diff --git a/AudioDataTutorial.ipynb b/AudioDataTutorial.ipynb
index 98dd2750156fdbfdec3f46d5c74e04f868ae1bf3..f628e1bfa49a05494a4efee0cc6c7933b61f7457 100644
--- a/AudioDataTutorial.ipynb
+++ b/AudioDataTutorial.ipynb
@@ -79,7 +79,7 @@
    "source": [
     "import requests\n",
     "\n",
-    "url = 'http://www-mmsp.ece.mcgill.ca/Documents/AudioFormats/WAVE/Samples/Microsoft/6_Channel_ID.wav'\n",
+    "url = 'http://www.mmsp.ece.mcgill.ca/Documents/AudioFormats/WAVE/Samples/Microsoft/6_Channel_ID.wav'\n",
     "file='6_Channel_ID.wav'\n",
     "r = requests.get(url, allow_redirects=True)\n",
     "open(file, 'wb').write(r.content)"
@@ -292,7 +292,7 @@
  ],
  "metadata": {
   "kernelspec": {
-   "display_name": "Python 3",
+   "display_name": "Python 3 (ipykernel)",
    "language": "python",
    "name": "python3"
   },
@@ -306,7 +306,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.9.7"
+   "version": "3.6.4"
   }
  },
  "nbformat": 4,
diff --git a/Auto_Cropping_Image_Tutorial/Auto_Image_Cropping.ipynb b/Auto_Cropping_Image_Tutorial/Auto_Image_Cropping.ipynb
index 473b7eb625c41f278c5c99019daf3060efd33980..a17135bc8072d12eadf4f3fb26540544174f3b1a 100644
--- a/Auto_Cropping_Image_Tutorial/Auto_Image_Cropping.ipynb
+++ b/Auto_Cropping_Image_Tutorial/Auto_Image_Cropping.ipynb
@@ -20,6 +20,15 @@
     "# !pip install rembg"
    ]
   },
+  {
+   "cell_type": "markdown",
+   "id": "9142d4fc-1e9c-4207-98fb-602fab721c97",
+   "metadata": {},
+   "source": [
+    "### 🛑 STOP\n",
+    "**Pause to restart your kernel!** We sometimes need to do this after installing new packages using pip. Otherwise, you might get a message saying the package doesn't exist, even though you just installed it."
+   ]
+  },
   {
    "cell_type": "markdown",
    "id": "07423b0f",
@@ -123,12 +132,14 @@
    "id": "f88accb8",
    "metadata": {},
    "source": [
-    "To perform a simple crop of the image, the .crop() method can be used. It requires a tuple denoting the 4 coordinates of the crop-rectangle (left, upper, right, and lower). For this example, the coordinates are specified manually."
+    "To perform a simple crop of the image, the .crop() method can be used. It requires a tuple denoting the 4 coordinates of the crop-rectangle (left, upper, right, and lower). For this example, the coordinates are specified manually.\n",
+    "\n",
+    "**For some intuition on how to choose values for left, upper, right, lower** (how you might want to crop): each value is the number of pixels \"in\" *from the left and top*. So in the cell below, we move 2300 pixels from the left to crop on the left, 10 pixels down from the top to crop for \"upper.\" But! we start on the left to crop for 'right', moving 3000 pixels from the **left** to crop. We also start on the top to crop the bottom, moving 1300 pixels from the **top** to crop."
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 121,
+   "execution_count": 21,
    "id": "37445d6f",
    "metadata": {},
    "outputs": [
@@ -139,7 +150,7 @@
        "<PIL.Image.Image image mode=RGB size=700x1290>"
       ]
      },
-     "execution_count": 121,
+     "execution_count": 21,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -629,7 +640,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.10.9"
+   "version": "3.8.11"
   }
  },
  "nbformat": 4,
diff --git a/GoogleSheetsTutorial.ipynb b/GoogleSheetsTutorial.ipynb
index fbb8583f1edee27c85d9e561cde6742543438e71..501391e01fa4118ecb3359d18c94f2d100060d85 100644
--- a/GoogleSheetsTutorial.ipynb
+++ b/GoogleSheetsTutorial.ipynb
@@ -24,7 +24,6 @@
    ]
   },
   {
-   "attachments": {},
    "cell_type": "markdown",
    "metadata": {
     "id": "yTqEB1KKhshX"
@@ -35,13 +34,15 @@
     "    (you must use your personal email not your MSU email)\n",
     "2. Inside that project enable the Google sheets API and Google Drive for your new project\n",
     "    1. This is found by using the search bar to search for Google API and Google Drive\n",
-    "3. Go to “APIs & Services > Credentials” and choose “Create credentials > Service account key”.\n",
-    "4. Once you open the "Create service account" screen, fill out the name and ID fields and click "Create and Continue" to move to the   next section. After filling out "1. Service account details" under "2. Grant this service account access to project" navigate to the "Role" drop down and choose "Basic" -> 'Editor." After you select the role, hit "Continue." You can skip the next section, "Grant users access to this service account" and click done. "\n",
-    "5. Press “Manage service accounts” above Service Accounts.\n",
-    "6. Press on ⋮ near recently created service account and select “Manage keys” and then click on “ADD KEY > Create new key”.\n",
-    "7. Select JSON key type and press “Create”.\n",
-    "8. In your python file import gspread\n",
-    "9. Copy Json key into python file as a dictionary\n",
+    "3. Create a Google service account through the Google developer portal\n",
+    "4. Go to “APIs & Services > Credentials” and choose “Create credentials > Service account key”.\n",
+    "5. Fill out the form (making sure to add editor privledges to the service account)\n",
+    "6. Click “Create” and “Done”.\n",
+    "7. Press “Manage service accounts” above Service Accounts.\n",
+    "8. Click on “ADD KEY > Create new key”. Jiaye Xie\n",
+    "9. Select JSON key type and press “Create”.\n",
+    "10. In your python file import gspread\n",
+    "11. Copy Json key into python file as a dictionary\n",
     "\n",
     "\n",
     "for more information/ documentation on gspread head here\n",
@@ -50,7 +51,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 7,
+   "execution_count": 6,
    "metadata": {
     "id": "9VYd9pBAgrKU"
    },
@@ -59,21 +60,21 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "Requirement already satisfied: gspread in c:\\programdata\\anaconda3\\lib\\site-packages (5.1.1)\n",
-      "Requirement already satisfied: google-auth>=1.12.0 in c:\\programdata\\anaconda3\\lib\\site-packages (from gspread) (2.6.0)\n",
-      "Requirement already satisfied: google-auth-oauthlib>=0.4.1 in c:\\programdata\\anaconda3\\lib\\site-packages (from gspread) (0.5.0)\n",
-      "Requirement already satisfied: pyasn1-modules>=0.2.1 in c:\\programdata\\anaconda3\\lib\\site-packages (from google-auth>=1.12.0->gspread) (0.2.8)\n",
-      "Requirement already satisfied: cachetools<6.0,>=2.0.0 in c:\\programdata\\anaconda3\\lib\\site-packages (from google-auth>=1.12.0->gspread) (5.0.0)\n",
-      "Requirement already satisfied: rsa<5,>=3.1.4; python_version >= \"3.6\" in c:\\programdata\\anaconda3\\lib\\site-packages (from google-auth>=1.12.0->gspread) (4.8)\n",
-      "Requirement already satisfied: six>=1.9.0 in c:\\programdata\\anaconda3\\lib\\site-packages (from google-auth>=1.12.0->gspread) (1.12.0)\n",
-      "Requirement already satisfied: requests-oauthlib>=0.7.0 in c:\\users\\sam\\appdata\\roaming\\python\\python37\\site-packages (from google-auth-oauthlib>=0.4.1->gspread) (1.3.0)\n",
-      "Requirement already satisfied: pyasn1<0.5.0,>=0.4.6 in c:\\programdata\\anaconda3\\lib\\site-packages (from pyasn1-modules>=0.2.1->google-auth>=1.12.0->gspread) (0.4.8)\n",
-      "Requirement already satisfied: requests>=2.0.0 in c:\\users\\sam\\appdata\\roaming\\python\\python37\\site-packages (from requests-oauthlib>=0.7.0->google-auth-oauthlib>=0.4.1->gspread) (2.27.1)\n",
-      "Requirement already satisfied: oauthlib>=3.0.0 in c:\\programdata\\anaconda3\\lib\\site-packages (from requests-oauthlib>=0.7.0->google-auth-oauthlib>=0.4.1->gspread) (3.1.1)\n",
-      "Requirement already satisfied: charset-normalizer~=2.0.0; python_version >= \"3\" in c:\\programdata\\anaconda3\\lib\\site-packages (from requests>=2.0.0->requests-oauthlib>=0.7.0->google-auth-oauthlib>=0.4.1->gspread) (2.0.10)\n",
-      "Requirement already satisfied: certifi>=2017.4.17 in c:\\programdata\\anaconda3\\lib\\site-packages (from requests>=2.0.0->requests-oauthlib>=0.7.0->google-auth-oauthlib>=0.4.1->gspread) (2021.5.30)\n",
-      "Requirement already satisfied: idna<4,>=2.5; python_version >= \"3\" in c:\\programdata\\anaconda3\\lib\\site-packages (from requests>=2.0.0->requests-oauthlib>=0.7.0->google-auth-oauthlib>=0.4.1->gspread) (2.8)\n",
-      "Requirement already satisfied: urllib3<1.27,>=1.21.1 in c:\\users\\sam\\appdata\\roaming\\python\\python37\\site-packages (from requests>=2.0.0->requests-oauthlib>=0.7.0->google-auth-oauthlib>=0.4.1->gspread) (1.26.8)\n"
+      "Requirement already satisfied: gspread in c:\\users\\elias\\miniconda3\\envs\\data_sc\\lib\\site-packages (6.0.0)\n",
+      "Requirement already satisfied: google-auth>=1.12.0 in c:\\users\\elias\\miniconda3\\envs\\data_sc\\lib\\site-packages (from gspread) (2.27.0)\n",
+      "Requirement already satisfied: google-auth-oauthlib>=0.4.1 in c:\\users\\elias\\miniconda3\\envs\\data_sc\\lib\\site-packages (from gspread) (1.2.0)\n",
+      "Requirement already satisfied: StrEnum==0.4.15 in c:\\users\\elias\\miniconda3\\envs\\data_sc\\lib\\site-packages (from gspread) (0.4.15)\n",
+      "Requirement already satisfied: cachetools<6.0,>=2.0.0 in c:\\users\\elias\\miniconda3\\envs\\data_sc\\lib\\site-packages (from google-auth>=1.12.0->gspread) (5.3.2)\n",
+      "Requirement already satisfied: pyasn1-modules>=0.2.1 in c:\\users\\elias\\miniconda3\\envs\\data_sc\\lib\\site-packages (from google-auth>=1.12.0->gspread) (0.3.0)\n",
+      "Requirement already satisfied: rsa<5,>=3.1.4 in c:\\users\\elias\\miniconda3\\envs\\data_sc\\lib\\site-packages (from google-auth>=1.12.0->gspread) (4.9)\n",
+      "Requirement already satisfied: requests-oauthlib>=0.7.0 in c:\\users\\elias\\miniconda3\\envs\\data_sc\\lib\\site-packages (from google-auth-oauthlib>=0.4.1->gspread) (1.3.1)\n",
+      "Requirement already satisfied: pyasn1<0.6.0,>=0.4.6 in c:\\users\\elias\\miniconda3\\envs\\data_sc\\lib\\site-packages (from pyasn1-modules>=0.2.1->google-auth>=1.12.0->gspread) (0.5.1)\n",
+      "Requirement already satisfied: oauthlib>=3.0.0 in c:\\users\\elias\\miniconda3\\envs\\data_sc\\lib\\site-packages (from requests-oauthlib>=0.7.0->google-auth-oauthlib>=0.4.1->gspread) (3.2.2)\n",
+      "Requirement already satisfied: requests>=2.0.0 in c:\\users\\elias\\miniconda3\\envs\\data_sc\\lib\\site-packages (from requests-oauthlib>=0.7.0->google-auth-oauthlib>=0.4.1->gspread) (2.31.0)\n",
+      "Requirement already satisfied: charset-normalizer<4,>=2 in c:\\users\\elias\\miniconda3\\envs\\data_sc\\lib\\site-packages (from requests>=2.0.0->requests-oauthlib>=0.7.0->google-auth-oauthlib>=0.4.1->gspread) (2.0.4)\n",
+      "Requirement already satisfied: idna<4,>=2.5 in c:\\users\\elias\\miniconda3\\envs\\data_sc\\lib\\site-packages (from requests>=2.0.0->requests-oauthlib>=0.7.0->google-auth-oauthlib>=0.4.1->gspread) (3.4)\n",
+      "Requirement already satisfied: urllib3<3,>=1.21.1 in c:\\users\\elias\\miniconda3\\envs\\data_sc\\lib\\site-packages (from requests>=2.0.0->requests-oauthlib>=0.7.0->google-auth-oauthlib>=0.4.1->gspread) (1.26.18)\n",
+      "Requirement already satisfied: certifi>=2017.4.17 in c:\\users\\elias\\miniconda3\\envs\\data_sc\\lib\\site-packages (from requests>=2.0.0->requests-oauthlib>=0.7.0->google-auth-oauthlib>=0.4.1->gspread) (2023.11.17)\n"
      ]
     }
    ],
@@ -83,7 +84,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 8,
+   "execution_count": 7,
    "metadata": {
     "colab": {
      "base_uri": "https://localhost:8080/"
@@ -91,7 +92,34 @@
     "id": "1PLVAwwKbCFi",
     "outputId": "02800dde-ca55-4a6d-a55d-4407bf44ef77"
    },
-   "outputs": [],
+   "outputs": [
+    {
+     "ename": "AttributeError",
+     "evalue": "partially initialized module 'charset_normalizer' has no attribute 'md__mypyc' (most likely due to a circular import)",
+     "output_type": "error",
+     "traceback": [
+      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
+      "\u001b[1;31mModuleNotFoundError\u001b[0m                       Traceback (most recent call last)",
+      "File \u001b[1;32mc:\\Users\\elias\\miniconda3\\envs\\data_sc\\Lib\\site-packages\\requests\\compat.py:11\u001b[0m\n\u001b[0;32m     10\u001b[0m \u001b[38;5;28;01mtry\u001b[39;00m:\n\u001b[1;32m---> 11\u001b[0m     \u001b[38;5;28;01mimport\u001b[39;00m \u001b[38;5;21;01mchardet\u001b[39;00m\n\u001b[0;32m     12\u001b[0m \u001b[38;5;28;01mexcept\u001b[39;00m \u001b[38;5;167;01mImportError\u001b[39;00m:\n",
+      "\u001b[1;31mModuleNotFoundError\u001b[0m: No module named 'chardet'",
+      "\nDuring handling of the above exception, another exception occurred:\n",
+      "\u001b[1;31mAttributeError\u001b[0m                            Traceback (most recent call last)",
+      "Cell \u001b[1;32mIn[7], line 1\u001b[0m\n\u001b[1;32m----> 1\u001b[0m \u001b[38;5;28;01mimport\u001b[39;00m \u001b[38;5;21;01mgspread\u001b[39;00m\n\u001b[0;32m      2\u001b[0m \u001b[38;5;28;01mimport\u001b[39;00m \u001b[38;5;21;01mjson\u001b[39;00m\n",
