Context

I love going to new restaurants and trying out their food and enjoying their ambience.

Content

This dataset contains information about the restaurant name, their location, the ratings, how many people have rated and also cuisine information.

Acknowledgements

I would like to thank TripAdvisor from where I scraped a little data to make a dataset of my own.

The dataset contains every sustained or not yet adjudicated violation citation from every full or special program inspection conducted up to three years prior to the most recent inspection for restaurants and college cafeterias in an active status on the RECORD DATE (date of the data pull). When an inspection results in more than one violation, values for associated fields are repeated for each additional violation record. Establishments are uniquely identified by their CAMIS (record ID) number. Keep in mind that thousands of restaurants start business and go out of business every year; only restaurants in an active status are included in the dataset. Records are also included for each restaurant that has applied for a permit but has not yet been inspected and for inspections resulting in no violations. Establishments with inspection date of 1/1/1900 are new establishments that have not yet received an inspection. Restaurants that received no violations are represented by a single row and coded as having no violations using the ACTION field. Because this dataset is compiled from several large administrative data systems, it contains some illogical values that could be a result of data entry or transfer errors. Data may also be missing. This dataset and the information on the Health Department’s Restaurant Grading website come from the same data source. The Health Department’s Restaurant Grading website is here: http://www1.nyc.gov/site/doh/services/restaurant-grades.page See the data dictionary file in the Attachments section of the OpenData website for a summary of data fields and allowable values.

Context

I was interested in behaviour of people ordering food online. For sample dataset Thane City was selected for analysis of the data. In Thane city, number of restaurants serving variety of cuisines are present. Currently there are approximately 1200 restaurants present in Thane City. I was interested in finding a) Density of restaurant b) Correlation between restaurant type and rating of restaurant c) Cuisines offered by the restaurant

Content Using the Zomato dataset, I could extract and analyse the following: a) Detailed information about restaurant including establishment type, type of cusines, pricing, no of reviews, average rating b) Number of restaurants per sq. km This will help in identifying the areas with high density of restaurants. c) Breakup of rating reviews assigned to restaurant by the users d) Types of cusine offered by restaurants and percentage distribution of the same. e) Frequency distribution of number of reviews by users.

f) Top dishes of each restaurants as per users reviews. Phase I, In Phase I, using Zomato API, top 100 restaurants of each establishment types were extracted. The detailed information as per point a) above was available. Phase II, In Phase II , using the restaurant url, top dishes of the restaurant was scraped from the website. Acknowledgements All copyrights for the data is owned by Zomato Media Pvt. Ltd. This data was extracted for educational purpose only.

