/World-Weather-Analysis

Using OpenWeatherMap API, retrieve the JSON weather data from different cities. Using Matplotlib, create a series of scatter plots showing relationship between the latitude and a variety of weather parameters for over 1500 cities around the world. Perform statistical calculations on the weather parameters using linear regression to predict future weather in chosen cities.

Primary LanguageJupyter Notebook

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.