+      "File \u001b[1;32mc:\\Users\\elias\\miniconda3\\envs\\data_sc\\Lib\\site-packages\\gspread\\__init__.py:7\u001b[0m\n\u001b[0;32m      3\u001b[0m __version__ \u001b[38;5;241m=\u001b[39m \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124m6.0.0\u001b[39m\u001b[38;5;124m\"\u001b[39m\n\u001b[0;32m      4\u001b[0m __author__ \u001b[38;5;241m=\u001b[39m \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mAnton Burnashev\u001b[39m\u001b[38;5;124m\"\u001b[39m\n\u001b[1;32m----> 7\u001b[0m \u001b[38;5;28;01mfrom\u001b[39;00m \u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01mauth\u001b[39;00m \u001b[38;5;28;01mimport\u001b[39;00m (\n\u001b[0;32m      8\u001b[0m     authorize,\n\u001b[0;32m      9\u001b[0m     oauth,\n\u001b[0;32m     10\u001b[0m     oauth_from_dict,\n\u001b[0;32m     11\u001b[0m     service_account,\n\u001b[0;32m     12\u001b[0m     service_account_from_dict,\n\u001b[0;32m     13\u001b[0m )\n\u001b[0;32m     14\u001b[0m \u001b[38;5;28;01mfrom\u001b[39;00m \u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01mcell\u001b[39;00m \u001b[38;5;28;01mimport\u001b[39;00m Cell\n\u001b[0;32m     15\u001b[0m \u001b[38;5;28;01mfrom\u001b[39;00m \u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01mclient\u001b[39;00m \u001b[38;5;28;01mimport\u001b[39;00m Client\n",
+      "File \u001b[1;32mc:\\Users\\elias\\miniconda3\\envs\\data_sc\\Lib\\site-packages\\gspread\\auth.py:17\u001b[0m\n\u001b[0;32m     15\u001b[0m \u001b[38;5;28;01mfrom\u001b[39;00m \u001b[38;5;21;01mgoogle\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01moauth2\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01mcredentials\u001b[39;00m \u001b[38;5;28;01mimport\u001b[39;00m Credentials \u001b[38;5;28;01mas\u001b[39;00m OAuthCredentials\n\u001b[0;32m     16\u001b[0m \u001b[38;5;28;01mfrom\u001b[39;00m \u001b[38;5;21;01mgoogle\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01moauth2\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01mservice_account\u001b[39;00m \u001b[38;5;28;01mimport\u001b[39;00m Credentials \u001b[38;5;28;01mas\u001b[39;00m SACredentials\n\u001b[1;32m---> 17\u001b[0m \u001b[38;5;28;01mfrom\u001b[39;00m \u001b[38;5;21;01mgoogle_auth_oauthlib\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01mflow\u001b[39;00m \u001b[38;5;28;01mimport\u001b[39;00m InstalledAppFlow\n\u001b[0;32m     19\u001b[0m \u001b[38;5;28;01mfrom\u001b[39;00m \u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01mclient\u001b[39;00m \u001b[38;5;28;01mimport\u001b[39;00m Client\n\u001b[0;32m     20\u001b[0m \u001b[38;5;28;01mfrom\u001b[39;00m \u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01mhttp_client\u001b[39;00m \u001b[38;5;28;01mimport\u001b[39;00m HTTPClient, HTTPClientType\n",
+      "File \u001b[1;32mc:\\Users\\elias\\miniconda3\\envs\\data_sc\\Lib\\site-packages\\google_auth_oauthlib\\__init__.py:21\u001b[0m\n\u001b[0;32m      1\u001b[0m \u001b[38;5;66;03m# Copyright 2019 Google LLC\u001b[39;00m\n\u001b[0;32m      2\u001b[0m \u001b[38;5;66;03m#\u001b[39;00m\n\u001b[0;32m      3\u001b[0m \u001b[38;5;66;03m# Licensed under the Apache License, Version 2.0 (the \"License\");\u001b[39;00m\n\u001b[1;32m   (...)\u001b[0m\n\u001b[0;32m     12\u001b[0m \u001b[38;5;66;03m# See the License for the specific language governing permissions and\u001b[39;00m\n\u001b[0;32m     13\u001b[0m \u001b[38;5;66;03m# limitations under the License.\u001b[39;00m\n\u001b[0;32m     15\u001b[0m \u001b[38;5;124;03m\"\"\"oauthlib integration for Google Auth\u001b[39;00m\n\u001b[0;32m     16\u001b[0m \n\u001b[0;32m     17\u001b[0m \u001b[38;5;124;03mThis library provides `oauthlib <https://oauthlib.readthedocs.io/>`__\u001b[39;00m\n\u001b[0;32m     18\u001b[0m \u001b[38;5;124;03mintegration with `google-auth <https://google-auth.readthedocs.io/>`__.\u001b[39;00m\n\u001b[0;32m     19\u001b[0m \u001b[38;5;124;03m\"\"\"\u001b[39;00m\n\u001b[1;32m---> 21\u001b[0m \u001b[38;5;28;01mfrom\u001b[39;00m \u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01minteractive\u001b[39;00m \u001b[38;5;28;01mimport\u001b[39;00m get_user_credentials\n\u001b[0;32m     23\u001b[0m __all__ \u001b[38;5;241m=\u001b[39m [\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mget_user_credentials\u001b[39m\u001b[38;5;124m\"\u001b[39m]\n",
+      "File \u001b[1;32mc:\\Users\\elias\\miniconda3\\envs\\data_sc\\Lib\\site-packages\\google_auth_oauthlib\\interactive.py:27\u001b[0m\n\u001b[0;32m     24\u001b[0m \u001b[38;5;28;01mimport\u001b[39;00m \u001b[38;5;21;01mcontextlib\u001b[39;00m\n\u001b[0;32m     25\u001b[0m \u001b[38;5;28;01mimport\u001b[39;00m \u001b[38;5;21;01msocket\u001b[39;00m\n\u001b[1;32m---> 27\u001b[0m \u001b[38;5;28;01mimport\u001b[39;00m \u001b[38;5;21;01mgoogle_auth_oauthlib\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01mflow\u001b[39;00m\n\u001b[0;32m     30\u001b[0m LOCALHOST \u001b[38;5;241m=\u001b[39m \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mlocalhost\u001b[39m\u001b[38;5;124m\"\u001b[39m\n\u001b[0;32m     31\u001b[0m DEFAULT_PORTS_TO_TRY \u001b[38;5;241m=\u001b[39m \u001b[38;5;241m100\u001b[39m\n",
+      "File \u001b[1;32mc:\\Users\\elias\\miniconda3\\envs\\data_sc\\Lib\\site-packages\\google_auth_oauthlib\\flow.py:65\u001b[0m\n\u001b[0;32m     62\u001b[0m \u001b[38;5;28;01mimport\u001b[39;00m \u001b[38;5;21;01mwsgiref\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01msimple_server\u001b[39;00m\n\u001b[0;32m     63\u001b[0m \u001b[38;5;28;01mimport\u001b[39;00m \u001b[38;5;21;01mwsgiref\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01mutil\u001b[39;00m\n\u001b[1;32m---> 65\u001b[0m \u001b[38;5;28;01mimport\u001b[39;00m \u001b[38;5;21;01mgoogle\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01mauth\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01mtransport\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01mrequests\u001b[39;00m\n\u001b[0;32m     66\u001b[0m \u001b[38;5;28;01mimport\u001b[39;00m \u001b[38;5;21;01mgoogle\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01moauth2\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01mcredentials\u001b[39;00m\n\u001b[0;32m     68\u001b[0m \u001b[38;5;28;01mimport\u001b[39;00m \u001b[38;5;21;01mgoogle_auth_oauthlib\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01mhelpers\u001b[39;00m\n",