Abstract Tina and Rick get married in Kosovo and head back to Italy, where they can start their happy life as a married couple. Or almost. Some problems with the chocolate factory are waiting for them back at home. While Ivan is ready to do anything to help his son and his friends, he also needs to deal with his complicated love life. Details Rick and Tina, with the help of their parents and their friends, start to plan the wedding. They planned a big party with 300 guests, including Rick’s mother, who flew from San Diego to Kosovo as soon as she received the invitation. She meets Tina for the first time while dressing up in her wedding gown, and although she is very happy for her son, she worries about them being unable to make it on their own. Surprisingly, Ivan reassures her this time, showing how this rescue trip changed his perspective. The celebration takes place smoothly with the help of their families, friends, and neighbours. The only person missing is Miriam, who, in the meantime, is fighting for the survival of the packaging team at the chocolate factory. When the time comes for the Abrate family to vote on merging with the French company, Miriam’s brother votes for it, betraying Miriam against everyone’s expectations. His wife, worried about the family’s finances, threatened to leave him if he did not vote against Miriam. The return journey to Italy is planned for the day following the wedding after Rick and Tina spent their first night together as a married couple. However, when they get to Italy, Miriam gives them the bad news that she is leaving the firm because the family decided to sell; they are out of a job. Miriam, who is deeply sad, refuses even to talk to Ivan. When he tries to call her and offers to meet with her, she turns him down again. The series fasts forward to three months later. The situation at the Abrate Factory has stayed the same, and Rick is missing his job and friends. Tired of Rick’s sadness, Ivan decides to take the matter into his own hands. He goes looking for Rick’s friends one by one and turns his home into a place where they can all hang out every day. Ready to do anything to find employment for his son, he shows up at Miriam’s house and convinces her to get into business together and turn her grandfather’s old Café into a classy restaurant where the packaging team could work. After weeks of training, the opening night finally comes. Many people show up, but it is far from being a success since the demanding tasks overwhelm Rick and his friends. After some time in which the situation at the restaurant has yet to progress, Miriam, Ivan, and the team start losing faith. Just as they are about to give up, one of them, Marione, starts hugging people walking down the street and invites them to come in and eat. As he does that, the place fills up, and just like that, Ivan realizes that they need a new business model that can benefit from the uniqueness of their team instead of considering their disability as a flaw. Therefore, they decided for the restaurant not to be a classy and pricy place but rather a simpler one, where people feel at home and loved. Meanwhile, Alessia reveals to Ivan that she still has feelings for him and has broken up with her partner. This comes unexpectedly for Ivan, who initially turns her down when she suggests giving their marriage another chance. He is still in love with Miriam, even though she still does not want to be in any relationship. One day, after Alessia helps him comfort Tina, who is feeling homesick, Ivan realizes that Alessia’s presence is positive and accepts her proposal to go back to living together. However, this only lasts for a while since Ivan cannot forget Miriam. In the meantime, working with Ivan makes her realize she is in love with him, but she is escaping from it because of the sense of guilt that her daughter’s death provoked in her. A karaoke night at the restaurant is the occasion that finally brings Miriam and Ivan together. When she invites him to sing on stage, he passionately kisses her, just like Rick had kissed Tina on their first karaoke night.

This data includes the name and location of active food service establishments and the violations that were found at the time of the inspection. Active food service establishments include only establishments that are currently operating. This dataset excludes inspections conducted in New York City (https://data.cityofnewyork.us/Health/Restaurant-Inspection-Results/4vkw-7nck), Suffolk County (http://apps.suffolkcountyny.gov/health/Restaurant/intro.html) and Erie County (http://www.healthspace.com/erieny). Inspections are a “snapshot” in time and are not always reflective of the day-to-day operations and overall condition of an establishment. Occasionally, remediation may not appear until the following month due to the timing of the updates. Update frequencies and availability of historical inspection data may vary from county to county. Some counties provide this information on their own websites and information found there may be updated more frequently. This dataset is refreshed on a monthly basis. The inspection data contained in this dataset was not collected in a manner intended for use as a restaurant grading system, and should not be construed or interpreted as such. Any use of this data to develop a restaurant grading system is not supported or endorsed by the New York State Department of Health. For more information, visit http://www.health.ny.gov/regulations/nycrr/title_10/part_14/subpart_14-1.htm or go to the “About” tab.

Context

This dataset contains lists of Restaurants and their menus in the USA that are partnered with Uber Eats. Data was collected via web scraping using python libraries.

*This dataset is dedicated to the awesome delivery drivers of Uber Eats, hence the cover image

Download

kaggle API Command !kaggle datasets download -d ahmedshahriarsakib/uber-eats-usa-restaurants-menus

Content

The dataset has two CSV files -

1. restaurants.csv (40k+ entries, 11 columns)

   • id (Restaurant id)
   • position (Restaurant position in the search result)
   • name (Restaurant name)
   • score (Restaurant score)
   • ratings (Ratings count)
   • category (Restaurant category)
   • price_range (Restaurant price range - $ = Inexpensive, $$ = Moderately expensive, $$$ = Expensive, $$$$ = Very Expensive) - Source - stackoverflow
   • full_address (Restaurant full address)
   • zip_code (Zip code)
   • lat (Latitude)
   • long (Longitude)

2. restaurant-menus.csv (3.71M entries, 5 columns)

   • restaurant_id (Restaurant id)
   • category (Menu category)
   • name (Menu Name)
   • description (Menu description)
   • price (Menu price)

Acknowledgements

Data was scraped from - - https://www.ubereats.com - An online food ordering and delivery platform launched by Uber in 2014. Users can read menus, reviews, ratings, order, and pay for food from participating restaurants using an application on the iOS or Android platforms, or through a web browser. Users are also able to tip for delivery. Payment is charged to a card on file with Uber. Meals are delivered by couriers using cars, scooters, bikes, or foot. It is operational in over 6,000 cities across 45 countries.

