Development of Weather Forecasting Using TensorFlow and Visual Crossing API

The goal of this project is to create an advanced weather forecasting system that utilizes weather data provided by the Visual Crossing API. The project aims to process and analyze historical and current data to create weather forecasts for the next 26 days. The system will be built using TensorFlow in the Google Colab environment, which will allow for the use of cloud computing resources and facilitate the work on the project. Requirements: Integration with Visual Crossing API: Implementation of integration with the Visual Crossing API is required to retrieve weather data. The data should cover a wide range of information, such as temperature, precipitation, humidity, wind speed, atmospheric pressure, and more. Data Processing and Analysis: Weather data should be processed and prepared for analysis. Attention should be paid to data cleaning, normalization, and the possible enrichment of data with additional variables that may improve the accuracy of the model. Model Development with TensorFlow: Use TensorFlow to create and train a weather forecasting model. The model should be able to predict various weather aspects for the next 26 days, based on available historical and current data. Model Evaluation and Optimization: The model should be thoroughly tested and optimized for forecast accuracy. Cross-validation should be performed, and model parameters should be adjusted to achieve the best possible results. User Interface and Data Presentation: Development of an aesthetic and intuitive user interface (UI) that will present the weather forecasts in a clear form. The UI can be built as a Google Colab notebook or as a separate web application that utilizes the model's results. Documentation: Creation of detailed project documentation, including a description of the technologies used, system architecture, data analysis process, model implementation details, and instructions for running the project. 10$