+      "File \u001b[1;32mc:\\Users\\elias\\miniconda3\\envs\\data_sc\\Lib\\site-packages\\google\\auth\\transport\\requests.py:26\u001b[0m\n\u001b[0;32m     23\u001b[0m \u001b[38;5;28;01mimport\u001b[39;00m \u001b[38;5;21;01mtime\u001b[39;00m\n\u001b[0;32m     25\u001b[0m \u001b[38;5;28;01mtry\u001b[39;00m:\n\u001b[1;32m---> 26\u001b[0m     \u001b[38;5;28;01mimport\u001b[39;00m \u001b[38;5;21;01mrequests\u001b[39;00m\n\u001b[0;32m     27\u001b[0m \u001b[38;5;28;01mexcept\u001b[39;00m \u001b[38;5;167;01mImportError\u001b[39;00m \u001b[38;5;28;01mas\u001b[39;00m caught_exc:  \u001b[38;5;66;03m# pragma: NO COVER\u001b[39;00m\n\u001b[0;32m     28\u001b[0m     \u001b[38;5;28;01mraise\u001b[39;00m \u001b[38;5;167;01mImportError\u001b[39;00m(\n\u001b[0;32m     29\u001b[0m         \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mThe requests library is not installed from please install the requests package to use the requests transport.\u001b[39m\u001b[38;5;124m\"\u001b[39m\n\u001b[0;32m     30\u001b[0m     ) \u001b[38;5;28;01mfrom\u001b[39;00m \u001b[38;5;21;01mcaught_exc\u001b[39;00m\n",
+      "File \u001b[1;32mc:\\Users\\elias\\miniconda3\\envs\\data_sc\\Lib\\site-packages\\requests\\__init__.py:45\u001b[0m\n\u001b[0;32m     41\u001b[0m \u001b[38;5;28;01mimport\u001b[39;00m \u001b[38;5;21;01mwarnings\u001b[39;00m\n\u001b[0;32m     43\u001b[0m \u001b[38;5;28;01mimport\u001b[39;00m \u001b[38;5;21;01murllib3\u001b[39;00m\n\u001b[1;32m---> 45\u001b[0m \u001b[38;5;28;01mfrom\u001b[39;00m \u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01mexceptions\u001b[39;00m \u001b[38;5;28;01mimport\u001b[39;00m RequestsDependencyWarning\n\u001b[0;32m     47\u001b[0m \u001b[38;5;28;01mtry\u001b[39;00m:\n\u001b[0;32m     48\u001b[0m     \u001b[38;5;28;01mfrom\u001b[39;00m \u001b[38;5;21;01mcharset_normalizer\u001b[39;00m \u001b[38;5;28;01mimport\u001b[39;00m __version__ \u001b[38;5;28;01mas\u001b[39;00m charset_normalizer_version\n",
+      "File \u001b[1;32mc:\\Users\\elias\\miniconda3\\envs\\data_sc\\Lib\\site-packages\\requests\\exceptions.py:9\u001b[0m\n\u001b[0;32m      1\u001b[0m \u001b[38;5;124;03m\"\"\"\u001b[39;00m\n\u001b[0;32m      2\u001b[0m \u001b[38;5;124;03mrequests.exceptions\u001b[39;00m\n\u001b[0;32m      3\u001b[0m \u001b[38;5;124;03m~~~~~~~~~~~~~~~~~~~\u001b[39;00m\n\u001b[0;32m      4\u001b[0m \n\u001b[0;32m      5\u001b[0m \u001b[38;5;124;03mThis module contains the set of Requests' exceptions.\u001b[39;00m\n\u001b[0;32m      6\u001b[0m \u001b[38;5;124;03m\"\"\"\u001b[39;00m\n\u001b[0;32m      7\u001b[0m \u001b[38;5;28;01mfrom\u001b[39;00m \u001b[38;5;21;01murllib3\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01mexceptions\u001b[39;00m \u001b[38;5;28;01mimport\u001b[39;00m HTTPError \u001b[38;5;28;01mas\u001b[39;00m BaseHTTPError\n\u001b[1;32m----> 9\u001b[0m \u001b[38;5;28;01mfrom\u001b[39;00m \u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01mcompat\u001b[39;00m \u001b[38;5;28;01mimport\u001b[39;00m JSONDecodeError \u001b[38;5;28;01mas\u001b[39;00m CompatJSONDecodeError\n\u001b[0;32m     12\u001b[0m \u001b[38;5;28;01mclass\u001b[39;00m \u001b[38;5;21;01mRequestException\u001b[39;00m(\u001b[38;5;167;01mIOError\u001b[39;00m):\n\u001b[0;32m     13\u001b[0m \u001b[38;5;250m    \u001b[39m\u001b[38;5;124;03m\"\"\"There was an ambiguous exception that occurred while handling your\u001b[39;00m\n\u001b[0;32m     14\u001b[0m \u001b[38;5;124;03m    request.\u001b[39;00m\n\u001b[0;32m     15\u001b[0m \u001b[38;5;124;03m    \"\"\"\u001b[39;00m\n",
+      "File \u001b[1;32mc:\\Users\\elias\\miniconda3\\envs\\data_sc\\Lib\\site-packages\\requests\\compat.py:13\u001b[0m\n\u001b[0;32m     11\u001b[0m     \u001b[38;5;28;01mimport\u001b[39;00m \u001b[38;5;21;01mchardet\u001b[39;00m\n\u001b[0;32m     12\u001b[0m \u001b[38;5;28;01mexcept\u001b[39;00m \u001b[38;5;167;01mImportError\u001b[39;00m:\n\u001b[1;32m---> 13\u001b[0m     \u001b[38;5;28;01mimport\u001b[39;00m \u001b[38;5;21;01mcharset_normalizer\u001b[39;00m \u001b[38;5;28;01mas\u001b[39;00m \u001b[38;5;21;01mchardet\u001b[39;00m\n\u001b[0;32m     15\u001b[0m \u001b[38;5;28;01mimport\u001b[39;00m \u001b[38;5;21;01msys\u001b[39;00m\n\u001b[0;32m     17\u001b[0m \u001b[38;5;66;03m# -------\u001b[39;00m\n\u001b[0;32m     18\u001b[0m \u001b[38;5;66;03m# Pythons\u001b[39;00m\n\u001b[0;32m     19\u001b[0m \u001b[38;5;66;03m# -------\u001b[39;00m\n\u001b[0;32m     20\u001b[0m \n\u001b[0;32m     21\u001b[0m \u001b[38;5;66;03m# Syntax sugar.\u001b[39;00m\n",
+      "File \u001b[1;32mc:\\Users\\elias\\miniconda3\\envs\\data_sc\\Lib\\site-packages\\charset_normalizer\\__init__.py:23\u001b[0m\n\u001b[0;32m      1\u001b[0m \u001b[38;5;124;03m\"\"\"\u001b[39;00m\n\u001b[0;32m      2\u001b[0m \u001b[38;5;124;03mCharset-Normalizer\u001b[39;00m\n\u001b[0;32m      3\u001b[0m \u001b[38;5;124;03m~~~~~~~~~~~~~~\u001b[39;00m\n\u001b[1;32m   (...)\u001b[0m\n\u001b[0;32m     21\u001b[0m \u001b[38;5;124;03m:license: MIT, see LICENSE for more details.\u001b[39;00m\n\u001b[0;32m     22\u001b[0m \u001b[38;5;124;03m\"\"\"\u001b[39;00m\n\u001b[1;32m---> 23\u001b[0m \u001b[38;5;28;01mfrom\u001b[39;00m \u001b[38;5;21;01mcharset_normalizer\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01mapi\u001b[39;00m \u001b[38;5;28;01mimport\u001b[39;00m from_fp, from_path, from_bytes, normalize\n\u001b[0;32m     24\u001b[0m \u001b[38;5;28;01mfrom\u001b[39;00m \u001b[38;5;21;01mcharset_normalizer\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01mlegacy\u001b[39;00m \u001b[38;5;28;01mimport\u001b[39;00m detect\n\u001b[0;32m     25\u001b[0m \u001b[38;5;28;01mfrom\u001b[39;00m \u001b[38;5;21;01mcharset_normalizer\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01mversion\u001b[39;00m \u001b[38;5;28;01mimport\u001b[39;00m __version__, VERSION\n",
+      "File \u001b[1;32mc:\\Users\\elias\\miniconda3\\envs\\data_sc\\Lib\\site-packages\\charset_normalizer\\api.py:10\u001b[0m\n\u001b[0;32m      7\u001b[0m     PathLike \u001b[38;5;241m=\u001b[39m Union[\u001b[38;5;28mstr\u001b[39m, \u001b[38;5;124m'\u001b[39m\u001b[38;5;124mos.PathLike[str]\u001b[39m\u001b[38;5;124m'\u001b[39m]  \u001b[38;5;66;03m# type: ignore\u001b[39;00m\n\u001b[0;32m      9\u001b[0m \u001b[38;5;28;01mfrom\u001b[39;00m \u001b[38;5;21;01mcharset_normalizer\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01mconstant\u001b[39;00m \u001b[38;5;28;01mimport\u001b[39;00m TOO_SMALL_SEQUENCE, TOO_BIG_SEQUENCE, IANA_SUPPORTED\n\u001b[1;32m---> 10\u001b[0m \u001b[38;5;28;01mfrom\u001b[39;00m \u001b[38;5;21;01mcharset_normalizer\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01mmd\u001b[39;00m \u001b[38;5;28;01mimport\u001b[39;00m mess_ratio\n\u001b[0;32m     11\u001b[0m \u001b[38;5;28;01mfrom\u001b[39;00m \u001b[38;5;21;01mcharset_normalizer\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01mmodels\u001b[39;00m \u001b[38;5;28;01mimport\u001b[39;00m CharsetMatches, CharsetMatch\n\u001b[0;32m     12\u001b[0m \u001b[38;5;28;01mfrom\u001b[39;00m \u001b[38;5;21;01mwarnings\u001b[39;00m \u001b[38;5;28;01mimport\u001b[39;00m warn\n",
+      "\u001b[1;31mAttributeError\u001b[0m: partially initialized module 'charset_normalizer' has no attribute 'md__mypyc' (most likely due to a circular import)"
