Using the Data Science process, we will be exploring data related to food and beverages. Within the food and beverage sector, we are capable of anticipating and managing demand for foodstuffs. Practical advantages of historical data will be highlighted in the implementation of our project, enabling us to better predict demand.

In a customer's perspective, today's customers are looking for quality of taste and want the desired food on their menu. Our goal is therefore to create a system that learns from data which factors have the greatest influence on the product, and the system is able to recognize patterns and trends that predict future behavior and understand customer preferences and as business owners perspective sometimes business owners don't pay attention to the wealth of the data, Thus, companies are able to take benefit from real time analysis and increase business and they do have an opportunity for growth while implementing a good data science method. Therefore, the focus is on those challenges and we are working to find solutions.

In data science, the forecasting system for foods is based on a number of fundamental factors which enable users to make precise and personalized recommendations. To develop an effective food recommendation system, it is necessary to take the most important as our project key factors.

Attributes of the data that we aimed at based on our business questions

• Restaurant Name: Name of the restaurant
• City: City in which restaurant is located
• Country: Country in which restaurant is located
• Rating: Average rating out of 5
• Votes: Number of votes casted by people
• Average Cost for two: Cost for two people in different currencies
• Has Table booking: yes/no
• Has Online delivery: yes/ no
• Cuisines: Cuisines offered by the restaurant

• Based upon a food review and restaurant rating, predict customer sentiment in the following ways: ○ Positive ○ Negative ○ Neutral
• Is it possible for us to suggest some restaurants based on their cuisines, ratings, costs, and cities?
• Doesn't the location also have an impact on achieving a high rating?
• Analysis of whether the rating for food is also influenced by ordering online(Over the internet).
• Can we predict customer future ratting on cuisine for the menu item?
• Can the location (city or locality) of a restaurant influence its average cost for two people?
• Is there a relationship between the type of cuisine offered by a restaurant and its aggregate rating?