Proof reading: python for data analysis scikitlearn

Author: liamjberrisford

individual_modules/python_for_data_analysis/Python_ScikitLearn.ipynb

@@ -17,7 +17,7 @@
     "- Understand the basic concepts and purpose of Scikit-Learn in machine learning tasks\n",
     "- Implement linear regression, k-means clustering, and decision tree models using Scikit-Learn\n",
     "- Understand the applications and limitations of each model\n",
-    "- Learn how to clean and preprocess data using pandas before feeding it into machine-learning models\n",
+    "- Learn how to clean and preprocess data using Pandas before feeding it into machine-learning models\n",
     "- Understand the importance of handling missing values and feature engineering\n",
     "- Learn how to evaluate the performance of different machine-learning models\n",
     "- Interpret the output of models to make informed decisions based on the data \n"
@@ -57,13 +57,19 @@
   {
    "cell_type": "markdown",
    "id": "601a8215-a645-4fe6-8334-1dc7434a589b",
-   "metadata": {},
+   "metadata": {
+    "editable": true,
+    "slideshow": {
+     "slide_type": ""
+    },
+    "tags": []
+   },
    "source": [
     "## What is Scikit-Learn?\n",
     "\n",
     "Scikit-Learn is a popular Python package that provides a set of algorithms and tools for machine learning that are both easy to use and effective. The package includes support for various tasks, including classification, regression, clustering, dimensionality reduction and model selection and normalization.\n",
     "\n",
-    "In scikit-learn the models that are avaliable include a massive number of possible arguements, and so for the purpose of this course the default arguements have been used. \n",
+    "In Scikit-Learn, the models that are available include a massive number of possible arguments, and so for the purpose of this course, the default arguments have been used. \n",
     "\n",
     "\n"
    ]
@@ -1294,10 +1300,16 @@
   {
    "cell_type": "markdown",
    "id": "06b1be83-6562-4c23-8313-9677f0814b8e",
-   "metadata": {},
+   "metadata": {
+    "editable": true,
+    "slideshow": {
+     "slide_type": ""
+    },
+    "tags": []
+   },
    "source": [
     "## K-Means Clustering \n",
-    "K-means clustering is a method for parritioning data into a specified number of clusters by minimizing the variance within each cluster."
+    "K-means clustering is a method for partitioning data into a specified number of clusters by minimizing the variance within each cluster."
    ]
   },
   {
@@ -2373,10 +2385,16 @@
   {
    "cell_type": "markdown",
    "id": "ff85e826-ccea-439b-ac60-555059067ed9",
-   "metadata": {},
+   "metadata": {
+    "editable": true,
+    "slideshow": {
+     "slide_type": ""
+    },
+    "tags": []
+   },
    "source": [
     "## Decision Tree Models\n",
-    "Decision Trees are a model framework that splits data into branches based on the some input data, in out case wind speed. The model resembles a tree structure with decision points and leaf nodes where each decision leads to further splits, eventually ending on a final prediction based on the input features."
+    "Decision Trees are a model framework that splits data into branches based on the some input data - in our case wind speed. The model resembles a tree structure with decision points and leaf nodes where each decision leads to further splits, eventually ending on a final prediction based on the input features."
    ]
   },
   {
@@ -3429,7 +3447,7 @@
    },
    "source": [
     "## Further Models\n",
-    "This section of the course has only introducded a very small subset of models that are avaliable, alongside not diving into the different arguements that are avaliable. If you are interested in learning more about the models themselves, and key practical aspects of deploying and ensuring the validity of models you develop then please attend this couses next course \"Introduction to Machine Learning\""
+    "This section of the course has only introduced a very small subset of models that are avaliable, alongside not diving into the different arguments that are available. If you are interested in learning more about the models themselves, and key practical aspects of deploying and ensuring the validity of models you develop, then please attend e course \"Introduction to Machine Learning\"."
    ]
   },
   {