Analyze, create visuals, and statistically graph the weather data for a potential travel company. Their app PlanMyTrip will use the data to recommend ideal hotels based on clients’ weather preferences.
- Data source: API from OpenWeatherMap, API from GoogleMaps
- Software: Python 3.6.9, Jupyter Notebook, Pandas Library, MatPLotLib, SciPy Libraries
- Provide real-time suggestions for our client’s ideal hotels
- Create a Pandas DataFrame with 500 or more of the world’s unique cities and their weather data in real time. This process will entail collecting, analyzing, and visualizing the data.
How many cities have recorded rainfall or snow at the time the API was pulled?
- 64 cites have experienced rainfall in the last 3 hours.
- 30 cities have experienced snowfall in the last 3 hours.
The client chose a location with a minimum temperature of 65 F and maximum temperature of 90 F, and where it has not been raining or snowing in the last 3 hours.
A map marking the location that meet the clients preferred criteria:
A map displaying pop-up markers for some of the locations that meet the clients preferred criteria:
The client decided to travel somewhere in South America, between Guatemala, Honduras, Belize, and El Salvador. A map (travel itinerary) is created that shows the route between five cities from the customer’s possible travel destinations in South America. Additionally, a map is created with pop-up markers for the five cities
A map displaying the routes between the five cities they are interested in visiting:
A map displaying the pop-up markers for some of the cites in the vacation itinerary:
