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.