Open ytz opened 2 years ago
[x] Repository: Is the source code for this data analysis available? Is the repository well organized and easy to navigate?
[x] Readability: Are scripts, functions, objects, etc., well named? Is it relatively easy to understand the code?
Nice work! This is really interesting and I thoroughly enjoyed reading the source of this analysis. The project looks pretty good, below are a few issues when I tried to replicate the process.
The main issue arrives in the usage section of the project:
This was derived from the JOSE review checklist and the ROpenSci review checklist.
~90 mins
This is a practical topic and I can totally see the possibility of real life use case in the e-commerce industry.
Statement of future direction
session. I know what to look forward to in the upcoming release. =)Since @Sanchit120496 focused on the script, I spent more time on the reading material
This was derived from the JOSE review checklist and the ROpenSci review checklist.
1.5
I enjoyed reading the report which is very well structured and highlights the importance of the analysis along with its practicality. For e.g., in the model selection metrics, the linking of focus areas on the errors with business context was excellent and as an ex-management consultant, I cannot stress enough how important this is to convince a decision making process at the senior management levels. After having a review of the work, here are my observations on some of the sections:
Rest, I think this is one of the best reports i have read, and commendable efforts put in here. I must say I learnt quite a lot from your analysis, such as smart use of feature engineering for one. All the best.
This was derived from the JOSE review checklist and the ROpenSci review checklist.
😊 Thanks for the feedbacks!
.gitkeep
instead of a dummy text file, as seen in https://github.com/UBC-MDS/online-shoppers-purchasing-intention/commit/6c0383e1cfe4846736afeb6887443c8720b29780
Submitting authors: @nicovandenhooff, @arijc76, @ytz
Repository: https://github.com/UBC-MDS/online-shoppers-purchasing-intention Report link: https://ubc-mds.github.io/online-shoppers-purchasing-intention/intro.html Abstract/executive summary: The research question that we are attempting to answer with our analysis is a predictive question, and is stated as follows:
Nowadays, it is common for companies to sell their products online, with little to no physical presence such as a traditional brick and mortar store. Answering this question is critical for these types of companies in order to ensure that they are able to remain profitable. This information can be used to nudge a potential customer in real-time to complete an online purchase, increasing overall purchase conversion rates. Examples of nudges include highlighting popular products through social proof, and exit intent overlay on webpages.
Our final model is a tuned random forest, outputting 268 false positives, and 88 false negatives. The macro average recall score is 0.827 and the macro average precision score is 0.748, which is above our budget of 0.60 that we set at the beginning of our project.
Editor: @flor14 Reviewer: @Sanchit120496, @MacyChan, @shivajena