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World_Weather_Analysis

Overview of the project

This project is created to provide real-time suggestions for a travel agency clients' ideal hotels based on their preferred travel criteria via the search page.

Results

Collection of the data

To collect the data I

  • Used the NumPy module to generate more than 1,500 random latitudes and longitudes.
  • Used the citipy module to list the nearest city to the latitudes and longitudes.
  • Used the OpenWeatherMap API to request the current weather data from each unique city in your list.
  • Parsed the JSON data from the API request.
  • Collected the following data from the JSON file and add it to a DataFrame:
    • City, country, and date
    • Latitude and longitude
    • Maximum temperature
    • Humidity
    • Cloudiness
    • Wind speed
Filtering and visualization of the data
  • Filtered the Pandas DataFrame based on user inputs for a minimum and maximum temperature.
  • Found hotels from the cities' coordinates using Google's Maps and Places API, and Search Nearby feature.
  • Stored names of the hotels in a new DataFrame.
  • Added pop-up markers to the map that display information about the city, current weather with maximum temperature, and a hotel in the city.
Creating a travel itinerary map
  • Used the Google Directions API to create a travel itinerary that shows the route between four cities chosen from the customer’s possible travel destinations.
  • Created a marker layer map with a pop-up marker for each city on the itinerary.

Summary

This code allows clients to choose cities and hotels based on their temperature preferences and creates a driving route between four chosen cities. It can be easily adjusted to let clients find places based on other weather criteria and change amount of cities to visit.