Cover Image -

Photo by eggbank on Unsplash

Disclaimer

The data and information in the data set provided here are intended to use for educational purposes only. I do not own any of the data and all rights are reserved to the respective owners.

Inspiration

• How many Restaurants are around the USA?
• What are the Most Popular/Highly Rated Restaurants and menus?
• Is there any relationship between the price level and the popularity of a restaurant?
• Which menus are more expensive?
• Which menus are very common in the USA?

Update Frequency

1. The dataset will be updated weekly

Hi 👋, The food industry has grown rapidly. It produces a lot of restaurants each one of them has his one value wither it was in the type of food or the price and locations, as it became the target market for any new business, So why we don't collect data about these restaurants and TripAdvisor is the place to find all the information that we need.

Context

This dataset was scraped from TripAdvisor Tripadvisor, the world's largest travel platform, it contains all the information that helps the travelers around the world, to find the best accommodations, restaurants, experiences, airlines, and cruises, by reviewing all the information the traveler needs to know about starting from the name to the reviews of the previous customers. Here we focused only on restaurants in Saudi Arabia since improving tourism was a hot topic in the last period of time.

Content

This data contains information about restaurants in 3 main cities in Saudi Arabia: JEDDAH , RYADH, DAMMAM. Also, there is 4csv file 3 represents each city and the last one is the big one that contains all the 3. The information is : name | the name of the restaurant type | type of food that it represents location | the full location of the restaurant review score| how many points did he get review number| how many people give there feedback city| where is he opening hours | when he opens and when he close price range| start from - until out_of| his place out of the other restaurants represent the same type of food address_line1| extracted from location address_line2|extracted from location type 2 |extracted from type

Acknowledgements

This data is taken fro Trip-advisor website, and this project was required in order to graduate from GA data science Immersive course

Inspiration

There are a lot of things inspired me to do this one of them is restaurants and cafes are really important destinations when it comes to entertainment and also if you look at it from a business perspective it almost Succesful business if it was well planned. So, i thought about classifying this data to find the best location for a specific type of food in order to help any user or a new business to choose the perfect location. Or, you can combine these Data to do prediction or even recommendations. After all, Due to the current circumstances I really missed going out😢Maybe that was the main reason🙈.

The overall goals of this project were to develop a clear theoretical understanding of coercive control and to develop a measure of "nonviolent coercive control" for use in the measurement of intimate partner violence (IPV). The psychometric properties of the newly developed coercive control measure were assessed between February and September 2004 in a total sample of 757 that included 302 males and 448 females from the metropolitan Washington, DC, and Boston areas. Of this sample, 139 reporting IPV victimization only, 39 reported IPV perpetration only, 245 reported both IPV victimization and perpetration, and 334 reported neither IPV victimization nor perpetration. Respondents were recruited from community agencies involving identified IPV victims and perpetrators, agencies providing non-IPV services to demographically similar participants, community college settings, and general public community settings, e.g., fast food restaurants. The sample was a convenience, not a representative, sample. Selection criteria included the following: (1) involvement in an intimate partner relationship within the past 12 months, and (2) being 18 years of age or older. Respondents were excluded if they exhibited signs of intoxication or other indications of a lack of coherence sufficient to complete the survey. Both data files contain demographic information. Respondents were asked several series of questions including those pertaining to demands received from their partner, whether their partner did anything to find out if the respondent had done what the partner had demanded, if their partner made them feel the partner might do something if the respondent did not do what the partner wanted, and whether they had done certain things when their partner demanded something. Respondents were then asked the same series of questions conversely. Respondents were read a statement and asked how often they felt this way in the past month, asked whether in the last 12 months they had experienced certain physical abuse or abused their partner physically, and they were asked whether in the last 12 months they had experienced certain types of emotional abuse or had abused their partner emotionally. Respondents were read a series of statements regarding their relationships with people in general and asked to tell whether the statement was true or false, asked how often they had experienced problems in response to a trauma, and asked how likely their partner might attempt to abuse the respondent in specific ways in the next year.

