chunk_size=500, chunk_overlap=100)\n",
+        "split_docs = text_splitter.split_documents(docs)\n"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "split_docs[0].page_content"
+      ],
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 36
+        },
+        "id": "y04EIegH0EbH",
+        "outputId": "4e1d67f6-f496-429c-e62b-fa260356e0f0"
+      },
+      "execution_count": null,
+      "outputs": [
+        {
+          "output_type": "execute_result",
+          "data": {
+            "text/plain": [
+              "'1. Probability'"
+            ],
+            "application/vnd.google.colaboratory.intrinsic+json": {
+              "type": "string"
+            }
+          },
+          "metadata": {},
+          "execution_count": 32
+        }
+      ]
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "#Setup the new embeddings model\n",
         "model_name = \"intfloat/multilingual-e5-base\"\n",
         "embeddings = HuggingFaceEmbeddings(model_name=model_name)\n",
         "\n",
@@ -559,6 +664,309 @@
         "\n",
         "# Create the retriever for document retrieval\n",
         "embedding_retriever = vector_store.as_retriever(search_kwargs={\"k\": 15})"
+      ],
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 503,
+          "referenced_widgets": [
+            "c5841a4ce6bb4080bfd12fe45f9ef0ac",
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+          ]
+        },
+        "id": "nOtDOvDBC8St",
+        "outputId": "96a533ef-e7f8-440d-8dcc-5dcb4a515825"
+      },
+      "execution_count": null,
+      "outputs": [
+        {
+          "output_type": "stream",
+          "name": "stderr",
+          "text": [
+            "<ipython-input-25-bc5d5ca3db5b>:3: LangChainDeprecationWarning: The class `HuggingFaceEmbeddings` was deprecated in LangChain 0.2.2 and will be removed in 1.0. An updated version of the class exists in the langchain-huggingface package and should be used instead. To use it run `pip install -U langchain-huggingface` and import as `from langchain_huggingface import HuggingFaceEmbeddings`.\n",
+            "  embeddings = HuggingFaceEmbeddings(model_name=model_name)\n",
+            "/usr/local/lib/python3.10/dist-packages/huggingface_hub/utils/_token.py:89: UserWarning: \n",
+            "The secret `HF_TOKEN` does not exist in your Colab secrets.\n",
+            "To authenticate with the Hugging Face Hub, create a token in your settings tab (https://huggingface.co/settings/tokens), set it as secret in your Google Colab and restart your session.\n",
+            "You will be able to reuse this secret in all of your notebooks.\n",
+            "Please note that authentication is recommended but still optional to access public models or datasets.\n",
+            "  warnings.warn(\n"
+          ]
+        },
+        {
+          "output_type": "display_data",
+          "data": {
+            "text/plain": [
+              "modules.json:   0%|          | 0.00/387 [00:00<?, ?B/s]"
+            ],
+            "application/vnd.jupyter.widget-view+json": {
+              "version_major": 2,
+              "version_minor": 0,
+              "model_id": "c5841a4ce6bb4080bfd12fe45f9ef0ac"
+            }
+          },
+          "metadata": {}
+        },
+        {
+          "output_type": "display_data",
+          "data": {
+            "text/plain": [
+              "README.md:   0%|          | 0.00/179k [00:00<?, ?B/s]"
+            ],
+            "application/vnd.jupyter.widget-view+json": {
+              "version_major": 2,
+              "version_minor": 0,
+              "model_id": "48e65d69916a481e99144e0dd20f7252"
+            }
+          },
+          "metadata": {}
+        },
+        {
+          "output_type": "display_data",
+          "data": {
+            "text/plain": [
+              "sentence_bert_config.json:   0%|          | 0.00/57.0 [00:00<?, ?B/s]"
+            ],
+            "application/vnd.jupyter.widget-view+json": {
+              "version_major": 2,
+              "version_minor": 0,
+              "model_id": "2d6b6e723b394db1bda1628a67f29cd0"
+            }
+          },
+          "metadata": {}
+        },
+        {
+          "output_type": "display_data",
+          "data": {
+            "text/plain": [
+              "config.json:   0%|          | 0.00/694 [00:00<?, ?B/s]"
+            ],
+            "application/vnd.jupyter.widget-view+json": {
+              "version_major": 2,
+              "version_minor": 0,
+              "model_id": "ddd607992f5a42598b5029a7d1c0735f"
+            }
+          },
+          "metadata": {}
+        },
+        {
+          "output_type": "display_data",
+          "data": {
+            "text/plain": [
+              "model.safetensors:   0%|          | 0.00/1.11G [00:00<?, ?B/s]"
+            ],
+            "application/vnd.jupyter.widget-view+json": {
+              "version_major": 2,
+              "version_minor": 0,
+              "model_id": "98fe33de61954aa2a0d6417c9ff12ee3"
+            }
+          },
+          "metadata": {}
+        },
+        {
+          "output_type": "display_data",
+          "data": {
+            "text/plain": [
+              "tokenizer_config.json:   0%|          | 0.00/418 [00:00<?, ?B/s]"
+            ],
+            "application/vnd.jupyter.widget-view+json": {
+              "version_major": 2,
+              "version_minor": 0,
+              "model_id": "101f51b1dc994c58aea7e505678d5b60"
+            }
+          },
+          "metadata": {}
+        },
+        {
+          "output_type": "display_data",
+          "data": {
+            "text/plain": [
+              "sentencepiece.bpe.model:   0%|          | 0.00/5.07M [00:00<?, ?B/s]"
+            ],
+            "application/vnd.jupyter.widget-view+json": {
+              "version_major": 2,
+              "version_minor": 0,
+              "model_id": "26007c6d5f7b4e26995158272fd85376"
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+          "metadata": {}
+        },
+        {
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+            }
+          },
+          "metadata": {}
+        },
+        {
+          "output_type": "display_data",
+          "data": {
+            "text/plain": [
+              "1_Pooling/config.json:   0%|          | 0.00/200 [00:00<?, ?B/s]"
+            ],
+            "application/vnd.jupyter.widget-view+json": {
+              "version_major": 2,
+              "version_minor": 0,
+              "model_id": "0abc174237c14c58881d5e47f4184d48"
+            }
+          },
+          "metadata": {}
+        }
+      ]
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "split_docs[19000]"
+      ],
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "iLviy4yFCvYP",
+        "outputId": "02233604-74ab-47d7-e4f0-8398e4ccfee9"
+      },
+      "execution_count": null,
+      "outputs": [
+        {
+          "output_type": "execute_result",
+          "data": {
+            "text/plain": [
+              "Document(metadata={'title': 'transcript'}, page_content=\"this for all your tens of  thousands of training examples and averaging the desired changes you get. But that's computationally slow, so instead you randomly subdivide the  data into mini-batches and compute each step with respect to a mini-batch. Repeatedly going through all of the mini-batches and making these adjustments,  you will converge towards a local minimum of the cost function,  which is to say your network will end up doing a really good job on the training examples. So with all of\")"
+            ]
+          },
+          "metadata": {},
+          "execution_count": 24
+        }
       ]
     },
     {
@@ -572,54 +980,57 @@
     },
     {
       "cell_type": "code",
-      "execution_count": 59,
+      "execution_count": null,
       "metadata": {
         "colab": {
-          "base_uri": "https://localhost:8080/",
-          "height": 390
+          "base_uri": "https://localhost:8080/"
         },
         "id": "pg9NqxCRoLPn",
-        "outputId": "d7695e09-4171-40c6-b055-08bba5b6e487"
+        "outputId": "56e0837b-db74-4ac2-d878-0e011ddf6270"
       },
       "outputs": [
         {
-          "name": "stdout",
           "output_type": "stream",
+          "name": "stdout",
           "text": [
             "Loading existing FAISS index...\n",
-            "Информация из базы знаний:\n",
+            "Information from knowledge base:\n",
+            "\n",
+            "Decision trees are a machine learning method used for classification and regression tasks. They work by making a series of decisions based on input features to arrive at a prediction or classification.\n",
             "\n",
-            "Шаги логистической регрессии:\n",
+            "Key points about decision trees:\n",
             "\n",
-            "1. Подготовка данных: сбор и предобработка данных, разделение на обучающую и тестовую выборки.\n",
+            "1. They make statements and decisions based on whether those statements are true or false.\n",
             "\n",
-            "2. Выбор функции активации: обычно используется сигмоидная функция.\n",
+            "2. Classification trees categorize data into classes, while regression trees predict numeric values.\n",
             "\n",
-            "3. Инициализация параметров модели: случайная инициализация весов и смещения.\n",
+            "3. Decision trees are built using training data and can then be used to make predictions on new data.\n",
             "\n",
-            "4. Определение функции потерь: чаще всего используется кросс-энтропия.\n",
+            "4. The tree is constructed by selecting features and thresholds that best split the data at each node.\n",
             "\n",
-            "5. Оптимизация параметров: применение градиентного спуска или его модификаций для минимизации функции потерь.\n",
+            "5. Pruning is used to prevent overfitting by removing unnecessary branches.\n",
             "\n",
-            "6. Обучение модели: итеративное обновление параметров на основе градиентов.\n",
+            "6. Decision trees can handle both categorical and numerical data.\n",
             "\n",
-            "7. Оценка модели: проверка точности на тестовой выборке.\n",
+            "7. They are interpretable and easy to visualize.\n",
             "\n",
-            "8. Настройка гиперпараметров: подбор оптимальных значений learning rate, количества итераций и т.д.\n",
+            "8. Missing data can be handled in various ways when building and using decision trees.\n",
             "\n",
-            "Что полезно добавить поверх базы знаний:\n",
+            "What I would add:\n",
             "\n",
-            "9. Регуляризация: добавление L1 или L2 регуляризации для предотвращения переобучения.\n",
+            "1. Decision trees use metrics like Gini impurity or information gain to determine the best splits.\n",
             "\n",
-            "10. Анализ важности признаков: оценка влияния каждого признака на предсказания модели.\n",
+            "2. They can be prone to overfitting, especially with deep trees, which is why techniques like pruning and setting a maximum depth are important.\n",
             "\n",
-            "11. Обработка несбалансированных данных: применение техник, таких как взвешивание классов или oversampling/undersampling.\n",
+            "3. Random forests and gradient boosting are ensemble methods that build on decision trees to create more robust models.\n",
             "\n",
-            "12. Интерпретация результатов: анализ коэффициентов модели для понимания влияния признаков.\n",
+            "4. Decision trees have advantages in interpretability but may not always perform as well as more complex algorithms for certain tasks.\n",
             "\n",
-            "13. Кросс-валидация: использование k-fold кросс-валидации для более надежной оценки производительности модели.\n",
+            "5. Feature importance can be derived from decision trees, helping identify which variables are most influential in making predictions.\n",
             "\n",
-            "14. Мониторинг процесса обучения: отслеживание изменения функции потерь и точности на валидационной выборке для определения момента остановки обучения.\n"
+            "6. Cross-validation is often used to tune hyperparameters and assess the model's performance.\n",
+            "\n",
+            "7. Decision trees can handle non-linear relationships and interactions between features naturally.\n"
           ]
         }
       ],
@@ -632,7 +1043,7 @@
         "import os\n",
         "\n",
         "# Anthropic API setup\n",
-        "client = anthropic.Client(api_key='Your_api_key')\n",
+        "client = anthropic.Client(api_key='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')\n",
         "\n",
         "# Prompt template\n",
         "prompt_template = '''Reply to the {input} as a seasoned machine learning professional. \\\n",
@@ -647,7 +1058,7 @@
         "def setup_rag(force_rebuild=False):\n",
         "    model_name = \"intfloat/multilingual-e5-base\"\n",
         "    embeddings = HuggingFaceEmbeddings(model_name=model_name)\n",
-        "    \n",
+        "\n",
         "    if not force_rebuild and os.path.exists(\"faiss_index\"):\n",
         "        print(\"Loading existing FAISS index...\")\n",
         "        return FAISS.load_local('faiss_index', embeddings=embeddings, allow_dangerous_deserialization=True), embeddings\n",
@@ -661,7 +1072,7 @@
         "                docs.append(Document(page_content=transcript, metadata={\"title\": title}))\n",
         "\n",
         "    # Split documents into chunks\n",
-        "    text_splitter = RecursiveCharacterTextSplitter(chunk_size=1500, chunk_overlap=200)\n",
+        "    text_splitter = RecursiveCharacterTextSplitter(chunk_size=500, chunk_overlap=100)\n",
         "    split_docs = text_splitter.split_documents(docs)\n",
         "\n",
         "    # Create the FAISS vector store and save it locally\n",
@@ -687,7 +1098,7 @@
         "    documents = retriever.get_relevant_documents(question)\n",
         "    context = \" \".join([doc.page_content for doc in documents])\n",
         "    prompt = prompt_template.format(context=context, input=question)\n",
-        "    return call_claude_api(prompt, client)\n",
+        "    return call_claude_api(prompt, client), documents\n",
         "\n",
         "# Main execution\n",
         "if __name__ == \"__main__\":\n",
@@ -695,18 +1106,43 @@
         "    vector_store, embeddings = setup_rag()\n",
         "\n",
         "    # Create the retriever for document retrieval\n",
-        "    embedding_retriever = vector_store.as_retriever(search_kwargs={\"k\": 15})\n",
+        "    embedding_retriever = vector_store.as_retriever(search_kwargs={\"k\": 20})\n",
         "\n",
         "    # Example usage\n",
-        "    question = 'Шаги логистической регрессии'\n",
-        "    answer = answer_question(question, embedding_retriever, client)\n",
+        "    question = 'How decision tree works?'\n",
+        "    answer, context = answer_question(question, embedding_retriever, client)\n",
         "    print(answer)"
       ]
     },
+    {
+      "cell_type": "code",
+      "source": [
+        "print(context)"
+      ],
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "KbaKVEAPkebf",
+        "outputId": "151781a1-f355-4e1c-d0b4-45cace52c199"
+      },
+      "execution_count": null,
+      "outputs": [
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "[Document(metadata={'title': 'title'}, page_content='Decision and Classification Trees, Clearly Explained!!!'), Document(metadata={'title': 'transcript'}, page_content=\"out the latest justin bieber video instead in general a decision tree makes a statement and then makes a decision based on whether or not that statement is true or false it's no big deal when a decision tree classifies things into categories it's called a classification tree and when a decision tree predicts numeric values it's called a regression tree in this case we're using diet to predict a numeric value for mouse size note for the remainder of this video we are going to focus on\"), Document(metadata={'title': 'transcript'}, page_content=\"quest otherwise you might not like stack quest wah wah this is a silly example but it illustrates a decision tree a simple machine learning method the purpose of this particular decision tree is to predict whether or not someone will love stack quest alternatively we could say that this decision tree classifies a person as either someone who loves stack quest or someone who doesn't since decision trees are a type of machine learning then if you understand how we use this tree to predict or\"), Document(metadata={'title': 'transcript'}, page_content=\"quest on decision trees we started with a table of data and built a decision tree that gave us a sense of how likely a patient might have heart disease if they have other symptoms we first asked if a patient had good blood circulation if so we then asked if they had blocked arteries and if so we then asked if they had chest pain if so there's a good chance that they have heart disease since 17 people with similar answers did and only three people with similar answers did not if they don't have\"), Document(metadata={'title': 'title'}, page_content='StatQuest: Decision Trees, Part 2 - Feature Selection and Missing Data'), Document(metadata={'title': 'transcript'}, page_content=\"testing data we can use the testing data to see how well our decision tree predicts if someone will love stat quest the first person in the testing data did not like silly songs so we go to the right side of the decision tree they didn't like machine learning either so we just keep on going down the right side of the decision tree they didn't like statistics either so the decision tree predicts that this person will not love stat quest however this person loves stat quest so the decision tree\"), Document(metadata={'title': 'transcript'}, page_content='that this person will not love stat quest however this person loves stat quest so the decision tree made a mistake want wall the second person in the testing data liked silly songs and that takes us down the left side of the decision tree they were also interested in machine learning so we predict that that person loves stat quest and since this person actually loves stat quest the decision tree did a good job hooray now we just run all of the other people in the testing data down the decision'), Document(metadata={'title': 'transcript'}, page_content=\"i like decision trees how about you stat quest hello i'm josh darmer and welcome to statquest today we're going to talk about decision and classification trees and they're going to be clearly explained here is a simple decision tree if a person wants to learn about decision trees then they should watch this stat quest in contrast if a person does not want to learn about decision trees then check out the latest justin bieber video instead in general a decision tree makes a statement and then\"), Document(metadata={'title': 'transcript'}, page_content=\"imported data identified and dealt with missing data formatted the data for a decision tree using one hot encoding built a preliminary decision tree for classification and that's what we use as a reference to know if pruning or optimizing the tree was going to make any different difference then we pruned with cost complexity pruning then we built drew interpreted and evaluated the final classification tree so hooray triple bam we made it oh a couple things before we go i want to say a lot of\"), Document(metadata={'title': 'transcript'}, page_content=\"in our next month we're doing support vector machines so uh so there you go oh someone did ask if decision trees are better for medical data than random forests and i don't know i like i love random forests i'm a random forest dude um i'm always thinking about them um uh i i think it might be appropriate in a medical setting uh it just sort of depends um uh i would like for it to be um because i like random forests uh but in terms of like sheer interpretability simplicity decision trees are the\"), Document(metadata={'title': 'transcript'}, page_content=\"and it just picks the first one that it gets to and if i've got multiple columns that have the exact same data and uh and this in this call that first column is really good for um classifying and so it does a great job separating and the other columns would do just as well it just uses that first one every time um so it ends up not being an issue if we have redundancy in our data set and that's one of the nice things about um decision trees um all right so i think we are ready to move on um all\"), Document(metadata={'title': 'transcript'}, page_content='did a good job hooray now we just run all of the other people in the testing data down the decision tree and compare the predictions to reality then we can compare this decision tree to the latest greatest machine learning method ultimately we pick the method that does the best job predicting if someone will love stat quest or not triple bam in summary machine learning is all about making predictions and classifications there are tons of fancy machine learning methods but the most important'), Document(metadata={'title': 'transcript'}, page_content=\"and pruner just takes care of them all in one fell swoop pruning a decision tree is all about finding the right value for the pruning parameter alpha which controls how little or how much pruning happens one way to find the optimal value for alpha is to plot the accuracy of the tree as a function of different values we do this for both the training data set and the testing data set so first we're going to extract the different values for alpha that are available for this tree and build a prune\"), Document(metadata={'title': 'transcript'}, page_content='to classify if someone loved stat quest based on a few questions to create the decision tree we collected data from people who loved stat quest and from people who did not love stat quest altogether this was the training data and we used it to build the decision tree got data from a few more people who love stat quest and a few more people who did not love stat quest altogether this forms the testing data we can use the testing data to see how well our decision tree predicts if someone will'), Document(metadata={'title': 'transcript'}, page_content=\"than others in this illustration the larger stumps get more say in the final classification than the smaller stumps lastly in a random forest each decision tree is made independently of the others in other words it doesn't matter if this tree was made first or this one in contrast in a forest of stumps made with adaboost order is important the errors that the first stump makes influence how the second stump is made and the air is that the second stone makes influence how the third stump is made\"), Document(metadata={'title': 'transcript'}, page_content=\"with the original data the data we use to make the tree but doesn't do well with any other data set decision trees have the downside of often being overfit requiring each split to make a large reduction in impurity helps a tree from being over fit so in a nutshell that's what feature selection is all about now let's talk about missing data in the first video on decision trees we calculated impurity for blocked arteries boota boota boota boota boota boota boo and we skipped this patient since we\"), Document(metadata={'title': 'transcript'}, page_content='a new tree we add it to all of the previous predictions and make new predictions that give us even smaller residuals then we build another tree based on the new residuals and we keep building trees until the residuals are super small or we have reached the maximum number of trees triple bam in summary when building x/g boost trees for classification we calculate similarity scores and game to determine how to split the data and we prune the tree by calculating the difference between gain values'), Document(metadata={'title': 'transcript'}, page_content=\"of the nice things about um decision trees um all right so i think we are ready to move on um all right so we're going