| Student's name | SCIPER |
|---|---|
| Nazlican Turan | 315262 |
| İlker Gül | 353296 |
| Berke Argin | 376695 |
Milestone 1 • Milestone 2 • Milestone 3
10% of the final grade
This is a preliminary milestone to let you set up goals for your final project and assess the feasibility of your ideas. Please, fill the following sections about your project.
(max. 2000 characters per section)
Find a dataset (or multiple) that you will explore. Assess the quality of the data it contains and how much preprocessing / data-cleaning it will require before tackling visualization. We recommend using a standard dataset as this course is not about scraping nor data processing.
Hint: some good pointers for finding quality publicly available datasets (Google dataset search, Kaggle, OpenSwissData, SNAP and FiveThirtyEight), you could use also the DataSets proposed by the ENAC (see the Announcements section on Zulip).
Frame the general topic of your visualization and the main axis that you want to develop.
- What am I trying to show with my visualization?
- Think of an overview for the project, your motivation, and the target audience.
Pre-processing of the data set you chose
- Show some basic statistics and get insights about the data
- What others have already done with the data?
- Why is your approach original?
- What source of inspiration do you take? Visualizations that you found on other websites or magazines (might be unrelated to your data).
- In case you are using a dataset that you have already explored in another context (ML or ADA course, semester project...), you are required to share the report of that work to outline the differences with the submission for this class.
The most significant website for the users searching for Michelin Restaurants is Guide Michelin which enables them to filter the restaurants based on their star ratings, price and cuisine preferances. Via Michelin is also allow users to view Michelin restaurants in a map. However, the website provides route planning services independent from Michelin-rated restaurants and offer basic filtering options based on star ratings and price which can be expanded much more. Our third inspiration is the platform The Fork where users can search any restaurant, see their menus and filter according to their features but it's not including every Michelin Restaurants and is limited to European countries only.
Our main motivation to focus on Michelin Guide data, beyond our gourmet interests, stems from the noticeable lack of effective visualizations that engage audiences by filtering restaurants according to various features via an interactive map to display these filtered results in an engaging way, including closest Michelin restaurants to a selected location and routes that feature the selected restaurants world-wide.
Our approach stands out in its originality with an extensive range of filtering options beyond just star rating and prices such as different dietary preferences (vegeterian, vegan, gluten free etc.), ambiance (pet friendly, wheelchair etc.), cuisine types and sustainability practices with a user friendly interface alongside an interactive map to present the filtered results with respect to selected locations.
By combining the past approaches and providing a platform to visualize both the restaurants and locations in an easy to navigate fashion, our visualization aims to address the existing gap and to become a valuable resource for food enthusiasts seeking personalized dining experiences based on their specific preferences and selected destination.
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10% of the final grade
80% of the final grade
- < 24h: 80% of the grade for the milestone
- < 48h: 70% of the grade for the milestone