Python代写|IB9JV0 Programming for Data Analytics Term Two 2022-23




Board games have been enjoyed for centuries, and continue to be a popular form of entertainment for people of all ages. There are a large number of different types of board games, such as strategy games like Catan, party games like Pictionary, adventure games like Dungeons and Dragons. Some games can be played by single players while some may require as many as 10 players.

According to a recent market research, the global board game market size was valued at $13.4 billion in 2021, and expected to reach $26.88 by 20271 . This is driven by an increase in the popularity of board games among all age groups, with millennials and Gen X leading the way. The COVID-19 pandemic has also played a role in the increase in sales, as people have been spending more time at home and looking for indoor activities.

It is expected that the board game market will continue to grow in the coming years, driven by factors such as the growing popularity of tabletop gaming, the rise of online platforms and streaming, and the increased availability of niche and indie titles.


  1. Create a hypothetical business scenario, identify and propose a practical business objective(s) or problem(s). e.g. To help a game manufacturer create a new board game; To help a board game club decide on new games to offer, etc. At least one assumption needs to be made for your scenario, such as that the new game targets young kids or the game club charges guests by playing time (i.e., follows a subscription model).
  1. Carry out a data science project based on your scenario. Your analysis should at least include machine learning based predictive modelling.
  1. Write an executive report summarizing your scenario and work and explaining how your work would help to solve the proposed problem(s).

A dataset of 20343 board games is provided in the bgg.csv file. The data is collected from (BGG).


  1. All codes must be implemented using Python.
  2. You should use Jupyter Notebook to work on this project and submit the .ipynb file.
  3. You are required to write an “executive summary” (word or pdf file) to present your work. The summary should be no more than three pages (double-spaced, excluding any figures, tables, and references)
  1. Codes must be well documented with comments.
  2. You should also include narratives along your codes using Markdown to explain and justify your works and decisions, as well as describe any insights gained from each step.
  1. You may search online or discuss with other students, but each student must work independently2 .


  1. Additional Python packages (not covered in class) are welcome to use. But they should be well documented through Markdown, including why they were needed.
  1. Comments are different from explanations using Markdown.
  2. Here is the relative importance and weighting of each component of your work. The percentage is only indicative.
  • Explanation/Description using Markdown (20% – 25%)
  • Code, including comments (60% – 65%)
  • Executive summary (15% – 20%)
  1. Depending on your scenario and proposed objective(s) or problem(s), you may collect additional data from BGG or other sources to help your analysis. Please properly document the source of the additional data, including why the additional data was needed.
  1. You might find similar datasets online but do not rely on the existing solutions as they may not work properly. The sample dataset has been adapted for this assessment.
  1. The accuracy (or other metrics) of your final prediction model is less important than the process to achieve and improve that value.
  1. You may try different algorithms (including those not covered in the lectures) and include them in your submission. However, a purposeful selection of a smaller number of algorithms with good justification is better than a random selection of a larger number of algorithms without good justification.


本网站支持淘宝 支付宝 微信支付  paypal等等交易。如果不放心可以用淘宝交易!

E-mail:  微信:itcsdx