+     ]
+    }
+   ],
    "source": [
     "import gspread\n",
     "import json"
@@ -108,7 +136,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 4,
+   "execution_count": 2,
    "metadata": {
     "id": "-OmbBoh5b90X"
    },
@@ -130,7 +158,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 5,
+   "execution_count": 3,
    "metadata": {
     "colab": {
      "base_uri": "https://localhost:8080/",
@@ -139,7 +167,19 @@
     "id": "Qa-Pff1ZhJSE",
     "outputId": "2b14636c-3150-479d-ca65-792abfd99b98"
    },
-   "outputs": [],
+   "outputs": [
+    {
+     "ename": "NameError",
+     "evalue": "name 'gspread' is not defined",
+     "output_type": "error",
+     "traceback": [
+      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
+      "\u001b[1;31mNameError\u001b[0m                                 Traceback (most recent call last)",
+      "Cell \u001b[1;32mIn[3], line 1\u001b[0m\n\u001b[1;32m----> 1\u001b[0m gc \u001b[38;5;241m=\u001b[39m \u001b[43mgspread\u001b[49m\u001b[38;5;241m.\u001b[39mservice_account_from_dict(credentials)\n\u001b[0;32m      2\u001b[0m sheet_id \u001b[38;5;241m=\u001b[39m \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124m1vt5SnUuAojBLPug41dBs3_LKpyVM2rxrJw6MwAQ1_c0\u001b[39m\u001b[38;5;124m\"\u001b[39m\n\u001b[0;32m      3\u001b[0m gsheet \u001b[38;5;241m=\u001b[39m gc\u001b[38;5;241m.\u001b[39mopen_by_key(sheet_id)\n",
+      "\u001b[1;31mNameError\u001b[0m: name 'gspread' is not defined"
+     ]
+    }
+   ],
    "source": [
     "gc = gspread.service_account_from_dict(credentials)\n",
     "sheet_id = \"1vt5SnUuAojBLPug41dBs3_LKpyVM2rxrJw6MwAQ1_c0\"\n",
@@ -228,7 +268,7 @@
    "provenance": []
   },
   "kernelspec": {
-   "display_name": "Python 3 (ipykernel)",
+   "display_name": "Python 3",
    "language": "python",
    "name": "python3"
   },
@@ -242,7 +282,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.8.12"
+   "version": "3.11.5"
   }
  },
  "nbformat": 4,
diff --git a/PyTorch_tutorial.ipynb b/PyTorch_tutorial.ipynb
index ba10a2884acd94b5c4454d443cc12658d4a46aaa..dfb48428de22a835cd4b31b4c9ab75572bfac349 100644
--- a/PyTorch_tutorial.ipynb
+++ b/PyTorch_tutorial.ipynb
@@ -180,6 +180,7 @@
     "\n",
     "num_epochs = 10\n",
     "\n",
+    "# For each epoch, calculate the loss\n",
     "for epoch in range(num_epochs):\n",
     "    total_loss = 0.0\n",
     "    for i, (images, labels) in enumerate(train_loader):\n",
@@ -244,7 +245,7 @@
  ],
  "metadata": {
   "kernelspec": {
-   "display_name": "Python 3",
+   "display_name": "Python 3 (ipykernel)",
    "language": "python",
    "name": "python3"
   },
@@ -258,7 +259,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.7.1"
+   "version": "3.9.12"
   }
  },
  "nbformat": 4,
diff --git a/Seaborn_Tutorial_DTTD.ipynb b/Seaborn_Tutorial_DTTD.ipynb
index 6bbb45f1fb0488830a886ba296f420b04b901888..73595bfbd1d76dc1e31fc4095cd8109f8247353d 100644
--- a/Seaborn_Tutorial_DTTD.ipynb
+++ b/Seaborn_Tutorial_DTTD.ipynb
@@ -266,6 +266,9 @@
    "metadata": {},
    "source": [
     "# Line Plots\n",
+    "\n",
+    "seaborn.lineplot(data=None, *, x=None, y=None, hue=None, size=None, style=None, units=None, weights=None, palette=None, hue_order=None, hue_norm=None, sizes=None, size_order=None, size_norm=None, dashes=True, markers=None, style_order=None, estimator='mean', errorbar=('ci', 95), n_boot=1000, seed=None, orient='x', sort=True, err_style='band', err_kws=None, legend='auto', ci='deprecated', ax=None, **kwargs)\n",
+    "\n",
     "Line plots can be used for many things including time series and regression plotting. The most basic form would be to call seaborn using sns, then clarify lineplot. In the parentheses you simply need to define your x variable and y variable, as well as the dataset you want the software to use."
    ]
   },
@@ -424,6 +427,9 @@
    "metadata": {},
    "source": [
     "# Violin Plots\n",
+    "\n",
+    "seaborn.violinplot(data=None, *, x=None, y=None, hue=None, order=None, hue_order=None, orient=None, color=None, palette=None, saturation=0.75, fill=True, inner='box', split=False, width=0.8, dodge='auto', gap=0, linewidth=None, linecolor='auto', cut=2, gridsize=100, bw_method='scott', bw_adjust=1, density_norm='area', common_norm=False, hue_norm=None, formatter=None, log_scale=None, native_scale=False, legend='auto', scale=<deprecated>, scale_hue=<deprecated>, bw=<deprecated>, inner_kws=None, ax=None, **kwargs)\n",
+    "\n",
     "- A Violin Plot is similar to box plots, in that it shows the distribution of quantitative data across several levels of one, or more, categorical variables.\n",
     "- Unlike a box plot, in which all of the plot components correspond to actual datapoints, the violin plot features a kernel density estimation of the underlying distribution.\n",
     "- This type of plot allows the distributions to be compared, a great tool for data analysis."
@@ -884,7 +890,7 @@
  ],
  "metadata": {
   "kernelspec": {
-   "display_name": "Python 3",
+   "display_name": "Python 3.11 (default)",
    "language": "python",
    "name": "python3"
   },
@@ -898,7 +904,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.8.8"
+   "version": "3.11.6"
   }
  },
  "nbformat": 4,
diff --git a/Streamlit/streamlit_tutorial.ipynb b/Streamlit/streamlit_tutorial.ipynb
index a560fcae29583436d8e5f16cf41a8e63657c1d91..49b010dfbaaf3b493b8f6d2a49ca660105c3559c 100644
--- a/Streamlit/streamlit_tutorial.ipynb
+++ b/Streamlit/streamlit_tutorial.ipynb
@@ -23,6 +23,15 @@
     "Streamlit is an open-source Python library that can create web applications for data science concepts including machine learning."
    ]
   },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### When running streamlit there are two possible installation methods\n",
+    "1. Installation on your own Computer\n",
+    "2. Installation on Shared Computer Cluster"
+   ]
+  },
   {
    "cell_type": "markdown",
    "metadata": {},
@@ -74,6 +83,29 @@
     "! streamlit run streamlit_tutorial.py"
    ]
   },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Installation on Shared Computer Cluster\n",
+    "In your terminal type and run each of these commands\n",
+    "\n",
+    "```bash\n",
+    "module load Anaconda/3\n",
+    "conda activate base\n",
+    "pip install streamlit\n",
+    "```\n",
+    "\n",
+    "\n",
+    "To check if this worked properly, run:\n",
+    "\n",
+    "```bash\n",
+    "streamlit hello\n",
+    "```\n",
+    "\n",
+    "This command should open the Streamlit Hello application in your browser."