Survey of Household Spending (SHS), average household spending on detailed food categories.

MIT Restaurant Corpus - CRFs (Conditional Random Fields) Dataset

A Funny Dive into Restaurant Reviews 🥳🍽️

Welcome to MIT Restaurant Corpus - CRF Dataset! If you are someone who loves food, restaurant and all the jargings that come with it, then you are for a treat! (Pun intended! 😉), Let's break it in the most delicious way!

This dataset obtained from MIT Restaurant Corpus (https://sls.csail.mit.edu/downloads/restaurant/) provides valuable restaurant review data for the NER (Named Entity Recognition) functions. With institutions such as ratings, locations and cuisine, it is perfect for the manufacture of CRF models. 🏷️🍴 Let's dive into this rich resource and find out its ability! 📊📍

🍔 What's Inside This Feast?

The MIT Restaurant Corpus is designed to help you understand the intricacies of restaurant reviews and data about restaurants can be pars and classified. It has a set of files that are structured to give you all ingredients required to make CRF (Conditional Random Field) models for NER (Named Entity Recognition). What is served here:

1.**‘sent_train’** 📝: This file contains a collection of sentences. But not just any sentences. These are sentences taken from real - world restaurant reviews! Each sentence is separated by a new line. It is like a dish of text, a sentence at a time.

2.**‘sent_test’** 🍽️: Just like the ‘sent_train’ file, this one contains sentences, but they’re for testing purposes. Think of it as the "taste test" phase of your restaurant review trip. The sentences here help you assess how well your model has learned the art of NER.

3.**‘label_train’** 🏷️: Now here’s where the magic happens. This file holds the NER labels or tags corresponding to each token in the ‘sent_train’ file. So, for every word in a sentence, there is a related label. It helps the model know what is - whether it’s a restaurant name, location, or dish. This review is like a guide to identify the stars of the show!

4.**‘label_test’** 📋: This file is just like ‘label_train’, but for testing. This allows you to verify if your model predictions are with the reality of the restaurant world. Will your model guess that "Burtito Palace" is the name of a restaurant? You will know here!

Therefore, in short, there is a beautiful one-to-one mapping between ‘sent_train’/‘sent_test’ files and ‘label_train’/‘label_test’ files. Each sentence is combined with its NER tag, which makes your model an ideal recipe for training and testing.

🍕 The NER Labels – What are we Tagging?

The real star of this dataset is the NER tags. If you’re thinking, "Okay, but in reality we are trying to identify in these restaurants reviews?" Well, here is the menu of NER label with which you are working:

• Rating ⭐: The stars or ratings (literally) that gives a reviewer to the restaurant. We all know those stars are important when it is to choosing where to eat!
• Amenity 🛋️: Think about that in the form of comfortable extra that comes with a restaurant, such as free Wi-Fi, or a pet-friendly courtyard.
• Location 📍: This tag marks the location of the restaurant. Therefore, when you see "on Fifth Avenue", you know that it points to the place.
• Restaurant_Name 🍴: Ah, place name! Is this "Burger Bonanza" or "Sushi Central"? What this tag recognizes.
• Price 💰: How much are we talking here? The price tag can be called "$ $ $" or "appropriate". We all need to know how different we are about, right?
• Hours ⏰: Because who wants to show in a restaurant when it is closed? It marks the tag opening and closing time.
• Dish 🍲: Which food is being talked about? "Pad Thai" or "Cheeseberger" are your examples.
• Cuisine 🍣: A tag for food type - whether it is Italian, Japanese, or good OL 'American comfort food.

These NER tags help create an understanding of all the data you encounter in a restaurant review. You will be able to easily pull names, prices, ratings, dishes, and more. Talk about a full-recourse data food!

🍤 CRF Model – The Chef’s Special!

Now, once you get your hand on this delicious dataset, what do you do with it? A ** CRF model ** cooking time!🍳

CRF (conditional random field) is a great way to label the sequences of data - such as sentences. Since NER work is about tagging each token (word) in a sentence, CRF models are ideal. They use reference around each word to perform predictions. So, when you were "wonderful for Sushi in Sushi Central!" As the sentence passes in, the model can find out that "Sushi Central" is a Restaurant_Name, and “sushi” is a Dish.

