Software Project Management Bonus Assignment #1

CS 587 Software Project Management
Bonus Assignment #1
General Instructions:

1. Due Date is 5/8/21 by 11:59pm.
2. There is NO PARTIAL credit for the bonus assignment submission that has
partial/not complete code.
3. All of your source code must be clearly documented and functional;
ZERO credit will be given to the submission that has nonfunctional code.
4. Submit your comparative analysis report for the results you obtained for
all experiments you executed.
5. ZERO credit will be given to the submission that has NO comparative
analysis report.
6. Submit your IPYNB script and live video of your run that has your code
and your output.
7. The dataset for Stackoverflow available from different sources :
 https://www.ics.uci.edu/~duboisc/stackoverflow/
 https://archive.org/download/stackexchange
 https://www.kaggle.com/stackoverflow/stackoverflow?select=v
otes
 https://cloud.google.com/bigquery/public-data
8. Before you start working on this assignment you must read/review the
following RAPIDS libraries :
 https://rapids.ai/start.html
 https://docs.rapids.ai/api
 https://docs.rapids.ai/api/cudf/stable/
 https://docs.rapids.ai/api/cuml/stable/
 https://docs.rapids.ai/api/cugraph/stable/
Requirements:
Use Anaconda Python 3.7, Tensorflow/Keras, RAPIDS (
https://rapids.ai/about.html ), the Recommender ipynb script discussed in
the class tutorial, and the provided Stackoverflow dataset to implement
the following features for Stackoverflow dataset:
1) Use the provided Stackoverflow dataset (answers.csv)
2) Use Google Colab (https://colab.research.google.com) or
your personal computer CPU and GPU
3) The intent is to make recommendations for a user who posted
a question and got answered, and find other questions that
you recommend to the same user based on the provided
tags and their scores. Basically, users working on specific
domain will ask similar questions and answers. If someone
interested in python related questions, we will recommend
similar/related questions in Python but not in Java for
example.
4) The provided dataset needs some preprocessing and
cleaning for the special characters.
5) Reuse and modify the ipynb script discussed in the lecture
and implement the 4 experiments provided but using the
Stackoverflow dataset.
6) Choose a class for any machine learning algorithm from
cuML library and call that Experiment #5 to make
recommendations.
7) Provide a comparative analysis report discussing the results
you obtain from the 5 experiments you executed.
Assignment Deliverables:
You are required to submit a SINGLE Zip file that has the following
deliverables are:
1. Your IPYNB script
2. All of your source code and output
3. Output report that has ALL captured screen-shots of your
assignment run saved in OUTPUT.pdf
4. Video recording of 10 minutes as a demo for the run of your
assignment using https://screencast-o-matic.com/
Post your assignment as a SINGLE ZIP file on Blackboard.


Dr. Atef Bader