Minor changes

.github/scripts/clean_kernelspecs.py

@@ -2,13 +2,13 @@
 import glob
 
 for nb_path in glob.glob("**/*.ipynb", recursive=True):
-    with open(nb_path) as f:
+    with open(nb_path, encoding = "utf-8") as f:
         nb = nbformat.read(f, as_version=4)
     nb['metadata']['kernelspec'] = {
         "name": "python3",
         "display_name": "Python 3",
         "language": "python"
     }
-    with open(nb_path, 'w') as f:
+    with open(nb_path, 'w', encoding = "utf-8") as f:
         nbformat.write(nb, f)
 

practicals_jn_book/week_1/finalbook_part1.ipynb

@@ -631,7 +631,7 @@
     "\n",
     "### Assignment 5\n",
     "\n",
-    "- **First sort all the data by Totalkg score, make sure the Totalkg is on top of your DataFrame. Print out the DataFrame. What do you notice?**\n",
+    "- **First sort all the data by Totalkg score, make sure the lowest Totalkg is on top of your DataFrame. Print out the DataFrame. What do you notice?**\n",
     "\n",
     "- **Remove all rows that contain missing values. Print out the DataFrame. What happened to the index?**\n",
     "\n",

practicals_jn_book/week_3/finalbook.ipynb

@@ -80,7 +80,7 @@
    "source": [
     "### Assignment 0: load and inspect data\n",
     "\n",
-    "The breast cancer dataset is available on the [GitHub](https://github.com/Alek050/big_data_practicals/tree/main/data/week_3). It's a binary DataFrame. Pandas also provides the convenient ``read_parquet()`` function. Parquet files are binary representations of tabular data, making it extremely fast with human readable formats such as csv and excel files. \n",
+    "The breast cancer dataset is available on the [GitHub](https://github.com/Alek050/big_data_practicals/tree/main/data/week_3). It's a binary DataFrame. Pandas also provides the convenient ``read_parquet()`` function. Parquet files are binary representations of tabular data, making it extremely fast compared with human readable formats such as csv and excel files. \n",
     "\n",
     "- **Read the data with the ``read_parquet()`` function and check what type it returns by using ``type()``.**\n",
     "- **Print the head of the DataFrame.**\n",
@@ -534,7 +534,7 @@
    "source": [
     "Take a look at the distribution of mean_radius for the benign and malignant cases. To do so you have to plot a histogram for both in the same figure. Do this using the ``matplotlib.pyplot`` sublibrary (import on top). Remember to first define a figure and then plot a hist?\n",
     "\n",
-    "- **Create a histogram for mean_radius for the malignant and benign cases. Add a legend, xlabel and ylabel. Choose wheter you use interactive plotting or object-oriented plotting**\n",
+    "- **Create a histogram for mean_radius for the malignant and benign cases. Add a legend, xlabel and ylabel. Choose whether you use interactive plotting or object-oriented plotting**\n",
     "\n",
     "You can spice up your vanilla matplotlib with the built-in [stylesheets](https://matplotlib.org/gallery/style_sheets/style_sheets_reference.html).\n",
     "\n",
@@ -543,7 +543,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 37,
+   "execution_count": null,
    "metadata": {
     "tags": [
      "hide-input"

practicals_jn_book/week_4/finalbook.ipynb

@@ -1691,6 +1691,11 @@
     "\n",
     "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.3, random_state=42)\n",
     "\n",
+    "scaler = StandardScaler()\n",
+    "scaler.fit(X_train)\n",
+    "X_train_scaled = scaler.transform(X_train)\n",
+    "X_test_scaled = scaler.transform(X_test)\n",
+    "\n",
     "def train_evaluate_model(model, X_train, X_test, y_train, y_test):\n",
     "    \"\"\" A function that calculates the R^2 and the RMSE based on a model and dataset\n",
     "\n",
@@ -1715,8 +1720,8 @@
     "    \n",
     "    return R2, RMSE\n",
     "\n",
-    "lasso_R2, lasso_RMSE = train_evaluate_model(lasso_model, X_train, X_test, y_train, y_test)\n",
-    "ridge_R2, ridge_RMSE = train_evaluate_model(ridge_model, X_train, X_test, y_train, y_test)"
+    "lasso_R2, lasso_RMSE = train_evaluate_model(lasso_model, X_train_scaled, X_test_scaled, y_train, y_test)\n",
+    "ridge_R2, ridge_RMSE = train_evaluate_model(ridge_model, X_train_scaled, X_test_scaled, y_train, y_test)"
    ]
   },
   {