🍜 Features – Spice It Up!

Next, we dive into defines features for CRF model. Features are like secret materials that work your model. You will learn how to define them in the python, so your model can recognize the pattern and make accurate predictions.

...

Context

Coronavirus infection is currently the most important health topic. It surely tested and continues to test to the fullest extent the healthcare systems around the world. Although big progress is made in handling this pandemic, a tremendous number of questions are needed to be answered. I hereby present to you the local Bulgarian COVID-19 dataset with some context. It could be used as a comparator because it stands out compared to other countries and deserves analysis.

Context for Bulgarian population: Population - 6 948 445 Median age - 44.7 years Aged >65 - 20.801 % Aged >70 - 13.272%

Summary of the results: - first pandemic wave was weak, probably because of the early state of emergency (5 days after the first confirmed case). Whether this was a good decision or it was too early and just postpone the inevitable is debatable. -healthcare system collapses (probably due to delayed measures) in the second and third waves which resulted in Bulgaria gaining the top ranks for mortality and morbidity tables worldwide and in the EU. - low percentage of vaccinated people results in a prolonged epidemic and delaying the lifting of the preventive measures.

Some of the important moments that should be considered when interpreting the data: 08.03.2020 - Bulgaria confirmed its first two cases. The government issued a nationwide ban on closed-door public events (first lockdown); 13.03.2020- after 16 reported cases in one day, Bulgaria declared a state of emergency for one month until 13.04.2020. Schools, shopping centres, cinemas, restaurants, and other places of business were closed. All sports events were suspended. Only supermarkets, food markets, pharmacies, banks, and gas stations remain open. 03.04.2020 - The National Assembly approved the government's proposal to extend the state of emergency by one month until 13.05.2020; 14.05.2020 - the national emergency was lifted, and in its place was declared a state of an emergency epidemic situation. Schools and daycares remain closed, as well as shopping centers and indoor restaurants; 18.05.2020 - Shopping malls and fitness centers opened; 01.06.2020 - Restaurants and gaming halls opened; 10.07.2020 - discos and bars are closed, the sports events are without an audience; 29.10.2020 - High school and college students are transitioning to online learning; 27.11.2020 - the whole education is online, restaurants, nightclubs, bars, and discos are closed (second lockdown 27.11 - 21.12); 05.12.2020 - the 14-day mortality rate is the highest in the world; 16.01.2021 - some of the students went back to school; 01.03.2021 - restaurants and casinos opened; 22.03.2021 - restaurants, shopping malls, fitness centers, and schools are closed (third lockdown for 10 days - 22.03 - 31.03); 19.04.2021 - children daycare facilities, fitness centers, and nightclubs are opened;

Content

This dataset consists of 447 rows with 29 columns and covers the period 08.03.2020 - 28.05.2021. In the beginning, there are some missing values until the proper statistical report was established.

Inspiration

A publication proposal is sent to anyone who wishes to collaborate. Based on the results and the value of the findings and the relevance of the topic it is expected to publish: - in a local journal (guaranteed); - in a SCOPUS journal (highly probable); - in an IF journal (if the results are really insightful).

The topics could be, but not limited to: - descriptive analysis of the pandemic outbreak in the country; - prediction of the pandemic or the vaccination rate; - discussion about the numbers compared to other countries/world; - discussion about the government decisions; - estimating cut-off values for step-down or step-up of the restrictions.

Error or query reporting

If you find an error, have a question, or wish to make a suggestion, I encourage you to reach me.

Context

The source is: https://opendata.paris.fr/explore/dataset/restaurants-casvp/export/?disjunctive.code&disjunctive.nom_restaurant&disjunctive.type

Content

What's inside is more than just rows and columns. Make it easy for others to get started by describing how you acquired the data and what time period it represents, too.

Acknowledgements

We wouldn't be here without the help of others. If you owe any attributions or thanks, include them here along with any citations of past research.

Inspiration

Your data will be in front of the world's largest data science community. What questions do you want to see answered?