Geographical Weather Analysis to Plan Vacations (API)

WeatherPy and VacationPy Data Analysis Project:

Part 1: WeatherPy - Visualizing Weather Patterns:
● Utilized citipy Python library to generate over 500 random cities based on geographic coordinates.
● Employed OpenWeatherMap API to retrieve weather data for each city.
● Created scatter plots to showcase the relationships between weather variables and latitude.
- Latitude vs. Temperature
- Latitude vs. Humidity
- Latitude vs. Cloudiness
- Latitude vs. Wind Speed
● Computed linear regression for each relationship, differentiating between Northern and Southern Hemispheres.
● Generated scatter plots with linear regression lines, displaying model formulas and r values.
● Analyzed the linear regression plots, identifying relationships and summarizing findings.

Part 2: VacationPy - Planning Future Vacations:
● Used geoViews Python library and Geoapify API for map visualizations.
● Created a map displaying a point for every city with point size representing humidity.
● Narrowed down city data to find ideal weather conditions, such as max temperature, wind speed, and cloudiness criteria.
● Created a new DataFrame (hotel_df) to store information about hotels near selected cities.
● Utilized Geoapify API to find the first hotel within 10,000 meters of each city's coordinates.
● Added hotel name and country as additional information in the hover message for each city on the map.

Conclusion:
● Successfully completed a comprehensive data analysis project using Python, API integration, and visualization tools.
● Demonstrated proficiency in handling large datasets, extracting weather information, and visualizing geographic data.
● Displayed skills in creating informative and visually appealing scatter plots with linear regression lines.
● Utilized Python libraries such as citipy, geoViews, and Geoapify API to gather, analyze, and visualize data.
● Improved problem-solving skills by addressing requirements and interpreting results in both WeatherPy and VacationPy.