An analysis and visualization of weather data across 500+ cities worldwide for a travel app called "PlanMyTrip" that will use the data to recommend ideal hotels based on clients' weather preferences to travellers so that they can plan their itinerary.
- Data Source:
- Weather Database: WeatherPy_Database.csv
- Vacation Data : WeatherPy_vacation.csv
- Software: Python 3.8.8, Pandas Dataframe, Matplotlib, CitiPy, SciPy, Python Requests, APIs, JSON Traversals, Jupyter Notebook 6.3.0
- API's accessed: OpenWeatherMap API, Google Maps and Places API, Google Maps Directions API
Generated a set of 2,000 random latitudes and longitudes, and retrieved the nearest cities. Performed an API call with the OpenWeatherMap and the following information was extracted from the API call:
- Latitude and longitude
- Maximum temperature
- Percent humidity
- Percent cloudiness
- Wind speed
- Weather description (for example, clouds, fog, light rain, clear sky) The above data was captured in a Pandas Dataframe and exported to a "WeatherPy_Database.csv" file. An excerpt of the dataframe is as follows.
Using input statements from customer to get customer weather preferences, then used those preferences and data collected in "WeatherPy_Database.csv" file, to identify potential travel destinations and nearby hotels, and exported the data to a 'WeatherPy_vacation.csv" file. The data is visualized on a world map that show those destinations on a marker layer map with pop-up markers.
Using the Google Directions API and data from 'WeatherPy_vacation.csv", created a sample travel itinerary that shows the route between four cities chosen from the customer’s possible travel destinations. Then, created a marker layer map with a pop-up marker for each city on the itinerary.
Sample Travel Itinerary of four cities in USA starting and ending at North Myrtle Beach.
Vacation Travel Route
Vacation Hotel and Weather Pop-up Marker Map