+   ]
+  },
   {
    "cell_type": "markdown",
    "metadata": {},
diff --git a/pcatutorial.ipynb b/pcatutorial.ipynb
index 9a6524d549209cb33203d211412e200dd1306625..095f8354c60ba12ebe821a2cb94304a8400157a0 100644
--- a/pcatutorial.ipynb
+++ b/pcatutorial.ipynb
@@ -282,11 +282,11 @@
    "outputs": [
     {
      "data": {
+      "image/png": "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",
       "text/plain": [
-       "Text(0.5, 0, 'PC3')"
+       "<Figure size 640x480 with 1 Axes>"
       ]
      },
-     "execution_count": 7,
      "metadata": {},
      "output_type": "execute_result"
     },
@@ -305,12 +305,21 @@
    ],
    "source": [
     "# create the 3D visualization\n",
+    "from mpl_toolkits.mplot3d import Axes3D\n",
+    "\n",
     "fig = plt.figure()\n",
     "ax = fig.add_subplot(projection=\"3d\")\n",
-    "ax.scatter(X[:,0],X[:,1],X[:,2], c=y, s=10, alpha=.5)\n",
+    "scatter = ax.scatter(X[:, 0], X[:, 1], X[:, 2], c=y, s=10, alpha=0.5)\n",
+    "\n",
     "ax.set_xlabel(\"PC1\", fontsize=10)\n",
     "ax.set_ylabel(\"PC2\", fontsize=10)\n",
-    "ax.set_zlabel(\"PC3\", fontsize=10)"
+    "ax.set_zlabel(\"PC3\", fontsize=10)\n",
+    "\n",
+    "# Adding color legend\n",
+    "legend = ax.legend(*scatter.legend_elements(), title=\"Labels\")\n",
+    "ax.add_artist(legend)\n",
+    "\n",
+    "plt.show()"
    ]
   }
  ],
@@ -330,7 +339,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.9.12"
+   "version": "3.8.18"
   }
  },
  "nbformat": 4,
diff --git a/socail_media_scrapper/Requirements.txt b/socail_media_scrapper/Requirements.txt
new file mode 100644
index 0000000000000000000000000000000000000000..50c05f9d80440db723eabd2379928416325d51ee
--- /dev/null
+++ b/socail_media_scrapper/Requirements.txt
@@ -0,0 +1,4 @@
+pytchat
+pandas
+matplotlib
+nltk
diff --git a/socail_media_scrapper/yt_scraper.ipynb b/socail_media_scrapper/yt_scraper.ipynb
index dd558ac7182709ae319ac1af9612fa7eb6ae8ff0..5c3eba96129b2ff2876263e13e237f6770eeb8bb 100644
--- a/socail_media_scrapper/yt_scraper.ipynb
+++ b/socail_media_scrapper/yt_scraper.ipynb
@@ -26,10 +26,7 @@
    "outputs": [],
    "source": [
     "# uncomment and run to install packages\n",
-    "# !pip install pytchat\n",
-    "# !pip install pandas\n",
-    "# !pip install matplotlib\n",
-    "# !pip install pytchat"
+    "#pip install -r requirements.txt"
    ]
   },
   {
@@ -44,7 +41,6 @@
     "import time\n",
     "import re\n",
     "import nltk\n",
-    "import matplotlib.pyplot as plt\n",
     "from nltk.corpus import stopwords\n",
     "nltk.download(\"stopwords\")\n",
     "stop_words = set(stopwords.words('english'))\n",
@@ -238,7 +234,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.9.15"
+   "version": "3.10.11"
   }
  },
  "nbformat": 4,
diff --git a/tpot_tutorial.ipynb b/tpot_tutorial.ipynb
index 3aabfb921e9368a9efed3d8b7e5758c107be3065..d4730f78a05f6d3efdcc197a7c29a663339ba78f 100644
--- a/tpot_tutorial.ipynb
+++ b/tpot_tutorial.ipynb
@@ -2,6 +2,7 @@
  "cells": [
   {
    "cell_type": "markdown",
+   "id": "4b3db991",
    "metadata": {},
    "source": [
     "### Run this cell to install commands"
@@ -9,9 +10,57 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 8,
-   "metadata": {},
-   "outputs": [],
+   "execution_count": 6,
+   "id": "9849686b",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Defaulting to user installation because normal site-packages is not writeable\n",
+      "Requirement already satisfied: torch in /opt/miniconda3/lib/python3.11/site-packages (2.1.1+cu118)\n",
+      "Requirement already satisfied: filelock in /opt/miniconda3/lib/python3.11/site-packages (from torch) (3.13.1)\n",
+      "Requirement already satisfied: typing-extensions in /opt/miniconda3/lib/python3.11/site-packages (from torch) (4.9.0)\n",
+      "Requirement already satisfied: sympy in /opt/miniconda3/lib/python3.11/site-packages (from torch) (1.12)\n",
+      "Requirement already satisfied: networkx in /opt/miniconda3/lib/python3.11/site-packages (from torch) (3.0)\n",
+      "Requirement already satisfied: jinja2 in /opt/miniconda3/lib/python3.11/site-packages (from torch) (3.1.2)\n",
+      "Requirement already satisfied: fsspec in /opt/miniconda3/lib/python3.11/site-packages (from torch) (2023.4.0)\n",
+      "Requirement already satisfied: triton==2.1.0 in /opt/miniconda3/lib/python3.11/site-packages (from torch) (2.1.0)\n",
+      "Requirement already satisfied: MarkupSafe>=2.0 in /opt/miniconda3/lib/python3.11/site-packages (from jinja2->torch) (2.1.3)\n",
+      "Requirement already satisfied: mpmath>=0.19 in /opt/miniconda3/lib/python3.11/site-packages (from sympy->torch) (1.3.0)\n",
+      "Note: you may need to restart the kernel to use updated packages.\n",
+      "Defaulting to user installation because normal site-packages is not writeable\n",
+      "Requirement already satisfied: xgboost in /home/kozlow86/.local/lib/python3.11/site-packages (2.0.3)\n",
+      "Requirement already satisfied: numpy in /opt/miniconda3/lib/python3.11/site-packages (from xgboost) (1.26.2)\n",
+      "Requirement already satisfied: scipy in /opt/miniconda3/lib/python3.11/site-packages (from xgboost) (1.10.1)\n",
+      "Note: you may need to restart the kernel to use updated packages.\n",
+      "Defaulting to user installation because normal site-packages is not writeable\n",
+      "Requirement already satisfied: tpot in /home/kozlow86/.local/lib/python3.11/site-packages (0.12.1)\n",
+      "Requirement already satisfied: numpy>=1.16.3 in /opt/miniconda3/lib/python3.11/site-packages (from tpot) (1.26.2)\n",
+      "Requirement already satisfied: scipy>=1.3.1 in /opt/miniconda3/lib/python3.11/site-packages (from tpot) (1.10.1)\n",
+      "Requirement already satisfied: scikit-learn>=0.22.0 in /opt/miniconda3/lib/python3.11/site-packages (from tpot) (1.3.2)\n",
+      "Requirement already satisfied: deap>=1.2 in /home/kozlow86/.local/lib/python3.11/site-packages (from tpot) (1.4.1)\n",
+      "Requirement already satisfied: update-checker>=0.16 in /home/kozlow86/.local/lib/python3.11/site-packages (from tpot) (0.18.0)\n",
+      "Requirement already satisfied: tqdm>=4.36.1 in /opt/miniconda3/lib/python3.11/site-packages (from tpot) (4.66.1)\n",
+      "Requirement already satisfied: stopit>=1.1.1 in /home/kozlow86/.local/lib/python3.11/site-packages (from tpot) (1.1.2)\n",
+      "Requirement already satisfied: pandas>=0.24.2 in /opt/miniconda3/lib/python3.11/site-packages (from tpot) (2.0.3)\n",
+      "Requirement already satisfied: joblib>=0.13.2 in /opt/miniconda3/lib/python3.11/site-packages (from tpot) (1.3.2)\n",
+      "Requirement already satisfied: xgboost>=1.1.0 in /home/kozlow86/.local/lib/python3.11/site-packages (from tpot) (2.0.3)\n",
+      "Requirement already satisfied: python-dateutil>=2.8.2 in /opt/miniconda3/lib/python3.11/site-packages (from pandas>=0.24.2->tpot) (2.8.2)\n",
+      "Requirement already satisfied: pytz>=2020.1 in /opt/miniconda3/lib/python3.11/site-packages (from pandas>=0.24.2->tpot) (2023.3.post1)\n",
+      "Requirement already satisfied: tzdata>=2022.1 in /opt/miniconda3/lib/python3.11/site-packages (from pandas>=0.24.2->tpot) (2023.3)\n",
+      "Requirement already satisfied: threadpoolctl>=2.0.0 in /opt/miniconda3/lib/python3.11/site-packages (from scikit-learn>=0.22.0->tpot) (3.2.0)\n",
+      "Requirement already satisfied: requests>=2.3.0 in /opt/miniconda3/lib/python3.11/site-packages (from update-checker>=0.16->tpot) (2.31.0)\n",
+      "Requirement already satisfied: six>=1.5 in /opt/miniconda3/lib/python3.11/site-packages (from python-dateutil>=2.8.2->pandas>=0.24.2->tpot) (1.16.0)\n",
+      "Requirement already satisfied: charset-normalizer<4,>=2 in /opt/miniconda3/lib/python3.11/site-packages (from requests>=2.3.0->update-checker>=0.16->tpot) (2.0.4)\n",
+      "Requirement already satisfied: idna<4,>=2.5 in /opt/miniconda3/lib/python3.11/site-packages (from requests>=2.3.0->update-checker>=0.16->tpot) (3.4)\n",
+      "Requirement already satisfied: urllib3<3,>=1.21.1 in /opt/miniconda3/lib/python3.11/site-packages (from requests>=2.3.0->update-checker>=0.16->tpot) (1.26.18)\n",
+      "Requirement already satisfied: certifi>=2017.4.17 in /opt/miniconda3/lib/python3.11/site-packages (from requests>=2.3.0->update-checker>=0.16->tpot) (2023.11.17)\n",
+      "Note: you may need to restart the kernel to use updated packages.\n"
+     ]
+    }
+   ],
    "source": [
     "%pip install torch\n",
     "%pip install xgboost\n",
@@ -20,6 +69,7 @@
   },
   {
    "cell_type": "markdown",
+   "id": "f0f5a4dc",
    "metadata": {},
    "source": [
     "* What is TPOT?\n",
@@ -48,6 +98,7 @@
   },
   {
    "cell_type": "markdown",
+   "id": "b48af34c",
    "metadata": {},
    "source": [
     "### Run this cell to import all of the necessary libraries"
@@ -55,7 +106,8 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 9,
+   "execution_count": 1,
+   "id": "c1c41528",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -68,6 +120,7 @@
   },
   {
    "cell_type": "markdown",
+   "id": "4da91ecb",
    "metadata": {},
    "source": [
     "## Example 1"
@@ -75,9 +128,32 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 4,
-   "metadata": {},
-   "outputs": [],
+   "execution_count": 2,
+   "id": "cd96a5e7",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "(array([[5.1, 3.5, 1.4, 0.2],\n",
+       "        [4.9, 3. , 1.4, 0.2],\n",
+       "        [4.7, 3.2, 1.3, 0.2],\n",
+       "        [4.6, 3.1, 1.5, 0.2],\n",
+       "        [5. , 3.6, 1.4, 0.2]]),\n",
+       " array([0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,\n",
+       "        0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,\n",
+       "        0, 0, 0, 0, 0, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,\n",
+       "        1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,\n",
+       "        1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2,\n",
+       "        2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2,\n",
+       "        2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2]))"
+      ]
+     },
+     "execution_count": 2,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
    "source": [
     "#load in all of the data\n",
     "iris = load_iris()\n",
@@ -86,9 +162,21 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 5,
+   "execution_count": 3,
+   "id": "60d59c8f",
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "((112, 4), (38, 4), (112,), (38,))"
+      ]
+     },
+     "execution_count": 3,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
    "source": [
     "#split data into a test and train data set\n",
     "X_train, X_test, y_train, y_test = train_test_split(iris.data, iris.target, train_size=0.75, test_size=0.25)\n",
@@ -97,15 +185,53 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 10,
-   "metadata": {},
-   "outputs": [],
+   "execution_count": 4,
+   "id": "0316f5a5",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "7575a243384549d8aceebdfd353bcd3b",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Optimization Progress:   0%|          | 0/100 [00:00<?, ?pipeline/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\n",
+      "Generation 1 - Current best internal CV score: 0.9739130434782609\n",
+      "\n",
+      "Generation 2 - Current best internal CV score: 0.9739130434782609\n",
+      "\n",
+      "Generation 3 - Current best internal CV score: 0.9739130434782609\n",
+      "\n",
+      "3.09 minutes have elapsed. TPOT will close down.\n",
+      "TPOT closed during evaluation in one generation.\n",
+      "WARNING: TPOT may not provide a good pipeline if TPOT is stopped/interrupted in a early generation.\n",
+      "\n",
+      "\n",
+      "TPOT closed prematurely. Will use the current best pipeline.\n",
+      "\n",
+      "Best pipeline: MLPClassifier(input_matrix, alpha=0.01, learning_rate_init=0.001)\n",
+      "0.9736842105263158\n"
+     ]
+    }
+   ],
    "source": [
     "# Fit the model based on the training data, get a score based on testing data.\n",
     "# Will report the score of the best found pipeline\n",
     "# Change max_time_mins to a higher time to allow TPOT to run without interruption. #issue number 25\n",
-    "# It is currently at 2 mins for sake of not taking to long\n",
-    "tpot = TPOTClassifier(verbosity=2, max_time_mins=3)\n",
+    "# It is currently at 2 mins for sake of not taking too long\n",
+    "tpot = TPOTClassifier(verbosity=2, max_time_mins=10) # increased max time to give best results\n",
     "tpot.fit(X_train, y_train)\n",
     "print(tpot.score(X_test, y_test))"
    ]
@@ -115,12 +241,13 @@
    "id": "22bb780f",
    "metadata": {},
    "source": [
-    "Issued warning of TPOT closed prematurely. I am increasing the max_time to 4 so tpot can completely run and the results are more accurate"
+    "Issued warning of TPOT closed prematurely. I am increasing the max_time to 10 so TPOT can completely run and the results are more accurate"
    ]
   },
   {
    "cell_type": "code",
    "execution_count": 11,
+   "id": "013af850",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -130,6 +257,7 @@
   },
   {
    "cell_type": "markdown",
+   "id": "3f331d68",
    "metadata": {},
    "source": [
     "## Example 2"
@@ -137,9 +265,153 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 12,
-   "metadata": {},
-   "outputs": [],
+   "execution_count": 5,
+   "id": "2b756a95",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>PassengerId</th>\n",
+       "      <th>Survived</th>\n",
+       "      <th>Pclass</th>\n",
+       "      <th>Name</th>\n",
+       "      <th>Sex</th>\n",
+       "      <th>Age</th>\n",
+       "      <th>SibSp</th>\n",
+       "      <th>Parch</th>\n",
+       "      <th>Ticket</th>\n",
+       "      <th>Fare</th>\n",
+       "      <th>Cabin</th>\n",
+       "      <th>Embarked</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>3</td>\n",
+       "      <td>Braund, Mr. Owen Harris</td>\n",
+       "      <td>male</td>\n",
+       "      <td>22.0</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>A/5 21171</td>\n",
+       "      <td>7.2500</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>S</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>2</td>\n",
+       "      <td>1</td>\n",
+       "      <td>1</td>\n",
+       "      <td>Cumings, Mrs. John Bradley (Florence Briggs Th...</td>\n",
+       "      <td>female</td>\n",
+       "      <td>38.0</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>PC 17599</td>\n",
+       "      <td>71.2833</td>\n",
+       "      <td>C85</td>\n",
+       "      <td>C</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>3</td>\n",
+       "      <td>1</td>\n",
+       "      <td>3</td>\n",
+       "      <td>Heikkinen, Miss. Laina</td>\n",
+       "      <td>female</td>\n",
+       "      <td>26.0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>STON/O2. 3101282</td>\n",
+       "      <td>7.9250</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>S</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>4</td>\n",
+       "      <td>1</td>\n",
+       "      <td>1</td>\n",
+       "      <td>Futrelle, Mrs. Jacques Heath (Lily May Peel)</td>\n",
+       "      <td>female</td>\n",
+       "      <td>35.0</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>113803</td>\n",
+       "      <td>53.1000</td>\n",
+       "      <td>C123</td>\n",
+       "      <td>S</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>5</td>\n",
+       "      <td>0</td>\n",
+       "      <td>3</td>\n",
+       "      <td>Allen, Mr. William Henry</td>\n",
+       "      <td>male</td>\n",
+       "      <td>35.0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>373450</td>\n",
+       "      <td>8.0500</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>S</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "   PassengerId  Survived  Pclass  \\\n",
+       "0            1         0       3   \n",
+       "1            2         1       1   \n",
+       "2            3         1       3   \n",
+       "3            4         1       1   \n",
+       "4            5         0       3   \n",
+       "\n",
+       "                                                Name     Sex   Age  SibSp  \\\n",
+       "0                            Braund, Mr. Owen Harris    male  22.0      1   \n",
+       "1  Cumings, Mrs. John Bradley (Florence Briggs Th...  female  38.0      1   \n",
+       "2                             Heikkinen, Miss. Laina  female  26.0      0   \n",
+       "3       Futrelle, Mrs. Jacques Heath (Lily May Peel)  female  35.0      1   \n",
+       "4                           Allen, Mr. William Henry    male  35.0      0   \n",
+       "\n",
+       "   Parch            Ticket     Fare Cabin Embarked  \n",
+       "0      0         A/5 21171   7.2500   NaN        S  \n",
+       "1      0          PC 17599  71.2833   C85        C  \n",
+       "2      0  STON/O2. 3101282   7.9250   NaN        S  \n",
+       "3      0            113803  53.1000  C123        S  \n",
+       "4      0            373450   8.0500   NaN        S  "
+      ]
+     },
+     "execution_count": 5,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
    "source": [
     "#read in data\n",
     "titanic = pd.read_csv('titanic_train.csv')\n",
@@ -148,7 +420,8 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 13,
+   "execution_count": 6,
+   "id": "1b663c97",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -158,9 +431,22 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 14,
+   "execution_count": 7,
+   "id": "50d6673f",
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Number of levels in category 'Name': 891.00 \n",
+      "Number of levels in category 'Sex': 2.00 \n",
+      "Number of levels in category 'Ticket': 681.00 \n",
+      "Number of levels in category 'Cabin': 148.00 \n",
+      "Number of levels in category 'Embarked': 4.00 \n"
+     ]
+    }
+   ],
    "source": [
     "# Find out how many different categories there are for each of these 5 features\n",
     "for cat in ['Name', 'Sex', 'Ticket', 'Cabin', 'Embarked']:\n",
@@ -169,9 +455,19 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 15,
-   "metadata": {},
-   "outputs": [],
+   "execution_count": 8,
+   "id": "d160467e",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Levels for catgeory 'Sex': ['male' 'female']\n",
+      "Levels for catgeory 'Embarked': ['S' 'C' 'Q' nan]\n"
+     ]
+    }
+   ],
    "source": [
     "#print out what those categories are for 'Sex' and 'Embarked'\n",
     "for cat in ['Sex', 'Embarked']:\n",
@@ -180,7 +476,8 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 16,
+   "execution_count": 9,
+   "id": "ab1f5255",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -191,9 +488,33 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 17,
-   "metadata": {},
-   "outputs": [],
+   "execution_count": 10,
+   "id": "6f88cded",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "PassengerId    False\n",
+       "class          False\n",
+       "Pclass         False\n",
+       "Name           False\n",
+       "Sex            False\n",
+       "Age            False\n",
+       "SibSp          False\n",
+       "Parch          False\n",
+       "Ticket         False\n",
+       "Fare           False\n",
+       "Cabin          False\n",
+       "Embarked       False\n",
+       "dtype: bool"
+      ]
+     },
+     "execution_count": 10,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
    "source": [
     "# fill na values and then double check there are non left\n",
     "titanic = titanic.fillna(-999)\n",
@@ -202,7 +523,8 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 18,
+   "execution_count": 11,
+   "id": "7b82a7fc",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -214,16 +536,35 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 19,
-   "metadata": {},
-   "outputs": [],
+   "execution_count": 12,
+   "id": "6c7468e4",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "array([[1, 0, 0, ..., 0, 0, 0],\n",
+       "       [0, 0, 0, ..., 0, 0, 0],\n",
+       "       [1, 0, 0, ..., 0, 0, 0],\n",
+       "       ...,\n",
+       "       [1, 0, 0, ..., 0, 0, 0],\n",
+       "       [0, 0, 0, ..., 0, 0, 0],\n",
+       "       [1, 0, 0, ..., 0, 0, 0]])"
+      ]
+     },
+     "execution_count": 12,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
    "source": [
     "CabinTrans"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 20,
+   "execution_count": 13,
+   "id": "b9e9c1d2",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -233,7 +574,8 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 21,
+   "execution_count": 14,
+   "id": "8c78ab53",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -243,7 +585,8 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 22,
+   "execution_count": 15,
+   "id": "2f2c8be2",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -254,9 +597,21 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 23,
-   "metadata": {},
-   "outputs": [],
+   "execution_count": 16,
+   "id": "76fc3385",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "False"
+      ]
+     },
+     "execution_count": 16,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
    "source": [
     "# make sure there are no nas in the data\n",
     "np.isnan(titanic_new).any()"
@@ -264,16 +619,29 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 24,
-   "metadata": {},
-   "outputs": [],
+   "execution_count": 17,
+   "id": "d26a0d0f",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "156"
+      ]
+     },
+     "execution_count": 17,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
    "source": [
     "titanic_new[0].size"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 25,
+   "execution_count": 18,
+   "id": "38fe863d",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -283,9 +651,21 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 26,
-   "metadata": {},
-   "outputs": [],
+   "execution_count": 19,
+   "id": "cad1b5b2",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "(668, 223)"
+      ]
+     },
+     "execution_count": 19,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
    "source": [
     "# split the data into testing and training- this will give us indices\n",
     "training_indices, validation_indices = training_indices, testing_indices = train_test_split(titanic.index, stratify = titanic_class, train_size=0.75, test_size=0.25)\n",
@@ -294,9 +674,25 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 27,
+   "execution_count": null,
+   "id": "bb29fe1c",
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "77018f7b307d4184bc84729d4417703e",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Optimization Progress:   0%|          | 0/40 [00:00<?, ?pipeline/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
    "source": [
     "# create the classifier and fit the model, reports the best pipeline\n",
     "# Parameters within the TPOT Classifier can be changed to allow for longer run time across more models\n",
@@ -309,12 +705,13 @@
    "id": "910d6e80",
    "metadata": {},
    "source": [
-    "Issued warning of TPOT closed prematurely. I am increasing the max_time so tpot can completely run and the results are more accurate"
+    "Issued warning of TPOT closed prematurely. I am increasing the max_time_mins so TPOT can completely run and the results are more accurate"
    ]
   },
   {
    "cell_type": "code",
    "execution_count": 28,
+   "id": "e9f5c9af",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -325,6 +722,7 @@
   {
    "cell_type": "code",
    "execution_count": 29,
+   "id": "61c375c3",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -335,6 +733,7 @@
   {
    "cell_type": "code",
    "execution_count": 30,
+   "id": "f348eb12",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -346,6 +745,7 @@
   {
    "cell_type": "code",
    "execution_count": 31,
+   "id": "64c48124",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -358,6 +758,7 @@
   {
    "cell_type": "code",
    "execution_count": 32,
+   "id": "b051001d",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -369,6 +770,7 @@
   {
    "cell_type": "code",
    "execution_count": 33,
+   "id": "e0b4c999",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -380,6 +782,7 @@
   {
    "cell_type": "code",
    "execution_count": 34,
+   "id": "c931cf65",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -394,6 +797,7 @@
   {
    "cell_type": "code",
    "execution_count": 35,
+   "id": "00d7bd45",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -404,6 +808,7 @@
   {
    "cell_type": "code",
    "execution_count": 36,
+   "id": "db5c1909",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -413,6 +818,7 @@
   {
    "cell_type": "code",
    "execution_count": 37,
+   "id": "3f2f4888",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -422,6 +828,7 @@
   {
    "cell_type": "code",
    "execution_count": 38,
+   "id": "250d23c7",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -432,6 +839,7 @@
   {
    "cell_type": "code",
    "execution_count": 40,
+   "id": "0132c44e",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -441,6 +849,7 @@
   {
    "cell_type": "code",
    "execution_count": 41,
+   "id": "17714990",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -457,6 +866,7 @@
   {
    "cell_type": "code",
    "execution_count": 42,
+   "id": "98bbe243",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -465,6 +875,7 @@
   },
   {
    "cell_type": "markdown",
+   "id": "a275be3e",
    "metadata": {},
    "source": [
     "### References\n",
@@ -475,7 +886,7 @@
  ],
  "metadata": {
   "kernelspec": {
-   "display_name": "Python 3",
+   "display_name": "Python 3.11 (default)",
    "language": "python",
    "name": "python3"
   },
@@ -489,7 +900,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.6.4"
+   "version": "3.11.6"
   }
  },
  "nbformat": 4,