https://github.com/muhammadmoeezkhan/find-your-food-truck

Implementation for Xtern's FoodX Foodie Plan: Collected, Analyzed, and Visualized Findings On Local Food Trucks In Indianapolis To Create A Dataset & A 2-day Foodie Tour Plan For Xterns!
https://github.com/muhammadmoeezkhan/find-your-food-truck

folium geopy google-maps-api opensource-api rapid-api

Last synced: 9 months ago
JSON representation

Implementation for Xtern's FoodX Foodie Plan: Collected, Analyzed, and Visualized Findings On Local Food Trucks In Indianapolis To Create A Dataset & A 2-day Foodie Tour Plan For Xterns!

• Host: GitHub
• URL: https://github.com/muhammadmoeezkhan/find-your-food-truck
• Owner: MuhammadMoeezKhan
• Created: 2023-10-17T16:08:34.000Z (about 2 years ago)
• Default Branch: main
• Last Pushed: 2023-10-23T03:10:06.000Z (about 2 years ago)
• Last Synced: 2025-01-24T11:22:21.791Z (11 months ago)
• Topics: folium, geopy, google-maps-api, opensource-api, rapid-api
• Language: Jupyter Notebook
• Homepage:
• Size: 7.92 MB
• Stars: 0
• Watchers: 1
• Forks: 0
• Open Issues: 0
• Metadata Files:
  • Readme: README.md

Awesome Lists containing this project

README

          
# Indianapolis Xtern's FoodX Tour

## The Goal:

This project was super fun! My tasks revolved around data collecting, organizing, analyzing, and visualizing data related to local food trucks in Indiana. I aimed to create a comprehensive dataset of food trucks, including their basic information such as name, address, rating, website, open hours, and cuisine type. Additionally, my task was to plan a two-day weekend foodie tour for Xterns, including travel routes, time schedules, travel distances, and transportation methods.

## Summary of My Implementation:

1. **Data Collection:**

   - **Libraries and Technologies:** Python, Google Maps API (Places + Directions), OpenRouteService API (Routes), and Matplotlib, Pandas, Requests

   - **Process:** I utilized the Google Maps API to collect data on local food trucks. The API was used to retrieve information such as name, address, rating, website, open hours, and cuisine type for various food trucks. I then cleaned and organized the data into a DataFrame using Pandas.

   - **Data Collected:** The resulting dataset included details about each food truck's name, address, rating, website, open hours, and cuisine type.

2. **Data Analysis:**

   - **Libraries and Technologies:** Pandas, Matplotlib

   - **Concepts:** I conducted data analysis and visualization, including calculation of average ratings, distribution of open hours, and comparisons between Saturday and Sunday ratings.

   - **Visualizations:**

     - I created a bar chart showing the distribution of open hours on Saturdays.

     - I also prepared a bar chart comparing the average ratings of food trucks open on Saturdays and Sundays.

     - Additionally, I generated a bar plot to compare average ratings between Saturday and Sunday for open food trucks.

   

3. **Travel Plan Generation:**

   - **Libraries and Technologies:** Google Maps API (Places + Directions), RapidAPI (Route and directions), OpenRouteService API (Routes), and Matplotlib

   - **Process:** I generated a two-day weekend foodie plan for Xterns. It included location names, addresses, cuisine types, travel times, travel distances, and transportation methods (walking or driving). I also visualized travel routes.

   - **Visualizations:**

     - I created travel route visualizations with details on distance and duration for both walking and driving modes on Saturday and Sunday.

4. **Error Handling and Exception Handling:**

   - Throughout the implementation, I incorporated error handling to deal with missing or incomplete data. Exception handling was used to manage errors in data retrieval and data analysis processes.

5. **Geocoding and Mapping:**

   - **Libraries and Technologies:** Geopy, Folium

   - **Concepts:** I performed geocoding to obtain latitude and longitude coordinates for food truck locations. I used Folium to create maps and visualize food truck locations and travel routes.

6. **Key Concepts:**

   - Data collection and cleaning.

   - Data analysis and visualization.

   - Geocoding for mapping.

   - Error and exception handling.

   - API usage (Google Maps, RapidAPI, OpenService).

   - Data presentation in tabular format.




   

**I successfully achieved our goals by creating a valuable dataset of local food trucks and a well-organized two-day foodie plan. The visualizations enhanced my understanding of the data, helping in the creation of the travel plan and improving the overall experience for the Xterns!**