The Process
In this section, I will show you what I did in my second assignment.
I have been through web scrapping (PareHub) and data cleaning (OpenRefine, SQLite), and generate the desired results by writing codes (Python) as a filter.
I also followed the tutorials on ParseHub and successfully scrapped information about "cafes" in "Causeway Bay", then I used OpenRefine and SQLite to clean up invaild data (mostly missing info), and reduced the size of the data set. Then I generated my results by JupyterNotebook.
The Results
(A) Top 9 cafes in Casuseway Bay that have never received dislikes:
The number of dislikes can be really disturbing in the decision-making process. Recieving 0 dissatisfaction is highly valued because it ensures a feeling of "no worries".
(B) Top 5 cafes in Casuseway Bay price-friendly to students (below 100HKD) that have also have received over 500 in both likes and reviews:
Some restaurants are specifically cost-effective in terms of students' consuming ability. Therefore, this result can provide them with a reference of choosing where to grab a good coffee.
(C) Top 10 cafes in Casuseway Bay that have also have received 20K+ bookmarks:
OpenRice has its social media attribute - bookmark, where people can see how the coffee shop is desired by return customers and also new ones who find it attractive.