From c26b4ae56862b4fab12454d3ca68698c4f7dce43 Mon Sep 17 00:00:00 2001
From: =?UTF-8?q?=E2=80=9CMSS3-ALSULAIMA=E2=80=9D?=
<Mohammed3.Alsulaimani@live.uwe.ac.uk>
Date: Thu, 16 Nov 2023 06:25:53 +0400
Subject: [PATCH] Pseudo Code
---
UFCFVQ-15-M Programming Task 2 Template.ipynb | 11 +++++++++++
1 file changed, 11 insertions(+)
diff --git a/UFCFVQ-15-M Programming Task 2 Template.ipynb b/UFCFVQ-15-M Programming Task 2 Template.ipynb
index e58558e..796f4b1 100644
--- a/UFCFVQ-15-M Programming Task 2 Template.ipynb
+++ b/UFCFVQ-15-M Programming Task 2 Template.ipynb
@@ -573,6 +573,17 @@
"\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",
+ "Pseudo Code:\n",
+ "\n",
+ "- Read CSVs into DataFrames df1 and df2\n",
+ "- Merge df1 and df2 on 'id_student'\n",
+ "- Remove missing values\n",
+ "- Filter rows based on 'click_events'\n",
+ "- Rename and remove columns\n",
+ "- Save to new CSV\n",
+ "- Calculate averages by age group\n",
+ "- Visualize results\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"
--
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