代写New York Restaurant Recommendations Based on the Subway System调试Python程序

日期：2024-04-11 05:48

Project Proposal:

New York Restaurant Recommendations Based on the Subway System

Topic:

The project aims to develop a Python-based application that offers personalized restaurant recommendations to users in New York City, leveraging the convenience of the NYC Subway system. By integrating real-time Yelp API data with static NYC Transit Subway Entrance and Exit data, the application will guide users to gastronomic destinations accessible via subway, enhancing their dining experience with considerations for travel time, distance, and culinary preferences.

Data Sources:

1. Yelp API: To fetch real-time data on restaurants, including names, locations (latitude and longitude), categories (cuisine type), ratings, and price levels.

2. NYC Transit Subway Data:

https://www.kaggle.com/datasets/new-york-state/nys-nyc-transit-subway-entrance-and-exit-data?select=nyc-transit-subway-entrance-and-exit-data.csv

https://catalog.data.gov/dataset/mta-subway-stations-and-complexes

Technical Process:

1. Data Integration and Preprocessing:

Subway Data Preprocessing: Parse the CSV data to construct a graph data structure representing the subway system. Nodes will represent subway stations, and edges will signify direct routes between stations, annotated with distances or travel times.

Yelp API Integration: Implement a caching mechanism to store and update Yelp data efficiently, minimizing API calls and ensuring quick response times for user queries.

2. Graph Analysis for Route Optimization:

Utilize Dijkstra's algorithm or a similar shortest path finding technique to calculate the optimal subway route between any two given stations, considering factors like the number of stops and total travel distance.

Apply graph traversal algorithms to identify nearby subway stations within a user-defined radius from a selected restaurant.

3. User Interaction and Dynamic Recommendation System:

User Input Interface: Allow users to select a start subway station and specify preferences such as maximum travel distance (in terms of subway stops), desired restaurant categories, and price range.

Dynamic Restaurant Recommendations: Based on user inputs and graph analysis, query the Yelp API to find restaurants that match the criteria. Enhance recommendations by considering the connectivity of subway stations to identify easily accessible dining options.

Further Recommendations: Upon selecting a restaurant, offer additional suggestions for similar restaurants near the chosen or other nearby subway stations, leveraging the restaurant's features like category and user ratings for tailored suggestions.

4. Presentation and Interactivity:

Develop an interactive command-line interface (CLI) or a simple graphical user interface (GUI) for users to input their preferences and view recommendations.

Present results in a user-friendly format, displaying key information about recommended restaurants, such as name, category, rating, distance from subway station, and an option to view the restaurant on Yelp for more details.

Implement features allowing users to refine their search or explore new recommendations based on different criteria without restarting the application.