diff --git a/UFCFVQ-15-M Programming Task 2 Template.ipynb b/UFCFVQ-15-M Programming Task 2 Template.ipynb
index 975e1295c0da94e061aee25df58577baae884d3b..e58558eb020cb26e6f818d101ec5e4fc08c92182 100644
--- a/UFCFVQ-15-M Programming Task 2 Template.ipynb	
+++ b/UFCFVQ-15-M Programming Task 2 Template.ipynb	
@@ -568,7 +568,15 @@
   {
    "cell_type": "markdown",
    "metadata": {},
-   "source": []
+   "source": [
+    "Firstly I began by importing two CSV files into groups of data called DataFrames using Pythons Pandas library. This allowed me to handle the data efficiently. Then I merged these DataFrames based on the 'id_student' column to consolidate all the data in one place. Following that I performed data cleanup by removing any missing information and unnecessary columns enabling us to focus on the data.\n",
+    "\n",
+    "For clarity I renamed columns and filtered out rows with a low number of click events. Subsequently I saved this cleaned up dataset, into a CSV file. Additionally I calculated the number of clicks and final marks for age groups. This analysis provided insights into how various age groups engage with and perform in the material. To visualize these averages effectively and spot patterns easily I utilized graph making tools such as Matplotlib and Seaborn.\n",
+    "\n",
+    "The key strength of my approach was its simplicity while leveraging Pandas powerful capabilities, for handling this type of data. However it's worth noting that I didn't account for any data points that could skew the averages. In analyses it would be beneficial to identify and address outliers.\n",
+    "\n",
+    "One alternative approach to enhance your improvements is to consider employing techniques, such, as statistical methods or machine learning algorithms. These methods can help you gain an understanding and extract valuable insights, from the data.\n"
+   ]
   },
   {
    "cell_type": "markdown",