to build a preliminary classification tree this is a classification tree uh that is not optimized that's why it's preliminary then we'll go through how to optimize it once we get this going uh so the first thing we're going to do is we're going to split our our data into testing and training data uh sets subsets so we've got x underscore train x underscore test y underscore\"), Document(metadata={'title': 'transcript'}, page_content='and just like before when a leaf has less than a minimum number of observations which is usually 20 but we are using 7 we stop trying to divide them triple bow in summary rushon trees are a type of decision tree in a regression tree each leaf represents a numeric value we determine how to divide the observations by trying different thresholds in calculating the sum of squared residuals at each step pp boo boo beep beep boo boo peepee poo-poo the threshold with the smallest sum of squared'), Document(metadata={'title': 'transcript'}, page_content='values and the values predicted by the leaf and the first tree then we calculated the output values for each leaf and we scale this new tree with the learning rate as well this process repeats until we have made the maximum number of trees specified or the residuals get super small BAM now for the sake of keeping the example relatively simple imagine that we configured gradient boost to just make these two trees and we needed to classify a new person as someone who loves to roll - or does not')]\n"
+          ]
+        }
+      ]
+    },
     {
       "cell_type": "code",
       "execution_count": null,
-      "metadata": {},
+      "metadata": {
+        "id": "UB7fGE5sAajW"
+      },
       "outputs": [],
       "source": []
     }
@@ -730,8 +1166,3432 @@
       "nbconvert_exporter": "python",
       "pygments_lexer": "ipython3",
       "version": "3.11.7"
+    },
+    "widgets": {
+      "application/vnd.jupyter.widget-state+json": {
+        "c5841a4ce6bb4080bfd12fe45f9ef0ac": {
+          "model_module": "@jupyter-widgets/controls",
+          "model_name": "HBoxModel",
+          "model_module_version": "1.5.0",
+          "state": {
+            "_dom_classes": [],
+            "_model_module": "@jupyter-widgets/controls",
+            "_model_module_version": "1.5.0",
+            "_model_name": "HBoxModel",
+            "_view_count": null,
+            "_view_module": "@jupyter-widgets/controls",
+            "_view_module_version": "1.5.0",
+            "_view_name": "HBoxView",
+            "box_style": "",
+            "children": [
+              "IPY_MODEL_b02022ee58da420e8eb3cfd9eb3f43d5",
+              "IPY_MODEL_3461edf56f2b474898430f891d99674c",
+              "IPY_MODEL_57e03ace47344b7d8768230e87f9a59b"
+            ],
+            "layout": "IPY_MODEL_89f056a38392430eb597e1bae5be4659"
+          }
+        },
+        "b02022ee58da420e8eb3cfd9eb3f43d5": {
+          "model_module": "@jupyter-widgets/controls",
+          "model_name": "HTMLModel",
+          "model_module_version": "1.5.0",
+          "state": {
+            "_dom_classes": [],
+            "_model_module": "@jupyter-widgets/controls",
+            "_model_module_version": "1.5.0",
+            "_model_name": "HTMLModel",
+            "_view_count": null,
+            "_view_module": "@jupyter-widgets/controls",
+            "_view_module_version": "1.5.0",
+            "_view_name": "HTMLView",
+            "description": "",
+            "description_tooltip": null,
+            "layout": "IPY_MODEL_bc25be9987a14271a755ad0df670ca81",
+            "placeholder": "​",
+            "style": "IPY_MODEL_959555740acc4a6497f85eaa745e3517",
+            "value": "modules.json: 100%"
+          }
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+        "3461edf56f2b474898430f891d99674c": {
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+          "model_name": "FloatProgressModel",
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+            "_model_module_version": "1.5.0",
+            "_model_name": "FloatProgressModel",
+            "_view_count": null,
+            "_view_module": "@jupyter-widgets/controls",
+            "_view_module_version": "1.5.0",
+            "_view_name": "ProgressView",
+            "bar_style": "success",
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+            "layout": "IPY_MODEL_84444adba9c64c49b18f6416775cb4c0",
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+            "min": 0,
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+            "style": "IPY_MODEL_37c442a4df204027b2f34bbd3e88ce92",
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+          "model_module": "@jupyter-widgets/controls",
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+            "_model_module": "@jupyter-widgets/controls",
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